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Abductive inference: The blind spot of artificial intelligence

#artificialintelligence

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain. But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson's new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.


Abductive inference: The blind spot of artificial intelligence

#artificialintelligence

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain. But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson's new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.


Abductive inference is a major blind spot for AI

#artificialintelligence

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain. But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson's new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.


Abductive Inference & future path of #AI

#artificialintelligence

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain. But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson's new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.


Why Computers Will Likely Never Perform Abductive Inferences

#artificialintelligence

Humans, on the other hand, need none of this. On the basis of very limited or incomplete data, we nonetheless come to the right conclusion about many things (yes, we are fallible, but the miracle is that we are right so often). Noam Chomsky's entire claim to fame in linguistics really amounts to exploring this underdetermination problem, which he referred to as "the poverty of the stimulus." Humans pick up language despite very varied experiences with other human language speakers. Babies born in abusive and sensory deprived environments pick up language.