Rule-based in comparison with corpus-based

Corpus-based AI (the “Tanks” type; cf. introductory AI post) successfully overcame its weaknesses (cf. preceding post). This was the result of a combination of “brute force” (improved hardware) and an ideal window of opportunity, i.e. when during the super-hot phase of internet expansion, companies such as Google, Amazon, Facebook and many others were able to collect large volumes of data and feed their data corpora with them – and a sufficiently big data corpus is the linchpin of corpus-based AI.

Brute force was not enough for rule-based AI, however, nor was there any point in collecting lots of data, since data also have to be organised for rule construction – and largely manually at that, i.e. by human expert specialists.

Challenge 1: different mentalities

Not everyone is equally fascinated by the process of building algorithms. Building algorithms requires a particular faculty of abstraction combined with a very meticulous vein – with regard to abstractions, at any rate. No matter how small an error in the rule construction may be, it will inevitably have an impact. Mathematicians possess the consistently meticulous mentality that is called for here, but natural scientists and engineers are also favourably characterised by it. Of course, accountants must also be meticulous, but AI rule construction additionally requires creativity.

Salespersons, artists and doctors, however, work in a different field. Abstractions are often incidental; the importance lies in what is tangible and specific. Empathy for other people can also be very important, or someone has to be able to act with speed and precision, as is the case with surgeons. These characteristics are all very valuable, but they are less relevant to algorithm construction.

This is a problem for rule-based AI because rule construction requires the skills of one camp and the knowledge of the other: it requires the mentality that makes a good algorithm designer combined with the way of thinking and the knowledge of the specialist field to which the rules refer. Such combinations of specialist knowledge with a talent for abstraction are rare. In the hospitals in which I worked, both cultures were quite clearly visible in their separateness: on the one hand the doctors, who at best accepted computers for invoicing or certain expensive technical devices but had a low opinion of information technology in general, and on the other hand the computer scientists, who did not have a clue about what the doctors did and talked about. The two camps simply avoided each other most of the time. Needless to say, it was not surprising that the expert systems designed for medical purposes only worked for very small specialist fields – if they had progressed beyond the experimentation stage at all.

Challenge 2: where can I find the experts?

Experts who are creative and equally at home in both mentality camps are obviously hard to find. This is aggravated by the fact that there are no training facilities for such experts. Equally realistic are the following questions: where are the instructors who are conversant with the current challenges? Which diplomas are valid for what? And how can an investor in this new field evaluate whether the experts employed are fit for purpose and the project is moving in the right direction?

Challenge 3: the sheer volume of detailed rules required

The fact that a large volume of detailed knowledge is required to be able to draw meaningful conclusions in a real situation was already a challenge for corpus-based AI. After all, it was only with really large corpora, i.e. thanks to the internet and a boost in computer performance, that it succeeded in gathering the huge volume of detailed knowledge which is one of the fundamental prerequisites for every realistic expert system.

For rule-based AI, however, it is particularly difficult to provide the large volume of knowledge since this provision of knowledge requires people who manually package this large volume of knowledge into computer-processable rules. This is very time-consuming work, which additionally requires hard-to-find human specialist experts who are able to meet the above-mentioned challenges 1 and 2.

In this situation, the question arises as to how larger-scale rule systems which actually work can be built at all. Could there be any possibilities for simplifying the construction of such rule systems?

Challenge 4: complexity

Anyone who has ever tried to really underpin a specialist field with rules discovers that they quickly encounter complex questions to which they find no solutions in the literature. In my field of Natural Language Processing (NLP), this is obvious. The complexity cannot be overlooked here, which is why it is imperative to deal with it. In other words: the principle of hope is not adequate to the task; rather, the complexity must be made the subject of debate and be studied intensively.

What complexity means and how it can be countered will be the subject matter of a further post. Of course, complexity must not result in an excessive increase in rules (cf. challenge 3). The question which therefore arises for rule-based AI is: how can we build a rule system which takes into consideration the wealth of details and complexity while still remaining simple and manageable?

The good news is: there are definitely answers to this question.

In a following post, the challenges will be specified.

This is a post about artificial intelligence.

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Translation: Tony Häfliger and Vivien Blandford

Two AI variants: rule-based and corpus-based

In the preceding post, I mentioned the two fundamental approaches to attempting to imbue computers with intelligence, namely the rule-based approach and the corpus-based approach. In a rule-based system, the intelligence is situated in a rule pool that is deliberately designed by people. In the corpus-based method, the knowledge is contained in the corpus, i.e. in a data collection which is analysed by a sophisticated program.

The performance of both methods has been massively boosted since the 1990s. The most impressive boost has been achieved with the corpus-based method, which is now regarded as the artificial intelligence proper and is making headlines across the board today. What, then, are the crucial improvements of the two methods? To begin with, we’ll have a look at how corpus-based AI works.

How does corpus-based AI work?

Corpus-based AI (c-AI) consists of two parts:

1. the corpus,
2. algorithms (neural network).

The corpus, which is also called learning corpus, is a collection of data. This can consist of photographs of tanks or faces, but also of collections of search queries, for instance of Google. What is important is that the corpus already contains the data in a weighted form. In the tank example, it has been written into the corpus whether the tanks are friendly or hostile. The collection of faces contains information about the owners of those faces. In the case of the search queries, Google records the links that a searcher clicks, i.e. which suggestion offered by Google is successful. Thus the learning corpus contains knowledge which the corpus-based AI is going to use.

Now the c-AI has to learn. The aim is for the AI to be able to categorise a new tank image, a new face or a new query correctly. For this purpose, the c-AI makes use of the knowledge in the corpus, i.e. the pictures of the tank collection, where it is noted for each image whether the tank is ours or foreign – as represented in Fig. 1.

Now the second component of the c-AI comes into play: the algorithm. Essentially, this is a neural network. It consists of several layers of “neurons” which pick up the input signals, process them and then transmit their own signals to the next higher level. Fig. 1 shows how the first (yellow) neuron layer picks up the signals (pixels) from the image and, after processing them, forwards its own signals to the next (orange) layer until finally, the network arrives at the result of “our tank” or “foreign tank”.

When the neural network is now shown a new image that has not been assessed yet, the process is precisely the same as with the other picture. If the network has been trained well, the program should be able to categorise on its own, i.e. the neural network should be able to discern whether the tank is ours or someone else’s

The significance of the data corpus for corpus-based AI

A corpus-based AI finds its detailed knowledge in the corpus that has been specially compiled for it and evaluates the connections which it discovers there. The corpus therefore contains the knowledge which the c-AI evaluates. In our example, the knowledge consists in the connection of the photograph, i.e. a set of wildly arranged pixels, with a simple binary piece of information (our tank/foreign tank). This knowledge is already part of the corpus before the algorithms conduct an evaluation. The algorithms of the c-AI thus do not detect anything that is not already in the corpus. However, the c-AI is now also able to apply the knowledge found in the corpus to new, unassessed cases.

The challenges for corpus-based AI

The challenges for c-AI are unequivocal:

1. Corpus size: the more images there are in the corpus, the higher the certainty of the categorisation. A corpus that is too small will result in faulty results. The size of the corpus is crucial for the precision and reliability of the results.
2. Hardware: the processing power required by a c-AI is very high and becomes higher the more precise the method is intended to be. Hardware performance is the decisive factor for the practical applicability of the method.

This quickly clarifies how c-AI has been able to improve its performance so impressively in the last two decades:

1. The data volumes which Google and other organisations are capable of collecting in the internet have increased drastically. In this respect, Google profits from quite an important amplification effect: the more queries Google receives, the better the corpus and thus its hit rate. The better the hit rate, the more queries Google will receive.
2. The hardware that is required to evaluate the data is becoming less expensive and more performant. Today, internet companies and other organisations operate huge server farms, without which the processor-intensive evaluations would not be possible in the first place.

Besides the corpus and the hardware, the sophistication of the algorithms naturally also plays a part. However, the algorithms were not bad even decades ago. In comparison with the other two factors – hardware and corpus – the progress made in the field of algorithms only plays a modest part in the impressive success of c-AI.

The success of corpus-based AI

The challenges for c-AI were tackled by the big corporations and organisations extremely successfully.

The above description of the operating mode of c-AI, however, should also reveal the weaknesses immanent in the system, which are accorded less media attention. I will discuss them in more detail in a later post.

Next we will have a look at the challenges for rule-based AI.

This is a post about artificial intelligence.

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Translation: Tony Häfliger and Vivien Blandford