4 Why It’s Called Intelligence
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In 1956,
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Dartmouth College
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if computers could be programmed to engage in cognitive thought,
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COSA....ROBA TIPO GIOCARE A SCACCHI...DIMOSTRARE UN TEOREMA
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give computers choices
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agenda turned out to be more visionary than practical.
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not fast enough
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The early 1980s
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expert systems
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medical diagnosis,
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costly to develop, cumbersome, and could not address the myriad of exceptions
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“AI winter.”
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More data, better models,
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improve prediction.
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storage of big data
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more suitable processors,
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Predicting Churn
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managing churn is perhaps the most important marketing activity.
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identify at-risk customers.
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a statistical technique called “regression.”
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prediction based on the average of what has occurred in the past.
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building models that can take in more data about the context.
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“the conditional average.”
Note:LO STRUMENTO USATO DA AI...BAYES... LA PROBABILITA' DATO UN CONTESTO... NON UNA TENDENZA
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likelihood of rain
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We can condition averages on time of day, pollution, cloud cover, ocean temperature, or any other available information.
Note:INFINITI CONDIZIONAMENTI
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Calculating the average for these seven types of information alone creates 128 different combinations.
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multivariate regression
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minimizes prediction mistakes,
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“goodness of fit.”
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For churn in cable television, it might be how frequently people watch TV;
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REGRESSIONE...IMPORTANTE SCEGLIERE LE VARIABILI...ES...CHI NN VEDE CAMBIERÀ
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statistics joke:
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The physicist calculates
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missing the deer by five feet to the left.
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“You forgot to account for the wind.
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missing the deer by five feet to the right.
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the statistician cheers, “Woo hoo! We got it!”
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regression can mean never actually hitting the target.
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allowing some bias in exchange for reducing variance.
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difference between machine learning and regression
Note:Tttttttttttttttt REGRESSIONE E BAYES...LA REGRESSIONE MINIMIZZA L'ERRORE (E' BIASED), BAYES CONSIDERA LA MEDIA (E' STATISTICAMENTE UNBIASED)
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freedom to experiment drove rapid improvements
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advantage of the rich data and fast computers
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regression still generally performed better.
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Duke University’s Teradata Center held a data science tournament in 2004 to predict churn.
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By 2016, that had all changed.
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the data and computers were finally good enough to enable machine learning to dominate.
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Now researchers base churn prediction on thousands of variables and millions of customers.
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in a mobile phone churn model, researchers utilized data on hour-by-hour call records in addition to standard variables such as bill size and payment punctuality.
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combination of intuition and statistical tests to select the variables and model.
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variables can combine with each other in unexpected ways.
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People with large phone bills who rack up minutes early in the billing month might be less likely to churn than people with large bills who rack up their minutes later in the month.
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people with large weekend long-distance bills who also pay late and tend to text a lot may be particularly likely to churn.
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Such combinations are difficult to anticipate,
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Beyond Churn
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The financial crisis of 2008 was a spectacular failure of regression-based prediction methods.
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prediction was staggeringly wrong despite very rich data on past defaults.
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failure was not due to insufficient data, but instead how analysts used that data
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multiple regression–like models that assumed house prices in different markets were not correlated with one another.
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Analysts built their regression models on hypotheses of what they believed mattered and how—
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unnecessary for machine learning.
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analyst’s intuition
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hypotheses are less important.
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If It’s Just Prediction, Then Why Is It Called “Intelligence”?
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transformed how we use statistics to predict.
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“traditional statistics on steroids.”
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the predictions are so good that we can use prediction instead of rule-based logic.
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Effective prediction changes the way computers are programmed.
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“deep learning,”
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learning through example
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If you want a child to know the word for “cat,” then every time you see a cat, say the word.
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Many problems have transformed from algorithmic problems (“what are the features of a cat?”) to prediction problems (“does this image with a missing label have the same features as the cats I have seen before?”).
Note:IN SINTESI
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prediction—is a key component of intelligence,
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In his book On Intelligence, Jeff Hawkins was among the first to argue that prediction is the basis for human intelligence.
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Prediction is not just one of the things your brain does. It is the primary function
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information is fed back into our brain, which updates its algorithm,
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many computer scientists flatly reject his emphasis on the cortex as a model for prediction machines.
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We do not speculate on whether this progress heralds the arrival of general artificial intelligence, “the Singularity,”
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narrower focus on prediction
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from deterministic to probabilistic programming of computers
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Ian Hacking, in his book The Taming of Chance, said that, before the nineteenth century, probability was the domain of gamblers.
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moving from a Newtonian deterministic perspective to the uncertainties of quantum mechanics.
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advance of twenty-first-century computer science matches these previous advances in social and physical sciences:
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structured probabilistically, based on data.
IL NUOVO ALGORITMO
