4 Why It’s Called Intelligence

4 Why It’s Called Intelligence
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In 1956,
Note:BRIEFING SULL IA...QUANDO

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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
Note:OBBIETTIVO

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agenda turned out to be more visionary than practical.
Note:SOGNI NEGLI ANNI 50

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not fast enough
Note:LA MACCHINA

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The early 1980s
Note:SECONDA TAPPA

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expert systems

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medical diagnosis,
Note:ESEMPIO

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costly to develop, cumbersome, and could not address the myriad of exceptions
Note:INCONVENIENTI

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“AI winter.”
Note:DOPO

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More data, better models,
Note:OGGI

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improve prediction.
Note:LA NUOVA FASE

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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.
Note:FIDELIZZARE

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identify at-risk customers.
Note:PRIMA COSA DA FARE

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a statistical technique called “regression.”
Note:LO STRUMENTO USATO PRIMA

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prediction based on the average of what has occurred in the past.
Note:COSA FA LA REGRESSIONE

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building models that can take in more data about the context.
Note:OGGI

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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
Note:ESEMPIO... DIPENDE DAL POSTO GEOGRAFICO, DALLA STAGIONE...

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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.
Note:COMBINAZIONI

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multivariate regression
Note:ANTENATO DI BAYES

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minimizes prediction mistakes,
Note:SCOPO DELLA REGRESSIONE

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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:
Note:LA BARZELLETTA DEL CERVO

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The physicist calculates
Note:UN FISICO...UN INGEGNERE E UNO STATISTICO

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missing the deer by five feet to the left.
Note:FALLIMENTO

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“You forgot to account for the wind.
Note:L ING

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missing the deer by five feet to the right.
Note:ERRORE OPPOSTO

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the statistician cheers, “Woo hoo! We got it!”
Note:PER LO STAT LA CACCIA È ANDATA A BUON FINE

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regression can mean never actually hitting the target.
Note:MARGINE DI ERRORE

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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
Note:BAYES IMPARA

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advantage of the rich data and fast computers
Note:VANTAGGIO

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regression still generally performed better.
Note:ANNI 90 E 00

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Duke University’s Teradata Center held a data science tournament in 2004 to predict churn.
Note:TORNEI BAYES VS REGRESSIONE...VINCE REGRESSIONE

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By 2016, that had all changed.
Note:COMINCIA A VINCERE BAYES

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the data and computers were finally good enough to enable machine learning to dominate.
Note:CAMBIO DECISIVO

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Now researchers base churn prediction on thousands of variables and millions of customers.
Note:BASE DATI ATTUALE

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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.
Note:ESEMPIO

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combination of intuition and statistical tests to select the variables and model.
Note:COSA CONTAVA IERI

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variables can combine with each other in unexpected ways.
Note:OGGI

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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.
Note:ESEMPIO

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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.
Note:ALTRA COMBINAZIONE INATTESA

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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.
Note:CLASSICO FALLIMENTO REGRESSIONI

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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
Note:COLPA DEI DATI?

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multiple regression–like models that assumed house prices in different markets were not correlated with one another.
Note:MODELLI DELLE AGENZIE DI RATINGS

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Analysts built their regression models on hypotheses of what they believed mattered and how—
Note:NECESSITA' DI SELEZIONARE I DATI E IL TIPO DI CURVA

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unnecessary for machine learning.
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analyst’s intuition
Note:AL CENTRO IERI

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hypotheses are less important.
Note:IERI

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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.
Note:LA SVOLTA IA

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“traditional statistics on steroids.”
Note:CHI MINIMIZZA... IN UN CERTO SENSO E' VERO

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the predictions are so good that we can use prediction instead of rule-based logic.
Note:MA FORSE OLTRE UNA CERTA SOGLIA PREDIZIONE = INTELIGENZA?

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Effective prediction changes the way computers are programmed.
Note:BASTA STATISTICHE E ALGORITMI

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“deep learning,”
Note:NUOVA TECNICA

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learning through example
Note:ANALAGIA CON LA MENTE UMANA

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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.
Note:TIPO...

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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,
Note:PERCHE' PARLIAMO DI INTELLIGENZA

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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.
Note:REFERENZA

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Prediction is not just one of the things your brain does. It is the primary function
Note:FUNZIONE PRIMARIA

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information is fed back into our brain, which updates its algorithm,
Note:AGGIORNAMENTO CONTINUO...BAYES

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many computer scientists flatly reject his emphasis on the cortex as a model for prediction machines.
Note:MOLTI RESPINGONO L'ANALOGIA

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We do not speculate on whether this progress heralds the arrival of general artificial intelligence, “the Singularity,”
Note:MODESTIA

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narrower focus on prediction
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from deterministic to probabilistic programming of computers
Note:IL PASSAGGIO

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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.
Note:IERI LA PROB. AVEVA CATTIVA STAMPA

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moving from a Newtonian deterministic perspective to the uncertainties of quantum mechanics.
Note:LA SCIENZA GIA' C'E' PASSATA

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advance of twenty-first-century computer science matches these previous advances in social and physical sciences:
Note:ANALOGIA

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structured probabilistically, based on data.
IL NUOVO ALGORITMO