The AI Delusion最新文献

{"title":"Doing Without Thinking","authors":"Gary Smith","doi":"10.1093/oso/9780198824305.003.0004","DOIUrl":"https://doi.org/10.1093/oso/9780198824305.003.0004","url":null,"abstract":"Nigel Richards is a New Zealand–Malaysian professional Scrabble player (yes, there are professional Scrabble players). His mother recalled that, “When he was learning to talk, he was not interested in words, just numbers. He related everything to numbers.” When he was 28, she challenged him to play Scrabble: “I know a game you’re not going to be very good at, because you can’t spell very well and you weren’t very good at English at school.” Four years later, Richards won the Thailand International (King’s Cup), the world’s largest Scrabble tournament. He went on to win the U.S., U.K., Singapore, and Thailand championships multiple times. He won the Scrabble World Championship in 2007, 2011, and 2013. (The tournament is held every two years and he was runner-up in 2009). In May 2015, Richards decided to memorize the 386,000 words that are allowed in French Scrabble. (There are 187,000 allowable words in North American Scrabble.) He doesn’t speak French beyond bonjour and the numbers he uses to record his score each turn. Beyond that, Richards paid no attention to what the French words mean. He simply memorized them. Nine weeks later, he won the French-language Scrabble World Championship with a resounding score of 565–434 in the championship match. If he had studied 16 hours a day for 9 weeks, he would have an average of 9 seconds per word to memorize all 386,000 words in the French Scrabble book. However, Richards reportedly doesn’t memorize words one by one; instead, he goes page by page, with the letters absorbed into his memory, ready to be recalled as needed when he plays Scrabble. Richards played as quickly and incisively in the French tournament as he does in English-language tournaments, giving no clue that he cannot actually communicate in French. For experts like Richards, Scrabble is essentially a mathematical game of combining tiles to accumulate points while limiting the opponent’s opportunities to do the same and holding on to letters that may be useful in the future. The important skills are an ability to recognize patterns and calculate probabilities. There is no need to know what any of the words mean.","PeriodicalId":308433,"journal":{"name":"The AI Delusion","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134619810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conclusion","authors":"Gary Smith","doi":"10.1093/oso/9780198824305.003.0015","DOIUrl":"https://doi.org/10.1093/oso/9780198824305.003.0015","url":null,"abstract":"We live in an incredible period in history. The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that never could be done before. I am addicted to computers and you may be, too. But we shouldn’t let our love of computers cloud our recognition of their limitations. Yes, computers know more facts than we do. Yes, computers have better memories than we do. Yes, computers can make calculations faster than we can. Yes, computers do not get tired like we do. Robots far surpass humans at repetitive, monotonous tasks like tightening bolts, planting seeds, searching legal documents, and accepting bank deposits and dispensing cash. Computers can recognize objects, draw pictures, drive cars. You can surely think of a dozen other impressive— even superhuman—computer feats. It is tempting to think that because computers can do some things extremely well, they must be highly intelligent. However, being useful for specific tasks is very different from having a general intelligence that applies the lessons learned and skills required for one task to more complex tasks or to completely different tasks. With true intelligence, skills are portable. Computers are great and getting better, but computer algorithms are still designed to have the very narrow capabilities needed to perform well-defined chores, not the general intelligence needed to deal with unfamiliar situations by assessing what is happening, why it is happening, and what the consequences are of taking action. Humans can apply general knowledge to specific situations and use specific situations to improve their general knowledge. Computers today cannot. Artificial intelligence is not at all like the real intelligence that comes from human brains. Computers do not know what words mean because computers do not experience the world the way we do. They do not even know what the real world is. Computers do not have the common sense or wisdom that humans accumulate by living life. Computers cannot formulate persuasive theories. Computers cannot do inductive reasoning or make long-run plans.","PeriodicalId":308433,"journal":{"name":"The AI Delusion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125879191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Old Wine in New Bottles","authors":"M. Battin, L. Francis, J. Jacobson, Charles B. Smith","doi":"10.1093/ACPROF:OSO/9780195335842.003.0008","DOIUrl":"https://doi.org/10.1093/ACPROF:OSO/9780195335842.003.0008","url":null,"abstract":"The fashion industry is subject to recurring cycles of popularity that are regular enough to be dubbed the “20-year rule.” Activewear clothing that is suitable for the gym and the street was fashionable in the 1990s and, 20 years later, in the 2010s. Intellectually, the British economist Dennis Robertson once wrote, “Now, as I have often pointed out to my students, some of whom have been brought up in sporting circles, high-brow opinion is like a hunted hare; if you stand in the same place, or nearly the same place, it can be relied upon to come round to you in a circle.” In the same way, today’s data miners have rediscovered several statistical tools that were once fashionable. These tools have been given new life because they are mathematically complex, indeed beautifully complex, and many data miners are easily seduced by mathematical beauty. Too few think about whether the underlying assumptions make sense and if the conclusions are reasonable. Consider data mining with multiple regression models. Rummaging through a large data base looking for the combination of explanatory variables that gives the best fit can be daunting. With 100 variables to choose from, there are more than 17 trillion possible combinations of 10 explanatory variables. With 1,000 possible explanatory variables, there are nearly a trillion trillion possible combinations of 10 explanatory variables. With 1 million possible explanatory variables, the number of 10-variable combinations grows to nearly a million trillion trillion trillion trillion (if we were to write it out, there would be 54 zeros). Stepwise regression was born back when computers were much slower than today, but it has become a popular data-mining tool because it is less computationally demanding than a full search over all possible combinations of explanatory variables but, it is hoped, will still give a reasonable approximation to the results of a full search. The stepwise label comes from the fact that the calculations go through a number of steps, considering potential explanatory variables one by one. There are three main stepwise procedures. A forward-selection rule starts with the one explanatory variable that has the highest correlation with the variable being predicted. Then the procedure adds a second variable, the variable that improves the fit the most.","PeriodicalId":308433,"journal":{"name":"The AI Delusion","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122110226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}