Can intelligence be revolutionised?
Quantifying effective cognition and rethinking the idea of super-intelligence.
The intelligence revolution is in motion. Recent and significant growth in access to intelligent machines has been led by transformer architecture based AI models. If you haven't already, I would encourage you read my post introducing the intelligence revolution to understand the rationale for this narrative.
Recent progress does not stem entirely from any single breakthrough, but rather from a powerful convergence of forces. At the heart of this revolution is intelligence and its mysterious properties…
Measuring the immeasurable
How can we measure changes in intelligence? How can we even define intelligence? How can we make sense of the endless debate around super-intelligence and AGI?
There are many theories of intelligence, and they tend take the form of classifications of cognitive abilities. For example Cattell–Horn–Carroll suggest that it is a hierarchy starting with general cognition, branching into broad abilities such as comprehension, fluid reasoning, quantitative analysis, learning etc, leveraging, storage and retrieval, multi-modal processing and so on.
These elements map readily to those abilities we experience with our new AI models; transformers have taken people by surprise precisely because of their ability to respond well to novel questioning, switch between modes, and more recently with code interpretation, conduct rapid quantitative analysis. Neuromorphic computing is by its nature replicating our brains, starting to demonstrate multiple cognitive similarities, and may well be uncovering some of our biological tricks. Research suggests that there are detectable patterns in our brain activity that are analogous to the vector analysis LLMs use to attend to meaning, and that transformers are mathematically equivalent to models used by the grid cells that fire in our hippocampus.
The universal computer
Some contend that intelligence is not a collection of features or abilities, nor is it definable on an incremental scale. The father of modern quantum computing, and author of The Fabric of Reality, David Deutsche proposes that humans are the only 'universal constructors', and that super-intelligence is a flawed premise as humans already have the fundamental potential to compute any problem:
“The brain is the only kind of object capable of understanding that the cosmos is even there, or why there are infinitely many prime numbers, or that apples fall because of the curvature of space-time, or that obeying its own inborn instincts can be morally wrong, or that it itself exists.”
David Deutsch
He argues that whilst artificial general intelligence may be achievable, for now human intelligence has a defining attribute; creativity. In particular we have a unique ability to create new knowledge and invent new explanations via conjecture.
This may well be true, but there seem to be no scaling limits beyond the energy reserves of the universe, that bound the degree to which humans and machines working together can extend the overall sum of their cognitive activity? From adding analytical speed and semantic access to vast knowledgebases, to the accelerative power of on-demand expert models, autonomous agents that never sleep, and exo-cognitive architectures... the new combinatory potential for human-machine systems appears boundless.
Alien minds
And what of the varied differences in intelligence between the machines and our brains? A fascinating recent visual exploration by Stephen Wolfram looks at the perceptual space of what he calls an 'alien mind', a diffusion model not constrained by typical human perceptions of reality.
Once there are many billions of machine mind instances running simultaneously the proportion of these 'alien' minds will become the majority. Researchers have recently trained an LLM to understand the less explored areas of science, and employed its lateral alien thinking it to generate innovative new hypotheses that humans would not think of. This intentional bias, and diversity of thought, can unlock new conjecture. Famously AlphaGo helped human players find new and creative ways to play a game that has been studied for over 4,000 years.
“I believe players more or less have all been affected by Professor Alpha. AlphaGo play makes us feel more free and no move is impossible to play any more. Now everyone is trying to play in a style that hasn’t been tried before.”
Zhou Ruiyang, Go World Champion
And of course AI does not suffer from the burden of communication or learning inefficiency of humans, it can pass new ‘weights’ almost instantly in perfect fidelity to new copies of itself, and as Geoff Hinton recently realised, already has the potential to surpass humans in some underlying cognitive processes:
“I used think that the computer models that we were developing weren’t as good as the brain, as the aim was to see what you could understand about the brain so you can improve the computer models. Over the last few months, I have changed my mind completely… the computer models are working in a different way that the brain. They are using backpropagation and the brain… is not... I think backpropagation is a much better learning model that we have.”
Geoff Hinton
A super-collective
The total sum of cognitive activity on planet earth is today a factor of our population size, but will be rapidly expanded in the intelligence revolution. If super-intelligence isn't a point on any meaningful measurement scale for an individual mind, super collective intelligence relative to current state is potentially achievable through shear numbers of minds. Collective, hybrid, and collective-hybrid systems can combine at many levels to align, compliment and sustain new and productive relationships:
Sadly, those studying modern industrial socio-technical systems and exploring the use of collective intelligence suggest that to-date we have not made the most of recent potential:
“But it’s a paradox, perhaps the paradox, of our times that proliferating smart technologies have so often coincided with stupider systems.”
Sir Geoff Mulgan, Professor of Collective Intelligence, UCL
The extent to which large organisations are frustratingly inefficient, empires collapse, and our post-industrial trajectory has become mis-aligned with the survival objectives of humanity, suggests there is work to be done.
Human-machine potential
Continuously learning, participatory, transparent, value sensitive, mindfully designed intelligent systems are the key to the safe achievement of our human-machine potential. While the essence of intelligence remains elusive, the promise of augmented collaboration is real and immediate. AI should not slavishly replicate human cognition, but properly aligned, it can amplify. Where human creativity flags, lightning-fast computation can step in. When machine inference is overwhelmed with options, human intuition and judgement can be decisive. Super-collective hybrid intelligence offers more than duplication of minds, it offers rich diversity.
But what of the practicalities and timelines for super-CHI? I cover the engine of compute in this post, and the current constraints that may delay the revolution in the short-term.