New data analysis competitions
- Kaggle's Porto Seguro’s Safe Driver Prediction. Up to $25,000 in prizes.
Deepmind, Google’s London-based AI research sibling, has opened a new unit focused on the ethical and societal questions raised by artificial intelligence.
The new research unit will aim “to help technologists put ethics into practice, and to help society anticipate and direct the impact of AI so that it works for the benefit of all”, according to the company, which hit headlines in 2016 for building the first machine to beat a world champion at the ancient Asian board game Go.
The company is bringing in external advisers from academia and the charitable sector, including Columbia development professor Jeffrey Sachs, Oxford AI professor , and climate change campaigner Christiana Figueres to advise the unit.
Nowadays, government is armed with algorithms that can forecast domestic violence and employee effectiveness, allowing it to perform its duties more effectively and to achieve correct results more often. But these algorithms can encode hidden biases that disproportionately and adversely impact minorities. What, then, should government consider when implementing predictive algorithms? Where should it draw the line between effectiveness and equality?
Panelists speaking at the University of Pennsylvania Law School grappled with these questions during the second of four workshops that are part of a larger Optimizing Government Project that seeks to inform the use of machine learning in government. The panel, moderated by Penn Law professor Cary Coglianese, sought to conceptualize fairness and equality and to distill their philosophical and legal implications for machine learning.
[About not knowing how deep neural nets really work] Last month, a YouTube video of a conference talk in Berlin, shared widely among artificial-intelligence researchers, offered a possible answer. In the talk, Naftali Tishby, a computer scientist and neuroscientist from the Hebrew University of Jerusalem, presented evidence in support of a new theory explaining how deep learning works. Tishby argues that deep neural networks learn according to a procedure called the “information bottleneck,” which he and two collaborators . The idea is that a network rids noisy input data of extraneous details as if by squeezing the information through a bottleneck, retaining only the features most relevant to general concepts. Striking new computer experiments by Tishby and his student Ravid Shwartz-Ziv reveal how this squeezing procedure happens during deep learning, at least in the cases they studied.
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