Lifeboat Foundation

“Dark fantasies, however, distract attention from more urgent questions. How will AI affect employment, especially higher-paying work? When will robot writers and artists alter the way humans consume creative content? Who will be held accountable for accidents when humans are no longer in the decision or action loop? Instead of ‘wolf’ criers of the Musk sort, humans need a serious discussion about new norms and practices that will shape and govern AI.” Read more

We are quickly approaching the point at which medical (and industrial) nanotech lives up to the hype!

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“A revolution in artificial intelligence is currently sweeping through computer science. The technique is called deep learning and it’s affecting everything from facial and voice to fashion and economics. But one area that has not yet benefitted is natural language processing—the ability to read a document and then answer questions about it…Today, that changes thanks to the work of Karl Moritz Hermann at Google DeepMind in London and a few pals. These guys say the special way that the Daily Mail and CNN write online news articles allows them to be used in this way.” Read more

“American sci-fi novelist Philip K. Dick once famously asked, Do Androids Dream of Electric Sheep? While he was on the right track, the answer appears to be, no, they don’t. They dream of dog-headed knights atop horses, of camel-birds and pig-snails, and of Dali-esque mutated landscapes.”

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Mourning Aibo.

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Do androids dream of electric sheep? The day dreams of A.I. Neural networks.

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Odds are you’re a decent, upstanding person. You abide by the laws, pay taxes, and don’t rock the boat. You value security but not at the expense of privacy. Like the majority of people around you, you’re driven by self interest but not to the detriment of others. And if someone is clearly in need, you’ll try to help them, if you feel you can make a difference.

In other words, you’re a moral human being…or so you think. Read more

Automative robotics is advancing quickly.

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They took each word and looked for other words that often appear nearby in a large corpus of text. They then use an algorithm to see how these words are clustered. The final step is to look up the different meanings of a word in a dictionary and then to match the clusters to each meaning.

This can be done automatically because the dictionary definition includes sample sentences in which the word is used in each different way. So by calculating the vector representation of these sentences and comparing them to the vector representation in each cluster, it is possible to match them. Read more