Imagine if your smart speaker could be trained to recognize your accent, or if a pair of running shoes could alert you in real time if your gait changed, indicating fatigue. Or if, in the industrial world, sensors could parse vibration information from a machine that changed location and function often in real time, halting the machine if that information suggested there was a problem. We often write about the value of on-device machine learning (ML), but what we're generally discussing is running existing models on a device and matching incoming data against the established model. This is known as inference. So when you say the name "Alexa," your smart speaker matches the pattern and wakes up.

Intel has done pretty well for itself by consistently figuring out ways of making CPUs faster and more efficient. But with the end of Moore's Law lurking on the horizon, Intel has been exploring ways of extending computing with innovative new architectures at Intel Labs. Quantum computing is one of these initiatives, and Intel Labs has been testing its own 49-qubit processors. Beyond that, Intel Labs is exploring neuromorphic computing (emulating the structure and, hopefully, some of the functionality of the human brain with artificial neural networks) as well as probabilistic computing, which is intended to help address the need to quantify uncertainty in artificial intelligence applications. Rich Uhlig has been the director of Intel Labs since December of 2018, which is really not all that long, but he's been at Intel since 1996 (most recently as Director of Systems and Software Research for Intel Labs) so he seems well qualified to hit the ground running.

Intel is betting big on probabilistic computing as a major component to AI that would allow future systems to comprehend and compute with uncertainties inherent in natural data and will allow researchers to build computers capable of understanding, predicting and decision-making. Mayberry also noted in a post that a key barrier to AI today is that natural data fed to a computer is largely unstructured and'noisy'. Probabilistic computing can make computers efficient at dealing with probabilities at scale that is the key to transforming current systems and applications from advanced computational aids into intelligent partners for understanding and decision-making, he emphasised.

Intel said this week that a system based on its Loihi chip planned for 2019 will include the equivalent of 100 billion synapses, which is about the same brain complexity as a common mouse. Last September, Intel introduced the world to Loihi, a chip designed for what Intel calls probabilistic computing. Intel sees probabilistic computing as an important step on the road to artificial intelligence. Unlike a Core chip, which uses a sequential pipeline of instructions, Loihi is designed to mimic the way the brain works. The version of the Loihi chip that Intel introduced last year included 130,000 silicon "neurons" connected with 130 million "synapses," the junctions that in humans connect the neurons within the brain.

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.

Intel announced today that it is forming a strategic research alliance to take artificial intelligence to the next level. Autonomous systems don't have good enough ways to respond to the uncertainties of the real world, and they don't have a good enough way to understand how the uncertainties of their sensors should factor into the decisions they need to make. According to Intel CTO Mike Mayberry the answer is "probabilistic computing", which he says could be AI's next wave. IEEE Spectrum: What motivated this new research thrust? Mike Mayberry: We're trying to figure out what the next wave of AI is.