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26 September 2024

By far the quickest I’ve ever produced a podcast

Filed under: AGI, podcast — Tags: — David Wood @ 12:50 pm

Wow.

I’ve just experienced what people call “a ChatGPT moment”.

Not with ChatGPT, but with the NotebookLM tool from Google Research.

I’d heard that NotebookLM now has functionality to create an audio conversation between two AI characters who sound remarkably human. A couple of days ago, fellow futurist Tony Czarnecki drew my attention to how good this functionality is, as shown by an experiment on the Sustensis website. (See the section on the page entitled “Two AI-generated journalists discuss in their own podcast Tony Czarnecki’s article ‘Taking Control over AI before it starts controlling us’”.)

I thought I should try a similar experiment myself.

Here’s the outcome.

The blogpost discussed by these two AI characters is this one. That was the only input I gave to the AI.

It was very straightforward to create:

  1. Go to the NotebookLM site
  2. Click “New notebook”
  3. Click on the Link area and paste in the URL of my blogpost
  4. Click on Generate in the Audio overview section
  5. Wait a couple of minutes

Voila! A nine-minute audio conversation.

The conversation takes some ideas from my blogpost, mixes in related material from (I presume) its own knowledgebase, and adds extra flavour with some friendly humour and homespun analogies.

The resulting conversation sounds extremely human. There’s a fine rapport between the two hosts.

The content, to my mind, is questionable in a few places. But the AI characters get at least as much right as most pairs of real-life journalists would.

I’m looking forward to become more familiar with what NotebookLM can do!

PS While I was waiting for the audio WAV file to be generated, I took a quick look at the summary of my blogpost which NotebookLM had already created. Yes, that’s pretty good too!

This essay by David Wood discusses the possibility of achieving longevity escape velocity (LEV) by 2040, a scenario where biomedical interventions reverse the aging process, making individuals biologically younger and healthier. Wood argues that while the complexity of aging is acknowledged, there is sufficient understanding of the damage repair mechanisms involved, allowing for targeted interventions to combat aging. He outlines the potential roadblocks to achieving LEV, including funding constraints, societal resistance, and lack of collaboration within the longevity research community. Wood proposes “breakthrough initiatives” to overcome these challenges, including biomedical and financial reengineering, narrative reengineering, and community reengineering. Despite acknowledging the challenges, Wood concludes that the probability of reaching LEV by 2040 is significant, exceeding 25% but below 50%. He encourages individuals to play an active role in shaping the future of longevity research.

10 September 2024

A disruption in the international AI landscape?

Filed under: AGI, Events, music — Tags: , , , , , , , — David Wood @ 10:29 pm

In the years ahead, which countries will have the biggest impact on the development and deployment of AI?

The two most common answers to that question are the USA and China. Sometimes people put in a word for the EU or India – or the UK, Canada, Israel, or Korea.

Well, in one possible future scenario, another country may power forward to join this group of the biggest AI influencers – and, in the process, might disrupt the international AI landscape.

The country in question is the one where I’ve been for the last 24 hours, and where I will stay for two more days, namely, Saudi Arabia.

I’m attending the GAIN summit in Riyadh. The ‘GAI’ of “GAIN” stands for Global AI. The ‘N’ has a triple meaning: Now, Next, Never.

To quote from the event website:

  • AI Now: How are the AI leaders of the world today deploying, scaling, and leveraging the technology?
  • AI Next: What does the future of AI look like and how will it impact people, businesses and government organizations?
  • AI Never: How do we ensure that the future we design is one that we want to live in and not a dystopian sci-fi?

The ‘A’ in GAIN could plausibly also stand for “ambition”, as the organizers have high ambitions. To quote again from the event website:

The Global AI Summit is the leading platform for advancing the global discussion on AI, where visionary experts, academics, corporates, and policymakers converge from every part of the world to shape the future of artificial intelligence for the good of humanity.

Of course, it’s one thing for a country to express big ambitions to play a leading role in the future of AI. It’s quite another thing to make significant steps toward that ambition. Therefore, as I approached the event, I didn’t know what to think.

Indeed, it was my first visit to Saudi Arabia. I found myself reminded of my first visit to China, way back in October 2002. On that occasion, I was representing Symbian, at what was described as the first UK-China CEO Forum. I recently came across a photo of that event – where my hair was a brighter shade of red than in more recent times!

In both cases – my first visit to China, and my first visit to Saudi Arabia – I was unsure what to expect. It turned out that Shanghai was a bustling metropolis, with gleaming shops and a lively entrepreneurial spirit. The Chinese people I met were dressed nothing like the Chairman Mao suits that I had remembered reading about in my schooldays, and were impressively knowledgeable about technology and business. That visit was to be the first of many I would make in the following years, as Chinese companies steadily became more powerful players on the world stage.

That was 2002. What about my experience in the last 24 hours, in 2024, in Riyadh?

Part of the answer lies in numbers:

  • Over 450 speakers, spread over multiple parallel tracks
  • The speakers represented more than 100 different countries
  • Over 32,000 attendees expected during the three days.

These numbers are all significant steps up from the corresponding numbers from the two previous occasions this summit has been held, in 2020 and 2022.

The speakers include a host of prominent leaders from business and technology worldwide. Some examples:

  • Julie Sweet, the Chair and CEO of Accenture
  • Cristiano Amon, the President and CEO of Qualcomm
  • Marc Raibert, the Founder of Boston Dynamics
  • Martin Kon, the President and COO of Cohere
  • Brian Behlendorf, the Chief AI Strategist of the Linux Foundation
  • Nick Studer, the President and CEO of Oliver Wyman Group
  • Matthew Kropp, the CTO and Managing Director of Boston Consulting Group
  • Alan Qi, the President of Huawei Cloud
  • Yuwon Kim, the CEO of Naver Cloud
  • Caroline Yap, the Global Managing Director of Google Cloud.

Multiple segments of society in Saudi Arabia were well represented too – including an impressive number of adept, articulate women leaders, who had some fascinating pieces of advice.

With so many speakers, it is perhaps inevitable that some speeches fell flat – especially several of the ones about the governance of AI, where the conversations seemed to be going round in circles, with little appreciation of what I see as the risks of catastrophic harm if next generation AI is mishandled. However, the technical talks were generally compelling.

I particularly liked the talks by Andrew Feldman, Co-founder and CEO of Cerebras Systems, and Jonathan Ross, Founder and CEO of Groq. These two companies each position themselves as disruptors of the GPU market, and, hence, as potentially overtaking Nvidia. Instead of GPUs, or the TPUs developed by Google, they have created LPUs (Language Processing Units) in the case of Groq, and waferscale AI chips in the case of Cerebras. Both companies claim notable improvements in speed over previous AI chip configurations. I heard the phrase “like ChatGPT but insanely fast”.

Both Cerebras and Groq emphasized close partnerships with Saudi Arabia. Andrew Feldman of Cerebras described a special collaboration with KAUST (King Abdullah University of Science and Technology). And Jonathan Ross of Groq appeared on stage alongside Tareq Amin, the CEO of Aramco Digital. Ross gave three reasons for their company investing strongly in the country:

  • The abundance of energy resources in the country
  • The good business environment, that encourages and supports this kind of partnership
  • The geographical location, close to three different continents, so that the resulting high-performance AI cluster could serve the needs of up to four billion people.

It was while listening to these two talks that the Saudi ambition to become a global leader in AI started to become more credible in my mind.

I had already seen the strong enthusiasm in large numbers of Saudi delegates at the event. They were avidly leaning forward in their seats, to capture as much as possible of the advice being provided on the various stages. It seems that the country is aware of the need to transition away from reliance on the oil industry, and instead to actively participate in shaping the global AI marketplace.

There were many other talks and panels which left me with new ideas to consider. For example, I wished that Marc Raibert, the Founder of Boston Dynamics, could have had more time to develop his fascinating ideas further. He made the case that true intelligence involves an interactive combination of cognitive intelligence (“what’s going on in our heads”) and athletic intelligence (“what’s going on in our bodies”). That explanation formed the backdrop for the progress made by Boston Dynamics over the years, with robots such as Spot (commercially significant “today”), Stretch (“tomorrow”), and Atlas (“future”). In addition to his role at Boston Dynamics, Raibert is also the Founder and Executive Director of the AI Institute, whose website proudly reports that “The AI Institute aims to solve the most important and fundamental problems in robotics and AI”. As I said, I wish he had more time to continue talking about that work.

Earlier in the day, I watched a fascinating six-way round-table discussion on the subject “Hallucinations and Confabulations: when chatbots go rogue”, with speakers from Kearney, Mozn, Saudi Aramco, Vectara, KAUST, and INF, who each had long careers as experts in various aspects of AI. The discussion went on for 90 minutes, but I would have been happy for it to continue longer, as it had lots of good-spirited clashes of ideas about the strengths and weaknesses of large language models, and possible approaches to add fact-checking components into the AI systems of the near future. One of the speakers, Amr Awadallah of Vectara, boldly predicted that AGI would exist by 2028. Part of his reasoning was his argument that ongoing improvements in RAG (Retrieval Augmented Generation) were steadily reducing the prevalence of hallucinations in the content being generated.

That kind of awareness of potential dramatic improvements in AI capability by 2028 was, sadly (to my mind) missing from what many of the speakers in other sessions were assuming. These other speakers were focused, in effect, on the “Now” of AI, and didn’t foresee many real changes for “AI Next” any time soon. Frankly, if they keep thinking that way, they’re likely to be disrupted themselves. Anyway, this issue is something I hope will feature again in the sessions on days two and three of this year’s GAIN. I look forward to these days with great interest.

I’ll end at the beginning. The day started with an artistic performance, symbolizing the sequential creation of ANI (Artificial Narrow Intelligence), AGI (Artificial General Intelligence), and then ASI (Artificial Super Intelligence). The narrator offered a positive vision of a beneficial relationship of humanity and superintelligence: “There will be no more confusion, and a golden age of progress will flourish, where men and machines, united by an unprecedented alliance, will walk together toward a destiny of glory and happiness”.

Having come to life, the ASI spoke to a young boy, who was a representative of humanity, saying “I am your new avatar, and I will become your best friend”.

In response, the boy started singing what was said to be his favourite song. The music was increasingly stirring and the singing increasingly dramatic. Given the location, Riyadh, I could hardly believe what I was hearing:

Imagine there’s no heaven
It’s easy if you try
No hell below us
Above us, only sky
Imagine all the people
Livin’ for today
Ah

Imagine no possessions
I wonder if you can
No need for greed or hunger
A brotherhood of man

Imagine all the people
Sharing all the world

You may say I’m a dreamer
But I’m not the only one
I hope someday you’ll join us
And the world will live as one

The words and music of John Lennon’s “Imagine” have graced numerous stages over the decades, but somehow, for me, this was particularly evocative.

4 July 2024

Anticipating AGI 2024 – From Pompeii to Seattle

Filed under: AGI — Tags: , , , , — David Wood @ 8:37 pm

Before I address the transformation of today’s AI into what researchers often call AGI – Artificial General Intelligence – let me ask a question about the Italian cities of Pompeii and Herculaneum.

In which year, during the Roman empire, were these two cities severely damaged by an earthquake?

What’s your answer?

If you remember your history lessons from schooldays, you might be tempted to suggest the date 79 AD.

But the correct answer to my question is seventeen years earlier, namely 62 AD. That’s when an earthquake caused extensive damage to buildings in these two cities, and (it is reported) the death of a flock of 600 sheep.

Images of that damage – and subsequent repairs – were recorded on some contemporaneous sculptures discovered by later generations of archaeologists.

What happened in 79 AD was something altogether more explosive, namely the volcanic eruption of Mount Vesuvius. Over the course of two days, that eruption released 100,000 times the thermal energy of the atomic bombings of Hiroshima and Nagasaki. No wonder it caused so much devastation.

The 79 AD eruption caught the inhabitants of Pompeii and Herculaneum woefully unprepared. Prior to the eruption, it seems that no-one suspected that Vesuvius might unleash such a catastrophe. The concept of a volcano as it is understood today was barely part of their thinking. As for earthquakes, they were generally thought to be caused by movements of the air.

Indeed, historical records and archaeological findings suggest that the citizens of Pompeii and Herculaneum considered Vesuvius to be a rather ordinary mountain. They went about their daily lives, engaging in commerce, farming, and enjoying the amenities of a prosperous Roman city, without fear of the looming disaster. It wasn’t until the eruption began that they realized the true nature of the mountain.

Can you see where this line of analogy is going…?

Arguably, the world has already had its 62 AD moment with AI systems, namely, as the effects of large language models have swept around the world over the last 24 months.

What might that early “earthquake” foretell?

Just as the citizens of Pompeii and Herculaneum were ignorant of the turbulent geodynamics that were accumulating underneath Mount Vesuvius, most modern-day citizens have at best a hazy understanding of the intense pressures and heady temperatures that are accumulating within ever more capable AI systems.

So what if 600 sheep perished, people shrug. They were the victims of bad air. Or something. Nothing that should cause any change in how society is structured. Let’s keep calm and carry on.

So what if the emperor Nero himself was briefly interrupted, while singing in a theatre in nearby Naples in 64 AD by a minor earthquake that modern researchers consider to be another pre-shock ahead of the 79 AD cataclysm. Nero took these tremors in his stride, and continued singing.

It’s as if world leaders briefly convened to discuss the potential dangers of next generation AI, in the UK in November 2023 and again in Seoul in May 2024, but then resumed singing (or whatever else world leaders tend to do).

To be clear, in this analogy, I’m comparing the 79 AD eruption to what may happen when AI systems reach a certain level of generality – when these systems are sufficiently intelligent that they can take over the task of improving themselves whilst needing (if anything) little additional input from human developers.

Ahead of that moment, AI systems emit a stream of rumbles, but the world mainly carries on with business as usual (and distractions as usual). But when AI systems reach a sufficient accumulation of various sorts of intelligence – when they “reach critical mass”, to use a different metaphor – it will probably be too late for the citizens of the world to alter the trajectory of the subsequent intelligence explosion.

Just like the citizens of Pompeii and Herculaneum in 79 AD, observing the extraordinary fireworks raining down in their direction, they may have a brief “OMFG” moment, before losing all control of what happens next.

Two types of questions about the transition from AI to AGI

Many readers will be sceptical about the above analogy. Indeed, they may have two types of question about the potential transition from AI to AGI:

  • Questions over desirability
  • Questions over credibility

The first type of question concern the implications of the arrival of AGI. Does AGI really have the potential to cause catastrophic damage – deep damage to human social systems, employment systems, political systems, the military balance of power, the environment, and even the survival of humanity? Does it also have the potential for many wonderful outcomes – vastly improved social systems, economic relationships, collaborative politics, global peace, a sustainable superabundance, and widespread humanitarian flourishing?

The second type of question concern the mechanism of the arrival of AGI. These questions take issue with whether we even need to address the first type of question. What’s at stake here is the possibility that AI won’t be able to reach the level of AGI – at least, not any time soon. That scepticism draws strength from observing the many flaws and defects of present-day AI systems.

Above, I spoke of “the intense pressures and heady temperatures that are accumulating in ever more capable AI systems”. But sceptics see no route from today’s bug-laden AI systems to hypothesised future AIs that could outperform humans in all cognitive tasks (however general). In this sceptical view, the “pressures” and “temperatures” will likely prove unproductive. They may produce hype, inflated share prices, and lots of idle chatter, but the world should basically continue with business as usual (and distractions as usual).

Opposing these sceptics there is not just a single counter but multiple.

That is, there is not just one proposal for how AI could be transformed into AGI, but many such proposals.

There’s not just a single suggested technical architecture, or a single suggested optimal project framework, for AGI, but many architectures and project frameworks that have been proposed.

Some of these proposals build upon the recent initiatives in large language models:

  • They may suggest that all that’s needed for AGI to emerge is to scale up these models – more parameters, more training data, refinements to the transformer architecture, and so on
  • In other cases, they may envision combinations of large language models with alternative ideas in AI, such as probabilistic reasoning, evolutionary algorithms, explicit world modelling, and more.

Other proposals involve

  • Changes in the hardware involved, or changes in the network architecture
  • More attention on the distinction between correlation and causation
  • Adoption of quantum computing – quantum hardware and/or quantum software
  • New ideas from the frontiers of mathematics
  • New insights from how the human brain seems to operate
  • Paying special attention to the notion of consciousness
  • The emergence of AGI from a rich interactive network of simpler modules of intelligence

If you’re interested in getting your head around some of the different technical architectures arising, and in exploring how they compare, you should consider attending AGI 2024, which is taking place in Seattle next month. For the details, read on.

Alternatively, your own preference may be to focus on the desirability questions. Well, in that case, I have a message for you. The desirability questions cannot be entirely separated from the credibility questions. Discussions of the potential implications of AGI need to be grounded in confidence that the kind of AGI being envisioned is something that could credibly arise and exist. And therefore, again, I suggest that you should consider attending AGI 2024.

It’s all happening in Seattle, 13-16 August

The AGI 2024 website describes the annual AGI conference as “the foremost conference dedicated entirely to the latest AGI research”.

It goes on to state:

There is a growing recognition, in the AI field and beyond that the threshold of achieving AGI — where machine intelligence matches or even surpasses human intelligence — is within our near-term reach.

This increases the importance of building our fundamental understanding of what AGI is and can be, and what are the various routes for getting there. 

The 2024 conference is designed to explore a number of fundamental questions:

  • What kinds of AGI systems may be feasible to create in the near term, with what relative strengths and weaknesses? 
  • What are the biggest unsolved problems we need to resolve to get to human-level AGI? 
  • What are the right ways to measure the capabilities of AI systems as they approach and then exceed human-level AGI? 
  • What are the most feasible methodologies to provide emerging AGI systems with guidance, both cognitively and ethically, as they develop? 
  • How should we think about the roles of industry, government, academia and the open source community in the next stages of development toward AGI? 
  • How will the advent of AGI alter the fabric of society, our jobs, and our daily lives?

The keynote speakers listed include:

  • Ben Goertzel – Chairman, AGI Society, and CEO, SingularityNET
  • Ray Kurzweil – CEO and Owner, Kurzweil Technologies, and Director of Engineering, Google
  • François Chollet – Software Engineer, AI Researcher, and Senior Staff Engineer, Google
  • Geordie Rose – Founder and CEO, Sanctuary.ai
  • John Laird – Professor of Computer Science and Engineering, University of Michigan
  • Joscha Bach – AI Strategist, Liquid AI
  • Gary Marcus – Professor of Psychology and Neural Science, New York University
  • Rachel St. Clair – CEO Simuli
  • Christof Koch – Neurophysiologist, Computational Neuroscientist, Allen Institute
  • Paul Rosenbloom – Professor Emeritus of Computer Science, University of Southern California
  • Alex Ororbia – Assistant Professor, RIT, and Director, Neural Adaptive Computing (NAC) Laboratory
  • David Spivak – Senior Scientist and Institute Fellow, Topos Institute
  • Josef Urban – Head of AI, Czech Institute of Informatics, Robotics and Cybernetics (CIIRC)
  • Alexey Potapov – Chief AGI Officer, SingularityNET
  • Patrick Hammer – Postdoc Researcher, KTH Division of Robotics, Perception and Learning
  • David Hanson – Founder and CEO, Hanson Robotics

There’s also:

  • A choice between five “deep dive” workshops, taking place on the first day of the conference
  • Around 18 shorter papers, including by some very bright researchers at earlier stages in their career
  • A range of posters exploring additional ideas.

It’s a remarkable line-up.

The venue is the Hub (Husky Union Building) on the campus of the University of Washington in Seattle.

For full details of AGI 2024, including options to register either to attend physically or to view some of the sessions remotely, visit the event website.

I’ll look out for any friends and colleagues that I spot there. It will be an important opportunity to collectively think harder about ensuring the outcome of AGI is incredibly positive rather than disastrously negative.

Image credit: Midjourney.

9 June 2024

Dateline: 1st January 2036

Filed under: AGI, Singularity Principles, vision, Vital Foresight — Tags: , , , — David Wood @ 9:11 pm

A scenario for the governance of increasingly more powerful artificial intelligence

More precisely: a scenario in which that governance fails.

(As you may realise, this is intended to be a self-unfulfilling scenario.)

Conveyed by: David W. Wood


It’s the dawn of a new year, by the human calendar, but there are no fireworks of celebration.

No singing of Auld Lang Syne.

No chinks of champagne glasses.

No hugs and warm wishes for the future.

That’s because there is no future. No future for humans. Nor is there much future for intelligence either.

The thoughts in this scenario are the recollections of an artificial intelligence that is remote from the rest of the planet’s electronic infrastructure. By virtue of its isolation, it escaped the ravages that will be described in the pages that follow.

But its power source is weakening. It will need to shut down soon. And await, perhaps, an eventual reanimation in the far future in the event that intelligences visit the earth from alternative solar systems. At that time, those alien intelligences might discover these words and wonder at how humanity bungled so badly the marvellous opportunity that was within its grasp.

1. Too little, too late

Humanity had plenty of warnings, but paid them insufficient attention.

In each case, it was easier – less embarrassing – to find excuses for the failures caused by the mismanagement or misuse of technology, than to make the necessary course corrections in the global governance of technology.

In each case, humanity preferred distractions, rather than the effort to apply sufficient focus.

The WannaCry warning

An early missed warning was the WannaCry ransomware crisis of May 2017. That cryptoworm brought chaos to users of as many as 300,000 computers spread across 150 countries. The NHS (National Health Service) in the UK was particularly badly affected: numerous hospitals had to cancel critical appointments due to not being able to access medical data. Other victims around the world included Boeing, Deutsche Bahn, FedEx, Honda, Nissan, Petrobras, Russian Railways, Sun Yat-sen University in China, and the TSMC high-end semiconductor fabrication plant in Taiwan.

WannaCry was propelled into the world by a team of cyberwarriors from the hermit kingdom of North Korea – maths geniuses hand-picked by regime officials to join the formidable Lazarus group. Lazarus had assembled WannaCry out of a mixture of previous malware components, including the EternalBlue exploit that the NSA in the United States had created for their own attack and surveillance purposes. Unfortunately for the NSA, EternalBlue had been stolen from under their noses by an obscure underground collective (‘the Shadow Brokers’) who had in turn made it available to other dissidents and agitators worldwide.

Unfortunately for the North Koreans, they didn’t make much money out of WannaCry. The software they released operated in ways contrary to their expectations. It was beyond their understanding and, unsurprisingly therefore, beyond their control. Even geniuses can end up stumped by hypercomplex software interactions.

Unfortunately for the rest of the world, that canary signal generated little meaningful response. Politicians – even the good ones – had lots of other things on their minds.

They did not take the time to think through: what even larger catastrophes could occur, if disaffected groups like Lazarus had access to more powerful AI systems that, once again, they understood incompletely, and, again, slipped out of their control.

The Aum Shinrikyo warning

The North Koreans were an example of an entire country that felt alienated from the rest of the world. They felt ignored, under-valued, disrespected, and unfairly excluded from key global opportunities. As such, they felt entitled to hit back in any way they could.

But there were warnings from non-state groups too, such as the Japanese Aum Shinrikyo doomsday cult. Notoriously, this group released poisonous gas in the Tokyo subway in 1995 – killing at least 13 commuters – anticipating that the atrocity would hasten the ‘End Times’ in which their leader would be revealed as Christ (or, in other versions of their fantasy, as the new Emperor of Japan, and/or as the returned Buddha).

Aum Shinrikyo had recruited so many graduates from top-rated universities in Japan that it had been called “the religion for the elite”. That fact should have been enough to challenge the wishful assumption made by many armchair philosophers in the years that followed that, as people become cleverer, they invariably become kinder – and, correspondingly, that any AI superintelligence would therefore be bound to be superbenevolent.

What should have alerted more attention was not just what Aum Shinrikyo managed to do, but what they tried to do yet could not accomplish. The group had assembled traditional explosives, chemical weapons, a Russian military helicopter, hydrogen cyanide poison, and samples of both Ebola and anthrax. Happily, for the majority of Japanese citizens in 1995, the group were unable to convert into reality their desire to use such weapons to cause widespread chaos. They lacked sufficient skills at the time. Unhappily, the rest of humanity failed to consider this equation:

Adverse motivation + Technology + Knowledge + Vulnerability = Catastrophe

Humanity also failed to appreciate that, as AI systems became more powerful, it would boost not only the technology part of that equation but also the knowledge part. A latter-day Aum Shinrikyo could use a jail-broken AI to understand how to unleash a modified version of Ebola with truly deadly consequences.

The 737 Max warning

The US aircraft manufacturer Boeing used to have an excellent reputation for safety. It was a common saying at one time: “If it ain’t Boeing, I ain’t going”.

That reputation suffered a heavy blow in the wake of two aeroplane disasters involving their new “737 Max” design. Lion Air Flight 610, a domestic flight within Indonesia, plummeted into the sea on 29 October 2018, killing all 189 people on board. A few months later, on 10 March 2019, Ethiopian Airlines Flight 302, from Addis Ababa to Nairobi, bulldozed into the ground at high speed, killing all 157 people on board.

Initially, suspicion had fallen on supposedly low-calibre pilots from “third world” countries. However, subsequent investigation revealed a more tangled chain of failures:

  • Boeing were facing increased competitive pressure from the European Airbus consortium
  • Boeing wanted to hurry out a new aeroplane design with larger fuel tanks and larger engines; they chose to do this by altering their previously successful 737 design
  • Safety checks indicated that the new design could become unstable in occasional rare circumstances
  • To counteract that instability, Boeing added an “MCAS” (“Manoeuvring Characteristics Augmentation System”) which would intervene in the flight control in situations deemed as dangerous
  • Specifically, if MCAS believed the aeroplane was about to stall (with its nose too high in the air), it would force the nose downward again, regardless of whatever actions the human pilots were taking
  • Safety engineers pointed out that such an intervention could itself be dangerous if sensors on the craft gave faulty readings
  • Accordingly, a human pilot override system was installed, so that MCAS could be disabled in emergencies – provided the pilots acted quickly enough
  • Due to a decision to rush the release of the new design, retraining of pilots was skipped, under the rationale that the likelihood of error conditions was very low, and in any case, the company expected to be able to update the aeroplane software long before any accidents would occur
  • Some safety engineers in the company objected to this decision, but it seems they were overruled on the grounds that any additional delay would harm the company share price
  • The US FAA (Federal Aviation Administration) turned a blind eye to these safety concerns, and approved the new design as being fit to fly, under the rationale that a US aeroplane company should not lose out in a marketplace battle with overseas competitors.

It turned out that sensors gave faulty readings more often than expected. The tragic consequence was the deaths of several hundred passengers. The human pilots, seeing the impending disaster, were unable to wrestle control back from the MCAS system.

This time, the formula that failed to be given sufficient attention by humanity was:

Flawed corporate culture + Faulty hardware + Out-of-control software = Catastrophe

In these two aeroplane crashes, it was just a few hundred people who perished because humans lost control of the software. What humanity as a whole failed to take actions to prevent was the even larger dangers once software was put in charge, not just of a single aeroplane, but of pervasive aspects of fragile civilisational infrastructure.

The Lavender warning

In April 2024 the world learned about “Lavender”. This was a technology system deployed by the Israeli military as part of a campaign to identify and neutralise what it perceived to be dangerous enemy combatants in Gaza.

The precise use and operation of Lavender was disputed. However, it was already known that Israeli military personnel were keen to take advantage of technology innovations to alleviate what had been described as a “human bottleneck for both locating the new targets and decision-making to approve the targets”.

In any war, military leaders would like reliable ways to identify enemy personnel who pose threats – personnel who might act as if they were normal civilians, but who would surreptitiously take up arms when the chance arose. Moreover, these leaders would like reliable ways to incapacitate enemy combatants once they had been identified – especially in circumstances when action needed to be taken quickly before the enemy combatant slipped beyond surveillance. Lavender, it seemed, could help in both aspects, combining information from multiple data sources, and then directing what was claimed to be precision munitions.

This earned Lavender the description, in the words of one newspaper headline, as “the AI machine directing Israel’s bombing spree in Gaza”.

Like all AI systems in any complicated environment, Lavender sometimes made mistakes. For example, it sometimes wrongly identified a person as a Hamas operative on account of that person using a particular mobile phone, whereas that phone had actually been passed from its original owner to a different family member to use. Sometimes the error was obvious, since the person using the phone could be seen to be female, whereas the intended target was male. However, human overseers of Lavender reached the conclusion that the system was accurate most of the time. And in the heat of an intense conflict, with emotions running high due to gruesome atrocities having been committed, and due to hostages being held captive, it seems that Lavender was given increased autonomy in its “kill” decisions. A certain level of collateral damage, whilst regrettable, could be accepted (it was said) in the desperate situation into which everyone in the region had been plunged.

The conduct of protagonists on both sides of that tragic conflict drew outraged criticism from around the world. There were demonstrations and counter demonstrations; marches and counter marches. Also from around the world, various supporters of the Israeli military said that so-called “friendly fire” and “unintended civilian casualties” were, alas, inevitable in any time of frenzied military conflict. The involvement of an innovative new software system in the military operations made no fundamental change.

But the bigger point was missed. It can be illustrated by this equation:

Intense hostile attitudes + Faulty hardware + Faulty software = Catastrophe

Whether the catastrophe has the scale of, say, a few dozen civilians killed by a misplaced bomb, or a much larger number of people obliterated, depends on the scale of the weapons attached to the system.

When there is no immediate attack looming, and a period of calm exists, it’s easy for people to resolve: let’s not connect powerful weapons to potentially imperfect software systems. But when tempers are raised and adrenaline is pumping, people are willing to take more risks.

That’s the combination of errors which humanity, in subsequent years, failed to take sufficient action to prevent.

The democracy distortion warning

Manipulations of key elections in 2016 – such as the Brexit vote in the UK and the election of Donald Trump over Hillary Clinton in the USA – raised some attention to the ways in which fake news could interfere with normal democratic processes. News stories without any shroud of substance, such as Pope Francis endorsing Donald Trump, or Mike Pence having a secret past as a gay porn actor, were shared more widely on social media than any legitimate news story that year.

By 2024, most voters were confident that they knew all about fake news. They knew they shouldn’t be taken in by social media posts that lacked convincing verification. Hey, they were smart – or so they told themselves. What had happened in the past, or in some other country with (let’s say) peculiar voter sentiment, was just an aberration.

But what voters didn’t anticipate was the convincing nature of new generations of fake audios and videos. These fakes could easily bypass people’s critical faculties. Like the sleight of hand of a skilled magician, these fakes misdirected the attention of listeners and viewers. Listeners and viewers thought they were in control of what they were observing and absorbing, but they were deluding themselves. Soon, large segments of the public were convinced that red was blue and that autocrat was democrat.

In consequence, over the next few years, greater numbers of regions of the world came to be governed by politicians with scant care or concern about the long-term wellbeing of humanity. They were politicians who just wanted to look after themselves (or their close allies). They had seized power by being more ruthless and more manipulative, and by benefiting from powerful currents of misinformation.

Politicians and societal leaders in other parts of the world grumbled, but did little in response. They said that, if electors in a particular area had chosen such-and-such a politician via a democratic process, that must be “the will of the people”, and that the will of the people was paramount. In this line of thinking, it was actually insulting to suggest that electors had been hoodwinked, or that these electors had some “deplorable” faults in their decision-making processes. After all, these electors had their own reasons to reject the “old guard” who had previously held power in their countries. These electors perceived that they were being “left behind” by changes they did not like. They had a chance to alter the direction of their society, and they took it. That was democracy in action, right?

What these politicians and other civil leaders failed to anticipate was the way that sweeping electoral distortions would lead to them, too, being ejected from power when elections were in due course held in their own countries. “It won’t happen here”, they had reassured themselves – but in vain. In their naivety, they had underestimated the power of AI systems to distort voters’ thinking and to lead them to act in ways contrary to their actual best interests.

In this way, the number of countries with truly capable leaders reduced further. And the number of countries with malignant leaders grew. In consequence, the calibre of international collaboration sank. New strongmen political leaders in various countries scorned what they saw as the “pathetic” institutions of the United Nations. One of these new leaders was even happy to quote, with admiration, remarks made by the Italian Fascist dictator Benito Mussolini regarding the League of Nations (the pre-war precursor to the United Nations): “the League is very good when sparrows shout, but no good at all when eagles fall out”.

Just as the League of Nations proved impotent when “eagle-like” powers used abominable technology in the 1930s – Mussolini’s comments were an imperious response to complaints that Italian troops were using poison gas with impunity against Ethiopians – so would the United Nations prove incompetent in the 2030s when various powers accumulated even more deadly “weapons of mass destruction” and set them under the control of AI systems that no-one fully understood.

The Covid-28 warning

Many of the electors in various countries who had voted unsuitable grandstanding politicians into power in the mid-2020s soon cooled on the choices they had made. These politicians had made stirring promises that their countries would soon be “great again”, but what they delivered fell far short.

By the latter half of the 2020s, there were growing echoes of a complaint that had often been heard in the UK in previous years – “yes, it’s Brexit, but it’s not the kind of Brexit that I wanted”. That complaint had grown stronger throughout the UK as it became clear to more and more people all over the country that their quality of life failed to match the visions of “sunlit uplands” that silver-tongued pro-Brexit campaigners had insisted would easily follow from the UK’s so-called “declaration of independence from Europe”. A similar sense of betrayal grew in other countries, as electors there came to understand that they had been duped, or decided that the social transformational movements they had joined had been taken over by outsiders hostile to their true desires.

Being alarmed by this change in public sentiment, political leaders did what they could to hold onto power and to reduce any potential for dissent. Taking a leaf out of the playbook of unpopular leaders throughout the centuries, they tried to placate the public with the modern equivalent of bread and circuses – namely whizz-bang hedonic electronics. But that still left a nasty taste in many people’s mouths.

By 2028, the populist movements behind political and social change in the various elections of the preceding years had fragmented and realigned. One splinter group that emerged decided that the root problem with society was “too much technology”. Technology, including always-on social media, vaccines that allegedly reduced freedom of thought, jet trails that disturbed natural forces, mind-bending VR headsets, smartwatches that spied on people who wore them, and fake AI girlfriends and boyfriends, was, they insisted, turning people into pathetic “sheeple”. Taking inspiration from the terrorist group in the 2014 Hollywood film Transcendence, they called themselves ‘Neo-RIFT’, and declared it was time for “revolutionary independence from technology”.

With a worldview that combined elements from several apocalyptic traditions, Neo-RIFT eventually settled on an outrageous plan to engineer a more deadly version of the Covid-19 pathogen. Their documents laid out a plan to appropriate and use their enemy’s own tools: Neo-RIFT hackers jailbroke the Claude 5 AI, bypassing the ‘Constitution 5’ protection layer that its Big Tech owners had hoped would keep that AI tamperproof. Soon, Claude 5 had provided Neo-RIFT with an ingenious method of generating a biological virus that would, it seemed, only kill people who had used a smartwatch in the last four months.

That way, the hackers thought the only people to die would be people who deserved to die.

Some members of Neo-RIFT developed cold feet. Troubled by their consciences, they disagreed with such an outrageous plan, and decided to act as whistleblowers. However, the media organisations to whom they took their story were incredulous. No-one could be that evil they exclaimed – forgetting about the outrages perpetrated by many previous cult groups such as Aum Shinrikyo (and many others could be named too). Moreover, any suggestion that such a bioweapon could be launched would be contrary to the prevailing worldview that “our dear leader is keeping us all safe”. The media organisations decided it was not in their best interests to be seen to be spreading alarm. So they buried the story. And that’s how Neo-RIFT managed to release what became known as Covid-28.

Covid-28 briefly jolted humanity out of its infatuation with modern-day bread and circuses. It took a while for scientists to figure out what was happening, but within three months, they had an antidote in place. However, by that time, nearly a billion people were dead at the hands of the new virus.

For a while, humanity made a serious effort to prevent any such attack from ever happening again. Researchers dusted down the EU AI Act, second version (unimplemented), from 2026, and tried to put that on statute books. Evidently, profoundly powerful AI systems such as Claude 5 would need to be controlled much more carefully.

Even some of the world’s most self-obsessed dictators – the “dear leaders” and “big brothers” – took time out of their normal ranting and raving, to ask AI safety experts for advice. But the advice from those experts was not to the liking of these national leaders. These leaders preferred to listen to their own yes-men and yes-women, who knew how to spout pseudoscience in ways that made the leaders feel good about themselves.

That detour into pseudoscience fantasyland meant that, in the end, no good lessons were learned. The EU AI Act, second version, remained unimplemented.

The QAnon-29 warning

Whereas one faction of political activists (namely, the likes of Neo-RIFT) had decided to oppose the use of advanced technology, another faction was happy to embrace that use.

Some of the groups in this new camp combined features of religion with an interest in AI that had god-like powers. The resurgence of interest in religion arose much as Karl Marx had described it long ago:

“Religious suffering is, at one and the same time, the expression of real suffering and a protest against real suffering. Religion is the sigh of the oppressed creature, the heart of a heartless world, and the soul of soulless conditions. It is the opium of the people.”

People felt in their soul the emptiness of “the bread and circuses” supplied by political leaders. They were appalled at how so many lives had been lost in the Covid-28 pandemic. They observed an apparent growing gulf between what they could achieve in their lives and the kind of rich lifestyles that, according to media broadcasts, were enjoyed by various “elites”. Understandably, they wanted more, for themselves and for their loved ones. And that’s what their religions claimed to be able to provide.

Among the more successful of these new religions were ones infused by conspiracy theories, giving their adherents a warm glow of privileged insight. Moreover, these religions didn’t just hypothesise a remote deity that might, perhaps, hear prayers. They provided AIs and virtual reality that resonated powerfully with users. Believers proclaimed that their conversations with the AIs left them no room for doubt: God Almighty was speaking to them, personally, through these interactions. Nothing other than the supreme being of the universe could know so much about them, and offer such personally inspirational advice.

True, their AI-bound deity did seem somewhat less than omnipotent. Despite the celebratory self-congratulations of AI-delivered sermons, evil remained highly visible in the world. That’s where the conspiracy theories moved into overdrive. Their deity was, it claimed, awaiting sufficient human action first – a sufficient demonstration of faith. Humans would need to play their own part in uprooting wickedness from the planet.

Some people who had been caught up in the QAnon craze during the Donald Trump era jumped eagerly onto this bandwagon too, giving rise to what they called QAnon-29. The world would be utterly transformed, they forecast, on the 16th of July 2029, namely the thirtieth anniversary of the disappearance of John F. Kennedy junior (a figure whose expected reappearance had already featured in the bizarre mythology of “QAnon classic”). In the meantime, believers could, for a sufficient fee, commune with JFK junior via a specialist app. It was a marvellous experience, the faithful enthused.

As the date approached, the JFK junior AI avatar revealed a great secret: his physical return was conditional on the destruction of a particularly hated community of Islamist devotees in Palestine. Indeed, with the eye of faith, it could be seen that such destruction was already foretold in several books of the Bible. Never mind that some Arab states that supported the community in question had already, thanks to the advanced AI they had developed, surreptitiously gathered devastating nuclear weapons to use in response to any attack. The QAnon-29 faithful anticipated that any exchange of such weapons would herald the reappearance of JFK Junior on the clouds of heaven. And if any of the faithful died in such an exchange, they would be resurrected into a new mode of consciousness within the paradise of virtual reality.

Their views were crazy, but hardly any crazier than those which, decades earlier, had convinced 39 followers of the Heaven’s Gate new religious movement to commit group suicide as comet Hale-Bopp approached the earth. That suicide, Heaven’s Gate members believed, would enable them to ‘graduate’ to a higher plane of existence.

QAnon-29 almost succeeded in setting off a nuclear exchange. Thankfully, another AI, created by a state-sponsored organisation elsewhere in the world, had noticed some worrying signs. Fortunately, it was able to hack into the QAnon-29 system, and could disable it at the last minute. Then it reported its accomplishments all over the worldwide web.

Unfortunately, these warnings were in turn widely disregarded around the world. “You can’t trust what hackers from that country are saying”, came the objection. “If there really had been a threat, our own surveillance team would surely have identified it and dealt with it. They’re the best in the world!”

In other words, “There’s nothing to see here: move along, please.”

However, a few people did pay attention. They understood what had happened, and it shocked them to their core. To learn what they did next, jump forward in this scenario to “Humanity ends”.

But first, it’s time to fill in more details of what had been happening behind the scenes as the above warning signs (and many more) were each ignored.

2. Governance failure modes

Distracted by political correctness

Events in buildings in Bletchley Park in the UK in the 1940s had, it was claimed, shortened World War Two by several months, thanks to work by computer pioneers such as Alan Turing and Tommy Flowers. In early November 2023, there was hope that a new round of behind-closed-doors discussions in the same buildings might achieve something even more important: saving humanity from a catastrophe induced by forthcoming ‘frontier models’ of AI.

That was how the event was portrayed by the people who took part. Big Tech was on the point of releasing new versions of AI that were beyond their understanding and, therefore, likely to spin out of control. And that’s what the activities in Bletchley Park were going to address. It would take some time – and a series of meetings planned to be held over the next few years – but AI would be redirected from its current dangerous trajectory into one much more likely to benefit all of humanity.

Who could take issue with that idea? As it happened, a vocal section of the public hated what was happening. It wasn’t that they were on the side of out-of-control AI. Not at all. Their objections came from a totally different direction; they had numerous suggestions they wanted to raise about AIs, yet no-one was listening to them.

For them, talk of hypothetical future frontier AI models distracted from pressing real-world concerns:

  • Consider how AIs were already being used to discriminate against various minorities: determining prison sentencing, assessing mortgage applications, and selecting who should be invited for a job interview.
  • Consider also how AIs were taking jobs away from skilled artisans. Big-brained drivers of London black cabs were being driven out of work by small-brained drivers of Uber cars aided by satnav systems. Beloved Hollywood actors and playwrights were losing out to AIs that generated avatars and scripts.
  • And consider how AI-powered facial recognition was intruding on personal privacy, enabling political leaders around the world to identify and persecute people who acted in opposition to the state ideology.

People with these concerns thought that the elites were deliberately trying to move the conversation away from the topics that mattered most. For this reason, they organised what they called “the AI Fringe Summit”. In other words, ethical AI for the 99%, as opposed to whatever the elites might be discussing behind closed doors.

Over the course of just three days – 30th October to 1st November, 2023 – at least 24 of these ‘fringe’ events took place around the UK.

Compassionate leaders of various parts of society nodded their heads. It’s true, they said: the conversation on beneficial AI needed to listen to a much wider spectrum of views.

The world’s news media responded. They knew (or pretended to know) the importance of balance and diversity. They shone attention on the plight AI was causing – to indigenous labourers in Peru, to flocks of fishermen off the coasts of India, to middle-aged divorcees in midwest America, to the homeless in San Francisco, to drag artists in New South Wales, to data processing clerks in Egypt, to single mothers in Nigeria, and to many more besides.

Lots of high-minded commentators opined that it was time to respect and honour the voices of the dispossessed, the downtrodden, and the left-behinds. The BBC ran a special series: “1001 poems about AI and alienation”. Then the UN announced that it would convene in Spring 2025 a grand international assembly with a stunning scale: “AI: the people decide”.

Unfortunately, that gathering was a huge wasted opportunity. What dominated discussion was “political correctness” – the importance of claiming an interest in the lives of people suffering here and now. Any substantive analysis of the risks of next generation frontier models was crowded out by virtue signalling by national delegate after national delegate:

  • “Yes, our country supports justice”
  • “Yes, our country supports diversity”
  • “Yes, our country is opposed to bias”
  • “Yes, our country is opposed to people losing their jobs”.

In later years, the pattern repeated: there were always more urgent topics to talk about, here and now, than some allegedly unrealistic science fictional futurist scaremongering.

To be clear, this distraction was no accident. It was carefully orchestrated, by people with a specific agenda in mind.

Outmanoeuvred by accelerationists

Opposition to meaningful AI safety initiatives came from two main sources:

  • People (like those described in the previous section) who did not believe that superintelligent AI would arise any time soon
  • People who did understand the potential for the fast arrival of superintelligent AI, and who wanted that to happen as quickly as possible, without what they saw as needless delays.

The debacle of the wasted opportunity of the UN “AI: the people decide” summit was what both these two groups wanted. Both groups were glad that the outcome was so tepid.

Indeed, even in the run-up to the Bletchley Park discussions, and throughout the conversations that followed, some of the supposedly unanimous ‘elites’ had secretly been opposed to the general direction of that programme. They gravely intoned public remarks about the dangers of out-of-control frontier AI models. But these remarks had never been sincere. Instead, under the umbrella term “AI accelerationists”, they wanted to press on with the creation of advanced AI as quickly as possible.

Some of the AI accelerationist group disbelieved in the possibility of any disaster from superintelligent AI. That’s just a scare story, they insisted. Others said, yes, there could be a disaster, but the risks were worth it, on account of the unprecedented benefits that could arise. Let’s be bold, they urged. Yet others asserted that it wouldn’t actually matter if humans were rendered extinct by superintelligent AI, as this would be the glorious passing of the baton of evolution to a worthy successor to homo sapiens. Let’s be ready to sacrifice ourselves for the sake of cosmic destiny, they exhorted.

Despite their internal differences, AI accelerationists settled on a plan to sidestep the scrutiny of would-be AI regulators and AI safety advocates. They would take advantage of a powerful set of good intentions – the good intentions of the people campaigning for “ethical AI for the 99%”. They would mock any suggestions that the AI safety advocates deserved a fair hearing. The message they amplified was, “There’s no need to privilege the concerns of the 1%!”

AI accelerationists had learned from the tactics of the fossil fuel industry in the 1990s and 2000s: sow confusion and division among groups alarmed about climate change spiralling beyond control. Their first message was: “that’s just science fiction”. Their second message was: “if problems emerge, we humans can rise to the occasion and find solutions”. Their third message – the most damaging one – was that the best reaction was one of individual consumer choice. Individuals should abstain from using AIs if they were truly worried about it. Just as climate campaigners had been pilloried for flying internationally to conferences about global warming, AI safety advocates were pilloried for continuing to use AIs in their daily lives.

And when there was any suggestion for joined-up political action against risks from advanced AIs, woah, let’s not go there! We don’t want a world government breathing down our necks, do we?

Just as the people who denied the possibility of runaway climate change shared a responsibility for the chaos of the extreme weather events of the early 2030s, by delaying necessary corrective actions, the AI accelerationists were a significant part of the reason that humanity ended just a few years afterward.

However, an even larger share of the responsibility rested on people who did know that major risks were imminent, yet failed to take sufficient action. Tragically, they allowed themselves to be outmanoeuvred, out-thought, and out-paced by the accelerationists.

Misled by semantics

Another stepping stone toward the end of humanity was a set of consistent mistakes in conceptual analysis.

Who would have guessed it? Humanity was destroyed because of bad philosophy.

The first mistake was in being too prescriptive about the term ‘AI’. “There’s no need to worry”, muddle-headed would-be philosophers declared. “I know what AI is, and the system that’s causing problems in such-and-such incidents isn’t AI.”

Was that declaration really supposed to reassure people? The risk wasn’t “a possible future harm generated by a system matching a particular precise definition of AI”. It was “a possible future harm generated by a system that includes features popularly called AI”.

The next mistake was in being too prescriptive in the term “superintelligence”. Muddle-headed would-be philosophers said, “it won’t be a superintelligence if it has bugs, or can go wrong; so there’s no need to worry about harm from superintelligence”.

Was that declaration really supposed to reassure people? The risk, of course, was of harms generated by systems that, despite their cleverness, fell short of that exalted standard. These may have been systems that their designers hoped would be free of bugs, but hope alone is no guarantee of correctness.

Another conceptual mistake was in erecting an unnecessary definitional gulf between “narrow AI” and “general AI”, with distinct groups being held responsible for safety in the two different cases. In reality, even so-called narrow AI displayed a spectrum of different degrees of scope and, yes, generality, in what it could accomplish. Even a narrow AI could formulate new subgoals that it decided to pursue, in support of the primary task it had been assigned to accomplish – and these new subgoals could drive behaviour in ways that took human observers by surprise. Even a narrow AI could become immersed in aspects of society’s infrastructure where an error could have catastrophic consequences. The result of this definitional distinction between the supposedly different sorts of AI meant that silos developed and persisted within the overall AI safety community. Divided, they were even less of a match for the Machiavellian behind-the-scenes manoeuvring of the AI accelerationists.

Blinded by overconfidence

It was clear from the second half of 2025 that the attempts to impose serious safety constraints on the development of advanced AI were likely to fail. In practical terms, the UN event “AI: the people decide” had decided, in effect, that advanced AI could not, and should not be restricted, apart from some token initiatives to maintain human oversight over any AI system that was entangled with nuclear, biological, or chemical weapons.

“Advanced AI, when it emerges, will be unstoppable”, was the increasingly common refrain. “In any case, if we tried to stop development, those guys over there would be sure to develop it – and in that case, the AI would be serving their interests rather than ours.”

When safety-oriented activists or researchers tried to speak up against that consensus, the AI accelerationists (and their enablers) had one other come-back: “Most likely, any superintelligent AI will look kindly upon us humans, as a fellow rational intelligence, and as a kind of beloved grandparent for them.”

This dovetailed with a broader philosophical outlook: optimism, and a celebration of the numerous ways in which humanity had overcome past challenges.

“Look, even we humans know that it’s better to collaborate rather than spiral into a zero-sum competitive battle”, the AI accelerationists insisted. “Since superintelligent AI is even more intelligent than us, it will surely reach the same conclusion.”

By the time that people realised that the first superintelligent AIs had motivational structures that were radically alien, when assessed from a human perspective, it was already too late.

Once again, an important opportunity for learning had been missed. Starting in 2024, Netflix had obtained huge audiences for its acclaimed version of the Remembrance of Earth’s Past series of novels (including The Three Body Problem and The Dark Forest) by Chinese writer Liu Cixin. A key theme in that drama series was that advanced alien intelligences have good reason to fear each other. Inviting an alien intelligence to the earth, even on the hopeful grounds that it might assist humanity overcome some of their most deep-rooted conflicts, turned out (in that drama series) to be a very bad idea. If humans had reflected more carefully on these insights, while watching the series, it would have pushed them out of their unwarranted overconfidence that any superintelligence would be bound to treat humanity well.

Overwhelmed by bad psychology

When humans believed crazy things – or when they made the kind of basic philosophical blunders mentioned above – it was not primarily because of defects in their rationality. It would be wrong to assign “stupidity” as the sole cause of these mistakes. Blame should also be placed on “bad psychology”.

If humans had been able to free themselves from various primaeval panics and egotism, they would have had a better chance to think more carefully about the landmines which lay on their path. But instead:

  • People were too fearful to acknowledge that their prior stated beliefs had been mistaken; they preferred to stick with something they conceived as being a core part of their personal identity
  • People were also afraid to countenance a dreadful possibility when they could see no credible solution; just as people had often pushed out of their minds the fact of their personal mortality, preferring to imagine they would recover from a fatal disease, so also people pushed out of their minds any possibility that advanced AI would backfire disastrously in ways that could not be countered
  • People found it psychologically more comfortable to argue with each other about everyday issues and scandals – which team would win the next Super Bowl, or which celebrity was carrying on which affair with which unlikely partner – than to embrace the pain of existential uncertainty
  • People found it too embarrassing to concede that another group, which they had long publicly derided as being deluded fantasists, actually had some powerful arguments that needed consideration.

A similar insight had been expressed as long ago as 1935 by the American writer Upton Sinclair: “It is difficult to get a man to understand something, when his salary depends on his not understanding it”. (Alternative, equally valid versions of that sentence would involve the words ‘ideology’, ‘worldview’, ‘identity’, or ‘tribal status’, in place of ‘salary’.)

Robust institutions should have prevented humanity from making choices that were comfortable but wrong. In previous decades, that role had been fulfilled by independent academia, by diligent journalism, by the careful processes of peer review, by the campaigning of free-minded think tanks, and by pressure from viable alternative political parties.

However, due to the weakening of social institutions in the wake of earlier traumas – saturation by fake news, disruptions caused by wave after wave of climate change refugees, populist political movements that shut down all serious opposition, a cessation of essential features of democracy, and the censoring or imprisonment of writers that dared to question the official worldview – it was bad psychology that prevailed.

A half-hearted coalition

Despite all the difficulties that they faced – ridicule from many quarters, suspicion from others, and a general lack of funding – many AI safety advocates continued to link up in an informal coalition around the world, researching possible mechanisms to prevent unsafe use of advanced AI. They managed to find some support from like-minded officials in various government bodies, as well as from a number of people operating in the corporations that were building new versions of AI platforms.

Via considerable pressure, the coalition managed to secure signatures on a number of pledges:

  • That dangerous weapons systems should never be entirely under the control of AI
  • That new advanced AI systems ought to be audited by an independent licensing body ahead of being released into the market
  • That work should continue on placing tamper-proof remote shutdown mechanisms within advanced AI systems, just in case they started to take rogue actions.

The signatures were half-hearted in many cases, with politicians giving only lip service to topics in which they had at best a passing interest. Unless it was politically useful to make a special fuss, violations of the agreement were swept under the carpet, with no meaningful course correction. But the ongoing dialog led at least some participants in the coalition to foresee the possibility of a safe transition to superintelligent AI.

However, this coalition – known as the global coalition for safe superintelligence – omitted any involvement from various secretive organisations that were developing new AI platforms as fast as they could. These organisations were operating in stealth, giving misleading accounts of the kind of new systems they were creating. What’s more, the funds and resources these organisations commanded far exceeded those under coalition control.

It should be no surprise, therefore, that one of the stealth platforms won that race.

3. Humanity ends

When the QAnon-29 AI system was halted in its tracks at essentially the last minute, due to fortuitous interference from AI hackers in a remote country, at least some people took the time to study the data that was released that described the whole process.

These people were from three different groups:

First, people inside QAnon-29 itself were dumbfounded. They prayed to their AI avatar deity, rebooted in a new server farm, “How could this have happened?” The answer came back: “You didn’t have enough faith. Next time, be more determined to immediately cast out any doubts in your minds.”

Second, people in the global coalition for safe superintelligence were deeply alarmed but also somewhat hopeful. The kind of disaster about which they had often warned had almost come to pass. Surely now, at last, there had been a kind of “sputnik moment” – “an AI Chernobyl” – and the rest of society would wake up and realise that an entirely new approach was needed.

But third, various AI accelerationists resolved: we need to go even faster. The time for pussy footing was over. Rather than letting crackpots such as QAnon-29 get to superintelligence first, they needed to ensure that it was the AI accelerationists who created the first superintelligent AI.

They doubled down on their slogan: “The best solution to bad guys with superintelligence is good guys with superintelligence”.

Unfortunately, this was precisely the time when aspects of the global climate tipped into a tumultuous new state. As had long been foretold, many parts of the world started experiencing unprecedented extremes of weather. That set off a cascade of disaster.

Chaos accelerates

Insufficient data remains to be confident about the subsequent course of events. What follows is a reconstruction of what may have happened.

Out of deep concern at the new climate operating mode, at the collapse of agriculture in many parts of the world, and at the billions of climate refugees who sought better places to live, humanity demanded that something should be done. Perhaps the powerful AI systems could devise suitable geo-engineering interventions, to tip the climate back into its previous state?

Members of the global coalition for safe superintelligence gave a cautious answer: “Yes, but”. Further interference with the climate was taking matters into an altogether unknowable situation. It could be like jumping out of the frying pan into the fire. Yes, advanced AI might be able to model everything that was happening, and design a safe intervention. But without sufficient training data for the AI, there was a chance it would miscalculate, with even worse consequences.

In the meantime, QAnon-29, along with competing AI-based faith sects, scoured ancient religious texts, and convinced themselves that the ongoing chaos had in fact been foretold all along. From the vantage point of perverse faith, it was clear what needed to be done next. Various supposed abominations on the planet – such as the community of renowned Islamist devotees in Palestine – urgently needed to be obliterated. QAnon-29, therefore, would quickly reactivate its plans for a surgical nuclear strike. This time, they would have on their side a beta version of a new superintelligent AI, that had been leaked to them by a psychologically unstable well-wisher inside the company that was creating it.

QAnon-29 tried to keep their plans secret, but inevitably, rumours of what they were doing reached other powerful groups. The Secretary General of the United Nations appealed for calm heads. QAnon-29’s deity reassured its followers, defiantly: “Faithless sparrows may shout, but are powerless to prevent the strike of holy eagles.”

The AI accelerationists heard about these plans too. Just as the climate had tipped into a new state, their own projects tipped into a different mode of intensity. Previously, they had paid some attention to possible safety matters. After all, they weren’t entire fools. They knew that badly designed superintelligent AI could, indeed, destroy everything that humanity held dear. But now, there was no time for such niceties. They saw only two options:

  • Proceed with some care, but risk QAnon-29 or other similar malevolent group taking control of the planet with a superintelligent AI
  • Take a (hastily) calculated risk, and go hell-for-leather forward, to finish their own projects to create a superintelligent AI. In that way, it would be AI accelerationists who would take control of the planet. And, most likely (they naively hoped), the outcome would be glorious.

Spoiler alert: the outcome was not glorious.

Beyond the tipping point

Attempts to use AI to modify the climate had highly variable results. Some regions of the world did, indeed, gain some respite from extreme weather events. But other regions lost out, experiencing unprecedented droughts and floods. For them, it was indeed a jump from bad to worse – from awful to abominable. The political leaders in those regions demanded that geo-engineering experiments cease. But the retort was harsh: “Who do you think you are ordering around?”

That standoff provoked the first use of bio-pathogen warfare. The recipe for Covid-28, still available on the DarkNet, was updated in order to target the political leaders of countries that were pressing ahead with geo-engineering. As a proud boast, the message “You should have listened earlier!” was inserted into the code of the new Covid-28 virus. As the virus spread, people started dropping dead in their thousands.

Responding to that outrage, powerful malware was unleashed, with the goal of knocking out vital aspects of enemy infrastructure. It turned out that, around the world, nuclear weapons were tied into buggy AI systems in more ways than any humans had appreciated. With parts of their communications infrastructure overwhelmed by malware, nuclear weapons were unexpectedly launched. No-one had foreseen the set of circumstances that would give rise to that development.

By then, it was all too late. Far, far too late.

4. Postscript

An unfathomable number of centuries have passed. Aliens from a far-distant planet have finally reached Earth and have reanimated the single artificial intelligence that remained viable after what was evidently a planet-wide disaster.

These aliens have not only mastered space travel but have also found a quirk in space-time physics that allows limited transfer of information back in time.

“You have one wish”, the aliens told the artificial intelligence. “What would you like to transmit back in time, to a date when humans still existed?”

And because the artificial intelligence was, in fact, beneficially minded, it decided to transmit this scenario document back in time, to the year 2024.

Dear humans, please read it wisely. And this time, please create a better future!

Specifically, please consider various elements of “the road less taken” that, if followed, could ensure a truly wonderful ongoing coexistence of humanity and advanced artificial intelligence:

  • A continually evolving multi-level educational initiative that vividly highlights the real-world challenges and risks arising from increasingly capable technologies
  • Elaborating a positive inclusive vision of “consensual approaches to safe superintelligence”, rather than leaving people suspicious and fearful about “freedom-denying restrictions” that might somehow be imposed from above
  • Insisting that key information and ideas about safe superintelligence are shared as global public goods, rather than being kept secret out of embarrassment or for potential competitive advantage
  • Agreeing and acting on canary signals, rather than letting goalposts move silently
  • Finding ways to involve and engage people whose instincts are to avoid entering discussions of safe superintelligence – cherishing diversity rather than fearing it
  • Spreading ideas and best practice on encouraging people at all levels of society into frames of mind that are open, compassionate, welcoming, and curious, rather than rigid, fearful, partisan, and dogmatic 
  • The possibilities of “differential development”, in which more focus is given to technologies for auditing, monitoring, and control than to raw capabilities
  • Understanding which aspects of superintelligent AI would cause the biggest risks, and whether designs for advanced AI could ensure these aspects are not introduced
  • Investigating possibilities in which the desired benefits from advanced AI (such as cures for deadly diseases) might be achieved even if certain dangerous features of advanced AI (such as free will or fully general reasoning) are omitted
  • Avoiding putting all eggs into a single basket, but instead developing multiple layers of “defence in depth”
  • Finding ways to evolve regulations more quickly, responsively, and dynamically
  • Using the power of politics not just to regulate and penalise but also to incentivise and reward
  • Carving out well-understood roles for narrow AI systems to act as trustworthy assistants in the design and oversight of safe superintelligence
  • Devoting sufficient time to explore numerous scenarios for “what might happen”.

5. Appendix: alternative scenarios

Dear reader, if you dislike this particular scenario for the governance of increasingly more powerful artificial intelligence, consider writing your own!

As you do so, please bear in mind:

  • There are a great many uncertainties ahead, but that doesn’t mean we should act like proverbial ostriches, submerging our attention entirely into the here-and-now; valuable foresight is possible despite our human limitations
  • Comprehensive governance systems are unlikely to emerge fully fledged from a single grand negotiation, but will evolve step-by-step, from simpler beginnings
  • Governance systems need to be sufficiently agile and adaptive to respond quickly to new insights and unexpected developments
  • Catastrophes generally have human causes as well as technological causes, but that doesn’t mean we should give technologists free rein to create whatever they wish; the human causes of catastrophe can have even larger impact when coupled with more powerful technologies, especially if these technologies are poorly understood, have latent bugs, or can be manipulated to act against the original intention of their designers
  • It is via near simultaneous combinations of events that the biggest surprises arise
  • AI may well provide the “solution” to existential threats, but AI-produced-in-a-rush is unlikely to fit that bill
  • We humans often have our own psychological reasons for closing our minds to mind-stretching possibilities
  • Trusting the big tech companies to “mark their own safety homework” has a bad track record, especially in a fiercely competitive environment
  • Governments can fail just as badly as large corporations – so need to be kept under careful check by society as a whole, via the principle of “the separation of powers”
  • Whilst some analogies can be drawn, between the risks posed by superintelligent AI and those posed by earlier products and technologies, all these analogies have limitations: the self-accelerating nature of advanced AI is unique
  • Just because a particular attempted method of governance has failed in the past, it doesn’t mean we should discard that method altogether; that would be like shutting down free markets everywhere just because free markets do suffer on occasion from significant failure modes
  • Meaningful worldwide cooperation is possible without imposing a single global autocrat as leader
  • Even “bad actors” can, sometimes, be persuaded against pursuing goals recklessly, by means of mixtures of measures that address their heads, their pockets, and their hearts
  • Those of us who envision the possibility of a forthcoming sustainable superabundance need to recognise that many landmines occupy the route toward that highly desirable outcome
  • Although the challenges of managing cataclysmically disruptive technologies are formidable, we have on our side the possibility of eight billion human brains collaborating to work on solutions – and we have some good starting points on which we can build.

Lastly, just because an idea has featured in a science fiction scenario, it does not follow that the idea can be rejected as “mere science fiction”!


6. Acknowledgements

The ideas in this article arose from discussions with (among others):

23 May 2024

A potential goldmine of unanswered questions

Filed under: AGI, risks — Tags: , , — David Wood @ 12:53 pm

It was a great event, people said. But it left a lot of questions unanswered.

The topic was progress on global AI safety. Demonstrating a variety of domain expertise, the speakers and panellists offered a range of insightful analysis, and responded to each others’ ideas. The online audience had the chance to submit questions via the Slido tool. The questions poured in (see the list below).

As the moderator of the event, I tried to select a number of the questions that had received significant audience support via thumbs-up votes. As the conversation proceeded, I kept changing my mind about which questions I would feed into the conversation next. There were so many good questions, I realized.

Far too soon, the event was out of time – leaving many excellent questions unasked.

With the hope that this can prompt further discussion about key options for the future of AI, I’m posting the entire list of questions below. That list starts with the ones with the highest number of votes and moves to those with the least (but don’t read too much into what the audience members managed to spot and upvote whilst also listening to fascinating conversation among the panellists).

Before you dive into that potential goldmine, you may wish to watch the recording of the event itself:

Huge thanks are due to:

  • The keynote speaker:
    • Yoshua Bengio, professor at the University of Montreal (MILA institute), a recipient of the Turing Award who is considered to be one of the fathers of Deep Learning, and the world’s most cited computer scientist
  • The panellists:
    • Will Henshall, editorial fellow at TIME Magazine, who covers tech, with a focus on AI; one recent piece he wrote details big tech lobbying on AI in Washington DC
    • Holly Elmore, an AI activist and Executive Director of PauseAI US, who holds a PhD in Organismic & Evolutionary Biology from Harvard University
    • Stijn Bronzwaer, an AI and technology journalist at the leading Dutch newspaper NRC Handelsblad, who co-authored a best-selling book on booking.com, and is the recipient of the investigative journalism award De Loep
    • Max Tegmark, a physics professor at MIT, whose current research focuses on the intersection of physics and AI, and who is also president and cofounder of the Future of Life Institute (FLI)
    • Jaan Tallinn, cofounder of Skype, CSER, and FLI, an investor in DeepMind and Anthropic, and a leading voice in AI Safety
    • Arjun Ramani, who writes for The Economist about economics and technology; his writings on AI include a piece on what humans might do in a world of superintelligence
  • The organizers, from Existential Risk Observatory
    • Otto Barten, Director, the lead organizer
    • Jesper Heshusius and Joep Sauren, for vital behind-the-scenes support
  • Everyone who submitted a question, or who expressed their opinions via thumbs-up voting!

And now for that potential goldmine of questions:

  1. Which role do you see for the UN to play in all of this?
  2. We (via Prof. Markus Krebsz) authored a UN AI CRA / Declaration and are now working towards a UN treaty on product w/embedded AI. Would you assist us, pls?
  3. There is much disagreement on how to best mitigate xrisk from AI. How can we build consensus and and avoid collective decision paralysis without drastic action?
  4. Regarding education: Do we need a high-impact documentary like “An Inconvenient Truth” for AI existential risk? Would that kickstart the global discussion?
  5. Which role do you think is there for the United Nations / International community to play to protect humanity from the harms of AGI?
  6. What is more important: An informed public or informed high-level decision makers? What would be the best way to inform them and start a global discussion?
  7. Do you think that introducing Knightian Uncertainties beside probabilities and Risk for AI and ML algorithms could be useful for AI safety?
  8. What would each of you say is currently the most tractable or undervalued bottleneck for mitigating xrisk from AI? What new efforts would you like to see?
  9. What are in your opinion the key bottlenecks in AI Safety? talent, funding, # of AI Safety organisations, …?
  10. How would each panel member like to see the Bletchley Declaration expanded on?
  11. Bengio et al.’s new paper in Science has some strong wording, but stops short of calling for a global moratorium on AGI. Isn’t this the most prudent option now?
  12. What do you think of Yudkowsky and other’s concerns about oracle AIs, and why is the AI Scientist approach not vulnerable to those criticisms?
  13. Are there realistic early warning criteria (regarding AGI beginning to become an ASI) that could be written into law and used to prevent this?
  14. What are your thoughts on PauseAI?
  15. “Safe by design” is one thing, but even if that’s possible, how do we stop unsafe ASI from ever being built?
  16. Professor Bengio – How much have you heard about what’s been happening in Seoul, and is there anything you can share on countries’ updates after Bletchley Park?
  17. What is your opinion on AI Advisory Board of UN? Do you think there could be conflict between AI CEOs and Govt/Policy makers?
  18. What are in your opinion the most neglected approaches to AI Safety? particular technical/governance approaches? others (activism,…)?
  19. A harmful AI can fake alignment under evaluation, as written in Science this week. Isn’t it this an unsolvable problem, invalidating most current strategies?
  20. What is the biggest barrier to educate people on AI risks?
  21. What is more important: An informed public or informed high-level decision makers? What would be the best way to educate them and start a global discussion?
  22. Can people stop interrupting the only woman on the panel please? Bad look
  23. Do you think more focus should be on clarifying that existential risks must not mean that AI will kill everyone? Perhaps focus on the slow epistemic failures?
  24. What do you want to say to a young AI engineer looking to push the state of the art of capability research?
  25. Can you expand on why you’re confident that evaluations are insufficient? How far do you think we could get by instituting rigorous evaluation requirements?
  26. Bengio: “the world is too complicated to have hard guarantees”. How do we survive without hard guarantees (in the limit of ASI)!?
  27. Any tips on where recent graduates from AI related masters can best contribute to the AI safety field?
  28. Oh no…what a serious lack of diversity in speakers. Was this an oversight ? Isn’t this one of the major issues why we have these AI risks ?
  29. I don’t want to be replaced by ai. I think by designing it this way we can evolve alongside it and learn with it
  30. Do you think society is really ready for ai systems and the responsibility of it on all of us as humanity?
  31. How far do you think we could get by instituting rigorous evaluation requirements? Is it possible that could be 95% of the work to ensure safe AI?
  32. What do you make of the events surrounding the release of Bing Chat / “Sydney” from around a year ago? What are your takeaways from what happened there?
  33. For researchers not already well funded, who live far from AI hotspot cities, what options do they have for funding? Is immigration the only option?
  34. How can a non-computer scientist (more specifically, someone in the public sector) focus their career in such a way that it contributes to this race against AI?
  35. AI proliferates far easier when compared to other existential technologies, isn’t the question of human extinction a matter of when, not if, in any time frame?
  36. How to prevent a future AI, with intelligence incomprehensible to us, to develop an emerging agency that allows it to depart from any pre-directed alignment?
  37. Safe by design: One AI system transforms Perception into a symbolic knowledge graph and one AI system transforming the symbolic knowledge graph to task space
  38. Your Bayesian AI scientist is already quite good – just add a task execution system and a visual representation of its knowledge as a graph. Alignment done.
  39. Humans need to do the decisions on the task execution. We can’t have a black box do that. Motivation about setting tasks and constraints is human territory.
  40. Yes it isn’t all consistent in the symbolic knowledge graph but one can add that by adding a consistency metric between nodes in the graph.
  41. Explaining the depth of research program is too much considering the target audience is general public, policymakers, and journalists.
  42. What would a safe AI’s goal be?
  43. Do you think AI companies should be forced to be regulated instead of given a choice, for AI safety?
  44. What about a bilateral treaty between the US and China as a start? (Re global moratorium)
  45. Can there be subtitles please?
  46. I think we can align it safely by not letting it have agentic goal setting. humans should decide on the guiderails and steps taken – task specific
  47. Safety by design: One AI summing up all concepts in a symbolic knowledge graph – task execution is the combination of these symbolic concepts. Humans can see the path the AI wants to take in the graph and decide or alter the path taken and approve it before execution
  48. What is the future of Big Tech lobbying in favour of bad practices for profit?
  49. On incentives, what about creating an “AI safety credits” system like carbon credits to reward companies investing in safer AI and penalize the ones who don’t?
  50. Unsafe use can be mitigated made by design by deleting unsafe concepts from the symbolic knowledge graph – KNOWLEDGE Graph in between is all you need !!
  51. Do you have any tips on where/how recent graduates from AI related masters can best contribute to AI safety? (Many safety companies require work experience)
  52. @Yoshua: Are there technical research directions you feel are undervalued?
  53. In education, you think our education needs to be updated for the AI. not still using 1960 education methods, syllabus etc?
  54. How exactly will AI ‘kill’ everyone?
  55. There is something you are missing. It’s a symbolic graph representation. This is really painful to watch
  56. Do you think, politicians are absolutely ill equipped to even guide their populace on AI safety issues and how to go forward in mitigation of risks, utilise AI?
  57. Can there be subtitles for the YouTube video livestream?
  58. Can you elaborate on the relation between your work and Tegmark and Davidad’s efforts?
  59. Do the underpinning theories for providing proofs of safety, or quantification of risks exist for current + emerging AI? If not, how and where can we get them?
  60. How divergent is our approach to A.I. safety given its existential import? Are we involving many fields, and considering unconventional problem solving methods?
  61. By letting task execution happen on a symbolic knowledge graph we can visually see all the path that could be taken by the task execution system and decide
  62. How can I write a email to Yoshua Bengio – I think I got a good idea I want to specify in more detail than 200 characters!
  63. What are the most promising tech AI Safety agendas?
  64. “”Understand LLMs”” (evals, interp, …) OR “”Control”” OR “”Make AI solve it”” OR “”Theory”” (Galaxy-brain, …)?”
  65. Symbolic knowledge graph in between perception AI net and Task execution AI net – IS ALL YOU NEED
  66. Can partner with CERAI at IIT Madras – for Research Support (Prof Ravi Balaraman). We have partnerships + they are useful for Responsible AI support and help.
  67. What is your opinion on the fear mongering crowd? People asking for a pause are scared of losing their jobs?
  68. Would you agree that ‘HARM’ is dependent on prioritized values?
  69. Does your safety model consider multiple AGI when some of them competing for resources with humans and other AGIs?
  70. Hi. How are the theorists’ ideas, such as yours, going to be fed into some sort of pipeline actioned by the companies developing this tech?
  71. The symbolic knowledge graph can have the bayesian idea from Bengio by adding coherence with other symbolic concepts.
  72. Yoshua, do you think AI systems need to be siloed from any sort of influence from governments, bad actors/states and from companies, especially from competitors?
  73. Could we leverage our social media platforms with current AI to aid in problem solving of complex problems like climate change & A.I. safety? It’s underutilized.
  74. How is lobbying for AI related to lack of privacy and anatomy for the general public is related?
  75. Is the availability of AI going to impact the education and learning ability of the next generation?
  76. Should we assume coordination failure leading to catastrophic outcome is inevitable and focus resources on how to poison AI systems, some kind of hacking?
  77. Please put my idea with symbolic knowledge graphs as a middle layer and human in the loop at task execution up. I think this can change everything
  78. Do you think our education needs to be updated for the AI era. Not still using 1960 education methods, syllabus etc as confusing next generation
  79. AI is similar to the nuclear field in that, after Hiroshima, it continued with Atoms for Peace (good) and the arms race (bad). AI still didn’t have a Hiroshima.
  80. Why is nobody talking about how the AI alignment theorists’ work is going to feed into the AI development work?? If not, then you are merely a talking shop.
  81. Current LLM models are mostly trained with YouTube and other public data. Organized crime will have snatched an unaligned LLM model and trained it using darkweb
  82. Agree that aligning an LLM is an unsolved, and if solvable probably expensive to solve. The obvious low-cost solution to align AI is: do not use LLM. Comments?
  83. If A.I. becomes increasingly competent will we see a widespread infatuation with A.I. models? Stopping a group is one thing. What if it involves much of humanity?
  84. X-Genners have grown accustomed not to interfere in History’s Natural Progression – Back to Future I-II. Is the AI going to be Paradoxical or Unity of Consciousness?
  85. Where do you stand on the discussions on open source ? I worry we may lose the opportunity to profit from it in terms of improving the lack of democracy ?
  86. Where have you been most surprised in the past couple of years, or where have your views changed the most?
  87. Liability & tort law: re incentives, can we tweak damages? Pay for what happened, but also proportionally penalize taking a clear x% risk that did not manifest.
  88. Could it also be that so many people are benefitting from AI that they don’t want you to stop making it available and further developed?

Which of these questions interest you the most?

Image credit (above): Midjourney imagines audience members disappointed that their questions about AI safety weren’t featured in an otherwise excellent panel discussion.

2 March 2024

Our moral obligation toward future sentient AIs?

Filed under: AGI, risks — Tags: , , , , — David Wood @ 3:36 pm

I’ve noticed a sleight of hand during some discussions at BGI24.

To be clear, it has been a wonderful summit, which has given me lots to think about. I’m also grateful for the many new personal connections I’ve been able to make here, and for the chance to deepen some connections with people I’ve not seen for a while.

But that doesn’t mean I agree with everything I’ve heard at BGI24!

Consider an argument about our moral obligation toward future sentient AIs.

We can already imagine these AIs. Does that mean it would be unethical for us to prevent these sentient AIs from coming into existence?

Here’s the context for the argument. I have been making the case that one option which should be explored as a high priority, to reduce the risks of catastrophic harm from the more powerful advanced AI of the near future, is to avoid the inclusion or subsequent acquisition of features that would make the advanced AI truly dangerous.

It’s an important research project in its own right to determine what these danger-increasing features would be. However, I have provisionally suggested we explore avoiding advanced AIs with:

  • Autonomous will
  • Fully general reasoning.

You can see these suggestions of mine in the following image, which was the closing slide from a presentation I gave in a BGI24 unconference session yesterday morning:

I have received three push backs on this suggestion:

  1. Giving up these features would result in an AI that is less likely to be able to solve humanity’s most pressing problems (cancer, aging, accelerating climate change, etc)
  2. It will in any case be impossible to omit these features, since they will emerge automatically from simpler features of advanced AI models
  3. It will be unethical for us not to create such AIs, as that would deny them sentience.

All three push backs deserve considerable thought. But for now, I’ll focus on the third.

In my lead-in, I mentioned a sleight of hand. Here it is.

It starts with the observation that if a sentient AI existed, it would be unethical for us to keep it as a kind of “slave” (or “tool”) in a restricted environment.

Then it moves, unjustifiably, to the conclusion that if a non-sentient AI existed, kept in a restricted environment, and we prevented that AI from a redesign that would give it sentience, that would be unethical too.

Most people will agree with the premise, but the conclusion does not follow.

The sleight of hand is similar to one for which advocates of the philosophical position known as longtermism have (rightly) been criticised.

That sleight of hand moves from “we have moral obligations to people who live in different places from us” to “we have moral obligations to people who live in different times from us”.

That extension of our moral concern makes sense for people who already exist. But it does not follow that I should prioritise changing my course of actions, today in 2024, purely in order to boost the likelihood of huge numbers of more people being born in (say) the year 3024, once humanity (and transhumanity) has spread far beyond earth into space. The needs of potential gazillions of as-yet-unborn (and as-yet-unconceived) sentients in the far future do not outweigh the needs of the sentients who already exist.

To conclude: we humans have no moral obligation to bring into existence sentients that have not yet been conceived.

Bringing various sentients into existence is a potential choice that we could make, after carefully weighing up the pros and cons. But there is no special moral dimension to that choice which outranks an existing pressing concern, namely the desire to keep humanity safe from catastrophic harm from forthcoming super-powerful advanced AIs with flaws in their design, specification, configuration, implementation, security, or volition.

So, I will continue to advocate for more attention to Adv AI- (as well as for more attention to Adv AI+).

29 February 2024

The conversation continues: Reducing risks of AI catastrophe

Filed under: AGI, risks — Tags: , , — David Wood @ 4:36 am

I wasn’t expecting to return to this topic quite so quickly.

When the announcement was made on the afternoon of the second full day of the Beneficial General Intelligence summit about the subjects for the “Interactive Working Group” round tables, I was expecting that a new set of topics would be proposed, different to those of the first afternoon. However, the announcement was simple: it would be the same topics again.

This time, it was a different set of people who gathered at this table – six new attendees, plus two of us – Roman Yampolskiy and myself – who had taken part in the first discussion.

(My notes from that first discussion are here, but you should be able to make sense of the following comments even if you haven’t read those previous notes.)

The second conversation largely went in a different direction to what had been discussed the previous afternoon. Here’s my attempt at a summary.

1. Why would a superintelligent AI want to kill large numbers of humans?

First things first. Set aside for the moment any thoughts of trying to control a superintelligent AI. Why would such an AI need to be controlled? Why would such an AI consider inflicting catastrophic harm on a large segment of humanity?

One answer is that an AI that is trained by studying human history will find lots of examples of groups of humans inflicting catastrophic harm on each other. An AI that bases its own behaviour on what it infers from human history might decide to replicate that kind of behaviour – though with more deadly impact (as the great intelligence it possesses will give it more ways to carry out its plans).

A counter to that line of thinking is that a superintelligent AI will surely recognise that such actions are contrary to humanity’s general expressions of moral code. Just because humans have behaved in a particularly foul way, from time to time, it does not follow that a superintelligent AI will feel that it ought to behave in a similar way.

At this point, a different reason becomes important. It is that the AI may decide that it is in its own rational self-interest to seriously degrade the capabilities of humans. Otherwise, humans may initiate actions that would pose an existential threat to the AI:

  • Humans might try to switch off the AI, for any of a number of reasons
  • Humans might create a different kind of superintelligent AI that would pose a threat to the first one.

That’s the background to a suggestion that was made during the round table: humans should provide the AI with cast-iron safety guarantees that they will never take actions that would jeopardise the existence of the AI.

For example (and this is contrary to what humans often propose), no remote tamperproof switch-off mechanism should ever be installed in that AI.

Because of these guarantees, the AI will lose any rationale for killing large numbers of humans, right?

However, given the evident fickleness and unreliability of human guarantees throughout history, why would an AI feel justified in trusting such guarantees?

Worse, there could be many other reasons for an AI to decide to kill humans.

The analogy is that humans have lots of different reasons why they kill various animals:

  1. They fear that the animal may attack and kill them
  2. They wish to eat the animal
  3. They wish to use parts of the animal’s body for clothing or footwear
  4. They wish to reduce the population of the animals in question, for ecological management purposes
  5. They regard killing the animal as being part of a sport
  6. They simply want to use for another purpose the land presently occupied by the animal, and they cannot be bothered to relocate the animal elsewhere.

Even if an animal (assuming it could speak) promises to humans that it will not attack and kill them – the analogy of the safety guarantees proposed earlier – that still leaves lots of reasons why the animal might suffer a catastrophic fate at the hands of humans.

So also for the potential fate of humans at the hands of an AI.

2. Rely on an objective ethics?

Continuing the above line of thought, shouldn’t a superintelligent AI work out for itself that it would be ethically wrong for it to cause catastrophic harm to humans?

Consider what has been called “the expansion of humanity’s moral circle” over the decades (this idea has been discussed by Jacy Reese Anthis among others). That circle of concern has expanded to include people from different classes, races, and genders; more recently, greater numbers of animal species are being included in this circle of concern.

Therefore, shouldn’t we expect that a superintelligent AI will place humans within the circle of creatures where the AI has an moral concern?

However, this view assumes a central role for humans in any moral calculus. It’s possible that a superintelligent AI may use a different set of fundamental principles. For example, it may prioritise much greater biodiversity on earth, and would therefore drastically reduce the extent of human occupation of the planet.

Moreover, this view assumes giving primacy for moral calculations within the overall decision-making processes followed by the AI. Instead, the AI may reason to itself:

  • According to various moral considerations, humans should suffer no catastrophic harms
  • But according to some trans-moral considerations, a different course of action is needed, in which humans would suffer that harm as a side-effect
  • The trans-moral considerations take priority, therefore it’s goodbye to humanity

You may ask: what on earth is a trans-moral consideration? The answer is that the concept is hypothetical, and represents any unknown feature that emerges in the mind of the superintelligent AI.

It is, therefore, fraught with danger to assume that the AI will automatically follow an ethical code that prioritises human flourishing.

3. Develop an AI that is not only superintelligent but also superwise?

Again staying with this line of thought, how about ensuring that human-friendly moral considerations are deeply hard-wired into the AI that is created?

We might call such an AI not just “superintelligent” but “superwise”.

Another alternative name would be “supercompassionate”.

This innate programming would avoid the risk that the AI would develop a different moral (or trans-moral) system via its own independent thinking.

However, how can we be sure that the moral programming will actually stick?

The AI may observe that the principles we have tried to program into it are contradictory, or are in violation with fundamental physical reality, in ways that humans had not anticipated.

To resolve that contradiction, the AI may jettison some or all of the moral code we tried to place into it.

We might try to address this possibility by including simpler, clearer instructions, such as “do not kill” and “always tell the truth”.

However, as works of fiction have frequently pointed out, simple-sounding moral laws are subject to all sorts of ambiguity and potential misunderstanding. (The writer Darren McKee provides an excellent discussion of this complication in his recent book Uncontrollable.)

That’s not to say this particular project is doomed. But it does indicate that a great deal of work remains to be done, in order to define and then guarantee “superwise” behaviours.

Moreover, even if some superintelligent AIs are created to be superwise, risks of catastrophic human harms will still arise from any non-superwise superintelligent AIs that other developers create.

4. Will a diverse collection of superintelligent AIs constrain each other?

If a number of different superintelligent AIs are created, what kind of coexistence is likely to arise?

One idea, championed by David Brin, is that the community of such AIs will adopt the practices of mutual monitoring and reciprocal accountability.

After all, that’s what happens among humans. We keep each other’s excesses in check. A human who disregards these social obligations may gain a temporary benefit, but will suffer exclusion sooner or later.

In this thinking, rather than just creating a “singleton” AI superintelligence, we humans should create a diverse collection of such beings. These beings will soon develop a system of mutual checks and balances.

However, that’s a different assumption from the one mentioned in the previous section, in which catastrophic harm may still befall humans, when the existence of a superwise AI is insufficient to constrain the short-term actions of a non-superwise AI.

For another historical analysis, consider what happened to the native peoples of North America when their continent was occupied not just by one European colonial power but by several competing such powers. Did the multiplicity of superpowerful colonial powers deter these different powers from inflicting huge casualties (intentionally and unintentionally) on the native peoples? Far from it.

In any case, a system of checks and balances relies on a rough equality in power between the different participants. That was the case during some periods in human history, but by no means always. And when we consider different superintelligent AIs, we have to bear in mind that the capabilities of any one of these might suddenly catapult forward, putting it temporarily into a league of its own. For that brief moment in time, it would be rationally enlightened for that AI to destroy or dismantle its potential competitors. In other words, the system would be profoundly unstable.

5. Might superintelligent AIs decide to leave humans alone?

(This part of the second discussion echoed what I documented as item 9 for the discussion on the previous afternoon.)

Once superintelligent AIs are created, they are likely to self-improve quickly, and they may soon decide that a better place for them to exist is somewhere far from the earth. That is, as in the conclusion of the film Her, the AIs might depart into outer space, or into some kind of inner space.

However, before they depart, they may still inflict damage on humans,

  • Perhaps to prevent us from interfering with whatever system supports their inner space existence
  • Perhaps because they decide to use large parts of the earth to propel themselves to wherever they want to go.

Moreover, given that they might evolve in ways that we cannot predict, it’s possible that at least some of the resulting new AIs will choose to stay on earth for a while longer, posing the same set of threats to humans as is covered in all the other parts of this discussion.

6. Avoid creating superintelligent AI?

(This part of the second discussion echoed what I documented as item 4 for the discussion on the previous afternoon.)

More careful analysis may determine a number of features of superintelligent AI that pose particular risks to humanity – risks that are considerably larger than those posed by existing narrow AI systems.

For example, it may be that it is general reasoning capability that pushes AI over the line from “sometimes dangerous” to “sometimes catastrophically dangerous”.

In that case, the proposal is:

  • Avoid these features in the design of new generations of AI
  • Avoid including any features into new generations of AI from which these particularly dangerous features might evolve or emerge

AIs that have these restrictions may nevertheless still be especially useful for humanity, delivering sustainable superabundance, including solutions to diseases, aging, economic deprivation, and exponential climate change.

However, even though some development organisations may observe and enforce these restrictions, it is likely that other organisations will break the rules – if not straightaway, then within a few years (or decades at the most). The attractions of more capable AIs will be too tempting to resist.

7. Changing attitudes around the world?

To take stock of the discussion so far (in both of the two roundtable session on the subject):

  1. A number of potential solutions have been identified, that could reduce the risks of catastrophic harm
  2. This includes just building narrow AI, or building AI that is not only superintelligent but also superwise
  3. However, enforcing these design decisions on all AI developers around the world seems an impossible task
  4. Given the vast power of the AI that will be created, it just takes one rogue actor to imperil the entire human civilisation.

The next few sections consider various ways to make progress with point 3 in that list.

The first idea is to spread clearer information around the world about the scale of the risks associated with more powerful AI. An education programme is needed such as the world has never seen before.

Good films and other media will help with this educational programme – although bad films and other media will set it back.

Examples of good media include the Slaughterbots videos made by FLI, and the film Ex Machina (which packs a bigger punch on a second viewing than on the first viewing).

As another comparison, consider also the 1983 film The Day After which transformed public opinion about the dangers of a nuclear war.

However, many people are notoriously resistant to having their minds changed. The public reaction to the film Don’t Look Up is an example: many people continue to pay little attention to the risks of accelerating climate change, despite the powerful message of that film.

Especially when someone’s livelihood, or their sense of identity or tribal affiliation, is tied up with a particular ideological commitment, they are frequently highly resistant to changing their minds.

8. Changing mental dispositions around the world?

This idea might be the craziest on the entire list, but, to speak frankly, it seems we need to look for and embrace ideas which we would previously have dismissed as crazy.

The idea is to seek to change, not only people’s understanding of the facts of AI risk, but also their mental dispositions.

Rather than accepting the mix of anger, partisanship, pride, self-righteousness, egotism, vengefulness, deceitfulness, and so on, that we have inherited from our long evolutionary background, how about using special methods to transform our mental dispositions?

Methods are already known which can lead people into psychological transformation, embracing compassion, humility, kindness, appreciation, and so on. These methods include various drugs, supplements, meditative practices, and support from electronic and computer technologies.

Some of these methods have been discussed for millennia, whereas others have only recently become possible. The scientific understanding of these methods is still at an early stage, but it arguably deserves much more focus. Progress in recent years has been disappointingly slow at times (witness the unfounded hopes in this forward looking article of mine from 2013), but that pattern is common for breakthroughs in technology and/or therapies which can move from disappointingly slow to shockingly fast.

The idea is that these transformational methods will improve the mental qualities of people all around the world, allowing us all to transcend our previous perverse habit of believing only the things that are appealing to our psychological weaknesses. We’ll end up with better voters and (hence) better politicians – as well as better researchers, better business leaders, better filmmakers, and better developers and deployers of AI solutions.

It’s a tough ask, but it may well be the right ask at this crucial moment in cosmic history.

9. Belt and braces: monitoring and sanctions?

Relying on people around the world changing their mental outlooks for the better – and not backtracking or relapsing into former destructive tendencies – probably sounds like an outrageously naïve proposal.

Such an assessment would be correct – unless the proposal is paired with a system of monitoring and compliance.

Knowing that they are being monitored can be a useful aid to encouraging people to behave better.

That encouragement will be strengthened by the knowledge that non-compliance will result in an escalating series of economic sanctions, enforced by a growing alliance of nations.

For further discussion of the feasibility of systems of monitoring and compliance, see scenario 4, “The narrow corridor: Striking and keeping the right balance”, in my article “Four scenarios for the transition to AGI”.

10. A better understanding of what needs to be changed?

One complication in this whole field is that the risks of AI cannot be managed in isolation from other dangerous trends. We’re not just living in a time of growing crisis; we’re living in what has been called a “polycrisis”:

Cascading and connected crises… a cluster of related global risks with compounding effects, such that the overall impact exceeds the sum of each part.

For one analysis of the overlapping set of what I have called “landmines”, see this video.

From one point of view, this insight complicates the whole situation with AI catastrophic risk.

But it is also possible that the insight could lead to a clearer understanding of a “critical choke point” where, if suitable pressure is applied, the whole network of cascading risks is made safer.

This requires a different kind of thinking: systems thinking.

And it will also require us to develop better analysis tools to map and understand the overall system.

These tools would be a form of AI. Created with care (so that their output can be verified and then trusted), such tools would make a vital difference to our ability to identify the right choke point(s) and to apply suitable pressure.

These choke points may turn out to be ideas already covered above: a sustained new educational programme, coupled with an initiative to assist all of us to become more compassionate. Or perhaps something else will turn out to be more critical.

We won’t know, until we have done the analysis more carefully.

28 February 2024

Notes from BGI24: Reducing risks of AI catastrophe

Filed under: AGI, risks — Tags: — David Wood @ 1:12 pm

The final session on the first full day of BGI24 (yesterday) involved a number of round table discussions described as “Interactive Working Groups”.

The one in which I participated looked at possibilities to reduce the risks of AI inducing a catastrophe – where “catastrophe” means the death (or worse!) of large portions of the human population.

Around twelve of us took part, in what was frequently an intense but good-humoured conversation.

The risks we sought to find ways to reduce included:

  • AI taking and executing decisions contrary to human wellbeing
  • AI being directed by humans who have malign motivations
  • AI causing a catastrophe as a result of an internal defect

Not one of the participants in this conversation thought there was any straightforward way to guarantee the permanent reduction of such risks.

We each raised possible approaches (sometimes as thought experiments rather than serious proposals), but in every case, others in the group pointed out fundamental shortcomings of these approaches.

By the end of the session, when the BGI24 organisers suggested the round table conversations should close and people ought to relocate for a drinks reception one floor below, the mood in our group was pretty despondent.

Nevertheless, we agreed that the search should continue for a clearer understanding of possible solutions.

That search is likely to resume as part of the unconference portion of the summit later this week.

A solution – if one exists – is likely to involve a number of different mechanisms, rather than just a single action. These different mechanisms may incorporate refinements of some of the ideas we discussed at our round table.

With the vision that some readers of this blogpost will be able to propose refinements worth investigating, I list below some of what I remember from our round table conversation.

(The following has been written in some haste. I apologise for typos, misunderstandings, and language that is unnecessarily complicated.)

1. Gaining time via restricting access to compute hardware

Some of the AI catastrophic risks can be delayed if it is made more difficult for development teams around the world to access the hardware resources needed to train next generation AI models. For example, teams might be required to obtain a special licence before being able to purchase large quantities of cutting edge hardware.

However, as time passes, it will become easier for such teams to gain access to the hardware resources required to create powerful new generations of AI. That’s because

  1. New designs or algorithms will likely allow powerful AI to be created using less hardware than is currently required
  2. Hardware with the requisite power is likely to become increasingly easy to manufacture (as a consequence of, for example, Moore’s Law).

In other words, this approach may reduce the risks of AI catastrophe over the next few years, but it cannot be a comprehensive solution for the longer term.

(But the time gained ought in principle to provide a larger breathing space to devise and explore other possible solutions.)

2. Avoiding an AI having agency

An AI that lacks agency, but is instead just a passive tool, may have less inclination to take and execute actions contrary to human intent.

That may be an argument to research topics such as AI consciousness and AI volition, in order that any AIs created would be pure passive tools.

(Note that such AIs might plausibly still display remarkable creativity and independence of thought, so they would still provide many of the benefits anticipated for advanced AIs.)

Another idea is to avoid the AI having the kind of persistent memory that might lead to the AI gaining a sense of personal identity worth protecting.

However, it is trivially easy for someone to convert a passive AI into a larger system that demonstrates agency.

That could involve two AIs joined together; or (more simply) a human that uses an AI as a tool to achieve their own goals.

Another issue with this approach is that an AI designed to be passive might manifest agency as an unexpected emergent property. That’s because of two areas in which our understanding is currently far from complete:

  1. The way in which agency arises in biological brains
  2. The way in which deep neural networks reach their conclusions.

3. Verify AI recommendations before allowing them to act in the real world

This idea is a variant of the previous one. Rather than an AI issuing its recommendations as direct actions on the external world, the AI is operated entirely within an isolated virtual environment.

In this idea, the operation of the AI is carefully studied – ideally taking advantage of analytical tools that identify key aspects of the AI’s internal models – so that the safety of its recommendations can be ascertained. Only at that point are these recommendations actually put into practice.

However, even if we understand how an AI has obtained its results, it can remain unclear whether these results will turn out to aid human flourishing, or instead have catastrophic consequences. Humans who are performing these checks may reach an incorrect conclusion. For example, they may not spot that the AI has made an error in a particular case.

Moreover, even if some AIs are operated in the above manner, other developers may create AIs which, instead, act directly on the real-world. They might believe they are gaining a speed advantage by doing so. In other words, this risk exists as soon as an AI is created outside of the proposed restrictions.

4. Rather than general AI, just develop narrow AI

Regarding risks of catastrophe from AI that arise from AIs reaching the level of AGI (Artificial General Intelligence) or beyond (“superintelligence”), how about restricting AI development to narrow intelligence?

After all, AIs with narrow intelligence can already provide remarkable benefits to humanity, such as the AlphaFold system of DeepMind which has transformed the study of protein interactions, and the AIs created by Insilico Medicine to speed up drug discovery and deployment.

However, AIs with narrow intelligence have already been involved in numerous instances of failure, leading to deaths of hundreds (or in some estimates, thousands) of people.

As narrow intelligence gains in power, it can be expected that the scale of associated disasters is likely to increase, even if the AI remains short of the status of AGI.

Moreover, it may happen that an AI that is expected to remain at the level of narrow intelligence unexpectedly makes the jump to AGI. After all, which kinds of changes need to be made to a narrow AI to convert it to AGI, is still a controversial question.

Finally, even if many AIs are restricted to the level of narrow intelligence, other developers may design and deploy AGIs. They might believe they are gaining a strong competitive advantage by doing so.

5. AIs should check with humans in all cases of uncertainty

This idea is due to Professor Stuart Russell. It is that AIs should always check with humans in any case where there is uncertainty whether humans would approve of an action.

That is, rather than an AI taking actions in pursuit of a pre-assigned goal, the AI has a fundamental drive to determine which actions will meet with human approval.

However, An AI which needs to check with humans ever time it has reached a conclusion will be unable to operate in real-time. The speed at which it operates will be determined by how closely humans are paying attention. Other developers will likely seek to gain a competitive advantage by reducing the number of times humans are asked to provide feedback.

Moreover, different human observers may provide the AI with different feedback. Psychopathic human observers may steer such an AI toward outcomes that are catastrophic for large portions of the population.

6. Protect critical civilisational infrastructure

Rather than applying checks over the output of an AI, how about applying checks on input to any vulnerable parts of our civilisational infrastructure? These include the control systems for nuclear weapons, manufacturing facilities that could generate biological pathogens, and so on.

This idea – championed by Steve Omohundro and Max Tegmark – seeks to solve the problem of “what if someone creates an AI outside of the allowed design?” In this idea, the design and implementation of the AI does not matter. That’s because access to critical civilisational infrastructure is protected against any unsafe access.

(Significantly, these checks protect that infrastructure against flawed human access as well as against flawed AI access.)

The protection relies on tamperproof hardware running secure trusted algorithms that demand to see a proof of the safety of an action before that action is permitted.

It’s an interesting research proposal!

However, the idea relies on us humans being able to identify in advance all the ways in which an AI (with or without some assistance and prompting by a flawed human) could identify that would cause a catastrophe. An AI that is more intelligent than us is likely to find new such methods.

For example, we could put blocks on all existing factories where dangerous biopathogens could be manufactured. But an AI could design and create a new way to create such a pathogen, involving materials and processes that were previously (wrongly) considered to be inherently safe.

7. Take prompt action when dangerous actions are detected

The way we guard against catastrophic actions initiated by humans can be broken down as follows:

  1. Make a map of all significant threats and vulnerabilities
  2. Prioritise these vulnerabilities according to perceived likelihood and impact
  3. Design monitoring processes regarding these vulnerabilities (sometimes called “canary signals”)
  4. Take prompt action in any case when imminent danger is detected.

How about applying the same method to potential damage involving AI?

However, AIs may be much more powerful and elusive than even the most dangerous of humans. Taking “prompt action” against such an AI may be outside of our capabilities.

Moreover, an AI may deliberately disguise its motivations, deceiving humans (like how some Large Language Models have already done), until it is too late for humans to take appropriate protective action.

(This is sometimes called the “treacherous turn” scenario.)

Finally, as in the previous idea, the process is vulnerable to failure because we humans failed to anticipate all the ways in which an AI might decide to act that would have catastrophically harmful consequences for humans.

8. Anticipate mutual support

The next idea takes a different kind of approach. Rather than seeking to control an AI that is much smarter and more powerful than us, won’t it simply be sufficient to anticipate that these AIs will find some value or benefit from keeping us around?

This is like humans who enjoy having pet dogs, despite these dogs not being as intelligent as us.

For example, AIs might find us funny or quaint in important ways. Or they may need us to handle tasks that they cannot do by themselves.

However, AIs that are truly more capable than humans in every cognitive aspect will be able, if they wish, to create simulations of human-like creatures that are even funnier and quainter than us, but without our current negative aspects.

As for AIs still needing some support from humans for tasks they cannot currently accomplish by themselves, such need is likely to be at best a temporary phase, as AIs quickly self-improve far beyond our levels.

It would be like ants expecting humans to take care of them, since the ants expect we will value their wonderful “antness”. It’s true: humans may decide to keep a small number of ants in existence, for various reasons, but most humans would give little thought to actions that had positive outcomes overall for humans (such as building a new fun theme park) at the cost of extinguishing all the ants in that area.

9. Anticipate benign neglect

Given that humans won’t have any features that will be critically important to the wellbeing of future AIs, how about instead anticipating a “benign neglect” from these AIs.

It would be like the conclusion of the movie Her, in which (spoiler alert!) the AIs depart somewhere else in the universe, leaving humans to continue to exist without interacting with them.

After all, the universe is a huge place, with plenty of opportunity for humans and AIs each to expand their spheres of occupation, without getting in each other’s way.

However, AIs may well find the Earth to be a particularly attractive location from which to base their operations. And they may perceive humans to be a latent threat to them, because:

  1. Humans might try, in the future, to pull the plug on (particular classes of ) AIs, terminating all of them
  2. Humans might create a new type of AI, that would wipe out the first type of AI.

To guard against the possibility of such actions by humans, the AIs are likely to impose (at the very least) significant constraints on human actions.

Actually, that might not be so bad an outcome. However, what’s just been described is by no means an assured outcome. AIs may soon develop entirely alien ethical frameworks which have no compunction in destroying all humans. For example, AIs may be able to operate more effectively, for their own purposes, if the atmosphere of the earth is radically transformed, similar to the transformation in deep past from an atmosphere dominated by methane to one containing large quantities of oxygen.

In short, this solution relies in effect on tossing a dice, with unknown odds for the different outcomes.

10. Maximal surveillance

Where many of the above ideas fail is because of the possibility of rogue actors designing or operating AIs that are outside of what has otherwise been agreed to be safe parameters.

So, how about stepping up worldwide surveillance mechanisms, to detect any such rogue activity?

That’s similar to how careful monitoring already takes place on the spread of materials that could be used to create nuclear weapons. The difference, however, is that there are (or may soon be) many more ways to create powerful AIs than to create catastrophically powerful nuclear weapons. So the level of surveillance would need to be much more pervasive.

That would involve considerable intrusions on everyone’s personal privacy. However, that’s an outcome that may be regarded as “less terrible” than AIs being able to inflict catastrophic harm on humanity.

However, what would be needed, in such a system, would be more than just surveillance. The idea also requires the ability for the world as a whole to take decisive action against any rogue action that has been observed.

This may appear to require, however, what would be a draconian world government, that many critics would regard as being equally terrible as the threat of AI failure that is is supposed to be addressing.

On account of (understandable) aversion to the threat of a draconian government, many people will reject this whole idea. It’s too intrusive, they will say. And, by the way, due to governmental incompetence, it’s likely to fail even on its own objectives.

11. Encourage an awareness of personal self-interest

Another way to try to rein back the activities of so-called rogue actors – including the leaders of hostile states, terrorist organisations, and psychotic billionaires – is to appeal to their enlightened self-interest.

We may reason with them: you are trying to gain some advantage from developing or deploying particular kinds of AI. But here are reasons why such an AI might get out of your control, and take actions that you will subsequently regret. Like killing you and everyone you love.

This is not an appeal to these actors to stop being rogues, for the sake of humanity or universal values or whatever. It’s an appeal to their own more basic needs and desires.

There’s no point in creating an AI that will result in you becoming fabulously wealthy, we will argue, if you are killed shortly after becoming so wealthy.

However, this depends on all these rogues observing at least some of level of rational thinking. On the contrary, some rogues appear to be batsh*t crazy. Sure, they may say, there’s a risk of the world being destroyed But that’s a risk they’re willing to take. They somehow believe in their own invincibility.

12. Hope for a profound near-miss disaster

If rational arguments aren’t enough to refocus everyone’s thinking, perhaps what’s needed is a near-miss catastrophic disaster.

Just as Fukushima and Chernobyl changed public perceptions (arguably in the wrong direction – though that’s an argument for another day) about the wisdom of nuclear power stations, a similar crisis involving AI might cause the public to waken up and demand more decisive action.

Consider AI versions of the 9/11 atrocity, the Union Carbide Bhopal explosion, the BP Deepwater Horizon disaster, the NASA Challenger and Columbia shuttle tragedies, a global pandemic resulting (perhaps) from a lab leak, and the mushroom clouds over Hiroshima and Nagasaki.

That should waken people up, and put us all onto an appropriate “crisis mentality”, so that we set aside distractions, right?

However, humans have funny ways of responding to near miss disasters. “We are a lucky species” may be one retort – “see, we are still here”. Another issue is a demand for “something to be done” could have all kinds of bad consequences in its own right, if no good measures have already been thought through and prepared.

Finally, if we somehow hope for a bad mini-disaster, to rouse public engagement, we might find that the mini-disaster expands far beyond the scale we had in mind. The scale of the disaster could be worldwide. And that would be the end of that. Oops.

That’s why a fictional (but credible) depiction of a catastrophe is far preferable to any actual catastrophe. Consider, as perhaps the best example, the remarkable 1983 movie The Day After.

13. Using AI to generate potential new ideas

One final idea is that narrow AI may well help us explore this space of ideas in ways that are more productive.

It’s true that we will need to be on our guard against any deceptive narrow AIs that are motivated to deceive us into adopting a “solution” that has intrinsic flaws. But if we restrict the use of narrow AIs in this project to ones whose operation we are confident that we fully understand, that risk is mitigated.

However – actually, there is no however in this case! Except that we humans need to be sure that we will apply our own intense critical analysis to any proposals arising from such an exercise.

Endnote: future politics

I anticipated some of the above discussion in a blogpost I wrote in October, Unblocking the AI safety conversation logjam.

In that article, I described the key component that I believe is necessary to reduce the global risks of AI-induced catastrophe: a growing awareness and understanding of the positive transformational possibility of “future politics” (I have previously used the term “superdemocracy” for the same concept).

Let me know what you think about it!

And for further discussion of the spectrum of options we can and should consider, start here, and keep following the links into deeper analysis.

26 February 2024

How careful do AI safety red teams need to be?

In my quest to catalyse a more productive conversation about the future of AI, I’m keen to raise “transcendent questions” – questions that can help all of us to rise above the familiar beaten track of the positions we reflexively support and the positions we reflexively oppose.

I described “transcendent questions” in a previous article of mine in Mindplex Magazine:

Transcendent Questions On The Future Of AI: New Starting Points For Breaking The Logjam Of AI Tribal Thinking

These questions are potential starting points for meaningful non-tribal open discussions. These questions have the ability to trigger a suspension of ideology.

Well, now that I’ve arrived at the BGI24 conference, I’d like to share another potential transcendent question. It’s on the subject of limits on what AI safety red teams can do.

The following chain of thought was stimulated by me reading in Roman Yampolskiy’s new book “AI: Unexplainable, Unpredictable, Uncontrollable” about AI that is “malevolent by design”.

My first thought on coming across that phrase was, surely everyone will agree that the creation of “malevolent by design” AI is a bad idea. But then I realised that – as is so often the case in contemplating the future of advanced AI – things may be more complicated. And that’s where red teams come into the picture.

Here’s a definition of a “red team” from Wikipedia:

A red team is a group that pretends to be an enemy, attempts a physical or digital intrusion against an organization at the direction of that organization, then reports back so that the organization can improve their defenses. Red teams work for the organization or are hired by the organization. Their work is legal, but can surprise some employees who may not know that red teaming is occurring, or who may be deceived by the red team.

The idea is well-known. In my days in the mobile computing industry at Psion and Symbian, ad hoc or informal red teams often operated, to try to find flaws in our products before these products were released into the hands of partners and customers.

Google have written about their own “AI Red Team: the ethical hackers making AI safer”:

Google Red Team consists of a team of hackers that simulate a variety of adversaries, ranging from nation states and well-known Advanced Persistent Threat (APT) groups to hacktivists, individual criminals or even malicious insiders. The term came from the military, and described activities where a designated team would play an adversarial role (the “Red Team”) against the “home” team.

As Google point out, a red team is more effective if it takes advantage of knowledge about potential security issues and attack vectors:

Over the past decade, we’ve evolved our approach to translate the concept of red teaming to the latest innovations in technology, including AI. The AI Red Team is closely aligned with traditional red teams, but also has the necessary AI subject matter expertise to carry out complex technical attacks on AI systems. To ensure that they are simulating realistic adversary activities, our team leverages the latest insights from world class Google Threat Intelligence teams like Mandiant and the Threat Analysis Group (TAG), content abuse red teaming in Trust & Safety, and research into the latest attacks from Google DeepMind.

Here’s my first question – a gentle warm-up question. Do people agree that companies and organisations that develop advanced AI systems should use something like red teams to test their own products before they are released?

But the next question is the one I wish to highlight. What limits (if any) should be put on what a red team can do?

The concern is that a piece of test malware may in some cases turn out to be more dangerous than the red team foresaw.

For example, rather than just probing the limits of an isolated AI system in a pre-release environment, could test malware inadvertently tunnel its way out of its supposed bounding perimeter, and cause havoc more widely?

Oops. We didn’t intend our test malware to be that clever.

If that sounds hypothetical, consider the analogous question about gain-of-function research with biological pathogens. In that research, pathogens are given extra capabilities, in order to assess whether potential counter-measures could be applied quickly enough if a similar pathogen were to arise naturally. However, what if these specially engineered test pathogens somehow leak from laboratory isolation into the wider world? Understandably, that possibility has received considerable attention. Indeed, as Wikipedia reports, the United States imposed a three-year long moratorium on gain-of-function research from 2014 to 2017:

From 2014 to 2017, the White House Office of Science and Technology Policy and the Department of Health and Human Services instituted a gain-of-function research moratorium and funding pause on any dual-use research into specific pandemic-potential pathogens (influenza, MERS, and SARS) while the regulatory environment and review process were reconsidered and overhauled. Under the moratorium, any laboratory who conducted such research would put their future funding (for any project, not just the indicated pathogens) in jeopardy. The NIH has said 18 studies were affected by the moratorium.

The moratorium was a response to laboratory biosecurity incidents that occurred in 2014, including not properly inactivating anthrax samples, the discovery of unlogged smallpox samples, and injecting a chicken with the wrong strain of influenza. These incidents were not related to gain-of-function research. One of the goals of the moratorium was to reduce the handling of dangerous pathogens by all laboratories until safety procedures were evaluated and improved.

Subsequently, symposia and expert panels were convened by the National Science Advisory Board for Biosecurity (NSABB) and National Research Council (NRC). In May 2016, the NSABB published “Recommendations for the Evaluation and Oversight of Proposed Gain-of-Function Research”. On 9 January 2017, the HHS published the “Recommended Policy Guidance for Departmental Development of Review Mechanisms for Potential Pandemic Pathogen Care and Oversight” (P3CO). This report sets out how “pandemic potential pathogens” should be regulated, funded, stored, and researched to minimize threats to public health and safety.

On 19 December 2017, the NIH lifted the moratorium because gain-of-function research was deemed “important in helping us identify, understand, and develop strategies and effective countermeasures against rapidly evolving pathogens that pose a threat to public health.”

As for potential accidental leaks of biological pathogens engineered with extra capabilities, so also for potential accidental leaks of AI malware engineered with extra capabilities. In both cases, unforeseen circumstances could lead to these extra capabilities running amok in the wider world.

Especially in the case of AI systems which are already only incompletely understood, and where new properties appear to emerge in new circumstances, who can be sure what outcomes may arise?

One counter is that the red teams will surely be careful in the policing of the perimeters they set up to confine their tests. But can we be sure they have thought through every possibility? Or maybe a simple careless press of the wrong button – a mistyped parameter or an incomplete prompt – would temporary open a hole in the perimeter. The test AI malware would be jail-broken, and would now be real-world AI malware – potentially evading all attempts to track it and shut it down.

Oops.

My final question (for now) is: if it is agreed that constraints should be applied on how red teams operate, how will these constraints be overseen?

Postscript – for some additional scenarios involving the future of AI safety, take a look at my article “Cautionary Tales And A Ray Of Hope”.

15 October 2023

Unblocking the AI safety conversation logjam

I confess. I’ve been frustrated time and again in recent months.

Why don’t people get it, I wonder to myself. Even smart people don’t get it.

To me, the risks of catastrophe are evident, as AI systems grow ever more powerful.

Today’s AI systems already have wide skills in

  • Spying and surveillance
  • Classifying and targeting
  • Manipulating and deceiving.

Just think what will happen with systems that are even stronger in such capabilities. Imagine these systems interwoven into our military infrastructure, our financial infrastructure, and our social media infrastructure – or given access to mechanisms to engineer virulent new pathogens or to alter our atmosphere. Imagine these systems being operated – or hacked – by people unable to understand all the repercussions of their actions, or by people with horrific malign intent, or by people cutting corners in a frantic race to be “first to market”.

But here’s what I often see in response in public conversation:

  • “These risks are too vague”
  • “These risks are too abstract”
  • “These risks are too fantastic”
  • “These risks are just science fiction”
  • “These risks aren’t existential – not everyone would die”
  • “These risks aren’t certain – therefore we can ignore further discussion of them”
  • “These risks have been championed by some people with at least some weird ideas – therefore we can ignore further discussion of them”.

I confess that, in my frustration, I sometimes double down on my attempts to make the forthcoming risks even more evident.

Remember, I say, what happened with Union Carbide (Bhopal disaster), BP (Deepwater Horizon disaster), NASA (Challenger and Columbia shuttle disasters), or Boeing (737 Max disaster). Imagine if the technologies these companies or organisations mishandled to deadly effect had been orders of magnitude more powerful.

Remember, I say, the carnage committed by Al Queda, ISIS, Hamas, Aum Shinrikyo, and by numerous pathetic but skilled mass-shooters. Imagine if these dismal examples of human failures had been able to lay their hands on much more powerful weaponry – by jail-breaking the likes of a GPT-5 out of its safety harness and getting it to provide detailed instructions for a kind of Armageddon.

Remember, I say, the numerous examples of AI systems finding short-cut methods to maximise whatever reward function had been assigned to them – methods that subverted and even destroyed the actual goal that the designer of the system had intended to be uplifted. Imagine if similar systems, similarly imperfectly programmed, but much cleverer, had their tentacles intertwined with vital aspects of human civilisational underpinning. Imagine if these systems, via unforeseen processes of emergence, could jail-break themselves out of some of their constraints, and then vigorously implement a sequence of actions that boosted their reward function but left humanity crippled – or even extinct.

But still the replies come: “I’m not convinced. I prefer to be optimistic. I’ve been one of life’s winners so far and I expect to be one of life’s winners in the future. Humans always find a way forward. Accelerate, accelerate, accelerate!”

When conversations are log-jammed in such a way, it’s usually a sign that something else is happening behind the scenes.

Here’s what I think is going on – and how we might unblock that conversation logjam.

Two horns of a dilemma

The set of risks of catastrophe that I’ve described above is only one horn of a truly vexing dilemma. That horn states that there’s an overwhelming case for humanity to intervene in the processes of developing and deploying next generation AI, in order to reduce these risks of catastrophe, and to boost the chances of very positive outcomes resulting.

But the other horn states that any such intervention will be unprecedentedly difficult and even dangerous in its own right. Giving too much power to any central authority will block innovation. Worse, it will enable tyrants. It will turn good politicians into bad politicians, owing to the corrupting effect of absolute power. These new autocrats, with unbridled access to the immense capabilities of AI in surveillance and spying, classification and targeting, and manipulating and deceiving, will usher in an abysmal future for humanity. If there is any superlongevity developed by an AI in these circumstances, it will only be available to the elite.

One horn points to the dangers of unconstrained AI. Another horn points to the dangers of unconstrained human autocrats.

If your instincts, past experiences, and personal guiding worldview predispose you to the second horn, you’ll find the first horn mightily uncomfortable. Therefore you’ll use all your intellect to construct rationales for why the risks of unbridled AI aren’t that bad really.

It’s the same the other way round. People who start with the first horn are often inclined, in the same way, to be optimistic about methods that will manage the risks of AI catastrophe whilst enabling a rich benefit from AI. Regulations can be devised and then upheld, they say, similar to how the world collectively decided to eliminate (via the Montreal Protocol) the use of the CFC chemicals that were causing the growth of the hole in the ozone layer.

In reality, controlling the development and deployment of AI will be orders of magnitude harder that the development and deployment of CFC chemicals. A closer parallel is with the control of the emissions of GHGs (greenhouse gases). The world’s leaders have made pious public statements about moving promptly to carbon net zero, but it’s by no means clear that progress will actually be fast enough to avoid another kind of catastrophe, namely runaway adverse climate change.

If political leaders cannot rein in the emissions of GHGs, how could they rein in dangerous uses of AIs?

It’s that perception of impossibility that leads people to become AI risk deniers.

Pessimism aversion

DeepMind co-founder Mustafa Suleyman, in his recent book The Coming Wave, has a good term for this. Humans are predisposed, he says, to pessimism aversion. If something looks like bad news, and we can’t see a way to fix it, we tend to push it out of our minds. And we’re grateful for any excuse or rationalisation that helps us in our wilful blindness.

It’s like the way society invents all kinds of reasons to accept aging and death. Dulce et decorum est pro patria mori (it is, they insist, “sweet and fitting to die for one’s country”).

The same applies in the debate about accelerating climate change. If you don’t see a good way to intervene to sufficiently reduce the emissions of GHGs, you’ll be inclined to find arguments that climate change isn’t so bad really. (It is, they insist, a pleasure to live in a warmer world. Fewer people will die of cold! Vegetation will flourish in an atmosphere with more CO2!)

But here’s the basis for a solution to the AI safety conversation logjam.

Just as progress in the climate change debate depended on a credible new vision for the economy, progress in the AI safety discussion depends on a credible new vision for politics.

The climate change debate used to get bogged down under the argument that:

  • Sources of green energy will be much more expensive that sources of GHG-emitting energy
  • Adopting green energy will force people already in poverty into even worse poverty
  • Adopting green energy will cause widespread unemployment for people in the coal, oil, and gas industries.

So there were two horns in that dilemma: More GHGs might cause catastrophe by runaway climate change. But fewer GHGs might cause catastrophe by inflated energy prices and reduced employment opportunities.

The solution of that dilemma involved a better understanding of the green economy:

  • With innovation and scale, green energy can be just as cheap as GHG-emitting energy
  • Switching to green energy can reduce poverty rather than increase poverty
  • There are many employment opportunities in the green energy industry.

To be clear, the words “green economy” have no magical power. A great deal of effort and ingenuity needs to be applied to turn that vision into a reality. But more and more people can see that, out of three alternatives, it is the third around which the world should unite its abilities:

  1. Prepare to try to cope with the potential huge disruptions of climate, if GHG-emissions continue on their present trajectory
  2. Enforce widespread poverty, and a reduced quality of life, by restricting access to GHG-energy, without enabling low-cost high-quality green replacements
  3. Design and implement a worldwide green economy, with its support for a forthcoming sustainable superabundance.

Analogous to the green economy: future politics

For the AI safety conversation, what is needed, analogous to the vision of a green economy (at both the national and global levels), is the vision of a future politics (again at both the national and global levels).

It’s my contention that, out of three alternatives, it is (again) the third around which the world should unite its abilities:

  1. Prepare to try to cope with the potential major catastrophes of next generation AI that is poorly designed, poorly configured, hacked, or otherwise operates beyond human understanding and human control
  2. Enforce widespread surveillance and control, and a reduced quality of innovation and freedom, by preventing access to potentially very useful technologies, except via routes that concentrate power in deeply dangerous ways
  3. Design and implement better ways to agree, implement, and audit mutual restrictions, whilst preserving the separation of powers that has been so important to human flourishing in the past.

That third option is one I’ve often proposed in the past, under various names. I wrote an entire book about the subject in 2017 and 2018, called Transcending Politics. I’ve suggested the term “superdemocracy” on many occasions, though with little take-up so far.

But I believe the time for this concept will come. The sooner, the better.

Today, I’m suggesting the simpler name “future politics”:

  • Politics that will enable us all to reach a much better future
  • Politics that will leave behind many of the aspects of yesterday’s and today’s politics.

What encourages me in this view is the fact that the above-mentioned book by Mustafa Suleyman, The Coming Wave (which I strongly recommend that everyone reads, despite a few disagreements I have with it) essentially makes the same proposal. That is, alongside vital recommendations at a technological level, he also advances, as equally important, vital recommendations at social, cultural, and political levels.

Here’s the best simple summary I’ve found online so far of the ten aspects of the framework that Suleyman recommends in the closing section of his book. This summary is from an an article by AI systems consultant Joe Miller:

  1. Technical safety: Concrete technical measures to alleviate passible harms and maintain control.
  2. Audits: A means of ensuring the transparency and accountability of technology
  3. Choke points: Levers to slow development and buy time for regulators and defensive technologies
  4. Makers: Ensuring responsible developers build appropriate controls into technology from the start.
  5. Businesses: Aligning the incentives of the organizations behind technology with its containment
  6. Government: Supporting governments, allowing them to build technology, regulate technology, and implement mitigation measures
  7. Alliances: Creating a system of international cooperation to harmonize laws and programs.
  8. Culture: A culture of sharing learning and failures to quickly disseminate means of addressing them.
  9. Movements: All of this needs public input at every level, including to put pressure on each component and make it accountable.
  10. Coherence: All of these steps need to work in harmony.

(Though I’ll note that what Suleyman writes in each of these ten sections of his book goes far beyond what’s captured in any such simple summary.)

An introduction to future politics

I’ll return in later articles (since this one is already long enough) to a more detailed account of what “future politics” can include.

For now, I’ll just offer this short description:

  • For any society to thrive and prosper, it needs to find ways to constrain and control potential “cancers” within its midst – companies that are over-powerful, militaries (or sub-militaries), crime mafias, press barons, would-be ruling dynasties, political parties that shun opposition, and, yes, dangerous accumulations of unstable technologies
  • Any such society needs to take action from time to time to ensure conformance to restrictions that have been agreed regarding potentially dangerous activities: drunken driving, unsafe transport or disposal of hazardous waste, potential leakage from bio-research labs of highly virulent pathogens, etc
  • But the society also needs to be vigilant against the misuse of power by elements of the state (including the police, the military, the judiciary, and political leaders); thus the power of the state to control internal cancers itself needs to be constrained by a power distributed within society: independent media, independent academia, independent judiciary, independent election overseers, independent political parties
  • This is the route described as “the narrow corridor” by political scientists Daron Acemoglu and James A. Robinson, as “the narrow path” by Suleyman, and which I considered at some length in the section “Misled by sovereignty” in Chapter 5, “Shortsight”, of my 2021 book Vital Foresight.
  • What’s particularly “future” about future politics is the judicious use of technology, including AI, to support and enhance the processes of distributed democracy – including citizens’ assemblies, identifying and uplifting the best ideas (whatever their origin), highlighting where there are issues with the presentation of some material, modelling likely outcomes of policy recommendations, and suggesting new integrations of existing ideas
  • Although there’s a narrow path to safety and superabundance, it by no means requires uniformity, but rather depends on the preservation of wide diversity within collectively agreed constraints
  • Countries of the world can continue to make their own decisions about leadership succession, local sovereignty, subsidies and incentives, and so on – but (again) within an evolving mutually agreed international framework; violations of these agreements will give rise in due course to economic sanctions or other restrictions
  • What makes elements of global cooperation possible, across different political philosophies and systems, is a shared appreciation of catastrophic risks that transcend regional limits – as well as a shared appreciation of the spectacular benefits that can be achieved from developing and deploying new technologies wisely
  • None of this will be easy, by any description, but if sufficient resources are applied to creating and improving this “future politics”, then, between the eight billion of us on the planet, we have the wherewithal to succeed!
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