Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Can computer systems develop to the point where they can think creatively, identify people or items they have never seen before, and adjust accordingly -- all while working more efficiently, with less power? Intel Labs is betting on it, with a new hardware and software approach using neuromorphic computing, which, according to a recent blog post, "uses new algorithmic approaches that emulate how the human brain interacts with the world to deliver capabilities closer to human cognition." While this may sound futuristic, Intel's neuromorphic computing research is already fostering interesting use cases, including how to add new voice interaction commands to Mercedes-Benz vehicles; create a robotic hand that delivers medications to patients; or develop chips that recognize hazardous chemicals. Machine learning-driven systems, such as autonomous cars, robotics, drones, and other self-sufficient technologies, have relied on ever-smaller, more-powerful, energy-efficient processing chips.

Scientists have tapped neuromorphic computing to keep robots learning about new objects after they've been deployed. For the uninitiated, neuromorphic computing replicates the neural structure of the human brain to create algorithms that can deal with the uncertainties of the natural world. Intel Labs has developed one of the most notable architectures in the field: the Loihi neuromorphic chip. Loihi is comprised of around 130,000 artificial neurons, which send information to each other across a "spiking" neural network (SNN). The chips had already powered a range of systems, from an smart artificial skin to an electronic "nose" that recognizes scents emitted from explosives.

Nilesh Jain is a Principal Engineer leading intelligent infrastructure and systems research at Intel Labs with a focus on visual/AI applications. The exponential growth of AI in every industry, from social media to drug discovery, has created a scaling challenge. Specifically, there is a need to design AI algorithms that map to disparate underlying AI platforms that can deploy efficiently and operate optimally. The adoption of automated machine learning (AutoML) is gaining momentum as major industry players implement automated solutions for every stage of development through to deployment. However, current AutoML technology only addresses half of the problem because it focuses only on automating the design of AI algorithms.

Scott Bair is a Senior Technical Creative Director for Intel Labs, chartered with growing awareness for Intel's leading-edge research activities, like AI, Neuromorphic Computing and Quantum Computing. Scott is responsible for driving marketing strategy, messaging, and asset creation for Intel Labs and its joint-research activities. In addition to his work at Intel, he has a passion for audio technology and is an active father of 5 children. Scott has over 23 years of experience in the computing industry bringing new products and technology to market. During his 15 years at Intel, he has worked in a variety of roles from R&D, architecture, strategic planning, product marketing, and technology evangelism.

Santiago Miret is a deep learning researcher at Intel Labs, where he focuses on developing artificial intelligence (AI) solutions and exploring the intersection of AI and the physical sciences. The successful design and deployment of novel material technologies in the last couple of decades has enabled tremendous innovations across various industries. Building today's smartphones, for example, would have cost about 100 million dollars in the 1980s and yielded a 14 meters tall device, both of which would be very impractical. Furthermore, materials innovations surrounding silicon have enabled advances in microelectronics and computer technologies that build the foundation of a technology-enabled world, including the recent proliferation of artificial intelligence (AI). Similar, albeit different advances, in silicon technology and perovskites, a class of semiconductor materials that transport the electric charge of light, have provided the basis for solar photovoltaic cells which enable the harvesting of renewable solar energy thereby driving a redesign of the energy industry to a more sustainable and less carbon-heavy system.

The current AI and Deep Learning of the present era have a few shortcomings like training a deep net can be very time-consuming, cloud computing can be costly and unavailability of sufficient data can also be a problem. To be rid of these, the scientists are all set in their search for a smarter version of AI, and there seem to be three ways they can progress in the future. Within the process of improving AI, the most focus is on high-performance computing. It is based on the deep neural net but aims to make them faster and easier to access. It aims to provide better general-purpose environments like TensorFlow, and greater utilization of GPUs and FPGAs in larger and larger data centers, with the promise of even more specialized chips not too far away.

As powerful as video game consoles have become, even the most graphically stunning video games don't look like realistic, real-world footage, which is arguably the ultimate goal. But researchers at Intel Labs may have found a shortcut by applying machine learning techniques to rendered footage from a console that takes it from beautiful to photorealistic. Over the past few decades, the graphics capabilities of home consoles have advanced by leaps and bounds. More processing power in the machines allows them to not only render more detail in the 3D models that make up a scene, but to also more accurately recreate the behavior of light so that reflections, highlights, and shadows behave and look more and more like they do in the real world. But the hardware isn't quite to the point where video games look as photo-realistic as the computer-generated visual effects that Hollywood blockbusters employ to wow audiences.

While the industry loves to combine "R&D" and we see this in every tech company's P&L, research and development are very different. Research is high risk, market making investments and discoveries that are unattached to products. Development is applying that research and other's IP to create an end product or services. Very few companies do research, and Intel has had a heritage in research for decades. One of the most exciting aspects of working as a tech analyst is, quite frankly, being one of the first to learn of these new, research-driven, cutting-edge technologies coming down the pipeline in the not-so-distant future--from the expected to the truly mind-boggling.

What's New: Today, Intel unveiled a new machine programming (MP) system – in conjunction with Massachusetts Institute of Technology (MIT) and Georgia Institute of Technology (Georgia Tech). The system, machine inferred code similarity (MISIM), is an automated engine designed to learn what a piece of software intends to do by studying the structure of the code and analyzing syntactic differences of other code with similar behavior. "Intel's ultimate goal for machine programming is to democratize the creation of software. When fully realized, MP will enable everyone to create software by expressing their intention in whatever fashion that's best for them, whether that's code, natural language or something else. That's an audacious goal, and while there's much more work to be done, MISIM is a solid step toward it."

Nature Machine Intelligence published a joint paper from researchers at Intel Labs and Cornell University demonstrating the ability of Intel's neuromorphic test chip, Loihi, to learn and recognize 10 hazardous chemicals, even in the presence of significant noise and occlusion. The work demonstrates how neuromorphic computing could be used to detect smells that are precursors to explosives, narcotics and more. Loihi learned each new odor from a single example without disrupting the previously learned smells, requiring up to 3000x fewer training samples per class compared to a deep learning solution and demonstrating superior recognition accuracy. The research shows how the self-learning, low-power, and "brain-like" properties of neuromorphic chips – combined with algorithms derived from neuroscience – could be the answer to creating "electronic nose" systems that recognize odors under real-world conditions more effectively than conventional solutions. "We are developing neural algorithms on Loihi that mimic what happens in your brain when you smell something," said Nabil Imam, senior research scientist in Intel's Neuromorphic Computing Lab.