Hypothesis

What is the hypothesis in Artificial Intelligence?

A Hypothesis is an inherently logical assumption made on the basis of assumptions and experience and not yet proven is.

For the Artificial Intelligence (AI) there are AI hypotheses in two different schools of thought. With the Strong AI hypothesis the machine is able to actually think like a human being and develop real self-awareness. On the other hand, the Weak AI hypothesis the machine is only able to take over individual intelligent functions as a substitute for humans. For example, there are pattern recognition, search programmes or even systems (such as cars, computer programmes) that optimally adapt their behaviour to the operator (as in the case of sporty drivers or also preferred functions).

Since little is generally known yet about how human intelligence can be described holistically, a useful benchmark for a strong AI hypothesis is lacking. The hardware of the brain is not fundamentally different from that of a computer. Each element, whether it is a transistor or a nerve cell, performs very simple functions - these can be replicated by computers.

Technological singularity impressively describes a widespread hypothesis according to which the development of an artificial superintelligence is possible, which can enable rapid technological growth. Thus, a powerful AI could evolve with unstoppable sequences of self-improvement cycles, resulting in an "intelligence explosion". Such a superintelligence could far surpass human intelligence.

Which applications with hypothesis generation are known?

There is AI in healthcare that can be used successfully. One of the best known is IBM Watson. This system understands natural language and it can respond to questions that are asked. Such a system evaluates patient data and can form hypotheses based on this.

There are also AI applications such as virtual online health assistants and functional chatbotswhich help patients and clients in the health sector to find medical information directly. Appointments can also be made. AI applications can help understand billing processes and handle various administrative procedures.

Can robust hypotheses be created through AI?

The human brain has the ability to build mental models from experience and thus to systematically draw broader conclusions beyond current sensory impressions, which even make explanations possible. There is the form of explanatory structure that can also be called a generative model. This is based on a corresponding multitude of different experiences and generates a hypothesis through certain sensory perceptions. In this way, statements can be made about how objects move or whether they are hidden. These hypotheses are significantly more robust than classification-oriented systems, which are used in the Deep Learning underlying.

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