Interesting; “Human memory is not the same as computer memory,” said James Kozloski.
An inventor at IBM has patented technology for a cognitive assistant that could learn all about you, then remind you of a name you can’t remember the moment you need to say it.
Artificial intelligence researchers at Google DeepMind are celebrating after reaching a major breakthrough that’s been pursued for more than 20 years: The team taught a computer program the ancient game of Go, which has long been considered the most challenging game for an an artificial intelligence to learn. Not only can the team’s program play Go, it’s actually very good at it.
The computer program AlphaGo was developed by Google DeepMind specifically with the task of beating professional human players in the ancient game. The group challenged the three-time European Go Champion Fan Hui to a series of matches, and for the first time ever, the software was able to beat a professional player in all five of the games played on a full-sized board. The team announced the breakthrough in a Nature article published today.
Google has achieved one of the long-standing “grand challenges” of AI, building a computer capable of beating expert players of the board game Go.
In a paper published in Nature on 28th January 2016, we describe a new approach to computer Go. This is the first time ever that a computer program “AlphaGo” has defeated a human professional player.
The game of Go is widely viewed as an unsolved “grand challenge” for artificial intelligence. Games are a great testing ground for inventing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. The first classic game mastered by a computer was noughts and crosses (also known as tic-tac-toe) in 1952. But until now, one game has thwarted A.I. researchers: the ancient game of Go.
Despite decades of work, the strongest computer Go programs only played at the level of human amateurs. AlphaGo has won over 99% of games against the strongest other computer Go programs. It also defeated the human European champion by 5–0 in tournament games, a feat previously believed to be at least a decade away. In March 2016, AlphaGo will face its ultimate challenge: a 5-game challenge match in Seoul against the legendary Lee Sedol—the top Go player in the world over the past decade.
This video tells the story so far…
Another Quantum Breakthrough through ultra- low temp nanoelectronics- Sub-millikelvin nanoelectronic circuits and is another step on the way to develop new quantum technologies including quantum computers and sensors.
The first ever measurement of the temperature of electrons in a nanoelectronic device a few thousandths of a degree above absolute zero was demonstrated in a joint research project performed by Lancaster University, VTT Technical Research Centre of Finland Ltd, and Aivon Ltd.
The team managed to make the electrons in a circuit on a silicon chip colder than had previously been achieved.
Dr Rich Haley, Head of Ultra Low Temperature Physics at Lancaster, said: “This is a notable achievement in that the team has finally broken through the 4 millikelvin barrier, which has been the record in such structures for over 15 years.”
And, another breakthrough for Quantum by Russian Scientists. Russian scientists have developed a new way to solve a key problem with cooling plasmonic components, which makes optical chips and super-fast light-based computers a definite possibility. https://lnkd.in/b9kuiSa
Russian scientists discover how to cool plasmonic components to make light-based transistors possible.
Virtual Healthcare & IMSHealth is a major player in this service offering. Healthcare and clinic in your own home.
The University of Southern California Center for Body Computing has teamed with 8 partners to launch a Virtual Care Clinic. The idea with VCC is to create an integrated approach to the use of mobile apps, “virtual” doctors, artificial intelligence, data collection and analysis, as well as diagnostics and wearable sensors to create truly on-demand healthcare.
The partners involved in this effort are peer-reviewed clinical trial database startup Doctor Evidence, drug data resource IMS Health ($IMS), consumer design firm Karten Design, HIPAA-compliant cloud platform Medable, video creator Planet Grande, sensor-enabled pill startup Proteus Digital Health and vision player VSP Global.
VSP’s next-gen sensor-embedded eyewear prototype, dubbed Project Genesis, will be refined and tested at the VCC in consultation with USC CBC, which is the digital health innovation accelerator at Keck School of Medicine. The VCC will also involve USC’s Institute of Creative Technologies (ICT).
Interesting approach.
A group of scientists has created a neural network based on polymeric memristors — devices that can potentially be used to build fundamentally new computers. These developments will primarily help in creating technologies for machine vision, hearing, and other machine sensory systems, and also for intelligent control systems in various fields of applications, including autonomous robots.
Google’s machine learning group is licensing new chip technology to make the devices in our pockets more intelligent and autonomous.
An international team of researchers has developed a new algorithm that could one day help scientists reprogram cells to plug any kind of gap in the human body. The computer code model, called Mogrify, is designed to make the process of creating pluripotent stem cells much quicker and more straightforward than ever before.
A pluripotent stem cell is one that has the potential to become any type of specialised cell in the body: eye tissue, or a neural cell, or cells to build a heart. In theory, that would open up the potential for doctors to regrow limbs, make organs to order, and patch up the human body in all kinds of ways that aren’t currently possible.
It was Japanese researcher Shinya Yamanaka who first reprogrammed cells in this way back in 2007 — it later earned him a Nobel Prize — but Yamanaka’s work involved a lot of labourious trial and error, and the process he followed is not an easy one to reproduce. Mogrify aims to compute the required set of factors to change cells instead, and it’s passed its early tests with flying colours.
