Dispel fears and misunderstandings about artificial intelligence through Gary Smith’s book

Along with the superhuman abilities of artificial intelligence are fantasies and fears for the future of humanity. To explain thoroughly, debunk misunderstandings, and dispel illusions, author Gary Smith – Professor of Pomona College, USA wrote the book “Fantasy about artificial intelligence”.
Humanity is living in an incredible period of history. It was the age of perfect memories and superhuman computer capabilities. “Artificial Intelligence” (AI) is one of the most interested keywords. Now, thanks to AI, people can do things that were never possible before and never thought they could do.

The book “Fantasy of Artificial Intelligence. However, with AI’s capabilities are fantasies and fears for the future of humanity. The real danger is not that computers are smarter than humans, but that people think computers are smarter than they are, and therefore trust the computers to make important decisions for them, because computers are perfect. They can all make serious mistakes, when the way they process data is not the way humans think. In order to explain thoroughly, debunk misunderstandings, and dispel illusions, author Gary Smith – Professor of Pomona College, USA wrote the book “Fantasy about artificial intelligence”. The book is divided into 12 chapters, including many real-life examples from history such as the 2008 US presidential election, IBM’s AI Watson and many other famous artificial intelligence projects with the aim of clarifying how intelligence works. Artificial intelligence processes information, gives the final result and shows the difference between AI (Artificial Intelligence) and the human brain, thereby showing readers the risks of over-trusting. computer results. In the first 4 chapters, the author has taken a lot of real-life examples to explain in an easy way how artificial intelligence works and how they give the end result. The author claims that robots have surpassed humans in repetitive monotonous tasks, they have better memory, perform more accurate calculations and are tireless. But artificial intelligence is not programmed to simulate the human brain, and artificial intelligence cannot think, reason, and make analogies. Since then, the author asserts that up to now computers with comprehensive intelligence enough to compete with humans have not yet existed. In the next two chapters, by referring to “Data Mining”, it is proven fact that Data mining although there will sometimes be a real knowledge discovery, given the number of explanatory variables The larger the consideration, the more likely it is that a relationship discovered is just a coincidence, of a temporary nature. In the remaining chapters of the book, the author has introduced many computational models in many fields such as finance, politics, economics… to prove that statistical evidence is not enough to distinguish between knowledge real and fake. The computer cannot accurately judge whether the correlation is random or truly significant. That work is only for the human mind. In conclusion, human intelligence works differently from artificial intelligence, so it cannot be said that artificial intelligence is intelligent. Robots excel in memory, accuracy, and endurance, but they still have many shortcomings: they can’t think, can’t make analogies, and can’t distinguish between good-bad, meaningless-meaning data. Humans need computers to process large amounts of information, but also need human intelligence to make final judgments about the results returned by computers. About the author, Gary Smith has been involved in many research projects that highlight the questionable use of data in statistical analysis. He has published 8 textbooks, 7 business books, nearly 100 academic articles and 7 software programs in economics, finance and statistics. In addition to teaching at Pomona College, he is also an economic analyst and consultant. His other well-known books such as “Standard deviation: Flawed assumptions” and “Tortured Data” also warn about the dangers of confusing correlations. and causality in statistical analysis.