Artificial Intelligence – Making Use of Reasoning

The first edition of this book began with the flight of a three-metre long paper aeroplane. It served to illustrate a point. So, let us set it airborne it again. Launched by half a dozen young men at a run, it flies successfully, dare we even say "gracefully", the length of a research station canteen before making an unfortunate landing in the director of research's Christmas lunch. It was just a question of getting the aerodynamics right.

My school mathematics teacher reminded us on most days (several times on some) that all science is mathematics. But was it only the power of numbers he had in mind? Does science come down to the mechanical crunching of numbers, real and imaginary?

Contrary to the perceptions of many people outside science, as well as too many inside it, science is not about proving facts: it is about testing hypotheses and theories; ultimately, it is about people and their opinions. In many fields, human decision making may best be supported by reasoned argument or the use of analogy and not much helped by numerical procedures or answers. The minimum braking distance for a car travelling at 40 miles per hour is 24 metres, according to the UK Highway Code. Assuming you can countenance the required mixing of miles and metres, does this information help you to drive more safely? Have you any more idea than I have how far ahead an imaginary 24 metre boundary-marker precedes you along the road?

And there is a further problem. "Numbers out" implies "numbers in", so what do you do if you have no numbers to put in? A regrettably popular solution is to invent them – or at least to come up with dubious estimates to feed into a model that demands them, which is close to invention. It is the only option if you want to apply numerical methods and to give numbers to the people asking for solutions. That numbers make people feel comfortable is a bigger problem than it may at first appear to be, too. Uncritical recipients of numerical answers tend to believe them, and to act on them, without probing very deeply. More sceptical recipients want to judge for themselves how meaningful the answers are but often find that the supporting evidence associated with a numerical method is not much help. Many are the controversies over whether this or that numerical method is more precise but they are missing the point if the data are far less precise than the method. Perhaps numbers are unnecessary – even unsuitable – for expressing some kinds of scientific knowledge.

There are circumstances in which numerical methods are highly reliable. Aeroplanes stay up in the sky and make it safely to earth where they are supposed to do. Chemical plants run 24 hours a day, year in year out. Numerical methods work routinely in physical chemistry laboratories, and toxicology and pharmacology departments. But it is unlikely that the designers of the three-metre paper dart that took flight at the start of this chapter did any calculations at all. My guess is that they depended on analogy, drawing on years of experience making little ones.

This book is about uses of artificial intelligence (AI) and databases in computational chemistry and related science, in cases where qualitative output may be of more practical use than quantitative output. It touches on quantitative structure–activity relationships (QSAR) and how they can inform qualitative predictions, but it is not about QSAR. Neither is it a book about molecular modelling. Both subjects are well-covered in too many books to list comprehensively. A few examples are given in the references at the end of this chapter. This book focuses on less widely described and yet, probably, more widely-used applications of AI in chemistry.

The term "artificial intelligence" carries with it notions of thinking computers but, as a radio personality in former times would have had it, it all depends on what you mean by intelligence. If you type "Liebig Consender" into the Google™ search box, Google™ responds with "Showing results for Liebig Condenser". That is worryingly like intelligent behaviour whether it is intelligent behaviour or not (it is also very irritating if you really do want to look for consenders). Arguments continue about whether tests for artificial intelligence such as the Turing test are valid and whether a categorical test or set of tests can be devised. Perhaps it is sufficient to require that to be intelligent a system must be able to learn, be able to reason, be creative, and be able to explain itself persuasively. Currently, no AI system can claim to have all of these characteristics. Individual systems typically have two or three.

To count as intelligent, solving problems needs to involve a degree of novel thinking, i.e. creativity. Restating the known, specific answer to a question requires only memory. Compare the following questions and answers. The first answer merely reproduces a single fact. Generating the second answer, simple though it is, requires reasoning and a degree of creativity.

"Where's the sugar?"

"In the sugar bowl".

"Where will the sugar be in this supermarket?"

"A lot of supermarkets put it near the tea and coffee, so it could be along the aisle labelled 'tea and coffee'. Alternatively, it might be in the aisle labelled 'baking'. Let's try 'baking' first – it is nearer".

One of the first computer systems to behave like an expert using a logical sequence of questions and answers to solve a problem was MYCIN, a system to support medical diagnosis.

"Doctor, I keep getting these terrible headaches".

"Sorry to hear that. Is there any pattern to when the headaches occur?"

"Now you ask, they do seem to come mostly on Sunday mornings".

"And what do you do on Saturday evenings?"

The doctor's questions are not arbitrary. You can see how they are directed by the patient's responses. You...