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Category: information science – Page 300

Interest in rejuvenation biotechnology is growing rapidly and attracting investors.

- Jim Mellon has made an investment in Insilico Medicine to enable the company to validate the many molecules discovered using deep learning and launch multi-modal biomarkers of human aging

Monday, April 10, 2017, Baltimore, MD — Insilico Medicine, Inc, a big data analytics company applying deep learning techniques to drug discovery, biomarker development, and aging research today announced that it has closed an investment from the billionaire biotechnology investor Jim Mellon. Proceeds will be used to perform pre-clinical validation of multiple lead molecules developed using Insilico Medicine’s drug discovery pipelines and to advance research in deep learned biomarkers of aging and disease.

“Unlike many wealthy business people who rely entirely on their advisors to support their investment in biotechnology, Jim Mellon has spent a substantial amount of time familiarizing himself with recent developments in biogerontology. He does not just come in with the funding, but brings in expert knowledge and a network of biotechnology and pharmaceutical executives, who work very quickly and focus on the commercialization potential. We are thrilled to have Mr. Mellon as one of our investors and business partners”, said Alex Zhavoronkov, PhD, founder, and CEO of Insilico Medicine, Inc.

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Audio engineering can make computerized customer support lines seem friendlier and more helpful.

Say you’re on the phone with a company and the automated virtual assistant needs a few seconds to “look up” your information. And then you hear it. The sound is unmistakable. It’s familiar. It’s the clickity-clack of a keyboard. You know it’s just a sound effect, but unlike hold music or a stream of company information, it’s not annoying. In fact, it’s kind of comforting.

Michael Norton and Ryan Buell of the Harvard Business School studied this idea —that customers appreciate knowing that work is being done on their behalf, even when the only “person” “working” is an algorithm. They call it the labor illusion.

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China has made the precision medicine field a focus of its 13th five-year plan, and its companies have been embarking on ambitious efforts to collect a vast trove of genetic and health data, researching how to identify cancer markers in blood, and launching consumer technologies that aim to tap potentially life-saving information. The push offers insight into China’s growing ambitions in science and biotechnology, areas where it has traditionally lagged developed nations like the United States.

Precision medicine a focus of latest five-year plan.

PUBLISHED : Thursday, 09 February, 2017, 1:42pm.

UPDATED : Thursday, 09 February, 2017, 1:42pm.

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Microsoft is bringing its Cognitive Toolkit version 2.0 out of beta today and should be helping out a ton of companies who depend on tools to deploy deep learning at scale.

The Cognitive Toolkit or CNTK to some is a deep learning tool that helps companies speed up the process of image and speech recognition. Thanks to today’s update, CNTK can now be used by companies either on-premises or in the cloud combined with Azure GPUs.

Cognitive Toolkit is being used extensively by a wide variety of Microsoft products, by companies worldwide with a need to deploy deep learning at scale, and by students interested in the very latest algorithms and techniques. The latest version of the toolkit is available on GitHub via an open source license. Since releasing the beta in October 2016, more than 10 beta releases have been deployed with hundreds of new features, performance improvements and fixes.

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OpenAI vs. Deepmind in river raid ATARI.

AI research has a long history of repurposing old ideas that have gone out of style. Now researchers at Elon Musk’s open source AI project have revisited “neuroevolution,” a field that has been around since the 1980s, and achieved state-of-the-art results.

The group, led by OpenAI’s research director Ilya Sutskever, has been exploring the use of a subset of algorithms from this field, called “evolution strategies,” which are aimed at solving optimization problems.

Despite the name, the approach is only loosely linked to biological evolution, the researchers say in a blog post announcing their results. On an abstract level, it relies on allowing successful individuals to pass on their characteristics to future generations. The researchers have taken these algorithms and reworked them to work better with deep neural networks and run on large-scale distributed computing systems.

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OpenAI researchers were surprised to discover that a neural network trained to predict the next character in texts from Amazon reviews taught itself to analyze sentiment. This unsupervised learning is the dream of machine learning researchers.

Much of today’s artificial intelligence (AI) relies on machine learning: where machines respond or react autonomously after learning information from a particular data set. Machine learning algorithms, in a sense, predict outcomes using previously established values. Researchers from OpenAI discovered that a machine learning system they created to predict the next character in the text of reviews from Amazon developed into an unsupervised system that could learn representations of sentiment.

“We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment,” OpenAI, a non-profit AI research company whose investors include Elon Musk, Peter Thiel, and Sam Altman, explained on their blog. OpenAI’s neural network was able to train itself to analyze sentiment by classifying reviews as either positive or negative, and was able to generate text with a desired sentiment.

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The fast-advancing fields of neuroscience and computer science are on a collision course. David Cox, Assistant Professor of Molecular and Cellular Biology and Computer Science at Harvard, explains how his lab is working with others to reverse engineer how brains learn, starting with rats. By shedding light on what our machine learning algorithms are currently missing, this work promises to improve the capabilities of robots – with implications for jobs, laws and ethics.

http://www.weforum.org/

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Arranging employees in an office is like creating a 13-dimensional matrix that triangulates human wants, corporate needs, and the cold hard laws of physics: Joe needs to be near Jane but Jane needs natural light, and Jim is sensitive to smells and can’t be near the kitchen but also needs to work with the product ideation and customer happiness team—oh, and Jane hates fans. Enter Autodesk’s Project Discover. Not only does the software apply the principles of generative design to a workspace, using algorithms to determine all possible paths to your #officegoals, but it was also the architect (so to speak) behind the firm’s newly opened space in Toronto.

That project, overseen by design firm The Living, first surveyed the 300 employees who would be moving in. What departments would you like to sit near? Are you a head-down worker or an interactive one? Project Discover generated 10,000 designs, exploring different combinations of high- and low-traffic areas, communal and private zones, and natural-light levels. Then it matched as many of the 300 workers as possible with their specific preferences, all while taking into account the constraints of the space itself. “Typically this kind of fine-resolution evaluation doesn’t make it into the design of an office space,” says Living founder David Benjamin. OK, humans—you got what you wanted. Now don’t screw it up.

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A team of researchers has developed artificial synapses that are capable of learning autonomously and can improve how fast artificial neural networks learn.

Developments and advances in artificial intelligence (AI) have been due in large part to technologies that mimic how the human brain works. In the world of information technology, such AI systems are called neural networks. These contain algorithms that can be trained, among other things, to imitate how the brain recognizes speech and images. However, running an Artificial Neural Network consumes a lot of time and energy.

Now, researchers from the National Center for Scientific Research (CNRS) in Thales, the University of Bordeaux in Paris-Sud, and Evry have developed an artificial synapse called a memristor directly on a chip. It paves the way for intelligent systems that required less time and energy to learn, and it can learn autonomously.

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SAN FRANCISCO, April 4, 2017 /PRNewswire/ — Enlitic, a medical deep learning company, is pleased to announce that it has executed a Memorandum of Understanding (“MOU”) with Beijing Hao Yun Dao Information & Technology Co., Ltd (“Paiyipai”) to provide Enlitic’s deep learning solution to Paiyipai for diagnostic imaging in Health Check centers across China.

Paiyipai is a medical big data company. The company is a market leader in China in the analysis of individual laboratory medical test results, and the storage and distribution of user medical records.

The MOU forms the basis of collaboration for the first large-scale commercial deployment of Enlitic’s deep learning technology in China. It was executed following a successful 10,000 chest x-ray trial of Enlitic’s patient triage platform.

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