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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.

Continue reading “We Just Created an Artificial Synapse That Can Learn Autonomously” »

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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This wasn’t the first such event – the agricultural revolution had upended human lives 12,000 years earlier.

A growing number of experts believe that a third revolution will occur during the 21st century, through the invention of machines with intelligence which far surpasses our own. These range from Stephen Hawking to Stuart Russell, the author of the best-selling AI textbook, AI: A Modern Approach.

Rapid progress in machine learning has raised the prospect that algorithms will one day be able to do most or all of the mental tasks currently performed by humans. This could ultimately lead to machines that are much better at these tasks than humans.

Continue reading “Positively shaping development of artificial intelligence” »

One of the goals of biomimetics is to take inspiration from the functioning of the brain in order to design increasingly intelligent machines. This principle is already at work in information technology, in the form of the algorithms used for completing certain tasks, such as image recognition; this, for instance, is what Facebook uses to identify photos. However, the procedure consumes a lot of energy. Vincent Garcia (Unité mixte de physique CNRS/Thales) and his colleagues have just taken a step forward in this area by creating directly on a chip an artificial synapse that is capable of learning. They have also developed a physical model that explains this learning capacity. This discovery opens the way to creating a network of synapses and hence intelligent systems requiring less time and energy.

Our brain’s learning process is linked to our synapses, which serve as connections between our neurons. The more the synapse is stimulated, the more the connection is reinforced and learning improved. Researchers took inspiration from this mechanism to design an artificial synapse, called a memristor. This electronic nanocomponent consists of a thin ferroelectric layer sandwiched between two electrodes, and whose resistance can be tuned using voltage pulses similar to those in neurons. If the resistance is low the synaptic connection will be strong, and if the resistance is high the connection will be weak. This capacity to adapt its resistance enables the synapse to learn.

Although research focusing on these artificial synapses is central to the concerns of many laboratories, the functioning of these devices remained largely unknown. The researchers have succeeded, for the first time, in developing a physical model able to predict how they function. This understanding of the process will make it possible to create more complex systems, such as a series of artificial neurons interconnected by these memristors.

Continue reading “Electronic synapses that can learn: towards an artificial brain?” »

Artificial intelligence has reached peak hype. News outlets report that companies have replaced workers with IBM Watson and that algorithms are beating doctors at diagnoses. New AI startups pop up everyday, claiming to solve all your personal and business problems with machine learning.

Ordinary objects like juicers and Wi-Fi routers suddenly advertise themselves as “powered by AI.” Not only can smart standing desks remember your height settings, they can also order you lunch.

Much of the AI hubbub is generated by reporters who’ve never trained a neural network and by startups or those hoping to be acqui-hired for engineering talent despite not having solved any real business problems. No wonder there are so many misconceptions about what AI can and cannot do.

Continue reading “Understanding the limits of deep learning” »

Artificial intelligence, machine learning, evolving algorithms — we know it can get confusing. So let’s take a look at AI and what it means.

Bosch and the car manufacturer behind Mercedes, Daimler, have announced they are joining forces “to advance the development of fully automated and driverless driving”.

The two companies are to enter into a development agreement that they say will bring fully automated driving to urban roads by “the beginning of the next decade”.

To do this the two companies will develop software and algorithms that lead to an autonomous driving system.

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Who owns the work?

Man or his machine?

Continue reading “Artificial Intelligence Is Already a Better Artist Than You Are” »

Wednesday, March 1st Baltimore, MD — In March 2016 Insilico Medicine initiated a research collaboration with Life Extension to apply advanced bioinformatic methods and deep learning algorithms to screen for naturally occurring compounds that may slow down or even reverse the cellular and molecular mechanisms of aging. Today Life Extension (LE) launched a new line of nutraceuticals called GEROPROTECT^TM, and the first product in the series called Ageless Cell^TM combines some of the natural compounds that were shortlisted by Insilico Medicine’s algorithms and are generally recognized as safe (GRAS).

The first research results on human biomarkers of aging and the product will be presented at the Re-Work Deep Learning in Healthcare Summit in London 28.02−01.03, 2017, one of the popular multidisciplinary conferences focusing on the emerging area of deep learning and machine intelligence.

“We salute Life Extension on the launch of GEROPROTECT^TM: Ageless Cell, the first combination of nutraceuticals developed using our artificial intelligence algorithms. We share the common passion for extending human productive longevity and investing every quantum of our energy and resources to identify novel ways to prevent age-related decline and diseases. Partnering with Life Extension has multiple advantages. LE has spent the past 37 years educating consumers on the latest in nutritional therapies for optimal health and anti-aging and is an industry leader and a premium brand in the supplement industry. Also, LE also has a unique mail order blood test service that allows US customers to perform comprehensive blood tests to help identify potential health concerns and to track the effects of the nutraceutical products,” said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.

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