Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 2,414

Billionaire investor to accelerate research in artificial intelligence in healthcare

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.

Artificial intelligence and drones ‘future of policing’

Artificial intelligence and drones will be key policing tools in the future amid budget and job cuts, Gwent Police’s chief constable has said.

Jeff Farrar said he foresees every police vehicle carrying a drone in the years to come and for more computers to do jobs “that do not involve emotion”.

Gwent has had £50m of funding cuts and still needs to make £9m of savings. It has also lost 300 officers since 2011.

Deep Learning Technologies Breaking Through to the Enterprise

Science fiction has always provided an outlet where we can let our imaginations run wild about the possibilities of technological advances. Not long ago, science fiction concepts like self-aware robots, autonomous cars, or 3D printers may have been unimaginable, but we’re watching many of these things come to life before our eyes in the digital age.

Deep learning was previously a technology that seemed straight from the plot of the latest blockbuster, yet these days it’s no longer fiction and is proliferating across real-life applications. Deep learning, which falls under the umbrella of technologies known as artificial intelligence (AI), teaches machines to mimic the thought and decision-making processes of the human brain. Computers are trained using extremely large historical datasets to help them adapt and learn from prior experience, identify anomalous patterns in large datasets, and improve predictive analysis.

These techniques are becoming so popular that Gartner recently named AI and Advanced Machine Learning (which includes technologies such as deep learning) their #1 Strategic Technology Trend for 2017. The firm went on to predict that these technologies will begin to increasingly augment and extend virtually every technology-enabled service, thing, or application, and therefore will become the primary battleground for technology vendors through at least 2020.

Ben Goertzel’s answer to What do I have to study to work on artificial intelligence?

If what you want to do is to apply machine learning toolkits to datasets, then you don’t need to study much. Study the field to which you want to apply machine learning, then maybe the good old text “Data Mining: Practical Machine Learning Tools and Techniques” … then when you want to apply some particular machine learning technique, look up a paper or two on that technique and read it…

Audio engineering is making call center robots more ‘human’ and less annoying

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.

Self-charging robots sorting system helps Chinese delivery company finish at least 200,000 packages a day in the warehouse

Chinese delivery firm is moving to embrace automation. Orange robots at the company’s sorting stations are able to identify the destination of a package through a code-scan, virtually eliminating sorting mistakes.

The army of robots can sort up to 200,000 packages a day, and are self-charging, meaning they are operational 24/7. The company estimates its robotic sorting system is saving around 70-percent of the costs a human-based sorting line would require.

Innovation in the Bay Area: Q&A with Nidhi Kalra

For people in that area, and it may be worth while to try reaching out to them for funding for anti aging stuff.

Why is RAND opening a Bay Area office?

The San Francisco Bay Area is really at the center of technology and transformation. That’s also been a focus at RAND since our very first report, Preliminary Design of an Experimental World-Circling Spaceship, in 1946, which foretold the creation of satellites more than a decade before Sputnik.

Today, our researchers are working on important questions related to autonomous vehicles, drones, cybersecurity, education technology, virtual medicine—the same questions driving Silicon Valley startups and billion-dollar Bay Area corporations. At the same time, we’re looking at issues surrounding social inequality, drug policy, water resource management, and transportation, all of which directly relate to the Bay Area.