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7 March 2023

What are the minimum conditions for software global catastrophe?

Filed under: AGI, risks — Tags: , — David Wood @ 11:55 am

Should we be seriously concerned that forthcoming new software systems might cause a global catastrophe?

Or are there, instead, good reasons to dismiss any such concern?

(image by Midjourney)

It’s a vitally important public debate. Alas, this debate is bedevilled by false turnings.

For example, dismissers often make claims with this form:

  • The argument for being concerned assumes that such-and-such a precondition holds
  • But that precondition is suspect (or false)
  • Therefore the concern can be dismissed.

Here’s a simple example – which used to be common, though it appears less often these days:

  • The argument for being concerned assumes that Moore’s Law will hold for the next three decades
  • But Moore’s Law is slowing down
  • Therefore the concern can be dismissed.

Another one:

  • The argument for being concerned assumes that deep learning systems understand what they’re talking about
  • But by such-and-such a definition of understanding, these systems lack understanding
  • (They’re “just stochastic parrots”)
  • Therefore the concern can be dismissed.

Or a favourite:

  • You call these systems AI, meaning they’re (supposedly) artificially intelligent
  • But by such-and-such a definition of intelligence, these systems lack intelligence
  • Therefore the concern can be dismissed.

Perhaps the silliest example:

  • Your example of doom involves a software system that is inordinately obsessed with paperclips
  • But any wise philosopher would design an AI that has no such paperclip obsession
  • Therefore the concern can be dismissed.

My conclusion: those of us who are seriously concerned about the prospects of a software-induced global catastrophe should clarify what are the minimum conditions that would give rise to such a catastrophe.

To be clear, these minimum conditions don’t include the inexorability of Moore’s Law. Nor the conformance of software systems to particular academic models of language understanding. Nor that a fast take-off occurs. Nor that the software system becomes sentient.

Here’s my suggestion of these minimum conditions:

  1. A software system that can influence, directly or indirectly (e.g. by psychological pressure) what happens in the real world
  2. That has access, directly or indirectly, to physical mechanisms that can seriously harm humans
  3. That operates in ways which we might fail to understand or anticipate
  4. That can anticipate actions humans might take, and can calculate and execute countermeasures
  5. That can take actions quickly enough (and/or stealthily enough) to avoid being switched off or reconfigured before catastrophic damage is done.

Even more briefly: the software system operates outside our understanding and outside our control, with potential devastating power.

I’ve chosen to use the term “software” rather than “AI” in order to counter a whole posse of dismissers right at the beginning of the discussion. Not even the smuggest of dismissers denies that software exists and can, indeed, cause harm when it contains bugs, is misconfigured, is hacked, or has gaps in its specification.

Critically, note that software systems often do have real-world impact. Consider the Stuxnet computer worm that caused centrifuges to speed up and destroy themselves inside Iran’s nuclear enrichment facilities. Consider the WannaCry malware that disabled critical hospital equipment around the world in 2017.

Present-day chatbots have already influenced millions of people around the world, via the ideas emerging in chat interactions. Just as people can make life-changing decisions after talking with human therapists or counsellors, people are increasingly taking life-changing decisions following their encounters with the likes of ChatGPT.

Software systems are already involved in the design and operation of military weapons. Presently, humans tend to remain “in the loop”, but military leaders are making the case for humans instead being just “on the loop”, in order for their defence systems to be able to move “at the speed of relevance”.

So the possibility of this kind of software shouldn’t be disputed.

It’s not just military weapons where the potential risk exists. Software systems can be involved with biological pathogens, or with the generation of hate-inducing fake news, or with geoengineering. Or with the manipulation of parts of our infrastructure that we currently only understand dimly, but which might turn out to be horribly fragile, when nudged in particular ways.

Someone wanting to dismiss the risk of software-induced global catastrophe therefore needs to make one or more of the following cases:

  1. All such software systems will be carefully constrained – perhaps by tamperproof failsafe mechanisms that are utterly reliable
  2. All such software systems will remain fully within human understanding, and therefore won’t take any actions that surprise us
  3. All such software systems will fail to develop an accurate “theory of mind” and therefore won’t be able to anticipate human countermeasures
  4. All such software systems will decide, by themselves, to avoid humans experiencing significant harm, regardless of which other goals are found to be attractive by the alien mental processes of that system.

If you still wish to dismiss the risk of software global catastrophe, which of these four cases do you wish to advance?

Or do you have something different in mind?

And can you also be sure that all such software systems will operate correctly, without bugs, configuration failures, gaps in their specification, or being hacked?

Case 2, by the way, includes the idea that “we humans will merge with software and will therefore remain in control of that software”. But in that case, how confident are you that:

  • Humans can speed up their understanding as quickly as the improvement rate of software systems that are free from the constraints of the human skull?
  • Any such “superintelligent” humans will take actions that avoid the same kinds of global catastrophe (after all, some of the world’s most dangerous people have intelligence well above the average)?

Case 4 includes the idea that at least some aspects of morality are absolute, and that a sufficiently intelligent piece of software will discover these principles. But in that case, how confident are you that:

  • The software will decide to respect these principles of morality, rather than (like many humans) disregarding them in order to pursue some other objectives?
  • That these fundamental principles of morality will include the preservation and flourishing of eight billion humans (rather than, say, just a small representative subset in a kind of future “zoo”)?

Postscript: My own recommendations for how to address these very serious risks are in The Singularity Principles. Spoiler alert: there are no magic bullets.

26 February 2023

Ostriches and AGI risks: four transformations needed

Filed under: AGI, risks, Singularity, Singularity Principles — Tags: , — David Wood @ 12:48 am

I confess to having been pretty despondent at various times over the last few days.

The context: increased discussions on social media triggered by recent claims about AGI risk – such as I covered in my previous blogpost.

The cause of my despondency: I’ve seen far too many examples of people with scant knowledge expressing themselves with unwarranted pride and self-certainty.

I call these people the AGI ostriches.

It’s impossible for AGI to exist, one of these ostriches squealed. The probability that AGI can exist is zero.

Anyone concerned about AGI risks, another opined, fails to understand anything about AI, and has just got their ideas from Hollywood or 1950s science fiction.

Yet another claimed: Anything that AGI does in the world will be the inscrutable cosmic will of the universe, so we humans shouldn’t try to change its direction.

Just keep your hand by the off switch, thundered another. Any misbehaving AGI can easily be shut down. Problem solved! You didn’t think of that, did you?

Don’t give the robots any legs, shrieked yet another. Problem solved! You didn’t think of that, did you? You fool!

It’s not the ignorance that depressed me. It was the lack of interest shown by the AGI ostriches regarding alternative possibilities.

I had tried to engage some of the ostriches in conversation. Try looking at things this way, I asked. Not interested, came the answer. Discussions on social media never change any minds, so I’m not going to reply to you.

Click on this link to read a helpful analysis, I suggested. No need, came the answer. Nothing you have written could possibly be relevant.

And the ostriches rejoiced in their wilful blinkeredness. There’s no need to look in that direction, they said. Keep wearing the blindfolds!

(The following image is by the Midjourney AI.)

But my purpose in writing this blogpost isn’t to complain about individual ostriches.

Nor is my purpose to lament the near-fatal flaws in human nature, including our many cognitive biases, our emotional self-sabotage, and our perverse ideological loyalties.

Instead, my remarks will proceed in a different direction. What most needs to change isn’t the ostriches.

It’s the community of people who want to raise awareness of the catastrophic risks of AGI.

That includes me.

On reflection, we’re doing four things wrong. Four transformations are needed, urgently.

Without these changes taking place, it won’t be surprising if the ostriches continue to behave so perversely.

(1) Stop tolerating the Singularity Shadow

When they briefly take off their blindfolds, and take a quick peak into the discussions about AGI, ostriches often notice claims that are, in fact, unwarranted.

These claims confuse matters. They are overconfident claims about what can be expected about the advent of AGI, also known as the Technological Singularity. These claims form part of what I call the Singularity Shadow.

There are seven components in the Singularity Shadow:

  • Singularity timescale determinism
  • Singularity outcome determinism
  • Singularity hyping
  • Singularity risk complacency
  • Singularity term overloading
  • Singularity anti-regulation fundamentalism
  • Singularity preoccupation

If you’ve not come across the concept before, here’s a video all about it:

Or you can read this chapter from The Singularity Principles on the concept: “The Singularity Shadow”.

People who (like me) point out the dangers of badly designed AGI often too easily make alliances with people in the Singularity Shadow. After all, both groups of people:

  • Believe that AGI is possible
  • Believe that AGI might happen soon
  • Believe that AGI is likely to be cause an unprecedented transformation in the human condition.

But the Singularity Shadow causes far too much trouble. It is time to stop being tolerant of its various confusions, wishful thinking, and distortions.

To be clear, I’m not criticising the concept of the Singularity. Far from it. Indeed, I consider myself a singularitarian, with the meaning I explain here. I look forward to more and more people similarly adopting this same stance.

It’s the distortions of that stance that now need to be countered. We must put our own house in order. Sharply.

Otherwise the ostriches will continue to be confused.

(2) Clarify the credible risk pathways

The AI paperclip maximiser has had its day. It needs to be retired.

Likewise the cancer-solving AI that solves cancer by, perversely, killing everyone on the planet.

Likewise the AI that “rescues” a woman from a burning building by hurling her out of the 20th floor window.

In the past, these thought experiments all helped the discussion about AGI risks, among people who were able to see the connections between these “abstract” examples and more complicated real-world scenarios.

But as more of the general public shows an interest in the possibilities of advanced AI, we urgently need a better set of examples. Explained, not by mathematics, nor by cartoonish simplifications, but in plain everyday language.

I’ve tried to offer some examples, for example in the section “Examples of dangers with uncontrollable AI” in the chapter “The AI Control Problem” of my book The Singularity Principles.

But it seems these scenarios still fail to convince. The ostriches find themselves bemused. Oh, that wouldn’t happen, they say.

So this needs more work. As soon as possible.

I anticipate starting from themes about which even the most empty-headed ostrich occasionally worries:

  1. The prospects of an arms race involving lethal autonomous weapons systems
  2. The risks from malware that runs beyond the control of the people who originally released it
  3. The dangers of geoengineering systems that seek to manipulate the global climate
  4. The “gain of function” research which can create ultra-dangerous pathogens
  5. The side-effects of massive corporations which give priority to incentives such as “increase click-through”
  6. The escalation in hatred stirred up by automated trolls with more ingenious “fake social media”

On top of these starting points, the scenarios I envision mix in AI systems with increasing power and increasing autonomy – AI systems which are, however, incompletely understood by the people who deploy them, and which might manifest terrible bugs in unexpected circumstances. (After all, AIs include software, and software generally contains bugs.)

If there’s not already a prize competition to encourage clearer communication of such risk scenarios, in ways that uphold credibility as well as comprehensibility, there should be!

(3) Clarify credible solution pathways

Even more important than clarifying the AGI risk scenarios is to clarify some credible pathways to managing these risks.

Without seeing such solutions, ostriches go into an internal negative feedback loop. They think to themselves as follows:

  • Any possible solution to AGI risks seems unlikely to be successful
  • Any possible solution to AGI risks seems likely to have bad consequences in its own right
  • These thoughts are too horrible to contemplate
  • Therefore we had better believe the AGI risks aren’t actually real
  • Therefore anyone who makes AGI risks seem real needs to be silenced, ridiculed, or mocked.

Just as we need better communication of AGI risk scenarios, we need better communication of positive examples that are relevant to potential solutions:

  • Examples of when society collaborated to overcome huge problems which initially seemed impossible
  • Successful actions against the tolerance of drunk drivers, against dangerous features in car design, against the industrial pollutants which caused acid rain, and against the chemicals which depleted the ozone layer
  • Successful actions by governments to limit the powers of corporate monopolies
  • The de-escalation by Ronald Reagan and Mikhail Gorbachev of the terrifying nuclear arms race between the USA and the USSR.

But we also need to make it clearer how AGI risks can be addressed in practice. This includes a better understanding of:

  • Options for AIs that are explainable and interpretable – with the aid of trusted tools built from narrow AI
  • How AI systems can be designed to be free from the unexpected “emergence” of new properties or subgoals
  • How trusted monitoring can be built into key parts of our infrastructure, to provide early warnings of potential AI-induced catastrophic failures
  • How powerful simulation environments can be created to explore potential catastrophic AI failure modes (and solutions to these issues) in the safety of a virtual model
  • How international agreements can be built up, initially from a “coalition of the willing”, to impose powerful penalties in cases when AI is developed or deployed in ways that violate agreed standards
  • How research into AGI safety can be managed much more effectively, worldwide, than is presently the case.

Again, as needed, significant prizes should be established to accelerate breakthroughs in all these areas.

(4) Divide and conquer

The final transformation needed is to divide up the overall huge problem of AGI safety into more manageable chunks.

What I’ve covered above already suggests a number of vitally important sub-projects.

Specifically, it is surely worth having separate teams tasked with investigating, with the utmost seriousness, a range of potential solutions for the complications that advanced AI brings to each of the following:

  1. The prospects of an arms race involving lethal autonomous weapons systems
  2. The risks from malware that runs beyond the control of the people who originally released it
  3. The dangers of geoengineering systems that seek to manipulate the global climate
  4. The “gain of function” research which can create ultra-dangerous pathogens
  5. The side-effects of massive corporations which give priority to incentives such as “increase click-through”
  6. The escalation in hatred stirred up by automated trolls with more ingenious “fake social media”

(Yes, these are the same six scenarios for catastrophic AI risk that I listed in section (2) earlier.)

Rather than trying to “boil the entire AGI ocean”, these projects each appear to require slightly less boiling.

Once candidate solutions have been developed for one or more of these risk scenarios, the outputs from the different teams can be compared with each other.

What else should be added to the lists above?

23 February 2023

Nuclear-level catastrophe: four responses

Filed under: AGI, risks, Singularity Principles — Tags: , , , , , — David Wood @ 2:11 pm

36% of respondents agree that it is plausible that AI could produce catastrophic outcomes in this century, on the level of all-out nuclear war.

That’s 36% of a rather special group of people. People who replied to this survey needed to meet the criterion of being a named author on at least two papers published in the last three years in accredited journals in the field of Computational Linguistics (CL) – the field sometimes also known as NLP (Natural Language Processing).

The survey took place in May and June 2022. 327 complete responses were received, by people matching the criteria.

A full report on this survey (31 pages) is available here (PDF).

Here’s a screenshot from page 10 of the report, illustrating the answers to questions about Artificial General Intelligence (AGI):

You can see the responses to question 3-4. 36% of the respondents either “agreed” or “weakly agreed” with the statement that

It is plausible that decisions made by AI or machine learning systems could cause a catastrophe this century that is at least as bad as an all-out nuclear war.

That statistic is a useful backdrop to discussions stirred up in the last few days by a video interview given by polymath autodidact and long-time AGI risk researcher Eliezer Yudkowsky:

The publishers of that video chose the eye-catching title “we’re all gonna die”.

If you don’t want to spend 90 minutes watching that video – or if you are personally alienated by Eliezer’s communication style – here’s a useful twitter thread summary by Liron Shapira:

In contrast to the question posed in the NLP survey I mentioned earlier, Eliezer isn’t thinking about “outcomes of AGI in this century“. His timescales are much shorter. His “ballpark estimate” for the time before AGI arrives is “3-15 years”.

How are people reacting to this sombre prediction?

More generally, what responses are there to the statistic that, as quoted above,

36% of respondents agree that it is plausible that AI could produce catastrophic outcomes in this century, on the level of all-out nuclear war.

I’ve seen a lot of different reactions. They break down into four groups: denial, sabotage, trust, and hustle.

1. Denial

One example of denial is this claim: We’re nowhere near an understanding the magic of human minds. Therefore there’s no chance that engineers are going to duplicate that magic in artificial systems.

I have two counters:

  1. The risks of AGI arise, not because the AI may somehow become sentient, and take on the unpleasant aspects of alpha male human nature. Rather, the risks arise from systems that operate beyond our understanding and outside our control, and which may end up pursuing objectives different from the ones we thought (or wished) we had programmed into them
  2. Many systems have been created over the decades without the underlying science being fully understood. Steam engines predated the laws of thermodynamics. More recently, LLMs (Large Language Model AIs) have demonstrated aspects of intelligence that the designers of these systems had not anticipated. In the same way, AIs with some extra features may unexpectedly tip over into greater general intelligence.

Another example of denial: Some very smart people say they don’t believe that AGI poses risks. Therefore we don’t need to pay any more attention to this stupid idea.

My counters:

  1. The mere fact that someone very smart asserts an idea – likely outside of their own field of special expertise – does not confirm the idea is correct
  2. None of these purported objections to the possibility of AGI risk holds water (for a longer discussion, see my book The Singularity Principles).

Digging further into various online discussion threads, I caught the impression that what was motivating some of the denial was often a terrible fear. The people loudly proclaiming their denial were trying to cope with depression. The thought of potential human extinction within just 3-15 years was simply too dreadful for them to contemplate.

It’s similar to how people sometimes cope with the death of someone dear to them. There’s a chance my dear friend has now been reunited in an afterlife with their beloved grandparents, they whisper to themselves. Or, It’s sweet and honourable to die for your country: this death was a glorious sacrifice. And then woe betide any uppity humanist who dares to suggests there is no afterlife, or that patriotism is the last refuge of a scoundrel!

Likewise, woe betide any uppity AI risk researcher who dares to suggest that AGI might not be so benign after all! Deny! Deny!! Deny!!!

(For more on this line of thinking, see my short chapter “The Denial of the Singularity” in The Singularity Principles.)

A different motivation for denial is the belief that any sufficient “cure” to the risk of AGI catastrophe would be worse than the risk it was trying to address. This line of thinking goes as follows:

  • A solution to AGI risk will involve pervasive monitoring and widespread restrictions
  • That monitoring and restrictions will only be possible if an autocratic world government is put in place
  • Any autocratic world government would be absolutely terrible
  • Therefore, the risk of AGI can’t be that bad after all.

I’ll come back later to the flaws in that particular argument. (In the meantime, see if you can spot what’s wrong.)

2. Sabotage

In the video interview, Eliezer made one suggestion for avoiding AGI catastrophe: Destroy all the GPU server farms.

These vast collections of GPUs (a special kind of computing chip) are what enables the training of many types of AI. If these chips were all put out of action, it would delay the arrival of AGI, giving humanity more time to work out a better solution to coexisting with AGI.

Another suggestion Eliezer makes is that the superbright people who are currently working flat out to increase the capabilities of their AI systems should be paid large amounts of money to do nothing. They could lounge about on a beach all day, and still earn more money than they are currently receiving from OpenAI, DeepMind, or whoever is employing them. Once again, that would slow down the emergence of AGI, and buy humanity more time.

I’ve seen other similar suggestions online, which I won’t repeat here, since they come close to acts of terrorism.

All these suggestions have in common: let’s find ways to stop the development of AI in its tracks, all across the world. Companies should be stopped in their tracks. Shadowy military research groups should be stopped in their tracks. Open source hackers should be stopped in their tracks. North Korean ransomware hackers must be stopped in their tracks.

This isn’t just a suggestion that specific AI developments should be halted, namely those with an explicit target of creating AGI. Instead, it recognises that the creation of AGI might occur via unexpected routes. Improving the performance of various narrow AI systems, including fact-checking, or emotion recognition, or online request interchange marketplaces – any of these might push the collection of AI modules over the critical threshold. Mixing metaphors, AI could go nuclear.

Shutting down all these research activities seems a very tall order. Especially since many of the people who are currently working flat out to increase AI capabilities are motivated, not by money, but by the vision that better AI could do a tremendous amount of good in the world: curing cancer, solving nuclear fusion, improving agriculture by leaps and bounds, and so on. They’re not going to be easy to persuade to change course. For them, there’s a lot more at stake than money.

I have more to say about the question “To AGI or not AGI” in this chapter. In short, I’m deeply sceptical.

In response, a would-be saboteur may admit that their chances of success are low. But what do you suggest instead, they will ask.

Read on.

3. Trust

Let’s start again from the statistic that 36% of the NLP survey respondents agreed, with varying degrees of confidence, that advanced AI could trigger a catastrophe as bad as an all-out nuclear war some time this century.

It’s a pity that the question wasn’t asked with shorter timescales. Comparing the chances of an AI-induced global catastrophe in the next 15 years with one in the next 85 years:

  • The longer timescale makes it more likely that AGI will be developed
  • The shorter timescale makes it more likely that AGI safety research will still be at a primitive (deeply ineffective) level.

Even since the date of the survey – May and June 2022 – many forecasters have shortened their estimates of the likely timeline to the arrival of AGI.

So, for the sake of the argument, let’s suppose that the risk of an AI-induced global catastrophe happening by 2038 (15 years from now) is 1/10.

There are two ways to react to this:

  • 1/10 is fine odds. I feel lucky. What’s more, there are plenty of reasons we ought to feel lucky about
  • 1/10 is terrible odds. That’s far too high a risk to accept. We need to hustle to find ways to change these odds in our favour.

I’ll come to the hustle response in a moment. But let’s first consider the trust response.

A good example is in this comment from SingularityNET founder and CEO Ben Goertzel:

Eliezer is a very serious thinker on these matters and was the core source of most of the ideas in Nick Bostrom’s influential book Superintelligence. But ever since I met him, and first debated these issues with him,  back in 2000 I have felt he had a somewhat narrow view of humanity and the universe in general.   

There are currents of love and wisdom in our world that he is not considering and seems to be mostly unaware of, and that we can tap into by creating self reflective compassionate AGIs and doing good loving works together with them.

In short, rather than fearing humanity, we should learn to trust humanity. Rather than fearing what AGI will do, we should trust that AGI can do wonderful things.

You can find a much longer version of Ben’s views in the review he wrote back in 2015 of Superintelligence. It’s well worth reading.

What are the grounds for hope? Humanity has come through major challenges in the past. Even though the scale of the challenge is more daunting on this occasion, there are also more people contributing ideas and inspiration than before. AI is more accessible than nuclear weapons, which increases the danger level, but AI could also be deployed as part of the solution, rather than just being a threat.

Another idea is that if an AI looks around for data teaching it which values to respect and uphold, it will find plenty of positive examples in great human literature. OK, that literature also includes lots of treachery, and different moral codes often conflict, but a wise AGI should be able to see through all these conclusions to discern the importance of defending human flourishing. OK, much of AI training at the moment focuses on deception, manipulation, enticement, and surveillance, but, again, we can hope that a wise AGI will set aside those nastier aspects of human behaviour. Rather than aping trolls or clickbait, we can hope that AGI will echo the better angels of human nature.

It’s also possible that, just as DeepMind’s AlphaGo Zero worked out by itself, without any human input, superior strategies at the board games Go and Chess, a future AI might work out, by itself, the principles of universal morality. (That’s assuming such principles exist.)

We would still have to hope, in such a case, that the AI that worked out the principles of universal morality would decide to follow these principles, rather than having some alternative (alien) ways of thinking.

But surely hope is better than despair?

To quote Ben Goertzel again:

Despondence is unwarranted and unproductive. We need to focus on optimistically maximizing odds of a wildly beneficial Singularity together.   

My view is the same as expressed by Berkeley professor of AI Stuart Russell, in part of a lengthy exchange with Steven Pinker on the subject of AGI risks:

The meta argument is that if we don’t talk about the failure modes, we won’t be able to address them…

Just like in nuclear safety, it’s not against the rules to raise possible failure modes like, what if this molten sodium that you’re proposing should flow around all these pipes? What if it ever came into contact with the water that’s on the turbine side of the system? Wouldn’t you have a massive explosion which could rip off the containment and so on? That’s not exactly what happened in Chernobyl, but not so dissimilar…

The idea that we could solve that problem without even mentioning it, without even talking about it and without even pointing out why it’s difficult and why it’s important, that’s not the culture of safety. That’s sort of more like the culture of the communist party committee in Chernobyl, that simply continued to assert that nothing bad was happening.

(By the way, my sympathies in that long discussion, when it comes to AGI risk, are approximately 100.0% with Russell and approximately 0.0% with Pinker.)

4. Hustle

The story so far:

  • The risks are real (though estimates of their probability vary)
  • Some possible “solutions” to the risks might produce results that are, by some calculations, worse than letting AGI take its own course
  • If we want to improve our odds of survival – and, indeed, for humanity to reach something like a sustainable superabundance with the assistance of advanced AIs – we need to be able to take a clear, candid view of the risks facing us
  • Being naïve about the dangers we face is unlikely to be the best way forward
  • Since time may be short, the time to press for better answers is now
  • We shouldn’t despair. We should hustle.

Some ways in which research could generate useful new insight relatively quickly:

  • When the NLP survey respondents expressed their views, what reasons did they have for disagreeing with the statement? And what reasons did they have for agreeing with it? And how do these reasons stand up, in the cold light of a clear analysis? (In other words, rather than a one-time survey, an iterative Delphi survey should lead to deeper understanding.)
  • Why have the various AI safety initiatives formed in the wake of the Puerto Rico and Asilomar conferences of 2015 and 2017 fallen so far short of expectations?
  • Which descriptions of potential catastrophic AI failure modes are most likely to change the minds of those critics who currently like to shrug off failure scenarios as “unrealistic” or “Hollywood fantasy”?

Constructively, I invite conversation on the strengths and weaknesses of the 21 Singularity Principles that I have suggested as contributing to improving the chances of beneficial AGI outcomes.

For example:

  • Can we identify “middle ways” that include important elements of global monitoring and auditing of AI systems, without collapsing into autocratic global government?
  • Can we improve the interpretability and explainability of advanced AI systems (perhaps with the help of trusted narrow AI tools), to diminish the risks of these systems unexpectedly behaving in ways their designers failed to anticipate?
  • Can we deepen our understanding of the ways new capabilities “emerge” in advanced AI systems, with a particular focus on preventing the emergence of alternative goals?

I also believe we should explore more fully the possibility that an AGI will converge on a set of universal values, independent of whatever training we provide it – and, moreover, the possibility that these values will include upholding human flourishing.

And despite me saying just now that these values would be “independent of whatever training we provide”, is there, nevertheless, a way for us to tilt the landscape so that the AGI is more likely to reach and respect these conclusions?

Postscript

To join me in “camp hustle”, visit Future Surge, which is the activist wing of London Futurists.

If you’re interested in the ideas of my book The Singularity Principles, here’s a podcast episode in which Calum Chace and I discuss some of these ideas more fully.

In a subsequent episode of our podcast, Calum and I took another look at the same topics, this time with Millennium Project Executive Director Jerome Glenn: “Governing the transition to AGI”.

19 December 2022

Rethinking

Filed under: AGI, politics, Singularity Principles — Tags: , , — David Wood @ 2:06 am

I’ve been rethinking some aspects of AI control and AI alignment.

In the six months since publishing my book The Singularity Principles: Anticipating and Managing Cataclysmically Disruptive Technologies, I’ve been involved in scores of conversations about the themes it raises. These conversations have often brought my attention to fresh ideas and different perspectives.

These six months have also seen the appearance of numerous new AI models with capabilities that often catch observers by surprise. The general public is showing a new willingness (at least some of the time) to consider the far-reaching implications of these AI models and their more powerful successors.

People from various parts of my past life have been contacting me. The kinds of things they used to hear me forecasting – the kinds of things they thought, at the time, were unlikely to ever happen – are becoming more credible, more exciting, and, yes, more frightening.

They ask me: What is to be done? And, pointedly, Why aren’t you doing more to stop the truly bad outcomes that now seem ominously likely?

The main answer I give is: read my book. Indeed, you can find all the content online, spread out over a family of webpages.

Indeed, my request is that people should read my book all the way through. That’s because later chapters of that book anticipate questions that tend to come to readers’ minds during earlier chapters, and try to provide answers.

Six months later, although I would give some different (newer) examples were I to rewrite that book today, I stand by the analysis I offered and the principles I championed.

However, I’m inclined to revise my thinking on a number of points. Please find these updates below.

An option to control superintelligent AI

I remain doubtful about the prospects for humans to retain control of any AGI (Artificial General Intelligence) that we create.

That is, the arguments I gave in my chapter “The AI Control Problem” still look strong to me.

But one line of thinking may have some extra mileage. That’s the idea of keeping AGI entirely as an advisor to humans, rather than giving it any autonomy to act directly in the world.

Such an AI would provide us with many recommendations, but it wouldn’t operate any sort of equipment.

More to the point: such an AI would have no desire to operate any sort of equipment. It would have no desires whatsoever, nor any motivations. It would simply be a tool. Or, to be more precise, it would simply be a remarkable tool.

In The Singularity Principles I gave a number of arguments why that idea is unsustainable:

  • Some decisions require faster responses than slow-brained humans can provide; that is, AIs with direct access to real-world levers and switches will be more effective than those that are merely advisory
  • Smart AIs will inevitably develop “subsidiary goals” (intermediate goals) such as having greater computational power, even when there is no explicit programming for such goals
  • As soon as a smart AI acquires any such subsidiary goal, it will find ways to escape any confinement imposed by human overseers.

But I now think this should be explored more carefully. Might a useful distinction be made between:

  1. AIs that do have direct access to real-world levers and switches – with the programming of such AIs being carefully restricted to narrow lines of thinking
  2. AIs with more powerful (general) capabilities, that operate purely in advisory capacities.

In that case, the damage that could be caused by failures of the first type of AI, whilst significant, would not involve threats to the entirety of human civilisation. And failures of the second type of AI would be restricted by the actions of humans as intermediaries.

This approach would require confidence that:

  1. The capabilities of AIs of the first type will remain narrow, despite competitive pressures to give these systems at least some extra rationality
  2. The design of AIs of the second type will prevent the emergence of any dangerous “subsidiary goals”.

As a special case of the second point, the design of these AIs will need to avoid any risk of the systems developing sentience or intrinsic motivation.

These are tough challenges – especially since we still have only a vague understanding of how desires and/or sentience can emerge as smaller systems combine and evolve into larger ones.

But since we are short of other options, it’s definitely something to be considered more fully.

An option for automatically aligned superintelligence

If controlling an AGI turns out to be impossible – as seems likely – what about the option that an AGI will have goals and principles that are fundamentally aligned with human wellbeing?

In such a case, it will not matter if an AGI is beyond human control. The actions it takes will ensure that humans have a very positive future.

The creation of such an AI – sometimes called a “friendly AI” – remains my best hope for humanity’s future.

However, there are severe difficulties in agreeing and encoding “goals and principles that are fundamentally aligned with human wellbeing”. I reviewed these difficulties in my chapter “The AI Alignment Problem”.

But what if such goals and principles are somehow part of an objective reality, awaiting discovery, rather than needing to be invented? What if something like the theory of “moral realism” is true?

In this idea, a principle like “treat humans well” would follow from some sort of a priori logical analysis, a bit like the laws of mathematics (such as the fact, discovered by one of the followers of Pythagoras, that the square root of two is an irrational number).

Accordingly, a sufficiently smart AGI would, all being well, reach its own conclusion that humans ought to be well treated.

Nevertheless, even in this case, significant risks would remain:

  • The principle might be true, but an AGI might not be motivated to discover it
  • The principle might be true, but an AGI, despite its brilliance, may fail to discover it
  • The principle might be true, and an AGI might recognise it, but it may take its own decision to ignore it – like the way that we humans often act in defiance of what we believe at the time to be overarching moral principles

The design criteria and initial conditions that we humans provide for an AGI may well influence the outcome of these risk factors.

I plan to return to these weighty matters in a future blog post!

Two different sorts of control

I’ve come to realise that there are not one but two questions of control of AI:

  1. Can we humans retain control of an AGI that we create?
  2. Can society as a whole control the actions of companies (or organisations) that may create an AGI?

Whilst both these control problems are profoundly hard, the second is less hard.

Moreover, it’s the second problem which is the truly urgent one.

This second control problem involves preventing teams inside corporations (and other organisations) from rushing ahead without due regard to questions of the potential outcomes of their work.

It’s the second control problem that the 21 principles which I highlight in my book are primarily intended to address.

When people say “it’s impossible to solve the AI control problem”, I think they may be correct regarding the first problem, but I passionately believe they’re wrong concerning the second problem.

The importance of psychology

When I review what people say about the progress and risks of AI, I am frequently struck by the fact that apparently intelligent people are strongly attached to views that are full of holes.

When I try to point out the flaws in their thinking, they hardly seem to pause in their stride. They portray a stubborn confidence that they are sure they are correct.

What’s at play here is more than logic. It’s surely a manifestation of humanity’s often defective psychology.

My book includes a short chapter “The denial of the Singularity” which touched on various matters of psychology. If I were to rewrite my book today, I believe that chapter would become larger, and that psychological themes would be spread more widely throughout the book.

Of course, noticing psychological defects is only the start of making progress. Circumventing or transcending these defects is an altogether harder question. But it’s one that needs a lot more attention.

The option of merging with AI

How can we have a better, more productive conversation about anticipating and managing AGI?

How can we avoid being derailed by ineffective arguments, hostile rhetoric, stubborn prejudices, hobby-horse obsessions, outdated ideologies, and (see the previous section) flawed psychology?

How might our not-much-better-than-monkey brains cope with the magnitude of these questions?

One possible answer is that technology can help us (so long as we use it wisely).

For example, the chapter “Uplifting politics”, from near the end of my book, listed ten ways for “technology improving politics”.

More broadly, we humans have the option to selectively deploy some aspects of technology to improve our capabilities in handling other aspects of technology.

We must recognise that technology is no panacea. But it can definitely make a big difference.

Especially if we restrict ourselves to putting heavy reliance only on those technologies – narrow technologies – whose mode of operation we fully understand, and where risks of malfunction can be limited.

This forms part of a general idea that “we humans don’t need to worry about being left behind by robots, or about being subjugated by robots, since we will be the robots”.

As I put it in the chapter “No easy solutions” in my book,

If humans merge with AI, humans could remain in control of AIs, even as these AIs rapidly become more powerful. With such a merger in place, human intelligence will automatically be magnified, as AI improves in capability. Therefore, we humans wouldn’t need to worry about being left behind.

Now I’ve often expressed strong criticisms of this notion of merger. I still believe these criticisms are sound.

But what these criticisms show is that any such merger cannot be the entirety of our response to the prospect of the emergence of AGI. They can only be part of the solution. That’s especially true because humans-augmented-by-technology are still very likely to lag behind pure technology systems, until such time as human minds might be removed from biological skulls and placed into new silicon hosts. That’s something that I’m not expecting to happen before the arrival of AGI, so it will be too late to solve (by itself) the problems of AI alignment and control.

(And since you ask, I probably won’t be in any hurry, even after the arrival of AGI, for my mind to be removed from my biological skull. I guess I might rethink that reticence in due course. But that’s rethinking for another day.)

The importance of politics

Any serious discussion about managing cataclysmically disruptive technologies (such as advanced AIs) pretty soon rubs up against the questions of politics.

That’s not just small-p “politics” – questions of how to collaborate with potential partners where there are many points of disagreement and even dislike.

It’s large-P “Politics” – interacting with presidents, prime ministers, cabinets, parliaments, and so on.

Questions of large-P politics occur throughout The Singularity Principles. My thoughts now, six months afterwards, is that even more focus should be placed on the subject of improving politics:

  • Helping politics to escape the clutches of demagogues and autocrats
  • Helping politics to avoid stultifying embraces between politicians and their “cronies” in established industries
  • Ensuring that the best insights and ideas of the whole electorate can rise to wide attention, without being quashed or distorted by powerful incumbents
  • Bringing everyone involved in politics rapidly up-to-date with the real issues regarding cataclysmically disruptive technologies
  • Distinguishing effective regulations and incentives from those that are counter-productive.

As 2022 has progressed, I’ve seen plenty new evidence of deep problems within political systems around the world. These problems were analysed with sharp insight in the book The Revenge of Power by Moisés Naím that I recently identified as “the best book that I read in 2022”.

Happily, as well as evidence of deep problems in our politics worldwide, there are also encouraging signs, as well as sensible plans for improvement. You can find some of these plans inside the book by Naím, and, yes, I offer suggestions in my own book too.

To accelerate improvements in politics was one of the reasons I created Future Surge a few months back. That’s an initiative on which I expect to spend a lot more of my time in 2023.

Note: the image underlying the picture at the top of this article was created by DALL.E 2 from the prompt “A brain with a human face on it rethinks, vivid stormy sky overhead, photorealistic style”.

3 November 2022

Four options for avoiding an AI cataclysm

Filed under: AGI, podcast, Singularity Principles — Tags: , , — David Wood @ 9:56 pm

Let’s consider four hard truths, and then four options for a solution.

Hard truth 1: Software has bugs.

Even when clever people write the software, and that software passes numerous verification tests, any complex software system generally still has bugs. If the software encounters a circumstance outside its verification suite, it can go horribly wrong.

Hard truth 2: Just because software becomes more powerful, that won’t make all the bugs go away.

Newer software may run faster. It may incorporate input from larger sets of training data. It may gain extra features. But none of these developments mean the automatic removal of subtle errors in the logic of the software, or shortcomings in its specification. It might still reach terrible outcomes – just quicker than before!

Hard truth 3: As AI becomes more powerful, there will be more pressure to deploy it in challenging real-world situations.

Consider the real-time management of:

  • Complex arsenals of missiles, anti-missile missiles, and so on
  • Geoengineering interventions, which are intended to bring the planet’s climate back from the brink of a cascade of tipping points
  • Devious countermeasures against the growing weapons systems of a group (or nation) with a dangerously unstable leadership
  • Social network conversations, where changing sentiments can have big implications for electoral dynamics or for the perceived value of commercial brands
  • Ultra-hot plasmas inside whirling magnetic fields in nuclear fusion energy generators
  • Incentives for people to spend more money than is wise, on addictive gambling sites
  • The buying and selling of financial instruments, to take advantage of changing market sentiments.

In each case, powerful AI software could be a very attractive option. A seductive option. Especially if it has been written by clever people, and appears to have a good track record of delivering results.

Until it goes wrong. In which case the result could be cataclysmic. (Accidental nuclear war. The climate walloped past a tipping point in the wrong direction. Malware going existentially wrong. Partisan outrage propelling a psychological loose cannon over the edge. Easy access to weapons of mass destruction. Etc.)

Indeed, the real risk of AI cataclysm – as opposed to the Hollywood version of any such risk – is that an AI system may acquire so much influence over human society and our surrounding environment that a mistake in that system could cataclysmically reduce human wellbeing all over the world. Billions of lives could be extinguished, or turned into a very pale reflection of their present state.

Such an outcome could arise in any of four ways – four catastrophic error modes. In brief, these are:

  1. Implementation defect
  2. Design defect
  3. Design overridden
  4. Implementation overridden.

Hard truth 4: There are no simple solutions to the risks described above.

What’s more, people who naively assume that a simple solution can easily be put in place (or already exists) are making the overall situation worse. They encourage complacency, whereas greater attention is urgently needed.

But perhaps you disagree?

That’s the context for the conversation in Episode 11 of the London Futurists Podcast, which was published yesterday morning.

In just thirty minutes, that episode dug deep into some of the ideas in my recent book The Singularity Principles. Co-host Calum Chace and I found plenty on which to agree, but had differing opinions on one of the most important questions.

Calum listed three suggestions that people sometimes make for how the dangers of potentially cataclysmic AI might be handled.

In response, I described a different approach – something that Calum said would be a fourth idea for a solution. As you can hear from the recording of the podcast, I evidently left him unconvinced.

Therefore, I’d like to dig even deeper.

Option 1: Humanity gets lucky

It might be the case that AI software that is smart enough, will embody an unshakeable commitment toward humanity having the best possible experience.

Such software won’t miscalculate (after all, it is superintelligent). If there are flaws in how it has been specified, it will be smart enough to notice these flaws, rather than stubbornly following through on the letter of its programming. (After all, it is superintelligent.)

Variants of this wishful thinking exist. In some variants, what will guarantee a positive outcome isn’t just a latent tendency of superintelligence toward superbenevolence. It’s the invisible hand of the free market that will guide consumer choices away from software that might harm users, toward software that never, ever, ever goes wrong.

My response here is that software which appears to be bug free can, nevertheless, harbour deep mistakes. It may be superintelligent, but that doesn’t mean it’s omniscient or infallible.

Second, software which is bug free may be monstrously efficient at doing what some of its designers had in mind – manipulating consumers into actions which increase the share price of a given corporation, despite all the externalities arising.

Moreover, it’s too much of a stretch to say that greater intelligence always makes your wiser and kinder. There are plenty of dreadful counterexamples, from humans in the worlds of politics, crime, business, academia, and more. Who is to say that a piece of software with an IQ equivalent to 100,000 will be sure to treat us humans any better than we humans sometimes treat swarms of insects (e.g. ant colonies) that get in our way?

Do you feel lucky? My view is that any such feeling, in these circumstances, is rash in the extreme.

Option 2: Safety engineered in

Might a team of brilliant AI researchers, Mary and Flo (to make up a couple of names), devise a clever method that will ensure their AI (once it is built) never harms humanity?

Perhaps the answer lies in some advanced mathematical wizardry. Or in chiselling a 21st century version of Asimov’s Laws of Robotics into the chipsets at the heart of computer systems. Or in switching from “correlation logic” to “causation logic”, or some other kind of new paradigm in AI systems engineering.

Of course, I wish Mary and Flo well. But their ongoing research won’t, by itself, prevent lots of other people releasing their own unsafe AI first. Especially when these other engineers are in a hurry to win market share for their companies.

Indeed, the considerable effort being invested by various researchers and organisations in a search for a kind of fix for AI safety is, arguably, a distraction from a sober assessment of the bigger picture. Better technology, better product design, better mathematics, and better hardware can all be part of the full solution. But that full solution also needs, critically, to include aspects of organisational design, economic incentives, legal frameworks, and political oversight. That’s the argument I develop in my book. We ignore these broader forces at our peril.

Option 3: Humans merge with machines

If we can’t beat them, how about joining them?

If human minds are fused into silicon AI systems, won’t the good human sense of these minds counteract any bugs or design flaws in the silicon part of the hybrid formed?

With such a merger in place, human intelligence will automatically be magnified, as AI improves in capability. Therefore, we humans wouldn’t need to worry about being left behind. Right?

I see two big problems with this idea. First, so long as human intelligence is rooted in something like the biology of the brain, the mechanisms for any such merger may only allow relatively modest increases in human intelligence. Our biological brains would be bottlenecks that constrain the speed of progress in this hybrid case. Compared to pure AIs, the human-AI hybrid would, after all, be left behind in this intelligence race. So much for humans staying in control!

An even bigger problem is the realisation that a human with superhuman intelligence is likely to be at least as unpredictable and dangerous as an AI with superhuman intelligence. The magnification of intelligence will allow that superhuman human to do all kinds of things with great vigour – settling grudges, acting out fantasies, demanding attention, pursuing vanity projects, and so on. Recall: power tends to corrupt. Such a person would be able to destroy the earth. Worse, they might want to do so.

Another way to state this point is that, just because AI elements are included inside a person, that won’t magically ensure that these elements become benign, or are subject to the full control of the person’s best intentions. Consider as comparisons what happens when biological viruses enter a person’s body, or when a cancer grows there. In neither case does the intruding element lose its ability to cause damage, just on account of being part of a person who has humanitarian instincts.

This reminds me of the statement that is sometimes heard, in defence of accelerating the capabilities of AI systems: “I am not afraid of artificial intelligence. I am afraid of human stupidity”.

In reality, what we need to fear is the combination of imperfect AI and imperfect humanity.

The conclusion of this line of discussion is that we need to do considerably more than enable greater intelligence. We also need to accelerate greater wisdom – so that any beings with superhuman intelligence will operate truly beneficently.

Option 4: Greater wisdom

The cornerstone insight of ethics is that, just because we can do something, and indeed may even want to do that thing, it doesn’t mean we should do that thing.

Accordingly, human societies since prehistory have placed constraints on how people should behave.

Sometimes, moral sanction is sufficient: people constrain their actions in deference to public opinion. In other cases, restrictions are codified into laws and regulations.

Likewise, just because a corporation could boost its profits by releasing a new version of its AI software, that doesn’t mean it should release that software.

But what is the origin of these “should” imperatives? And how do we resolve conflicts, when two different groups of people champion two different sets of ethical intuitions?

Where can we find a viable foundation for ethical restrictions – something more solid than “we’ve always done things like this” or “this feels right to me” or “we need to submit to the dictates in our favourite holy scripture”?

Welcome to the world of philosophy.

It’s a world that, according to some observers, has made little progress over the centuries. People still argue over fundamentals. Deontologists square off against consequentialists. Virtue ethicists stake out a different position.

It’s a world in which it is easier to poke holes in the views held by others, rather than defending a consistent view of your own.

But it’s my position that the impending threat of cataclysmic AI impels us to reach a wiser agreement.

It’s like how the devastation of the Covid pandemic impelled society to find significantly quicker ways to manufacture, verify, and deploy vaccines.

It’s like how society can come together, remarkably, in a wartime situation, notwithstanding the divisions that previously existed.

In the face of the threats of technology beyond our control, minds should focus, with unprecedented clarity. We’ll gradually build a wider consensus in favour of various restrictions and, yes, in favour of various incentives.

What’s your reaction? Is option 4 simply naïve?

Practical steps forward

Rather than trying to “boil the ocean” of philosophical disputes over contrasting ethical foundations, we can, and should, proceed in a kaizen manner.

To start with, we can give our attention to specific individual questions:

  • What are the circumstances when we should welcome AI-powered facial recognition software, and when should we resist it?
  • What are the circumstances when we should welcome AI systems that supervise aspects of dangerous weaponry?
  • What are the circumstances that could transform AI-powered monitoring systems from dangerous to helpful?

As we reach some tentative agreements on these individual matters, we can take the time to highlight principles with potential wider applicability.

In parallel, we can revisit some of the agreements (explicit and implicit) for how we measure the health of society and the liberties of individuals:

  • The GDP (Gross Domestic Product) statistics that provide a perspective on economic activities
  • The UDHR (Universal Declaration of Human Rights) statement that was endorsed in the United Nations General Assembly in 1948.

I don’t deny it will be hard to build consensus. It will be even harder to agree how to enforce the guidelines arising – especially in light of the wretched partisan conflicts that are poisoning the political processes in a number of parts of the world.

But we must try. And with some small wins under our belt, we can anticipate momentum building.

These are some of the topics I cover in the closing chapters of The Singularity Principles:

I by no means claim to know all the answers.

But I do believe that these are some of the most important questions to address.

And to help us make progress, something that could help us is – you guessed it – AI. In the right circumstances, AI can help us think more clearly, and can propose new syntheses of our previous ideas.

Thus today’s AI can provide stepping stones to the design and deployment of better, safer, wiser AI tomorrow. That’s provided we maintain human oversight.

Footnotes

The image above includes a design by Pixabay user Alexander Antropov, used with thanks.

See also this article by Calum in Forbes, Taking Back Control Of The Singularity.

15 May 2022

A year-by-year timeline to 2045

Filed under: Abundance, AGI, challenge, evolution, futurist, Singularity Principles, YouTube — Tags: , , , — David Wood @ 1:01 pm

The ground rules for the worldbuilding competition were attractive:

  • The year is 2045.
  • AGI has existed for at least 5 years.
  • Technology is advancing rapidly and AI is transforming the world sector by sector.
  • The US, EU and China have managed a steady, if uneasy, power equilibrium.
  • India, Africa and South America are quickly on the ride as major players.
  • Despite ongoing challenges, there have been no major wars or other global catastrophes.
  • The world is not dystopian and the future is looking bright.

Entrants were asked to submit four pieces of work. One was a new media piece. I submitted this video:

Another required piece was:

A timeline with entries for each year between 2022 and 2045 giving at least two events (e.g. “X invented”) and one data point (e.g. “GDP rises by 25%”) for each year.

The timeline I created dovetailed with the framework from the above video. Since I enjoyed creating it, I’m sharing my submission here, in the hope that it may inspire readers.

(Note: the content was submitted on 11th April 2022.)

2022

US mid-term elections result in log-jammed US governance, widespread frustration, and a groundswell desire for more constructive approaches to politics.

The collapse of a major crypto “stablecoin” results in much wider adverse repercussions than was generally expected, and a new social appreciation of the dangers of flawed financial systems.

Data point: Number of people killed in violent incidents (including homicides and armed conflicts) around the world: 590,000

2023

Fake news that is spread by social media driven by a new variant of AI provokes riots in which more than 10,000 people die, leading to much greater interest a set of “Singularity Principles” that had previously been proposed to steer the development of potentially world-transforming technologies.

G7 transforms into the D16, consisting of the world’s 16 leading democracies, proclaiming a profound shared commitment to champion norms of: openness; free and fair elections; the rule of law; independent media, judiciary, and academia; power being distributed rather than concentrated; and respect for autonomous decisions of groups of people.

Data point: Proportion of world population living in countries that are “full democracies” as assessed by the Economist: 6.4%

2024

South Korea starts a trial of a nationwide UBI scheme, in the first of what will become in later years a long line of increasingly robust “universal citizens’ dividends” schemes around the world.

A previously unknown offshoot of ISIS releases a bioengineered virus. Fortunately, vaccines are quickly developed and deployed against it. In parallel, a bitter cyber war takes place between Iran and Israel. These incidents lead to international commitments to prevent future recurrences.

Data point: Proportion of people of working age in US who are not working and who are not looking for a job: 38%

2025

Extreme weather – floods and storms – kills 10s of 1000s in both North America and Europe. A major trial of geo-engineering is rushed through, with reflection of solar radiation in the stratosphere – causing global political disagreement and then a renewed determination for tangible shared action on climate change.

The US President appoints a Secretary for the Future as a top-level cabinet position. More US states adopt rank choice voting, allowing third parties to grow in prominence.

Data point: Proportion of earth’s habitable land used to rear animals for human food: 38%

2026

A song created entirely by an AI tops the hit parade, and initiates a radical new musical genre.

Groundswell opposition to autocratic rule in Russia leads to the fall from power of the president and a new dedication to democracy throughout countries formerly perceived as being within Russia’s sphere of direct influence.

Data point: Net greenhouse gas emissions (including those from land-use changes): 59 billion tons of CO2 equivalent – an unwelcome record.

2027

Metformin approved for use as an anti-aging medicine in a D16 country. Another D16 country recommends nationwide regular usage of a new nootropic drug.

Exchanges of small numbers of missiles between North and South Korea leads to regime change inside North Korea and a rapprochement between the long-bitter enemies.

Data point: Proportion of world population living in countries that are “full democracies” as assessed by the Economist: 9.2%

2028

An innovative nuclear fusion system, with its design assisted by AI, runs for more than one hour and generates significantly more energy out than what had been put in.

As a result of disagreements about the future of an independent Taiwan, an intense destructive cyber battle takes place. At the end, the nations of the world commit more seriously than before to avoiding any future cyber battles.

Data point: Proportion of world population experiencing mental illness or dissatisfied with the quality of their mental health: 41%

2029

A trial of an anti-aging intervention in middle-aged dogs is confirmed to have increased remaining life expectancy by 25% without causing any adverse side effects. Public interest in similar interventions in humans skyrockets.

The UK rejoins a reconfigured EU, as an indication of support for sovereignty that is pooled rather than narrow.

Data point: Proportion of world population with formal cryonics arrangements: 1 in 100,000

2030

Russia is admitted into the D40 – a newly expanded version of the D16. The D40 officially adopts “Index of Human Flourishing” as more important metric than GDP, and agrees a revised version of the Universal Declaration of Human Rights, brought up to date with transhuman issues.

First permanent implant in a human of an artificial heart with a new design that draws all required power from the biology of the body rather than any attached battery, and whose pace of operation is under the control of the brain.

Data point: Net greenhouse gas emissions (including those from land-use changes): 47 billion tons of CO2 equivalent – a significant improvement

2031

An AI discovers and explains a profound new way of looking at mathematics, DeepMath, leading in turn to dramatically successful new theories of fundamental physics.

Widespread use of dynamically re-programmed nanobots to treat medical conditions that would previously have been fatal.

Data point: Proportion of world population regularly taking powerful anti-aging medications: 23%

2032

First person reaches the age of 125. Her birthday celebrations are briefly disrupted by a small group of self-described “naturality advocates” who chant “120 is enough for anyone”, but that group has little public support.

D40 countries put in place a widespread “trustable monitoring system” to cut down on existential risks (such as spread of WMDs) whilst maintaining citizens’ trust.

Data point: Proportion of world population living in countries that are “full democracies” as assessed by the Economist: 35.7% 

2033

For the first time since the 1850s, the US President comes from a party other than Republican and Democratic.

An AI system is able to convincingly pass the Turing test, impressing even the previous staunchest critics with its apparent grasp of general knowledge and common sense. The answers it gives to questions of moral dilemmas also impress previous sceptics.

Data point: Proportion of people of working age in US who are not working and who are not looking for a job: 58%

2034

The D90 (expanded from the D40) agrees to vigorously impose Singularity Principles rules to avoid inadvertent creation of dangerous AGI.

Atomically precise synthetic nanoscale assembly factories have come of age, in line with the decades-old vision of nanotechnology visionary Eric Drexler, and are proving to have just as consequential an impact on human society as AI.

Data point: Net greenhouse gas *removals*: 10 billion tons of CO2 equivalent – a dramatic improvement

2035

A novel written entirely by an AI reaches the top of the New York Times bestseller list, and is widely celebrated as being the finest piece of literature ever produced.

Successful measures to remove greenhouse gases from the atmosphere, coupled with wide deployment of clean energy sources, lead to a declaration of “victory over runaway climate change”.

Data point: Proportion of earth’s habitable land used to rear animals for human food: 4%

2036

A film created entirely by an AI, without any real human actors, wins Oscar awards.

The last major sceptical holdout, a philosophy professor from an Ivy League university, accepts that AGI now exists. The pope gives his blessing too.

Data point: Proportion of world population with cryonics arrangements: 24%

2037

The last instances of the industrial scale slaughter of animals for human consumption, on account of the worldwide adoption of cultivated (lab-grown) meat.

AGI convincingly explains that it is not sentient, and that it has a very different fundamental structure from that of biological consciousness.

Data point: Proportion of world population who are literate: 99.3%

2038

Rejuvenation therapies are in wide use around the world. “Eighty is the new fifty”. First person reaches the age of 130.

Improvements made by AGI upon itself effectively raise its IQ one hundred fold, taking it far beyond the comprehension of human observers. However, the AGI provides explanatory educational material that allows people to understand vast new sets of ideas.

Data point: Proportion of world population who consider themselves opposed to AGI: 0.1%

2039

An extensive set of “vital training” sessions has been established by the AGI, with all citizens over the age of ten participating for a minimum of seven hours per day on 72 days each year, to ensure that humans develop and maintain key survival skills.

Menopause reversal is common place. Women who had long ago given up any ideas of bearing another child happily embrace motherhood again.

Data point: Proportion of world population regularly taking powerful anti-aging medications: 99.2%

2040

The use of “mind phones” is widespread: new brain-computer interfaces that allow communication between people by mental thought alone.

People regularly opt to have several of their original biological organs replaced by synthetic alternatives that are more efficient, more durable, and more reliable.

Data point: Proportion of people of working age in US who are not working and who are not looking for a job: 96%

2041

Shared immersive virtual reality experiences include hyper-realistic simulations of long-dead individuals – including musicians, politicians, royalty, saints, and founders of religions.

The number of miles of journey undertaken by small “flying cars” exceeds that of ground-based powered transport.

Data point: Proportion of world population living in countries that are “full democracies” as assessed by the Economist: 100.0%

2042

First successful revival of mammal from cryopreservation.

AGI presents a proof of the possibility of time travel, but the resources required for safe transit of humans through time would require the equivalent of building a Dyson sphere around the sun.

Data point: Proportion of world population experiencing mental illness or dissatisfied with the quality of their mental health: 0.4%

2043

First person reaches the age of 135, and declares herself to be healthier than at any time in the preceding four decades.

As a result of virtual reality encounters of avatars of founders of religion, a number of new systems of philosophical and mystical thinking grow in popularity.

Data point: Proportion of world’s energy provided by earth-based nuclear fusion: 75%

2044

First human baby born from an ectogenetic pregnancy.

Family holidays on the Moon are an increasingly common occurrence.

Data point: Average amount of their waking time that people spend in a metaverse: 38%

2045

First revival of human from cryopreservation – someone who had been cryopreserved ten years previously.

Subtle messages decoded by AGI from far distant stars in the galaxy confirm that other intelligent civilisations exist, and are on their way to reveal themselves to humanity.

Data point: Number of people killed in violent incidents around the world: 59

Postscript

My thanks go to the competition organisers, the Future of Life Institute, for providing the inspiration for the creation of the above timeline.

Readers are likely to have questions in their minds as they browse the timeline above. More details of the reasoning behind the scenarios involved are contained in three follow-up posts:

7 February 2022

Options for controlling artificial superintelligence

Filed under: AGI, Events, friendly AI, London Futurists, safety, Singularity — Tags: , , — David Wood @ 5:10 pm

What are the best options for controlling artificial superintelligence?

Should we confine it in some kind of box (or simulation), to prevent it from roaming freely over the Internet?

Should we hard-wire into its programming a deep respect for humanity?

Should we avoid it from having any sense of agency or ambition?

Should we ensure that, before it takes any action, it always double-checks its plans with human overseers?

Should we create dedicated “narrow” intelligence monitoring systems, to keep a vigilant eye on it?

Should we build in a self-destruct mechanism, just in case it stops responding to human requests?

Should we insist that it shares its greater intelligence with its human overseers (in effect turning them into cyborgs), to avoid humanity being left behind?

More drastically, should we simply prevent any such systems from coming into existence, by forbidding any research that could lead to artificial superintelligence?

Alternatively, should we give up on any attempt at control, and trust that the superintelligence will be thoughtful enough to always “do the right thing”?

Or is there a better solution?

If you have clear views on this question, I’d like to hear from you.

I’m looking for speakers for a forthcoming London Futurists online webinar dedicated to this topic.

I envision three speakers each taking up to 15 minutes to set out their proposals. Once all the proposals are on the table, the real discussion will begin – with the speakers interacting with each other, and responding to questions raised by the live audience.

The date for this event remains to be determined. I will find a date that is suitable for the speakers who have the most interesting ideas to present.

As I said, please get in touch if you have questions or suggestions about this event.

Image credit: the above graphic includes work by Pixabay user Geralt.

PS For some background, here’s a video recording of the London Futurists event from last Saturday, in which Roman Yampolskiy gave several reasons why control of artificial superintelligence will be deeply difficult.

For other useful background material, see the videos on the Singularity page of the Vital Syllabus project.

1 March 2021

The imminence of artificial consciousness

Filed under: AGI, books, brain simulation, London Futurists — Tags: , , — David Wood @ 10:26 am

I’ve changed my mind about consciousness.

I used to think that, of the two great problems about artificial minds – namely, achieving artificial general intelligence, and achieving artificial consciousness – progress toward the former would be faster than progress toward the latter.

After all, progress in understanding consciousness had seemed particularly slow, whereas enormous numbers of researchers in both academia and industry have been attaining breakthrough after breakthrough with new algorithms in artificial reasoning.

Over the decades, I’d read a number of books by Daniel Dennett and other philosophers who claimed to have shown that consciousness was basically already understood. There’s nothing spectacularly magical or esoteric about consciousness, Dennett maintained. What’s more, we must beware being misled by our own introspective understanding of our consciousness. That inner introspection is subject to distortions – perceptual illusions, akin to the visual illusions that often mislead us about what we think our eyes are seeing.

But I’d found myself at best semi-convinced by such accounts. I felt that, despite the clever analyses in such accounts, there was surely more to the story.

The most famous expression of the idea that consciousness still defied a proper understanding is the formulation by David Chalmers. This is from his watershed 1995 essay “Facing Up to the Problem of Consciousness”:

The really hard problem of consciousness is the problem of experience. When we think and perceive, there is a whir of information-processing, but there is also a subjective aspect… There is something it is like to be a conscious organism. This subjective aspect is experience.

When we see, for example, we experience visual sensations: the felt quality of redness, the experience of dark and light, the quality of depth in a visual field. Other experiences go along with perception in different modalities: the sound of a clarinet, the smell of mothballs. Then there are bodily sensations, from pains to orgasms; mental images that are conjured up internally; the felt quality of emotion, and the experience of a stream of conscious thought. What unites all of these states is that there is something it is like to be in them. All of them are states of experience.

It is undeniable that some organisms are subjects of experience. But the question of how it is that these systems are subjects of experience is perplexing. Why is it that when our cognitive systems engage in visual and auditory information-processing, we have visual or auditory experience: the quality of deep blue, the sensation of middle C? How can we explain why there is something it is like to entertain a mental image, or to experience an emotion?

It is widely agreed that experience arises from a physical basis, but we have no good explanation of why and how it so arises. Why should physical processing give rise to a rich inner life at all? It seems objectively unreasonable that it should, and yet it does.

However, as Wikipedia notes,

The existence of a “hard problem” is controversial. It has been accepted by philosophers of mind such as Joseph Levine, Colin McGinn, and Ned Block and cognitive neuroscientists such as Francisco Varela, Giulio Tononi, and Christof Koch. However, its existence is disputed by philosophers of mind such as Daniel Dennett, Massimo Pigliucci, Thomas Metzinger, Patricia Churchland, and Keith Frankish, and cognitive neuroscientists such as Stanislas Dehaene, Bernard Baars, Anil Seth and Antonio Damasio.

With so many smart people apparently unable to agree, what hope is there for a layperson to have any confidence in an answering the question, is consciousness already explained in principle, or do we need some fundamentally new insights?

It’s tempting to say, therefore, that the question should be left to one side. Instead of squandering energy spinning circles of ideas with little prospect of real progress, it would be better to concentrate on numerous practical questions: vaccines for pandemics, climate change, taking the sting out of psychological malware, protecting democracy against latent totalitarianism, and so on.

That practical orientation is the one that I have tried to follow most of the time. But there are four reasons, nevertheless, to keep returning to the question of understanding consciousness. A better understanding of consciousness might:

  1. Help provide therapists and counsellors with new methods to address the growing crisis of mental ill-health
  2. Change our attitudes towards the suffering we inflict, as a society, upon farm animals, fish, and other creatures
  3. Provide confidence on whether copying of memories and other patterns of brain activity, into some kind of silicon storage, could result at some future date in the resurrection of our consciousness – or whether any such reanimation would, instead, be “only a copy” of us
  4. Guide the ways in which systems of artificial intelligence are being created.

On that last point, consider the question whether AI systems will somehow automatically become conscious, as they gain in computational ability. Most AI researchers have been sceptical on that score. Google Maps is not conscious, despite all the profoundly clever things that it can do. Neither is your smartphone. As for the Internet as a whole, opinions are a bit more mixed, but again, the general consensus is that all the electronic processing happening on the Internet is devoid of the kind of subjective inner experience described by David Chalmers.

Yes, lots of software has elements of being self-aware. Such software contains models of itself. But it’s generally thought (and I agree, for what it’s worth) that such internal modelling is far short of subjective inner experience.

One prospect this raises is the dark possibility that humans might be superseded by AIs that are considerably more intelligent than us, but that such AIs would have “no-one at home”, that is, no inner consciousness. In that case, a universe with AIs instead of humans might have much more information processing, but be devoid of conscious feelings. Mega oops.

The discussion at this point is sometimes led astray by the popular notion that any threat from superintelligent AIs to human existence is predicated on these AIs “waking up” or become conscious. In that popular narrative, any such waking up might give an AI an additional incentive to preserve itself. Such an AI might adopt destructive human “alpha male” combative attitudes. But as I say, that’s a faulty line of reasoning. AIs might well be motivated to preserve themselves without ever gaining any consciousness. (Look up the concept of “basic AI drives” by Steve Omohundro.) Indeed, a cruise missile that locks onto a target poses a threat to that target, not because the missile is somehow conscious, but because it has enough intelligence to navigate to its target and explode on arrival.

Indeed, AIs can pose threats to people’s employment, without these AIs gaining consciousness. They can simulate emotions without having real internal emotions. They can create artistic masterpieces, using techniques such as GANs (Generative Adversarial Networks), without having any real psychological appreciation of the beauty of these works of art.

For these reasons, I’ve generally urged people to set aside the question of machine consciousness, and to focus instead on the question of machine intelligence. (For example, I presented that argument in Chapter 9 of my book Sustainable Superabundance.) The latter is tangible and poses increasing threats (and opportunities), whereas the former is a discussion that never seems to get off the ground.

But, as I mentioned at the start, I’ve changed my mind. I now think it’s possible we could have machines with synthetic consciousness well before we have machines with general intelligence.

What’s changed my mind is the book by Professor Mark Solms, The Hidden Spring: A Journey to the Source of Consciousness.

Solms is director of neuropsychology in the Neuroscience Institute of the University of Cape Town, honorary lecturer in neurosurgery at the Royal London Hospital School of Medicine, and an honorary fellow of the American College of Psychiatrists. He has spent his entire career investigating the mysteries of consciousness. He achieved renown within his profession for identifying the brain mechanisms of dreaming and for bringing psychoanalytic insights into modern neuroscience. And now his book The Hidden Spring is bringing him renown far beyond his profession. Here’s a selection of the praise it has received:

  • A remarkably bold fusion of ideas from psychoanalysis, psychology, and the frontiers of theoretical neuroscience, that takes aim at the biggest question there is. Solms will challenge your most basic beliefs.
    – Matthew Cobb, author of The Idea of the Brain: The Past and Future of Neuroscience
  • At last the emperor has found some clothes! For decades, consciousness has been perceived as an epiphenomenon, little more than an illusion that can’t really make things happen. Solms takes a thrilling new approach to the problem, grounded in modern neurobiology but finding meaning in older ideas going back to Freud. This is an exciting book.
    – Nick Lane, author of The Vital Question
  • To say this work is encyclopaedic is to diminish its poetic, psychological and theoretical achievement. This is required reading.
    – Susie Orbach, author of In Therapy
  • Intriguing…There is plenty to provoke and fascinate along the way.
    – Anil Seth, Times Higher Education
  • Solms’s efforts… have been truly pioneering. This unification is clearly the direction for the future.
    – Eric Kandel, Nobel laureate for Physiology and Medicine
  • This treatment of consciousness and artificial sentience should be taken very seriously.
    – Karl Friston, scientific director, Wellcome Trust Centre for Neuroimaging
  • Solms’s vital work has never ignored the lived, felt experience of human beings. His ideas look a lot like the future to me.
    – Siri Hustvedt, author of The Blazing World
  • Nobody bewitched by these mysteries [of consciousness] can afford to ignore the solution proposed by Mark Solms… Fascinating, wide-ranging and heartfelt.
    – Oliver Burkeman, Guardian
  • This is truly a remarkable book. It changes everything.
    – Brian Eno

At times, I had to concentrate hard while listening to this book, rewinding the playback multiple times. That’s because the ideas kept sparking new lines of thought in my mind, which ran off in different directions as the narration continued. And although Solms explains his ideas in an engaging manner, I wanted to think through the deeper connections with the various fields that form part of the discussion – including psychoanalysis (Freud features heavily), thermodynamics (Helmholtz, Gibbs, and Friston), evolution, animal instincts, dreams, Bayesian statistics, perceptual illusions, and the philosophy of science.

Alongside the theoretical sections, the book contains plenty of case studies – from Solms’ own patients, and from other clinicians over the decades (actually centuries) – that illuminate the points being made. These studies involve people – or animals – with damage to parts of their brains. The unusual ways in which these subjects behave – and the unusual ways in which they express themselves – provide insight on how consciousness operates. Particularly remarkable are the children born with hydranencephaly – that is, without a cerebral cortex – but who nevertheless appear to experience feelings.

Having spent two weeks making my way through the first three quarters of the book, I took the time yesterday (Sunday) to listen to the final quarter, where there were several climaxes following on top of each other – addressing at length the “Hard Problem” ideas of David Chalmers, and the possibility of artificial consciousness.

It’s challenging to summarise such a rich set of ideas in just a few paragraphs, but here are some components:

  • To understand consciousness, the subcortical brain stem (an ancient part of our anatomy) is at least as important as the cognitive architecture of the cortex
  • To understand consciousness, we need to pay attention to feelings as much as to memories and thought processing
  • Likewise, the chemistry of long-range neuromodulators is at least as important as the chemistry of short-range neurotransmitters
  • Consciousness arises from particular kinds of homeostatic systems which are separated from their environment by a partially permeable boundary: a structure known as a “Markov blanket”
  • These systems need to take actions to preserve their own existence, including creating an internal model of their external environment, monitoring differences between incoming sensory signals and what their model predicted these signals would be, and making adjustments so as to prevent these differences from escalating
  • Whereas a great deal of internal processing and decision-making can happen automatically, without conscious thought, some challenges transcend previous programming, and demand greater attention

In short, consciousness arises from particular forms of information processing. (Solms provides good reasons to reject the idea that there is a basic consiciousness latent in all information, or, indeed, in all matter.) Whilst more work requires to be done to pin down the exact circumstances in which consciousness arises, this project is looking much more promising now, than it did just a few years ago.

This is no idle metaphysics. The ideas can in principle be tested by creating artificial systems that involve particular kinds of Markov blankets, uncertain environments that pose existential threats to the system, diverse categorical needs (akin to the multiple different needs of biologically conscious organisms), and layered feedback loops. Solms sets out a three-stage process whereby such systems could be built and evolved, in a relatively short number of years.

But wait. All kinds of questions arise. Perhaps the most pressing one is this: If such systems can be built, should we build them?

That “should we” question gets a lot of attention in the closing sections of the book. We might end up with AIs that are conscious slaves, in ways that we don’t have to worry about for our existing AIs. We might create AIs that feel pain beyond that which any previous conscious being has ever experienced it. Equally, we might create AIs that behave very differently from those without consciousness – AIs that are more unpredictable, more adaptable, more resourceful, more creative – and more dangerous.

Solms is doubtful about any global moratorium on such experiments. Now that the ideas are out of the bag, so to speak, there will be many people – in both academia and industry – who are motivated to do additional research in this field.

What next? That’s a question that I’ll be exploring this Saturday, 6th March, when Mark Solms will be speaking to London Futurists. The title of his presentation will be “Towards an artificial consciousness”.

For more details of what I expect will be a fascinating conversation – and to register to take part in the live question and answer portion of the event – follow the links here.

31 July 2020

The future of AI: 12 possible breakthroughs, and beyond

Filed under: AGI, books, disruption — Tags: , , , , — David Wood @ 1:30 pm

The AI of 5-10 years time could be very different from today’s AI. The most successful AI systems of that time will not simply be extensions of today’s deep neural networks. Instead, they are likely to include significant conceptual breakthroughs or other game-changing innovations.

That was the argument I made in a presentation on Thursday to the Global Data Sciences and Artificial Intelligence meetup. The chair of that meetup, Pramod Kunji, kindly recorded the presentation.

You can see my opening remarks in this video:

A copy of my slides can be accessed on Slideshare.

The ideas in this presentation raise many important questions, for which there are, as yet, only incomplete answers.

Indeed, the future of AI is a massive topic, touching nearly every area of human life. The greater the possibility that AI will experience cascading improvements in capability, the greater the urgency of exploring these scenarios in advance. In other words, the greater the need to set aside hype and predetermined ideas, in order to assess matters objectively and with an independent mind.

For that reason, I’ve joined with Rohit Talwar of Fast Future and Ben Goertzel of SingularityNET in a project to commission and edit chapters in a forthcoming book, “The Future of AI: Pathways to Artificial General Intelligence”.

forward-2083419_1920

We’re asking AI researchers, practitioners, analysts, commentators, policy makers, investors, futurists, economists, and writers from around the world, to submit chapters of up to 1,000 words, by the deadline of 15th September, that address one or more of the following themes:

  • Capability, Applications, and Impacts
    • How might the capabilities of AI systems evolve in the years ahead?
    • What can we anticipate about the potential evolution from today’s AI to AGI and beyond, in which software systems will match or exceed human cognitive abilities in every domain of thought?
    • What possible scenarios for the emergence of significantly more powerful AI deserve the most attention?
    • What new economic concepts, business models, and intellectual property ownership frameworks might be enabled and required as a result of advances that help us transition from today’s AI to AGI?
  • Pathways to AGI
    • What incremental steps might help drive practical commercial and humanitarian AI applications in the direction of AGI?
    • What practical ideas and experiences can be derived from real-world applications of technologies like transfer learning, unsupervised and reinforcement learning, and lifelong learning?
    • What are the opportunities and potential for “narrow AGI” applications that bring increasing levels of AGI to bear within specific vertical markets and application areas?
  • Societal Readiness
    • How can we raise society-wide awareness and understanding of the underlying technologies and their capabilities?
    • How can governments, businesses, educators, civil society organizations, and individuals prepare for the range of possible impacts and implications?
    • What other actions might be taken by individuals, by local groups, by individual countries, by non-governmental organizations (NGOs), by businesses, and by international institutions, to help ensure positive outcomes with advanced AI? How might we reach agreement on what constitutes a positive societal outcome in the context of AI and AGI?
  • Governance
    • How might societal ethical frameworks need to evolve to cope with the new challenges and opportunities that AGI is likely to bring?
    • What preparations can be made, at the present time, for the introduction and updating of legal and political systems to govern the development and deployment of AGI?

For more details of this new book, the process by which chapters will be selected, and processing fees that may apply, click here.

I’m very much looking forward to the insights that will arise – and to the critical new questions that will no doubt arise along the way.

 

1 October 2019

“Lifespan” – a book to accelerate the emerging paradigm change in healthcare

Filed under: AGI, aging, books, healthcare — Tags: , , , , , , , , — David Wood @ 6:11 am

Harvard Medical School professor David Sinclair has written a remarkable book that will do for an emerging new paradigm in healthcare what a similarly remarkable book by Oxford University professor Nick Bostrom has been doing for an emerging new paradigm in artificial intelligence.

In both cases, the books act to significantly increase the tempo of the adoption of the new paradigm.

Bostrom’s book, Superintelligence – subtitled Paths, Dangers, Strategies – caught the attention of Stephen Hawking, Bill Gates, Elon Musk, Barack Obama, and many more, who have collectively amplified its message. That message is the need to dramatically increase the priority of research into the safety of systems that contain AGI (artificial general intelligence). AGI will be a significant step up in capability from today’s “narrow” AI (which includes deep learning as well as “good old fashioned” expert systems), and therefore requires a significant step up in capability of safety engineering. In the wake of a wider appreciation of the scale of the threat (and, yes, the opportunity) ahead, funding has been provided for important initiatives such as the Future of Life Institute, OpenAI, and Partnership on AI. Thank goodness!

Sinclair’s book, Lifespan – subtitled Why We Age, and Why We Don’t Have To – is poised to be read, understood, and amplified by a similar group of key influencers of public thinking. In this case, the message is that a transformation is at hand in how we think about illness and health. Rather than a “disease first” approach, what is now possible – and much more desirable – is an “aging first” approach that views aging as the treatable root cause of numerous diseases. In the wake of a wider appreciation of the scale of the opportunity ahead (and, yes, the threat to society if healthcare continues along its current outdated disease-first trajectory), funding is likely to be provided to accelerate research into the aging-first paradigm. Thank goodness!

Bostom’s book drew upon the ideas of earlier writers, including Eliezer Yudkowsky and Ray Kurzweil. It also embodied decades of Bostrom’s own thinking and research into the field.

Sinclair’s book likewise builds upon ideas of earlier writers, including Aubrey de Grey and (again) Ray Kurzweil. Again, it also embodies decades of Sinclair’s own thinking and research into the field.

Both books are occasionally heavy going for the general reader – especially for a general reader who is in a hurry. But both take care to explain their thinking in a step-by-step process. Both contain many human elements in their narrative. Neither books contain the last word on their subject matter – and, indeed, parts will likely prove to be incorrect in the fullness of time. But both perform giant steps forwards for the paradigms they support.

The above remarks about the book Lifespan are part of what I’ll be talking about later today, in Brussels, at an open lunch event to mark the start of this year’s Longevity Month.

Longevity Month is an opportunity to celebrate recent progress, and to anticipate faster progress ahead, for the paradigm shift mentioned above:

  • Rather than studying each chronic disease separately, science should prioritise study of aging as the common underlying cause (and aggravator) of numerous chronic diseases
  • Rather than treating aging as an unalterable “fact of nature” (which, by the way, it isn’t), we should regard aging as an engineering problem which is awaiting an engineering solution.

In my remarks at this event, I’ll also be sharing my overall understanding of how paradigm shifts take place (and the opposition they face):

I’ll run through a simple explanation of the ideas behind the “aging-first” paradigm – a paradigm of regular medical interventions to repair or remove the damage caused at cellular and inter-cellular levels as a by-product of normal human metabolism:

Finally, I’ll be summarising the growing momentum of progress in a number of areas, and suggesting how that momentum has the potential to address the key remaining questions in the field:

In addition to me, four other speakers are scheduled to take part in today’s event:

It should be a great occasion!

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