{"id":100745,"date":"2016-08-22T10:54:14","date_gmt":"2016-08-22T10:54:14","guid":{"rendered":"https:\/\/gerdtestpress.online\/?guid=465007178b9e24d6db4744350239afbe"},"modified":"2016-08-22T10:54:14","modified_gmt":"2016-08-22T10:54:14","slug":"of-prediction-and-policy-good-read-via-the-economist","status":"publish","type":"post","link":"https:\/\/futuristgerd.com\/pt\/2016\/08\/of-prediction-and-policy-good-read-via-the-economist\/","title":{"rendered":"Sobre previs\u00e3o e pol\u00edtica, via The Economist"},"content":{"rendered":"
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Machine-learning systems excel at prediction. A common approach is to train a system by showing it a vast quantity of data on, say, students and their achievements. The software chews through the examples and learns which characteristics are most helpful in predicting whether a student will drop out. Once trained, it can study a different group and accurately pick those at risk. By helping to allocate scarce public funds more accurately, machine learning could save governments significant sums. According to Stephen Goldsmith, a professor at Harvard and a former mayor of Indianapolis, it could also transform almost every sector of public policy<\/strong><\/p>\n

For governments that embrace machine learning, the future will depend on how well they marry its predictive power with old-fashioned human wisdom.\u00a0To limit potential bias, Mr Ghani says, avoid prejudice in the training data and set machines the right goals. Machines are trained to find patterns that predict future criminality from past data. They can therefore be told to find patterns that both predict criminality and avoid disproportionate false categorisation of blacks (and others) as future offenders. When a new defendant is tested against these patterns, the risk of racial skewing should be lower.<\/span><\/p>\n

Bail decisions, in which judges estimate the risk of a prisoner fleeing or offending before trial, seem particularly ripe for help. Jens Ludwig of the University of Chic\"honourago and his colleagues claim that their algorithm, tested on a sample of past cases, would have yielded around 20% less crime (see chart), while leaving the number of releases unchanged. A similar reduction nationwide, they suggest, would require an extra 20,000 police officers at a cost of $2.6 billion. The White House is taking notice. Better bail decisions are a big priority of its Data-Driven Justice Initiative, which 67 states, cities and counties signed in June.<\/p><\/blockquote>\n

Read on:\u00a0Of prediction and policy<\/a><\/p>\n

This discussion is a perfect fit with many key themes addressed in\u00a0my new book ‘technology vs humanity'<\/a>.\u00a0\"tech-vs-human-book-300x227<\/p>\n

A related comment riffing off what I say in the book:<\/p>\n

Humanity will change more in the next 20 years than the previous 300 years:\u00a0<\/b>a\u00a0lot of people snicker at this statement because it sounds like grand-standing. I think it is actually an understatement given the reality of exponential and combinatorial technological change – the compound effect of these changes vastly surpasses the industrial revolution or the invention of the printing press. One key factor is that technology is no longer just outside of us<\/i> (such as the steam engine or the printing press which existed outside of human biology, of course) – it is actually moving inside of us (wearables, BCIs, nano-technology, human genome editing, AI etc) impacting the very definition of humanity<\/p>\n

Read more on Artificial Intelligence<\/a>\u00a0on this blog<\/p>\n<\/div>\n

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And some related images from my archives:<\/p>\n

\"responsible \"automation \"automation \"big \"computing<\/p>\n<\/div>\n

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\"Os sistemas de aprendizagem autom\u00e1tica s\u00e3o excelentes na previs\u00e3o. Uma abordagem comum \u00e9 treinar um sistema mostrando-lhe uma grande quantidade de dados sobre, por exemplo, estudantes e os seus resultados. O software analisa os exemplos e aprende quais as caracter\u00edsticas mais \u00fateis para prever se um aluno vai abandonar a escola. Uma vez treinado, pode estudar um grupo diferente e selecionar com precis\u00e3o os que est\u00e3o em risco. Ao ajudar a afetar com maior precis\u00e3o os escassos fundos p\u00fablicos, a aprendizagem autom\u00e1tica pode poupar aos governos somas significativas. De acordo com Stephen Goldsmith, professor em Harvard e antigo presidente da C\u00e2mara de Indian\u00e1polis, pode tamb\u00e9m transformar quase todos os sectores da pol\u00edtica p\u00fablica.\"<\/p>\n

De previs\u00e3o e pol\u00edtica
https:\/\/www.economist.com\/news\/finance-and-economics\/21705329-governments-have-much-gain-applying-algorithms-public-policy<\/a>
via Instapaper<\/div>\n

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