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Category: information science – Page 292

Billionaire investor Jim Mellon has joined the push to solve age-related diseases and bring rejuvenation biotechnology to the world.

Billionaire biotechnology investor Jim Mellon has unveiled an investment in an ambitious new venture which seeks to tackle ageing and age-related diseases.

Insilico Medicine is a big data analytics company which says its mission is to ‘extend healthy longevity’.

This is a ‘moonshot’ target in health which has seen investment from a number of ambitious research groups in the last few years.

Continue reading “Billionaire Jim Mellon invests in anti-ageing research firm” | >

By Leah Crane

Break out the censor’s black bars for naked singularities. Quantum effects could be obscuring these impossible predictions of general relativity, new calculations show.

Albert Einstein’s classical equations of general relativity do a fairly good job of describing gravity and space-time. But when it comes to the most extreme objects, such as black holes, general relativity runs into problems.

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Interesting link within concerning an injectable interface.

To be able to design a device that measures brain activity an understanding of the brains function is required. This section gives a high-level overview of some of the key elements of brain function. Human brains contain approximately 80 billion neurons, these neurons are interconnected with 7,000 synaptic connections each (on average). The combination of neurons firing and their communication is, in very simple terms the basis of all thoughts conscious and subconscious. Logically if the activity of these neurons and their connections were read in real-time, a sufficiently intelligent algorithm could understand all thoughts present. Similarly, if an input could be given at this level of granularity new thoughts could be implanted.

All human brains abide by the general structure shown in the picture below, certain areas, by and large do certain things. If higher levels of thoughts like creativity, idea generation and concentration want to be read, the frontal lobe is the place to look. If emotions and short-term memory are the target, the temporal lobe is the place to read from.

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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.

Continue reading “Billionaire investor to accelerate research in artificial intelligence in healthcare” | >

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.

Continue reading “Chinese biotech scientists plan to use big data in war on cancer” | >

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.

Continue reading “OpenAI Just Beat Google DeepMind at Atari With an Algorithm From the 80s” | >

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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