The history of artificial intelligence has been marked by repeated cycles of extreme optimism and promise followed by disillusionment and disappointment. Today's AI systems can perform complicated tasks in a wide range of areas, such as mathematics, games, and photorealistic image generation. But some of the early goals of AI like housekeeper robots and self-driving cars continue to recede as we approach them. Part of the continued cycle of missing these goals is due to incorrect assumptions about AI and natural intelligence, according to Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute and author of Artificial Intelligence: A Guide For Thinking Humans. In a new paper titled "Why AI is Harder Than We Think," Mitchell lays out four common fallacies about AI that cause misunderstandings not only among the public and the media, but also among experts.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. The history of artificial intelligence has been marked by repeated cycles of extreme optimism and promise followed by disillusionment and disappointment. Today's AI systems can perform complicated tasks in a wide range of areas, such as mathematics, games, and photorealistic image generation. But some of the early goals of AI like housekeeper robots and self-driving cars continue to recede as we approach them. Part of the continued cycle of missing these goals is due to incorrect assumptions about AI and natural intelligence, according to Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute and author of Artificial Intelligence: A Guide For Thinking Humans.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. The short excerpt below from the 1938 film La Femme du Boulanger (The Baker's Wife) ingeniously depicts how the human mind can extract deep meaning from life experiences and perceived situations. In the movie, directed by Marcel Pagnol, the baker Aimable welcomes his wife Aurelie, who has just come back after running off with a shepherd days earlier. While Aimable treats Aurelie with sweet words and a heart-shaped bread (which he had baked for himself), he shows no kindness toward Pomponette, his female cat who coincidentally returns home at the same time as Aurelie, after abandoning her mate Pompon for a chat de gouttière (alley cat). Aimable calls Pomponette ordur (junk) and a salope (a rude term) who has run off with un inconnu (a nobody) and bon-a-rien (good for nothing) while the poor Pompon has been miserably searching for her everywhere.

Welcome to TechTalks' AI book reviews, a series of posts that explore the latest literature on AI. It wouldn't be an overstatement to say that artificial intelligence is one of the most confusing and least understood fields of science. On the one hand, we have headlines that warn of deep learning outperforming medical experts, creating their own language and spinning fake news stories. On the other hand, AI experts point out that artificial neural networks, the key innovation of current AI techniques, fail at some of the most basic tasks that any human child can perform. Artificial intelligence is also marked with some of the most divisive disputes and rivalries.