AI – Its nature and future, by Margaret A. Boden. Oxford University Press. 2016.
The AI Delusion, by Gary Smith. Oxford University Press. 2018.
AI, machine learning, algorithms, robots, automation, chatbots, sexbots, androids – in recent years all these terms have regularly been appearing in the media, either to tell us about the latest achievements in technology, about exciting future possibilities, or in the context of warnings about threats to our jobs and freedoms.
Two recent books, from Margaret Boden and Gary Smith, respectively, are useful guides to the perplexed in explaining the issues. Each is clearly written and highly readable. Margaret Boden, Research Professor of Cognitive Science at the University of Sussex, begins with a basic definition:
Artificial intelligence (AI) seeks to make computers do the sorts of things that minds can do.
People who work in AI tend to work in one of two different camps (though occasionally both). They either take a technological approach, whereby they attempt to create systems that can perform certain tasks, regardless of how they do it; or they take a scientific approach, whereby they are interested in answering questions about human beings or other living things.
Boden’s book is essentially a potted history of the field, guiding the reader through the different approaches and philosophical arguments. Alan Turing, of Bletchley Park fame, seems to have envisaged all the current developments in the field, though during his lifetime the technology wasn’t available to implement these ideas. The first approach to hit the big time is now known as ‘Good Old-Fashioned AI (GOFAI)’. This assumes that intelligence arises from physical entities that can process symbols in the right kind of way, whether these entities are living organisms, arrangements of tin cans, silicon chips or whatever else. The other approaches are not reliant on sequential symbol processing. These are: 1. Artificial Neural Networks (ANNs), or connectionism, 2. Evolutionary programming, 3. Cellular automata (CA), and 4. Dynamical systems. Some researchers argue in favour of hybrid systems that combine elements of symbolic and non-symbolic processing.
For much of the 1950s, researchers of different theoretical persuasions all attended the same conferences and exchanged ideas, but in the late ’50s and 1960s a schism developed. In 1956 John McCarthy coined the term ‘Artificial Intelligence’ to refer to the symbol processing approach. This was seized upon by journalists, particularly as this approach began to have successes with the Logic Theory Machine (Newell & Simon) and General Problem Solver (Newell, Shaw, and Simon). By contrast, Frank Rosenblatt’s connectionist Perceptron model was found to have serious limitations and was contemptuously dismissed by many symbolists. Professional jealousies were aroused and communication between the symbolists and the others broke down. Worse, funding for the connectionist approach largely dried up.
Work within the symbol processing, or ‘classical’, approach has taught us some important lessons. These include the need to make problems tractable by directing attention to only part of the ‘search space’, by making simplifying assumptions and by ordering the search efficiently. However, the symbolic approaches also faced the issue of ‘combinatorial explosion’, meaning that logical processes would draw conclusions that were true but irrelevant. Likewise, in classical – or ‘monotonic’ – logic, once something is proved to be true it stays true, but in everyday life that is often not the case. Boden writes:
AI has taught us that human minds are hugely richer, and more subtle, than psychologists previously imagined. Indeed, that is the main lesson to be learned from AI.
Throughout the lean years for connectionist AI a number of researchers had plugged away regardless, and in the late 1980s there was a sudden explosion of research under the name of ‘Parallel Distributed Processing’ (PDP). These models consist of many interconnected units, each one capable of computing only one thing. There are multiple layers of units, including an input layer, an output layer, and a ‘hidden layer’ or layers in between. Some connections feed forward, others backwards, and others connect laterally. Concepts are represented within the state of the entire network rather than within individual units.
PDP models have had a number of successes, including their ability to deal with messy input. Perhaps the most notable finding occured when a network produced over-generalisation of past tense learning (e.g. saying ‘go-ed’ rather than ‘went’), indicating – contrary to Chomsky – that this aspect of language learning may not be an inborn linguistic rule. Consequently, the research funding tap was turned back on, especially from the US Department of Defense. Nonetheless, PDP models have their own weaknesses too, such as not being able to represent precision as well as classical models:
Q: What’s 2 + 2?
A: Very probably 4.
Learning within ANN’s usually involves changing the strength (the ‘weights’) of the links between units, as expressed in the saying “fire together, wire together”. It involves the application of ‘backprop’ (backwards propagation) algorithms which trace responsibility for performance back from the output layer into the hidden layers, identifying the units that need to be adopted, and thence to the input layer. The algorithm needs to know the precise state of the output layer when the network is giving the correct answer.
Although PDP propaganda plays up the similarity between network models and the brain’s neuronal connections, in fact there is no backwards propagation in the brain. Synapses feed forwards only. Also, brains aren’t strict hierarchies. Boden also notes (p.91):
a single neuron is as computationally complex as an entire PDP system, or even a small computer.
Subsequent to the 1980s PDP work it has been discovered that connections aren’t everything:
Biological circuits can sometimes alter their computational function (not merely make it more or less probable), due to chemicals diffusing through the brain.
One example of this is Nitrous Oxide. Researchers have now developed new types of ANNs, including GasNets, used to evolve “brains for autonomous robots.
Boden also discusses other approaches within the umbrella of AI, including robots and artificial life (‘A-life’), and evolutionary AI. These take in concepts such as distributed cognition (minds are not within individual heads), swarm intelligence (simple rules can lead to complex behaviours), and genetic algorithms (programs are allowed to change themselves, using random variation and non-random selection).
But are any of these systems intelligent? Many AI models have been very successful within specific domains and have outperformed human experts. However, the essence of human intelligence – even though the word itself does not have a standard definition among psychologists – is that it involves the ability to perform in many different domains, including perception, language, memory, creativity, decision making, social behaviour, morality, and so on. Emotions appear to be an important part of human thought and behaviour, too. Boden notes that there have been advances in the modelling of emotion, and there are programs that have demonstrated a certain degree of creativity. There are also some programs that operate in more than one domain, but are still nowhere near matching human abilities. However, unlike some people who have warned about the ‘singularity’ – the moment when machine intelligence exceeds that of humans – Boden does not envisage this happening. Indeed, whilst she holds the view that, in principle, truly intelligent behaviour could arise in non-biological systems, in practice this might not be the case.
Likewise, the title of Gary Smith’s book is not intended to decry all research within the field of AI. He also agrees that many achievements have occurred and will continue to do so. However, the ‘delusion’ of the title occurs when people assign to computers an ability that they do not in fact possess. Excessive trust can be dangerous. For Smith:
True intelligence is the ability to recognize and assess the essence of a situation.
This is precisely what he argues AI systems cannot do. He gives the example of a drawing of a box cart. Computer systems can’t identify this object, he says, whereas almost any human being could not only identify it, but suggest who might use it, what it might be used for, what the name on the side means, and so on.
Smith refers to the Winograd Schema Challenge. The Stanford Computer Science Professor, Terry Winograd, has put up a $25,000 prize for anyone who can design a system that is at least 90% accurate in interpreting sentences like this one:
I can’t cut that tree down with that axe; it is too [thick/small]
Most people realise that if the bracketed work is ‘thick’ it refers to the tree, whereas if it is ‘small’ it refers to the axe. Computers are typically – ahem – stumped by this kind of sentence, because they lack the real-world experience to put words in context.
Much of Smith’s concern is about the data-driven (rather than theory-driven) way that machine learning approaches use statistics. In essence, when a machine learning program processes data it does not stop to ask ‘Where did the data come from?’ or ‘Why these data?’ These are important questions to ask and Smith takes us through various problems that can arise with data (his previous book was called Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics).
One important limitation associated with data is the ‘survivor bias’. A study of Allied warplanes returning to Britain after bombing runs over Germany found that most of the bullet and shrapnel holes were on the wings and rear of the plane, but very few on the cockpit, engines, or fuel tanks. The top brass therefore planned to attach protective metal plates to the wings and rear of their aircraft. However, the statistician Abraham Wald pointed out that the planes that returned were, by definition, the ones that had survived the bullets and shrapnel. The planes that had not returned had most likely been struck in the areas that the returning planes had been spared. These were the areas that should be reinforced.
Another problem is the one discussed in my previous blog, that of fake or bad data, arising from the perverse incentives of academia and the publishing world. The ‘publish-or-perish’ climate, together with the wish of journals to publish ‘novel’ or ‘exciting’ results, has led to an exacerbation of ‘Questionable Research Practices’ or outright fakery, with the consequence that an unfortunately high proportion of published papers contain false findings.
Smith is particularly scathing about the practice of data mining, something that for decades was regarded as a major ‘no-no’ in academia. This is particularly problematic in the advent of big data, when machine learning algorithms can scour thousands upon thousands of variables looking for patterns and relationships. However, even among sequences that are randomly generated, correlations between variables will occur. Smith shows this to be the case with randomly generated sequences of his own. He laments that
The harsh truth is that data-mining algorithms are created by mathematicians who often are more interested in mathematical theory than practical reality.
The fundamental problem with data mining is that it is very good at finding models that fit the data, but totally useless in gauging whether the models are ludicrous.
When it comes to the choice of linear or non-linear models, Smith says that expert opinion is necessary to decide which is more realistic (though one recent systematic comparison of methods, involving a training set of data and a validation set, found that the non-linear methods associated with machine learning were dominated by the traditional linear methods). Other problems arise with particular forms of regression analysis, such as stepwise regression and ridge regression. Data reduction methods, such as factor analysis or principal components analysis, can also cause problems because the transformed data are hard to interpret. Especially if mined from thousands of variables they will contain nonsense. Smith looks at some dismal attempts to beat the stock market using data mining techniques.
But as if the statistical absurdities weren’t bad enough, Smith’s penultimate chapter – the one that everything else has been leading up to, he says – concerns the application of these techniques to our personal affairs in ways which impinge upon our privacy. For example, software exists that examines the online behaviour of job applicants. Executives who ought to know better may draw inappropriate causal inferences from the data. One of the major examples discussed earlier in the book is Hillary Clinton’s presidential campaign. Although not widely known, her campaign made use of a powerful computer program called Ada (after Ada Lovelace, an early pioneer in AI). This crunched masses of data about potential voters across the country, running 400,000 simulations per day. No-one knows exactly how Ada worked, but it was used to guide decisions about where to target campaigning resources. The opinions of seasoned campaigners were entirely sidelined, including perhaps the greatest campaigner of all – Bill Clinton (reportedly furious about this, too). We all know what happened next.