Daily Archives: February 20, 2017

When Binny Bansal, co-founder of Indias largest retailer Flipkart was studying at IIT-Delhi, nobody at his institute was interested in the subject of artificial intelligence. Because nothing was happening in India, Mr. Bansal told his co-panelists Satya Nadella, chief executive of Microsoft and Infosys co-founder Nandan Nilekani during a fireside chat at an event in Bengaluru.

That was 15 years ago and a lot has changed since. On Monday, Mr. Nadella, who leads the worlds largest software maker, announced a strategic partnership with Flipkart, where the e-commerce company would adopt the company's cloud computing platform, Microsoft Azure.

Flipkart said that it planned to leverage artificial intelligence (AI), machine learning and analytics capabilities in Azure to optimise its data for innovative merchandising, advertising, marketing and customer service.

AI is the simulation of human intelligence processes by computer systems and machine learning and gives computers the ability to learn without being explicitly programmed.

We are on the right ladder this time, I don't think we are going back to AI winter, said Mr. Nadella at the event where the audience consisted of hundreds of start-up founders, investors and industry experts. He said that the real challenge in AI is understanding of the human language, which still doesn't exist. (We) don't have anything that says we have the ability to write like Rabindranath Tagore, said Mr. Nadella.

Last March, Mr. Nadella faced a public communication fiasco when Microsofts teen-girl-inspired chatbot named Tay which had been programmed to interact with Twitter users mimicked racist and misogynistic lines, which other Twitter users had prompted her to repeat.

Tech entrepreneur Nandan Nilekani said that he was excited about the advent of technologies like AI and the cloud. He said that there was a challenge of taking the country from $2,000 per capita income to $20,000 and there was also a need to fix sectors like healthcare, education and financial services.

He said the classical way of getting more doctors or building education infrastructure would take time. The only way to square the circle is by using AI and cloud to deliver personalised health, education and financial services to a billion people, said Mr. Nilekani. Through his social enterprise EkStep, he said that he aimed to fix the learning challenges of Indias 200 million children using AI and smartphones.

Microsoft also runs an accelerator in Bengaluru which counts AI startups such as Uncanny Vision, Flutura and Altizon among its portfolio companies.

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Nadella woos India Inc. with artificial intelligence - The Hindu

Forbes

Alan Turing Predicts Machine Learning And The Impact Of Artificial Intelligence On Jobs Forbes This week's milestones in the history of technology include Alan Turing anticipating today's deep learning by intelligent machines and concerns about the impact of AI on jobs, Clifford Stoll anticipating Mark Zuckerberg, and establishing the FCC and NPR.

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Alan Turing Predicts Machine Learning And The Impact Of Artificial Intelligence On Jobs - Forbes

Computer programs odor predictions are on the nose.

Image Source/Alamy Stock Photo

By Robert F. ServiceFeb. 19, 2017 , 8:15 PM

Predicting color is easy: Shine a light with a wavelength of 510 nanometers, and most people will say it looks green. Yet figuring out exactly how a particular molecule will smell is much tougher. Now, 22 teams of computer scientists have unveiled a set of algorithms able to predict the odor of different molecules based on their chemical structure. It remains to be seen how broadly useful such programs will be, but one hope is that such algorithms may help fragrancemakers and food producers design new odorants with precisely tailored scents.

This latest smell prediction effort began with a recent study by olfactory researcher Leslie Vosshall and colleagues at The Rockefeller University in New York City, in which 49 volunteers rated the smell of 476 vials of pure odorants. For each one, the volunteers labeled the smell with one of 19 descriptors, including fish, garlic, sweet, or burnt. They also rated each odors pleasantness and intensity, creating a massive database of more than 1 million data points for all the odorant molecules in their study.

When computational biologist Pablo Meyer learned of the Rockefeller study 2 years ago, he saw an opportunity to test whethercomputer scientists could use it to predict how people would assess smells. Besides working at IBMs Thomas J. Watson Research Center in Yorktown Heights, New York, Meyer heads something called the DREAM challenges, contests that ask teams of computer scientists to solve outstanding biomedical problems, such as predicting the outcome of prostate cancer treatment based on clinical variables or detecting breast cancer from mammogram data. I knew from graduate school that olfaction was still one of the big unknowns, Meyer says. Even though researchers have discovered some 400 separate odor receptors in humans, he adds, just how they work together to distinguish different smells remains largely a mystery.

In 2015, Meyer and his colleagues set up theDREAM Olfaction Prediction Challenge. They divided the Rockefeller groups data set into three parts. Participants were given the volunteer ratings for two-thirds of the odors, along with the chemical structure of the molecules that produced them. They were also given more than 4800 descriptors for each molecule, such as the atoms included, their arrangement, and geometry, which constituted a separate set of more than 2 million data points. These data were then used to train their computer models in predicting smells from chemical structural information. The remaining groups of datatwo sets of 69 ratings and their corresponding chemical informationwere used to test how well the models predicted both how an average person would rate an odor and how each of the 49 individuals would rate them.

Twenty-two teams from around the globe took up the challenge. Many did well, but two stood out. A team led by Yuanfang Guan, a computer scientist at the University of Michigan in Ann Arbor, scored best at predicting how individual subjects rate smells. Another team led by Richard Gerkin at Arizona State University in Tempe best predicted how all the participants on average would rate smells, Meyer and his colleagues report today in Science.

We learned that we can very specifically assign structural features to descriptions of the odor, Meyer says. For example, molecules with sulfur groups tend to produce a garlicky smell, and molecules with a similar chemical structure to vanillin, from vanilla beans, predicts whether subjects will perceive a bakery smell.

Meyer suggests such models may help fragrance and flavor companies come up with new molecules tuned to trigger particular smells, such as sandalwood or citrus. But Avery Gilbert, a biological psychologist at Synesthetics in Fort Collins, Colorado, and a longtime veteran of the fragrance and flavor industry, says hes not so sure. Gilbert says the new work is useful in that it provides such a large data set. But the 19 different verbal descriptors of different scents, he says, is too limited. Thats really a slim number of attributes, he says. Alternative studies have had volunteers use 80 or more categories to rate different smells.

The upshot is that even though the current study showed computers can predict which of 19 words people will use to describe this set of odors, its not clear whetherthe same artificial intelligence programs would rise to the challenge if there were more categories. If you had different descriptors, you might have had different models predict them best. So Im not sure where that leaves us, Gilbert says. Perhaps it serves mostly as a reminder that odor perception remains a challenge both for human scientists and artificial intelligence.

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Artificial intelligence grows a nose | Science | AAAS - Science Magazine

Graphcore produced a series of striking images of computational graphs mapped to its "Intelligent Processing Unit."

The graph compiler builds up an intermediate representation of the computational graph to be scheduled and deployed across one or many IPU devices. The compiler can display this computational graph, so an application written at the level of a machine learning framework reveals an image of the computational graph which runs on the IPU.

The image below shows the graph for the full forward and backward training loop of AlexNet, generated from a TensorFlow description.

Our Poplar graph compiler has converted a description of the network into a computational graph of 18.7 million vertices and 115.8 million edges. This graph represents AlexNet as a highly-parallel execution plan for the IPU. The vertices of the graph represent computation processes and the edges represent communication between processes. The layers in the graph are labelled with the corresponding layers from the high level description of the network. The clearly visible clustering is the result of intensive communication between processes in each layer of the network, with lighter communication between layers.

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Gaetan Hadjeres and Francois Pachet at the Sony Computer Science Laboratories in Paris created DeepBach, then entered Bachs 352 chorales. The resulting composition is certainly in the style. So why does this work better than some other attempts?

My friend and Cool Tools business partner Kevin Kelly spoke at TEDSummit about the rapid rise of artificial intelligence. The talk is based on his excellent bestselling book, The Inevitable. The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable, says digital visionary Kevin Kelly []

Python is immensely popular in the data science world for the same reason it is in most other areas of computingit has highly readable syntax and is suitable for anything from short scripts to massive web services. One of its most exciting, newest applications, however, is in machine learning. You can dive into this booming []

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Instead of throwing out all the empties after your next party, why not transform them into some new DIY glassware? Cut back on waste and add some home ambiance with the Kinkajou Bottle Cutter and Candle Making Kit.The Kinkajou is designed as a clamp-on scoring blade to make precise cuts. Just slide abottle in, tighten []

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"brain scans" of artificial intelligence processes - Boing Boing