An AI Landscape — What Do You Mean by AI?
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” I use the landscape chart below. 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 I’ll also take as being synonymous with sophistication).
I’ve identified 4 main points on this axis — although it is undoubtedly a continuum, and boundaries will be blurred and even overlapping (and I’m probably also mixing categories too!):
- Simple Algorithms — 99% of most computer programmes, even complex ERP and CRM systems, they are highly linear and predictable.
- 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 level 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. An 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 made steady progress in recent years, but appears to now give a choice of a pretty human sounding generic voice or a slightly more robotic sounding person-specific voice. A lot of the work is bring driven my medical voice-banking and virtual voice-overs/actors!
- Speech Recognition: Can it recognise human speech without training. Systems like Siri have really driven this on recently but it’s still not 100% and pretty shaky in a lot of applications. Voice control within VR environments may be a new driver.
- Natural Language Understanding: Neural-network approaches seem to be ruling the roost at the moment, but hybrid approaches with a good dose of semantic and grammar understanding still seem a good bet for the future.
- Natural Language Generation: GPT-3 is being talked about a lot, but whilst it can look good on the surface it can break down in detail, and the bot has no idea of what it is saying. 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 — Abbatars being a case in point!.
- Avatar Body Animation: For gestures, movement etc. Again movies and decent motion-capture have pretty much solved this — but needed in real-time.
- Avatar Face Realism: All skin and hair so a lot harder and very much stuck in uncanny valley for any real-time rendering. See Abbatars again!
- 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. VR avatar development may help drive several of these avatar areas forward.
- Emotion: Debatable about whether this should be on the complexity/sophistication axis instead (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 and face-cams, EEG and other technology is beginning to give a handle on it.
The chart below gives my very rough assessment of the maturity of each (and I’m sure your view will vary), measured against a “Turing-test” like measure (sometimes referred to as Turing-capable) where the virtual human is readily confused for a physical human (for more detail see my book on Virtual Humans).
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.
Using the Chart
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” (we tried to get a survey going to do this in an evidenced-based way but couldn’t get enough responses — so this is my view again!).
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 me, 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 Channel 4’s “Humans” to Battlestar Galactica! The barrier there is that implicit in the diagram are three big barriers to “human-like AI” development, and I’ll cover those in the next post.