Intel Labs, in collaboration with the Italian Institute of Technology and the Technical University of Munich, has introduced a new approach to neural network-based object learning. It specifically targets future applications like robotic assistants that interact with unconstrained environments, including in logistics, healthcare or elderly care. This research is a crucial step in improving the capabilities of future assistive or manufacturing robots. It uses neuromorphic computing through new interactive online object learning methods to enable robots to learn new objects after deployment.

Using these new models, Intel and its collaborators successfully demonstrated continual interactive learning on Intel's neuromorphic research chip, Loihi, measuring up to 175x lower energy to learn a new object instance with similar or better speed and accuracy compared to conventional methods running on a central processing unit (CPU). To accomplish this, researchers implemented a spiking neural network architecture on Loihi that localized learning to a single layer of plastic synapses and accounted for different object views by recruiting new neurons on demand. This enabled the learning process to unfold autonomously while interacting with the user.

Intel Corporation today reported third-quarter 2021 financial results. "Q3 shone an even greater spotlight on the global demand for semiconductors, where Intel has the unique breadth and scale to lead. Our focus on execution continued as we started delivering on our IDM 2.0 commitments. We broke ground on new fabs, shared our accelerated path to regain process performance leadership, and unveiled our most dramatic architectural innovations in a decade. We also announced major customer wins across every part of our business," said Pat Gelsinger, Intel CEO. "We are still in the early stages of our journey, but I see the enormous opportunity ahead, and I couldn't be prouder of the progress we are making towards that opportunity."

In the third quarter, the company generated $9.9 billion in cash from operations and paid dividends of $1.4 billion. Intel CFO George Davis announced plans to retire from Intel in May 2022. He will continue to serve in his current role while Intel conducts a search for a new CFO and until his successor is appointed. Third-quarter revenue was led by strong recovery in the Enterprise portion of DCG and in IOTG, which saw higher demand amid recovery from the economic impacts of COVID-19. The Client Computing Group (CCG) was down due to lower notebook volumes due to industry-wide component shortages, and on lower adjacent revenue, partially offset by higher average selling prices (ASPs) and strength in desktop.

Today, Intel introduced Loihi 2, its second-generation neuromorphic research chip, and Lava, an open-source software framework for developing neuro-inspired applications. Their introduction signals Intel's ongoing progress in advancing neuromorphic technology. Neuromorphic computing, which draws insights from neuroscience to create chips that function more like the biological brain, aspires to deliver orders of magnitude improvements in energy efficiency, speed of computation and efficiency of learning across a range of edge applications: from vision, voice and gesture recognition to search retrieval, robotics, and constrained optimization problems.

"Loihi 2 and Lava harvest insights from several years of collaborative research using Loihi. Our second-generation chip greatly improves the speed, programmability, and capacity of neuromorphic processing, broadening its usages in power and latency constrained intelligent computing applications. We are open sourcing Lava to address the need for software convergence, benchmarking, and cross-platform collaboration in the field, and to accelerate our progress toward commercial viability." -- Mike Davies, director of Intel's

Today, Intel announced the readiness of Pohoiki Springs, its latest and most powerful neuromorphic research system providing the computational capacity of 100 million neurons. The cloud-based system will be made available to members of the Intel Neuromorphic Research Community (INRC), extending their neuromorphic work to solve larger, more complex problems.

"Pohoiki Springs scales up our Loihi neuromorphic research chip by more than 750 times, while operating at a power level of under 500 watts. The system enables our research partners to explore ways to accelerate workloads that run slowly today on conventional architectures, including high-performance computing (HPC) systems." -Mike Davies, director of Intel's Neuromorphic Computing Lab.

The potential impact of Artificial Intelligence (AI) has never been greater - but we'll only be successful if AI can deliver smarter and more intuitive answers. A key barrier to AI today is that natural data fed to a computer is largely unstructured and "noisy."

It's easy for humans to sort through natural data. For example: If you are driving a car on a residential street and see a ball roll in front of you, you would stop, assuming there is a small child not far behind that ball. Computers today don't do this. They are built to assist humans with precise productivity tasks. Making computers efficient at dealing with probabilities at scale is central to our ability to transform current systems and applications from advanced computational aids into intelligent partners for understanding and decision-making.

Today at the 2018 Consumer Electronics Show in Las Vegas, Intel announced two major milestones in its efforts to research and develop future computing technologies including quantum and neuromorphic computing, which have the potential to help industries, research institutions and society solve problems that currently overwhelm today's classical computers.

During his keynote address, Intel CEO Brian Krzanich announced the successful design, fabrication and delivery of a 49-qubit superconducting quantum test chip. The keynote also noted the promise of neuromorphic computing.

Intel has been steadily increasing its portfolio of products in the AI space, through the acquisition of multiple AI-focused companies such as Nervana, Mobileye, and others. Through its increased portfolio of AI-related IP, the company is looking to carve itself a slice of the AI computing market, and this sometimes means thinking inside the box more than outside of it. It really doesn't matter the amount of cores and threads you can put on your HEDT system: the human brain's wetware is still one of the most impressive computation machines known to man.

That idea is what's behind of neuromorphic computing, where chips are being designed to mimic the overall architecture of the human brain, with neurons, synapses and all. It marries the fields of biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, mimicking the morphology of individual neurons, circuits, applications, and overall architectures. This, in turn, affects how information is represented, influences robustness to damage due to the distribution of workload through a "many cores" design, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.