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The first generation of AI was rules-based and emulated classical logic to draw reasoned conclusions within a specific, narrowly defined problem domain. It was well suited to monitoring processes and improving efficiency, for example. The second, current generation is largely concerned with sensing and perception, such as using deep-learning networks to analyze the contents of a video frame. A coming next generation will extend AI into areas that correspond to human cognition, such as interpretation and autonomous adaptation. This is critical to overcoming the so-called “brittleness” of AI solutions based on neural network training and inference, which depend on literal, deterministic views of events that lack context and commonsense understanding.
Next-generation AI must be able to address novel situations and abstraction to automate ordinary human activities. Intel Labs is driving computer-science research that contributes to this third generation of AI. Key focus areas include neuromorphic computing, which is concerned with emulating the neural structure and operation of the human brain, as well as probabilistic computing, which creates algorithmic approaches to dealing with the uncertainty, ambiguity, and contradiction in the natural world.
Neuromorphic Computing Research Focus: The key challenges in neuromorphic research are matching a human's flexibility, and ability to learn from unstructured stimuli with the energy efficiency of the human brain. The computational building blocks within neuromorphic computing systems are logically analogous to neurons. Spiking neural networks (SNNs) are a novel model for arranging those elements to emulate natural neural networks that exist in biological brains. Each “neuron” in the SNN can fire independently of the others, and doing so, it sends pulsed signals to other neurons in the network that directly change the electrical states of those neurons. By encoding information within the signals themselves and their timing, SNNs simulate natural learning processes by dynamically remapping the synapses between artificial neurons in response to stimuli.
Producing a Silicon Foundation for Brain-Inspired Computation:

To provide functional systems for researchers to implement SNNs, Intel Labs designed Loih , its fifth-generation self-learning neuromorphic research test chip, which was introduced in November 2017. This 128-core design is based on a specialized architecture that is optimized for SNN algorithms and fabricated on 14nm process technology. Loihi supports the operation of SNNs that do not need to be trained in the conventional manner of a convolutional neural network, for example. These networks also become more capable (“smarter”) over time. The Loihi chip includes a total of some 130,000 neurons, each of which can communicate with thousands of others. Developers can access and manipulate on-chip resources programmatically by means of a learning engine that is embedded in each of the 128 cores. Because the hardware is optimized specifically for SNNs, it supports dramatically accelerated learning in unstructured environments for systems that require autonomous operation and continuous learning, with extremely low power consumption, plus high performance and capacity. Intel Labs is committed to enabling the research community at large with access to test systems based on Loihi.

Because the technology is still in a research phase (as opposed to production), there are only a limited number of Loihi-based test systems in existence; in order to expand access, Intel Labs has developed a cloud-based platform for research community access to scalable Loihi-based infrastructure. Contact us
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