Название: Understanding the Artificial Intelligence Revolution: Between Catastrophe and UtopiaАвтор: Shalom Lappin
Издательство: CRC Press
Год: 2025
Страниц: 168
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
After many years during which it languished in relative obscurity, in remote classrooms of Computer Science departments and in small prototype projects for tech companies, Artificial Intelligence (AI) is now a searingly hot topic across the media. Yet much of the public discussion is so feverish that an understanding of the basic scientific and engineering elements of the field is easily lost, often resulting in exaggerated claims, as well as dangerously neglected threats.
This concise and sober book presents a brief history of AI, explaining in clear language the central engineering innovations that have produced the current revolution. It distinguishes between imagined dangers and the very real problems that AI is creating. Spread across seven short and accessible chapters, this book explains the developments behind Deep Learning and the applications of deep neural networks (DNNs). It addresses both the imagined and actual risks posed by the AI revolution, before outlining the elements of a rational public policy on AI, covering topics like tech monopolies, disinformation, bias, hate speech, intellectual property rights, and inequality.
Automatic code generation applies NMT methods to mapping natural language text into computer code. It permits programmers to create, test, and correct code in a variety of languages. As DNN (Deep Neural Network) code generation improves in quality, it will replace much of the manual work in programming. This will allow software engineers to focus on high-level design and application problems, rather than the details of coding.
DNN-driven theorem proving is now producing interesting results in logic and mathematics. Some of its proofs significantly improve on those achieved by humans. It is also an effective tool for proof checking. As formal results are important for the soundness of theories in physics, and in numerous engineering applications, theorem proving is of more than theoretical interest.
Deep Learning is having a profound impact on medical practice and bioinformatics. It has surpassed human performance in many diagnostic tasks, and it guides robotic surgery. It generates medical reports and recommendations from data. Transformers are able to predict proteins and protein behaviour.1 This work has yielded important applications in pharmacology and in computational biology.
Finally, DNNs are generating visual images, videos, and musical compositions. Some of these are innovative and aesthetically interesting. In addition, DNNs are being used to identify artistic creators and to authenticate paintings. Some of the most interesting experiments in musical composition involve collaborations between generative transformers, which produce partial works of art and human interlocutors who develop and complete them. Using Deep Learning to create art is a new area of application. It remains to be seen to what extent it will replace human activity, or supplement it, in different modes of artistic expression. This very much depends on the reception that humans accord AI-generated and AI-assisted creations. At this point, this domain is still in an early experimental phase.
Suitable for the general reader, Understanding the Artificial Intelligence Revolution: Between Catastrophe and Utopia is the ideal book for anyone seeking a clear and informed introduction to AI.
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