[This piece has been inspired by reading about the University of Maryland’s new cross-disciplinary Artificial Intelligence Interdisciplinary Institute at Maryland.]
I’ve written and spoken quite a lot about AI, about how AI has changed its meaning since the term was introduced in 1956, or about technical issues, or about neuromorphic implementations, or even about the possible dangers of AI. But now, I want to write about the new directions that neurobiologically inspired AI is taking.
AI (as it currently stands, as of November 2024) is the result of the convergence of several technologies.
- the internet itself (allowing free and easy interconnectivity)
- lots of people and organisations using the internet, and posting vast amounts of data (text, sounds, graphics, images etc.) on it, making them freely available
- many years of research into adaptive systems such as neural networks
- and, of course, the electronic technologies that underly cheap digital computing and communications, the enabling technology for all of the above.
This technical convergence is having huge social effects well outside of technology.
The industrial revolution changed what was valued in people: being strong was no longer important when machines could provide power. Are we coming to a point where intelligence will no longer be important when machines can provide it? And if so, what will matter? What do we actually want from AI? And who wants it, who will control it, and to what end?
It is, of course, not quite that simple. Strength is quite easy to define, but intelligence is much more difficult. Chess-playing machines don’t replace chess players (even if they could) because the interest is in the game itself: the existence of a perfect chess playing machine would not stop people playing chess. And the nature of intelligence in chess playing machines is not applicable directly to other problems. We currently have machines that learn statistical patterns and regularities from enormous volumes of data, and we use these in conjunction with generative systesm which produce text without any understanding of it. These systems are trained, and this training uses many thousands of computers over a long period of time. This is so expensive that very few companies can undertake it. Accessing or using these trained machines is much less expensive, but relies on the trained system being made available.
Fortunately (or unfortunately) progress in AI is rapid. I am not referring to the use of AI, but to the underlying ideas. And these new ideas will revolutionise the way AI can be used. There is another convergence of technologies at work here: the convergence of neurophysiology and microelectronics. For many years researchers have worked to understand how neurons (brain cells) work and communicate, and this research is starting to produce results that could underlie a much better understanding of what underlies real intelligence – and hence allow better approximations of artificial intelligence. Current systems use a very basic concept of the neuron (in neural networks), but new ideas – specifically two point neurons, modelled on pyramidal neocortical neurons – are arriving. These are much more powerful for many deep mathematical/ statistical reasons, and one result of this much more powerful algorithm is that less computing power should be necessary to make them work. This could enable democratisation of training AI systems.
Perhaps more importantly, some researchers suggest that the way in which these neurons co-operate (rather than compete) may be critical in making the whole system work. Bill Phillips’ book “The co-operative neuron” analyses how this may work in real brains, but it is only a matter of time before the concepts are implemented electronically. This has huge implications because for the first time we begin to understand the way in which the brain produces the mind. And our electronic technology may be able to recreate this. Such synthetic intelligence could be very different from the relatively unsophisticated systems that we currently call intelligent.
This makes the development of interdisciplinary institutes like the one recently set up at the University of Maryland timely and critical. We urgently need the humanities here. These developments are too important to be left to the technologists.
Tags: ai, artificial general intelligence, Artificial Intelligence, artificial intelligence., deep-learning, machine-learning, neuroscience and AI, technology
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