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Games and Intelligence (2): Deep Learning

Go and chess The Asian game of Go shares many similarities with chess while being simpler and more sophisticated at the same time. The same as in chess: – Board game → clearly defined playing field – Two players (more would immediately increase complexity) – Unequivocally defined possibilities of playing the stones (clear rules) – The players place stones alternately (clear timeline). – No hidden information (as, for instance, in cards) – Clear objective (the player who has surrounded the larger territory wins) Simpler in Go: – Only one type of piece: the stone (unlike in chess: king, queen, etc.)

By Hans Rudolf Straub|2025-11-12T11:00:04+00:008. May 2020|Categories: Artificial Intelligence|Tags: Artificial Intelligence, chess, corpus-based, Intelligence, neural network|0 Comments

Overview of the AI systems

All the systems we have examined so far, including deep learning, can in essence be traced back to two methods: the rule-based method and the corpus-based method. This also applies to the systems we have not discussed to date, namely simple automata and hybrid systems, which combine the two above approaches. If we integrate these variants, we will arrive at the following overview: A: Rule-based systems Rule-based systems are based on calculation rules. These rules are invariably IF-THEN commands, i.e. instructions which assign a certain result to a certain input. These systems are always deterministic, i.e. a certain input always

By Hans Rudolf Straub|2025-11-12T10:58:42+00:005. May 2020|Categories: Artificial Intelligence|Tags: Artificial Intelligence, corpus-based, Intelligence, neural network, rule-based, search engine|0 Comments

Games and intelligence (1)

Chess or jass: what requires more intelligence? (Jass is a very popular Swiss card game of the same family as whist and bridge, though more homespun than the latter.) Generally, it is assumed that chess requires more intelligence, for obviously less intelligent players definitely stand a chance of winning at cards while they don’t in chess. If we consider, however, what a computer program must be able to do in order to win, the picture soon looks different: chess is clearly simpler for a machine. This may surprise you, but it is worth looking at the features the two games

By Hans Rudolf Straub|2025-11-12T11:00:30+00:0027. April 2020|Categories: Artificial Intelligence|Tags: Artificial Intelligence, chess, jass, probability|0 Comments

How real is the probable?

AI can only see whatever is in the corpus Corpus-based systems are on the road to success. They are “disruptive”, i.e. they change our society substantially within a very short period of time – reason enough for us to recall how these systems really work. In previous blog posts I explained that these systems consist of two parts, namely a data corpus and a neural network. Of course, the network is unable to recognise anything that is not already in the corpus. The blindness of the corpus automatically continues in the neural network, and the AI is ultimately only able

By Hans Rudolf Straub|2025-11-12T11:01:45+00:0022. April 2020|Categories: Artificial Intelligence|Tags: Artificial Intelligence, corpus-based, entropy, Intelligence, neural network, probability, search engine|0 Comments

Rule-based AI: Where is the intelligence situated

Two AI variants: rule-based and corpus-based The two AI variants mentioned in previous blog posts are still topical today, and they have registered some remarkable successes. The two differ from each other not least in where precisely their intelligence is situated. Let’s first have a look at the rule-based system. Structure of a rule-based system In the Semfinder company, we used a rule-based system. I drew the following sketch of it in 1999: Green: data Yellow: software Light blue: knowledge ware Dark blue: knowledge engineer The sketch consists of two rectangles, which represent different locations. The rectangle bottom left shows

By Hans Rudolf Straub|2025-11-12T11:02:54+00:0016. April 2020|Categories: Artificial Intelligence|Tags: Artificial Intelligence, Intelligence, Knowledge base, rule-based|0 Comments

Information Reduction 8: Different Macro States

Two states at the same time In my last article I showed how a system can be described at two levels: that of the micro and that of the macro state. At the micro level, all the information is present in full detail; at the macro level there is less information but what there is, is more stable. We have already discussed the example of the glass of water, where the micro state describes the movement of the individual water molecules, whereas the macro state encompasses the temperature of the liquid. In this paper I would like to discuss how

By Hans Rudolf Straub|2025-11-12T11:03:50+00:0016. April 2020|Categories: Artificial Intelligence, Logic|Tags: Benard cell, Biology, Information Reduction, Macro State, Micro State|0 Comments

What the corpus knows – and what it doesn’t

Compiling the corpus In a previous post we saw how the corpus – the basis for the neural network of AI – is compiled. The neural network is capable of interpreting the corpus in a refined manner, but of course the neural network cannot extract anything from the corpus that is not in it in the first place. Fig. 1: The neural network extracts knowledge from the corpus How is a corpus compiled? A domain expert assigns images of a certain class to a certain type, for instance “foreign tanks” vs “our tanks”. In Fig. 2, these categorisations carried

By Hans Rudolf Straub|2025-11-12T11:04:31+00:009. April 2020|Categories: Artificial Intelligence|Tags: Artificial Intelligence, corpus-based, neural network|0 Comments

Where is intelligence situated in corpus-based AI?

In a preceding post we saw that in rule-based AI, intelligence is situated in the rules. These rules are drawn up by people, and the system is as intelligent as the people who have formulated them. Where, then, is intelligence situated in corpus-based AI? The answer is somewhat more complicated than in the case of rule-based systems. Let us therefore have a closer look at the structure of such a corpus-based system. It is established in three steps: compiling as large a data collection as possible (corpus), assessing this data collection, training the neural network. The network can be applied as

By Hans Rudolf Straub|2025-11-12T11:05:18+00:009. April 2020|Categories: Artificial Intelligence|Tags: Artificial Intelligence, corpus-based, neural network|0 Comments

The three innovations of rule-based AI

Have the neural networks outpaced the rule-based systems? It cannot be ignored: corpus-based AI has overtaken rule-based AI by far. Neural networks are making the running wherever we look. Is the competition dozing? Or are rule-based systems simply incapable of yielding equivalent results to those of neural networks? My answer is that both methods are predisposed for performing very different functions as a matter of principle. A look at their respective modes of action makes clear what the two methods can usefully be employed for. Depending on the problem to be tackled, one or the other has an advantage. Yet

By Hans Rudolf Straub|2025-11-12T11:05:45+00:0030. March 2020|Categories: Artificial Intelligence|Tags: Artificial Intelligence, concept, Concept architecture, rule-based, semantics|0 Comments

Specification of the challenges for rule-based AI

Rule-based AI is lagging behind The distinction between rule-based AI and corpus-based AI makes sense in several respects since the two systems work in completely different ways. This does not only mean that their challenges are completely different, it also means that as a consequence, their development trajectories are not parallel in terms of time. In my view, the only reason for this is that rule-based AI has reached a dead end from which it will only be able to extricate itself once it has correctly identified its challenges. This is why these challenges will be described in more detail below.