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MIT Professor Leverages Machine Learning to Find Promising Cancer Treatments
When his father was diagnosed with stage IV, non-operable gastric cancer in 2007, Dr. Dimitris Bertsimas knew that combination chemotherapy was the best course of treatment. He visited several of the leading cancer hospitals in the nation--Dana Farber, Massachusetts General, MD Anderson and Memorial Sloan Kettering--to see what specific therapies they would propose for his father. "They each told me very distinct therapies, almost with no drugs in common," says Bertsimas. "I didn't know how to compare them." So Bertsimas, who is a professor of operational research at MIT did a simple back-of-the-envelope calculation.
Amazon Adds New Alexa Features for Fire TV
SEATTLE--(BUSINESS WIRE)--(NASDAQ:AMZN)--Amazon today announced new Alexa voice features are coming to Fire TV, including the ability to control playback of Amazon Video and Add-On Subscription content, launch apps, access local movie show times, search local businesses and restaurants, and more--all just by using your voice. Discovering and accessing content on Fire TV has never been easier with the new Alexa playback features, plus integrated voice search across 59 content partners including Hulu, Showtime, Starz, HBO Go and more. These new Alexa features will be automatically delivered via free, over-the-air software updates in the coming weeks. "Customers have told us they love having the convenience of Alexa on their Amazon Fire TV," said Tim Twerdahl, General Manager, Amazon Fire TV. "We're excited to add new Alexa features to Fire TV, making it easier than ever to enjoy their favorite TV shows, movies, and apps."
How physicists programmed AI to do their job โ by accident
A group of researchers recently developed an AI program to assist them in a complex procedure for an experiment involving finely optimized conditions. But rather than simply assist, the AI showed enough proficiency to run the experiment on its own and faster than humans or previous programs designed for the experiment. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," co-lead researcher Paul Wigley, a doctoral student at the Australian National University Research School of Physics and Engineering, said in a statement. The physicists from the ANU, University of Adelaide, and the University of New South Wales Australian Defence Force Academy, were attempting to recreate an experiment that won the 2001 Nobel Prize โ creating a Bose-Einstein condensate, a super chilled gas trapped in between laser beams. Bose-Einstein condensates are able to reach temperatures so low that they are some of the coldest areas of the universe, in some cases less than a billionth of a degree above absolute zero, the temperature where all atoms stop moving.
Amazon gives Alexa more control of your Fire TV
Amazon's virtual assistant was already hard at work helping with tasks via its Echo speakers and Fire TV, but now Alexa is getting more control of your television. The online retailer announced today that Alexa can handle more requests on its streaming gadgets, including launching apps, playing selections from Amazon video and add-on subscriptions (HBO Go, Starz, Showtime, SeeSo) and browsing local movie times. Fire TV already offered voice search and Alexa has been available on those devices as well, but this update expands the virtual assistant's workload.
Final EEOC rule sets limits for financial incentives on wellness programs
Employer wellness programs can gather medical information from employees and spouses -- so long as financial incentives or penalties don't exceed 30 percent of the annual cost for an individual in the company's group health plan, according to final rules issued by the Equal Employment Opportunity Commission Monday. Although such penalties or incentives could run into the hundreds or even thousands of dollars, the programs are considered voluntary -- and therefore legal, the commission said. The rules seek to ensure "wellness programs actually promote good health and are not just used to collect or sell sensitive medical information about employees and family members or to impermissibly shift health insurance costs to them," the EEOC said. But the final rules drew immediate concern from some groups. Jennifer Mathis, director of programs for the Bazelon Center for Mental Health Law, says the new rule rolls back protections in existing law.
The Sigmoid Function in Logistic Regression
In learning about logistic regression, I was at first confused as to why a sigmoid function was used to map from the inputs to the predicted output. I mean, sure, it's a nice function that cleanly maps from any real number to a range of -1 to 1, but where did it come from? This notebook hopes to explain. With classification, we have a sample with some attributes (a.k.a features), and based on those attributes, we want to know whether it belongs to a binary class or not. The regression algorithm could fit these weights to the data it sees, however, it would seem hard to map an arbitrary linear combination of inputs, each would may range from -\infty to \infty to a probability value in the range of 0 to 1 .
Tesla Pushes Nvidia Deeper Into The Datacenter
If you are trying to figure out what impact the new "Pascal" family of GPUs is going to have on the business at Nvidia, just take a gander at the recent financial results for the datacenter division of the company. If Nvidia had not spent the better part of a decade building its Tesla compute business, it would be a little smaller and quite a bit less profitable. In the company's first quarter of fiscal 2017, which ended on May 1, Nvidia posted sales of 1.31 billion, up 13 percent from the year ago period, and net income hit 196 million, up 46 percent over the same term. These are the kinds of growth numbers that all IT vendors like to show to Wall Street, especially with profit growth significantly outpacing revenue growth. The datacenter portion of Nvidia, which it only started reporting on separately last year and for which it has given two years of financial results since it has become materially relevant, is growing much faster than the overall business.
Artificial Intelligence Replicates Nobel-Prize Winning Physics Experiment In Less Than An Hour
The world's first Artificially Intelligent physicist is here, and it has already replicated a Nobel Prize-winning experiment -- one that involved creating an ultracold state of matter called Bose-Einstein condensate. Bose-Einstein condensates -- named after physicists Satyendra Nath Bose and Albert Einstein -- are a state of matter created when atoms are cooled to a temperature close to absolute zero (0 Kelvin or -459.6 degrees Fahrenheit). At such an ultralow temperature, all atoms gather in the lowest possible energy state, creating a "giant matter wave." Although Bose and Einstein predicted the existence of such a state of matter in 1924, scientists were only able to create this extreme state of matter in 1995 through an experiment that won them the Nobel Prize in 2001. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," Paul Wigley from the Australian National University, who used the AI algorithm to re-create the experiment, said in a statement released Monday.
IBM's ROSS becomes world's first artificially intelligent attorney
IBM's technology has won Jeopardy, managed companies and is now practicing law. ROSS, 'the world's first artificially intelligent attorney' powered by Watson, recently landed a position at New York law firm Baker & Hostetler handling the firm's bankruptcy practice. The machine is designed to understand language, provide answers to questions, formulate hypotheses and monitor developments in the legal system. IBM's technology has won Jeopardy, managed companies and is now practicing law. ROSS, 'the world's first artificially intelligent attorney' powered by Watson, has just landed a position at New York law firm Baker & Hostetler handling the firm's bankruptcy practice Lawyers ask ROSS research questions in natural language, just like they were talking to a colleague, and the AI'reads' through the law, gathers evidence, draws inferences and returns with a'highly relevant', evidence-based answer.
How the machine 'thinks': Understanding opacity in machine learning algorithms
This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1) opacity as intentional corporate or state secrecy, (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented), and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These are just some examples of mechanisms of classification that the personal and trace data we generate is subject to every day in network-connected, advanced capitalist societies. These mechanisms of classification all frequently rely on computational algorithms, and lately on machine learning algorithms to do this work. Opacity seems to be at the very heart of new concerns about'algorithms' among legal scholars and social scientists. The algorithms in question operate on data. Using this data as input, they produce an output; specifically, a classification (i.e. They are opaque in the sense that if one is a recipient of the output of the algorithm (the classification decision), rarely does one have any concrete sense of how or why a particular classification has been arrived at from inputs.