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An anonymous reader writes "Nataly Kelly writes in the Huffington Post about Google's strategy of hiring Ray Kurzweil and how the company likely intends to use language translation to revolutionize the way we share information. From the article: 'Google Translate is not just a tool that enables people on the web to translate information. It's a strategic tool for Google itself. The implications of this are vast and go beyond mere language translation. One implication might be a technology that can translate from one generation to another. Or how about one that slows down your speech or turns up the volume for an elderly person with hearing loss? That enables a stroke victim to use the clarity of speech he had previously? That can pronounce using your favorite accent? That can convert academic jargon to local slang? It's transformative. In this system, information can walk into one checkpoint as the raucous chant of a 22-year-old American football player and walk out as the quiet whisper of a 78-year-old Albanian grandmother.'"

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George E.P. Box, a statistician known for his body of work in time series analysis and Bayesian inference (and his quotes), recounts how he became a statistician while trying to solve actual problems. He was a 19-year-old college student studying chemistry. Instead of finishing, he joined the army, fed up with what the British government was doing to stop Hitler.

Before I could actually do any of that I was moved to a highly secret experimental station in the south of England. At the time they were bombing London every night and our job was to help to find out what to do if, one night, they used poisonous gas.

Some of England's best scientists were there. There were a lot of experiments with small animals, I was a lab assistant making biochemical determinations, my boss was a professor of physiology dressed up as a colonel, and I was dressed up as a staff sergeant.

The results I was getting were very variable and I told my colonel that what we really needed was a statistician.

He said "we can't get one, what do you know about it?" I said "Nothing, I once tried to read a book about it by someone called R. A. Fisher but I didn't understand it". He said "You've read the book so you better do it", so I said, "Yes sir".

Box eventually worked with Fischer, studied under E. S. Pearson in college after his discharge from the army, and started the Statistical Techniques Research Group at Princeton on the insistence of one John Tukey.

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The theory and practicum of type inference has been around for literally decades, but it remains a tricky and needlessly dry topic, even in academic circles. This talk will delve into the glorious details and subtle implications of type inference in industrial languages like C# and Scala, as well as highly mathematical languages like Haskell. We will uncover the sordid reasons beyond some of the many unnerving quirks of modern type inference schemes, as well as the the amazing power they proffer.

Love of math is not a prerequisite, though utter dread of such may result in minor hallucinations during the talk. Deep-seated hatred of static typing is welcomed! The primary focus of this talk will be on Scala, Haskell and SML, but prior knowledge of these languages is neither expected nor required.

Direct download: Uncovering_the_Unknown_-_Principles_of_Type_Inference.mp4
Category:ETE 2011
-- posted at: 11:36 PM

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AGI 2011 - Probabilistic Programs: A New Language for AI

The Fourth Conference on Artificial General Intelligence Mountain View, California, USA August 3-6, 2011 Probabilistic Programs: A New Language for AI Presented by Noah Goodman, Stanford University ABSTRACT How can logical and probabilistic approaches to understanding intelligence be reconciled? I will argue that probabilistic programming is the best way to merge logic and probability, providing a new set of tools for thinking about representation and inference in systems with human-like intelligence. I will illustrate these ideas by introducing the probabilistic programming language Church (a stochastic LISP), describing two universal inference algorithms (ie algorithms that can perform probabilistic inference for any Church program), and giving a series of examples. These examples, drawn from cognitive science and AI, will include multi-agent reasoning and concept learning. About Noah Goodman: stanford.edu
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Stories have been around as long as we have, helping us understand our world and ourselves. We learn and retain information best through stories, because they turn information into more than the sum of its parts. But what makes a story a story, and what does it mean for the digital world we’ve built? Elizabeth McGuane and Randall Snare weave an enchanting tale of attention, comprehension, inference, coherence, and shopping.

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