6 September 2017

An AI Landscape

In the old days there used to be a saying that "what we call ‘artificial intelligence’ is basically what computers can’t do yet" - so as things that were thought to take intelligence - like playing chess - were mastered by a computer they ceased to be things that needed "real" intelligence. Today, it's almost as though the situation has reversed, and to read most press-releases and media stories it now appears to be that "what we call 'artificial intelligence'" is basically anything that a computer can do today".

So in order to get a better handle on what we (should) mean by "artificial intelligence" we've come up with the landscape chart above. Almost any computer programme can be plotted on it - and so can the "space" that we might reasonably call "AI" - so we should be able to get a better sense of whether something has a right to be called AI or not.

The bottom axis shows complexity (which we'll also take as being synonymous with sophistication). We've identified 4 main points on this axis - although it is undoubtably a continuum, and boundaries will be blurred and even overlapping - and we are probably also mixing categories too!:

  • Simple Algorithms - 99% of most computer programmes, even complex ERP and CRM systems, they are highly linear and predicatable
  • Complex Algorithms - things like (but not limited to) machine learning, deep learning, neural networks, bayesian networks, fuzzy logic etc where the complexity of the inner code starts to go beyond simple linear relationships. Lots of what is currently called AI is here - but really falls short of a more traditional definition of an AI.
  • Artificial General Intelligence - the holy grail of AI developers, a system which can apply itself using common sense and  general knowledge to a wide range of problems and solve them to a similar laval as a human
  • Artificial Sentience - beloved of science-fiction, code which "thinks" and is "self-aware"

The vertical axis is about "presentation" - does the programme present itself as human (or indeed another animal or being) or as a computer. Our ERP or CRM system typically presents as a computer GUI - but if we add a chatbot in front of it it instantly presents as more human. The position on the axis is influenced by the programmes capability in a number of dimensions of "humanness":

  • Text-to-speech: Does it sound human? TTS has plateaued in recent years, good but certainly recognisably synthetic
  • Speech Recognition: Can it recognise human speech without training. Systems like Siri have really driven this on recently.
  • Natural Language Generation: This tends to be template driven or parroting back existing sentences. Lots more work needed, especially on argumentation and story-telling
  • Avatar Body Realism: CGI work in movies has made this pretty much 100% except for skin tones
  • Avatar Face Realism: All skin and hair so a lot harder and very much stuck in uncanny valley for any real-time rendering
  • Avatar Body Animation: For gestures, movement etc. Again movies and decent motion-capture have pretty much solved this.
  • Avatar Expression (& lip sync): Static faces can look pretty good, but try to get them to smile or grimace or just sync to speech and all realism is lost
  • Emotion: Debatable about whether this should be on the complexity/sophistication axis (and/or is an inherent part of an AGI or artificial sentient), but it's a very human characteristic and a programme needs to crack it to be taken as really human. Games are probably where we're seeing the most work here.
  • Empathy: Having cracked emotion the programme then needs to be able to "read" the person it is interacting with and respond accordingly - lots of work here but face-cams, EEG and other technology is beginning to give a handle on it.
The chart gives a very rough assessment of the maturity of each.

There are probably some alternative vertical dimensions we could use other than "presentation" to give us an view on interesting landscape - Sheridan's autonomy model could be a useful one which we'll cover in a later post.

So back on the chart we can now plot where current "AI" technologies and systems might sit:

The yellow area shows the space that we typically see marketeers and others use the term AI to refer to!

But compare this to the more popular, science-fiction derived, view of what is an "AI".

Big difference - and zero overlap!

Putting them both on the same chart makes this clear.

So hopefully a chart like this will give you, as it has us, a better understanding of what the potential AI landscape is, and where the current systems, and the systems of our SF culture, sit. Interestingly it also raises a question about the blank spaces and the gaps, and in particular how do we move from today's very "disappointing" marketing versions of AI to the one's we're promised in SF from "Humans" to Battlestar Galactica!

No comments:

Post a Comment