dw2

7 January 2010

Mobiles manifesting AI

Filed under: AGI, Apple, futurist, intelligence, m2020, vision — David Wood @ 12:15 am

If you get lists from 37 different mobile industry analysts of “five game-changing mobile trends for the next decade“, how many overlaps will there be?  And will the most important ideas be found in the “bell” of the aggregated curve of predictions, or instead in the tails of the curve?

Of the 37 people who took part in the “m2020” exercise conducted by Rudy De Waele, I think I was the only person to mention either of the terms “AI” (Artificial Intelligence) or “PDA” (Personal Digital Assistant), as in the first of my five predictions for the 2010’s:

  • Mobiles manifesting AI – fulfilling, at last, the vision of “personal digital assistants”

However, there were some close matches:

  • Rich Wong predicted “Smart Agents 2.0 (thank you Patty Maes) become real; the ability to deduce/impute context from blend of usage and location data”;
  • Marshall Kirkpatrick predicted “Mobile content recommendation”;
  • Carlo Longino predicted “The mobile phone will evolve into an enabler device, carrying users’ digital identities, preferences and possessions around with them”;
  • Steve O’Hear predicted “People will share more and more personal information. Both explicit e.g. photo and video uploads or status updates, and implicit data. Location sharing via GPS (in the background) is one current example of implicit information that can be shared, but others include various sensory data captured automatically via the mobile phone e.g. weather, traffic and air quality conditions, health and fitness-related data, spending habits etc. Some of this information will be shared privately and one-to-one, some anonymously and in aggregate, and some increasingly made public or shared with a user’s wider social graph. Companies will provide incentives, both at the service level or financially, in exchange for users sharing various personal data”;
  • Robert Rice predicted “Artificial Life + Intelligent Agents (holographic personalities)”.

Of course, these predictions cover a spread of different ideas.  Here’s what I had in mind for mine:

  • Our mobile electronic companions will know more and more about us, and will be able to put that information to good use to assist us better;
  • For example, these companion devices will be able to make good recommendations (e.g. mobile content, or activities) for us, suggest corrections and improvements to what we are trying to do, and generally make us smarter all-round.

The idea is similar to what former CEO of Apple, John Sculley, often talked about, during his tenure with Apple.  From a history review article about the Newton PDA:

John Sculley, Apple’s CEO, had toyed with the idea of creating a Macintosh-killer in 1986. He commissioned two high budget video mockups of a product he called Knowledge Navigator. Knowledge Navigator was going to be a tablet the size of an opened magazine, and it would have very sophisticated artificial intelligence. The machine would anticipate your needs and act on them…

Sculley was enamored with Newton, especially Newton Intelligence, which allowed the software to anticipate the behavior of the user and act on those assumptions. For example, Newton would filter an AppleLink email, hyperlink all of the names to the address book, search the email for dates and times, and ask the user if it should schedule an event.

As we now know, the Apple Newton fell seriously short of expectation.  The performance of “intelligent assistance” became something of a joke.  However, there’s nothing wrong with the concept itself.  It just turned out to be a lot harder to implement than originally imagined.  The passage of time is bringing us closer to actual useful systems.

Many of the interfaces on desktop computers already show an intelligent understanding of what the user may be trying to accomplish:

  • Search bars frequently ask, “Did you mean to search for… instead of…?” when I misspell a search clue;
  • I’ve almost stopped browsing through my list of URL bookmarks; I just type a few characters into the URL bar and the web-browser lists websites it thinks I might be trying to find – including some from my bookmarks, some pages I visit often, and some pages I’ve visited recently;
  • It’s the same for finding a book on Amazon.com – the list of “incrementally matching books” can be very useful, even after only typing part of a book’s title;
  • And it’s the same using the Google search bar – the list of “suggested search phrases” contains, surprisingly often, something I want to click on;
  • The set of items shown in “context sensitve menus” often seems a much smarter fit to my needs, nowadays, than it did when the concept was first introduced.

On mobile, search is frequently further improved by subsetting results depending on location.  As another example, typing a few characters into the home screen of the Nokia E72 smartphone results in a list of possible actions for people whose contact details match what’s been typed.

Improving the user experience with increasingly complex mobile devices, therefore, will depend not just on clearer graphical interfaces (though that will help too), but on powerful search engines that are able to draw upon contextual information about the user and his/her purpose.

Over time, it’s likely that our mobile devices will be constantly carrying out background processing of clues, making sense of visual and audio data from the environment – including processing the stream of nearby spoken conversation.  With the right algorithms, and with powerful hardware capabilities – and provided issues of security and privacy are handled in a satisfactory way – our devices will fulfill more and more of the vision of being a “personal digital assistant”.

That’s part of what I mean when I describe the 2010’s as “the decade of nanotechnology and AI”.

Advertisements

2 Comments »

  1. David,

    I’ve often said (or complained): “There are two types of application: those that do what they are told and those that do what they think I wanted them to do; I know which I prefer”. Part of this is that applications aren’t yet very good at anticipation, but part of it is genuine personal preference.

    Consider human personal assistants. Some people prefer assistants who effectively and efficiently do what they are told, others prefer assistants who try and anticipate and act upon their needs. It really is a matter of personal preference.

    There are two complementary problems for digital assistants. One is that of intrusiveness (the Microsoft paperclip announcing: “You seem to be writing a letter, do you want to…”). The other is silently doing the wrong thing (my word process or silently correcting a spelling error when in fact the word it was correcting was part of some Swedish text included in my document – rather than correcting an error it introduced one).

    The examples you give avoid these problems by providing a selection from which the user can easily reject the wrong items. It is neither intrusive, nor does it introduce errors.

    There are two challenges for digital assistants – anticipating what the user wants and providing that information in a non-intrusive and non-error-prone way. I don’t think we’ve seen much advance in either of these problems, other than in the narrow area of assisted search. So no, I don’t think we’ll see mobiles ‘fulfilling, at last, the vision of “personal digital assistants”’ within the next decade.

    Comment by Martin Budden — 7 January 2010 @ 10:23 am

  2. Henry Lieberman’s earlier work at MIT on ‘autonomous Interface agents’ and ‘Reconnaissance Agents’ offers some simple and powerful ideas that have unfortunately been neglected since.

    When I looked at implementing some of that on Symbian OS in trying to develop an intelligent mobile context sensitive recommender system, I discovered that the problem was _not_ the ‘AI’ (or whatever you want to call the magic) but the fact that you couldn’t source the context and user actions easily from the frameworks and apps in order to feed them into the ‘machine learning black box’ ; at least not without full access and major enhancements inside the system’s code. This seems to be true for most if not all mainstream environments desktop or otherwise (with notable exceptions Apple’s Open Scripting Architecture).

    For mobile’s to fulfil the “personal digital assistants” vision and infact Alan Kay’s Dynabook vision, in my opinion mobile platforms must be tuned/redesigned to allow proactive agents to take action. To achieve that they must be engineered to reflect what is going on in the system (apps, UI etc). This is the only way for agents to have valid info on what is going on and therefore to drive machine learning, until then application will be “narrow”. Nevertheless such “narrow” applicability can be very useful perhaps (like Lieberman’s Letizia, Bradley Rhodes’ Remembrance Agent, Balabanovic’ Fab/Slider or the Daily Learner by billsus and Pazzani).

    FYI:
    http://web.media.mit.edu/~lieber/Publications/Publications.html
    http://www.bradleyrhodes.com/Papers/physical-context-ieee-toc.pdf
    http://www.ics.uci.edu/~pazzani/Publications/billsuspazzanichen.pdf
    http://www.balabanovic.pwp.blueyonder.co.uk/marko/Marko-Balabanovic-Research.html

    Comment by John Pagonis — 7 January 2010 @ 11:37 am


RSS feed for comments on this post. TrackBack URI

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

Create a free website or blog at WordPress.com.

%d bloggers like this: