Why predicting the future is more than just horseplay

Christian Science Monitor

April 24, 2017 --Three years out of a PhD in physics in 1953, John Kelly Jr. published a breakthrough paper about insider information in horse racing in an unlikely place: the Bell Labs Technical Journal. Dr. Kelly had not just cracked the mathematics underlying a type of gambling, but he had also revealed deeper patterns about the nature of prediction. The formula is powerful in its simplicity. It tells us to put money on every horse for which we have an informational or statistical edge, and then calculates exactly what fraction of our bankroll to bet on each horse, depending on the strength of that edge. While this basic idea had long been known – the larger the difference in the track odds and the real odds, the bigger the opportunity for the gambler – Kelly quietly revolutionized the practice of prediction by writing down the optimal exchange rate between knowing something that others do not and the benefits of that knowledge.


Why bees could be the secret to superhuman intelligence

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Louis Rosenberg thinks he has found a way to make us all a lot smarter. Rosenberg runs a Silicon Valley startup called Unanimous AI, which has built a tool to support human decision-making by crowdsourcing opinions online. It lets hundreds of participants respond to a question all at once, pooling their collective insight, biases and varying expertise into a single answer. Since launching in June, Unanimous AI has registered around 50,000 users and answered 230,000 questions. Rosenberg thinks this hybrid human-computer decision-making machine – once dubbed an'artificial' artificial intelligence – could help us tackle some of the world's toughest questions.


An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples

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No discussion of ML would be complete without at least mentioning neural networks. Not only do neural nets offer an extremely powerful tool to solve very tough problems, but they also offer fascinating hints at the workings of our own brains, and intriguing possibilities for one day creating truly intelligent machines. Neural networks are well suited to machine learning problems where the number of inputs is gigantic. The computational cost of handling such a problem is just too overwhelming for the types of systems we've discussed above. As it turns out, however, neural networks can be effectively tuned using techniques that are strikingly similar to gradient descent in principle. A thorough discussion of neural networks is beyond the scope of this tutorial, but I recommend checking out our previous post on the subject.


A Guide to Solving Social Problems with Machine Learning

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You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like?


A Guide to Solving Social Problems with Machine Learning

#artificialintelligence

You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like?