neural network

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

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

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

AI: Vodka and tanks

AI in the last century AI is a big buzzword today but was already of interest to me in my field of natural language processing in the 1980s and 1990s. At that time, there were two methods which were occasionally labelled AI, but they could not have been more different from each other. The exciting thing is that these two different methods still exist today and continue to be essentially different from each other. AI-1: vodka The first method, i.e. the one already used by the very first computer pioneers, was purely algorithmic, i.e. rule-based. Aristotle’s syllogisms are a paradigm of this type