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The development of recursive self-improvement raises significant ethical and safety concerns, as such systems may evolve in unforeseen ways and could potentially surpass human control or understanding. There has been a number of proponents that have pushed to pause or slow down AI development for the potential risks of runaway AI systems. [3] [4]
Artificial Intelligence: A Guide for Thinking Humans is a 2019 nonfiction book by Santa Fe Institute professor Melanie Mitchell. [1] The book provides an overview of artificial intelligence (AI) technology, and argues that people tend to overestimate the abilities of artificial intelligence.
Life 3.0: Being Human in the Age of Artificial Intelligence [1] is a 2017 non-fiction book by Swedish-American cosmologist Max Tegmark. Life 3.0 discusses artificial intelligence (AI) and its impact on the future of life on Earth and beyond. The book discusses a variety of societal implications, what can be done to maximize the chances of a ...
Creatie.ai curated five of the biggest AI stories ... Artificial intelligence has changed how people work and live in 2024, as companies create tools that can write code, generate images, and ...
Recursive self improvement (aka seed AI) – speculative ability of strong artificial intelligence to reprogram itself to make itself even more intelligent. The more intelligent it got, the more capable it would be of further improving itself, in successively more rapid iterations, potentially resulting in an intelligence explosion leading to ...
Robots are coming for all our jobs, but we’ve still got the edge in a few key areas.
Artificial intelligence is used in astronomy to analyze increasing amounts of available data [160] [161] and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and distinguishing ...
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...