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Artificial intelligence has learnt a lot from neuroscience. It was the move away from symbolic to neural net (machine learning) approaches that led to the current surge of interest in AI. Neural net approaches have enabled AI systems to do humanlike things such as object recognition and categorisation that had eluded the symbolic approaches.
So it was with great interest that I attended Dr. Tim Kietzmann's talk at the Cognitive and Brain sciences Unit (CBU) in Cambridge UK, earlier this month (March 2019), on what artificial intelligence (AI) and neuroscience can learn from each other.
Tim is a researcher and graduate supervisor at the MRC CBU and investigates principles of neural information processing using tools from machine learning and deep learning, applied to neuroimaging data recorded at high temporal (EEG/MEG) and spatial (fMRI) resolution.
Both AI and neuroscience aim to understand information processing and decision making - neuroscience primarily through empirical studies and AI primarily through computational modelling. The talk had symmetry. The first half was 'how can neuroscience benefit from artificial intelligence', and the second half was 'how artificial intelligence benefits from neuroscience'.
Types of AI
It is important to distinguish between 'narrow', 'general' and 'super' AI. Narrow AI is what we have now. In this context, it is the ability of a machine learning algorithm to recognise or classify particular things. This is often something visual like a cat or a face, but it could be a sound (as when an algorithm is used to identify a piece of music or in speech recognition).
General AI is akin to what people have. When or if this will happen is speculative. Ray Kurzweil, Google’s Director of Engineering, predicts 2029 as the date when an AI will pass the Turing test (i.e. a human will not be able to tell the difference between a person and an AI when performing tasks). The singularity (the point when we will multiply our effective intelligence a billion fold by merging with the intelligence we have created), he predicts should happen by about 2045. Super AIs exceed human intelligence. Right now, they only appear in fiction and films.
It is impossible to predict how this will unfurl. After all, you could argue that the desktop calculator several decades ago exceeded human capability in the very narrow domain of performing mathematical calculations. It is possible to imagine many very narrow and deep skills like this becoming fully integrated within an overall control architecture capable of passing results between them. That might look quite different from human intelligence.
One Way or Another
Research in machine learning, a sub-discipline of AI, has given neuroscience researchers pattern recognition techniques that can be used to understand high-dimensional neural data. Moreover, the deep learning algorithms, that have been so successful in creating a new range of applications and interest in AI, offer an exciting new framework for researchers like Tim and colleagues, to advance knowledge of the computational principles at play in the brain. AI allows researchers to test different theories of brain computations and cognitive function by implementing and testing them. 'Today's computational neuroscience needs machine learning techniques from artificial intelligence'.
AI benefits from neuroscience by informing the development of a wide variety of AI applications from care robots to medical diagnosis and self-driving cars. Some principles that commonly apply in human learning (such as building on previous knowledge and unsupervised learning) are not yet integrated into AI systems.
For example, a child can quickly learn to recognise certain types of objects, even those such as a mythical 'Tufa' that they have never seen before. A machine learning algorithm, by contrast, would require tens of thousands of training instances in order to reliably perform that same task. Also, AI systems can easily be fooled in ways that a person never would. Adding a specially crafted 'noise' to an image of a dog, can lead an AI to misclassify it as an ostrich. A person would still see a dog and not make this sort of mistake. Having said that, children will over-generalise from exposure to a small number of instances, and so also make mistakes.
It could be that the column structures found in the cortex have some parallels to the multi-layered networks used in machine learning and might inform how they are designed. It is also worth noting that the idea of reinforcement learning used to train artificial neural nets, originally came out of behavioural psychology - in particular Pavlov and Skinner. This illustrates the 'intertwined' nature of all these disciplines.
The Neuroscience of Ethics
Although this was not covered in the talk, when it comes to ethics, neuroscience may have much to offer AI, especially as we move from narrow AI into artificial general intelligence (AGI) and beyond. Evidence is growing as to how brain structures, such as the pre-frontal cortex are involved in inhibiting thought and action. Certain drugs affect neuronal transmission and can disrupt these inhibitory signals. Brain lesions and the effects of strokes can also interfere with moral judgements. The relationship of neurological mechanisms to notions of criminal responsibility may also reveal findings relevant to AI. It seems likely that one day the understanding of the relationship between neuroscience, moral reasoning and the high-level control of behaviours will have an impact on the design of, and architectures for, artificial autonomous intelligent systems (i.e. see Neuroethics: Challenges for the 21st Century.Neil Levy - 2007 - Cambridge University Press or A Neuro-Philosophy of Human Nature: Emotional Amoral Egoism and the Five Motivators of Humankind - April 2019).
Understanding the Brain
The reality of the comparison between human and artificial intelligence comes home when you consider the energy requirements of the human brain and computer processors performing similar tasks. While the brain uses about 15 watts of energy, just a single graphics processing unit requires up to 250 watts.
It has often been said that you cannot understand something until you can build it. That provides a benchmark against which we can measure our understanding of neuroscience. Building machines that perform as well as humans is a necessary step in that understanding, although that still does not imply that the mechanisms are the same.
Writing about ethics in artificial intelligence and robotics can sometimes seem like it’s all doom and gloom. My last post for example covered two talks in Cambridge – one mentioning satellite monitoring and swarms of drones and the other going more deeply into surveillance capitalism where big companies (you know who) collect data about you and sell it on the behavioural futures market.
So it was really refreshing to go to a talk by Dr Danielle Belgrave at Microsoft Research in Cambridge last week that reflected a much more positive side to artificial intelligence ethics. Danielle has spent the last 11 years researching the application of probabilistic modelling to the medical condition of asthma. Using statistical techniques and machine learning approaches she has been able to differentiate between five more or less distinct conditions that are all labelled asthma. Just as with cancer there may be a whole host of underlying conditions that are all given the same name but may in fact have different underlying causes and environmental triggers.
This is important because treating a set of conditions that may have family resemblance (as Wittgenstein would have put it) with the same intervention(s) might work in some cases, not work in others and actually do harm to some people. Where this is leading, is towards personalised medicine, where each individual and their circumstances are treated as unique. This, in turn, potentially leads to the design of a uniquely configured set of interventions optimised for that individual.
The statistical techniques that Danielle uses, attempt to identify the underlying endotypes (sub-types of a condition) from set of phenotypes (the observable characteristics of an individual). Some conditions may manifest in very similar sets of symptoms while in fact they arise from quite different functional mechanisms.
Appearances can be deceptive and while two things can easily look the same, underneath they may in fact be quite different beasts. Labelling the appearance rather than the underlying mechanism can be misleading because it inclines us to assume that beast 1 and beast 2 are related when, in fact the only thing they have in common is how they appear.
It seems likely that for many years we have been administering some drugs thinking we are treating beast 1 when in fact some patients have beast 2, and that sometimes this does more harm than good. This view is supported by the common practice that getting the medication right in asthma, cancer, mental illness and many other conditions, is to try a few things until you find something that works.
But in the same way that, for example, it may be difficult to identify a person’s underlying intentions from the many things that they say (oops, perhaps I am deviating into politics here!), inferring underlying medical conditions from symptoms is not easy. In both cases you are trying to infer something that may be unobservable, complex and changing, from the things you can readily perceive.
We have come so far in just a few years. It was not long ago that some medical interventions were based on myth, guesswork and the unquestioned habits of deeply ingrained practices. We are currently in a time when, through the use of randomly controlled trials, interventions approved for use are at least effective ‘on average’, so to speak. That is, if you apply them to large populations there is significant net benefit, and any obvious harms are known about and mitigated by identifying them as side-effects. We are about to enter an era where it becomes commonplace to personalise medicine to targeted sub-groups and individuals.
It’s not yet routine and easy, but with dedication, skill and persistence together with advances in statistical techniques and machine learning, all this is becoming possible. We must thank people like Dr Danielle Belgrave who have devoted their careers to making this progress. I think most people would agree that teasing out the distinction between appearance and underlying mechanisms is both a generic and an uncontroversially ethical application of artificial intelligence.