This section is a brief introduction to Artificial Intelligence (AI). Our aim is to introduce some elementary concepts related to AI in a way that is hopefully understandable to people non-expert in the field. In this introduction, we emphasize that AI is much more than Machine Learning and Deep Learning, by introducing the two main different approaches to AI: Model Based AI and AI based on Machine Learning. We summarize the main pros and cons of both approaches. In spite of several AI success stories in the past by Model Based AI, there is no doubt that the current impact and high expectations raised by AI is due to the recent successes in data intensive (supervised) Machine Learning, and especially to Deep Learning. Deep learning has led to impressive gains on most key areas of AI, such as computer vision, natural language understanding, speech recognition, game playing, and robotics. In spite of the significant progress, we still need a lot of work in research and a paradigm shift in AI. The goal for the future will be to provide AI based solutions that can be of great help for our life and, at the same time, reliable, trustworthy solutions that can be used even in areas and systems at “high risk”, as stated by the Proposal for a Regulation of the European Union on AI.

Breve introduzione tecnica all’Intelligenza Artificiale

Paolo Traverso
2022

Abstract

This section is a brief introduction to Artificial Intelligence (AI). Our aim is to introduce some elementary concepts related to AI in a way that is hopefully understandable to people non-expert in the field. In this introduction, we emphasize that AI is much more than Machine Learning and Deep Learning, by introducing the two main different approaches to AI: Model Based AI and AI based on Machine Learning. We summarize the main pros and cons of both approaches. In spite of several AI success stories in the past by Model Based AI, there is no doubt that the current impact and high expectations raised by AI is due to the recent successes in data intensive (supervised) Machine Learning, and especially to Deep Learning. Deep learning has led to impressive gains on most key areas of AI, such as computer vision, natural language understanding, speech recognition, game playing, and robotics. In spite of the significant progress, we still need a lot of work in research and a paradigm shift in AI. The goal for the future will be to provide AI based solutions that can be of great help for our life and, at the same time, reliable, trustworthy solutions that can be used even in areas and systems at “high risk”, as stated by the Proposal for a Regulation of the European Union on AI.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/332507
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