The year is 2030 and we are at the world's largest tech conference, CES in Las Vegas. A crowd is gathered to watch a big tech company unveil its new smartphone. The CEO comes to the stage and announces the Nyooro, containing the most powerful processor ever seen in a phone. The Nyooro can perform an astonishing quintillion operations per second, which is a thousand times faster than smartphone models in 2020. It is also ten times more energy-efficient with a battery that lasts for ten days.

Fifty million artificial neurons--a number roughly equivalent to the brain of a small mammal--were delivered from Portland, Oregon-based Intel Corp. to Sandia National Laboratories last month, said Sandia project leader Craig Vineyard. The neurons will be assembled to advance a relatively new kind of computing, called neuromorphic, based on the principles of the human brain. Its artificial components pass information in a manner similar to the action of living neurons, electrically pulsing only when a synapse in a complex circuit has absorbed enough charge to produce an electrical spike. "With a neuromorphic computer of this scale," Vineyard said, "we have a new tool to understand how brain-based computers are able to do impressive feats that we cannot currently do with ordinary computers." Improved algorithms and computer circuitry can create wider applications for neuromorphic computers, said Vineyard. Sandia manager of cognitive and emerging computing John Wagner said, "This very large neural computer will let us test how brain-inspired processors use information at increasingly realistic scales as they come to actually approximate the processing power of brains.

In a technical paper quietly released earlier this year, IBM detailed what it calls the IBM Neural Computer, a custom-designed, reconfigurable parallel processing system designed to research and develop emerging AI algorithms and computational neuroscience. This week, the company published a preprint describing the first application demonstrated on the Neural Computer: a deep "neuroevolution" system that combines the hardware implementation of an Atari 2600, image preprocessing, and AI algorithms in an optimized pipeline. The coauthors report results competitive with state-of-the-art techniques, but perhaps more significantly, they claim that the system achieves a record training time of 1.2 million image frames per second. The Neural Computer represents something of a shot across the bow in the AI computational arms race. According to an analysis recently released by OpenAI, from 2012 to 2018, the amount of compute used in the largest AI training runs grew more than 300,000 times with a 3.5-month doubling time, far exceeding the pace of Moore's law. Video games are a well-established platform for AI and machine learning research.

The Google logo is displayed on a sign outside of the Google headquarters in Mountain View, California. Google's artificial intelligence (AI) platform DeepMind revolutionizes the field, being now capable of learning based on information already possessed. DeepMind is able of learning, or better said of teaching itself, based on data it already possesses. According to The Next Web, this is a significant step forward for artificial intelligence, a real breakthrough that revolutionizes the field. DeepMind technology is based on Alphabet's hybrid system called Differential Neural Computer (DNC).

A new kind of computer, devised by researchers at Google DeepMind in the U.K., could broaden the abilities of today's best AI systems by giving them an important new feature--a kind of working memory. The researchers show that the computer, which consists of a large neural network connected to a unique form of memory, can perform relatively complex tasks by figuring out for itself what information to hold in its memory. The tasks include figuring out the best way to get from one station to another on London's spaghetti-like Underground transit network, after exploring diagrams of other types of networks and learning about the most salient features. The Google DeepMind researchers call their system a differentiable neural computer. It is differentiable in the sense that its behavior--including what to store in memory--can be learned using the mathematical process, called backpropagation, that underlies the working of neural networks.

Artificial-intelligence systems can do increasingly complex tasks but they can't yet figure much out on their own without help from humans. In a paper published Wednesday in the journal Nature, researchers at Alphabet Inc.'s Google DeepMind describe experimental software that they say gets closer to that goal and could be more accurate and less costly than current systems. "There's a lot of things it could be used for," he said. One obvious future application is "chatbots," software that answers questions autonomously, he said. The new DeepMind prototype couples so-called artificial neural networks -- which are widely used for image and speech recognition -- with an external memory.

But the really exciting part about this trend is the increasing ability to marry robotics with artificial intelligence. When this happens, robotics and machinery don't need to be programmed to perform certain tasks…they learn by trial and error. But first, there are three things that must come together to make advanced AI possible: Big Data, the computer-programming technique of deep learning and new concepts in both computer chips and how to use them. Big Data is the virtually limitless wilderness of facts, videos, infographics, statistics, public records and everything else stored on and collected from the internet. Software tools allow researchers to mine data to find useful nuggets -- they're digital needles in this infinite haystack of facts and information.

Two dimensional image motion detection neural networks have been implemented using a general purpose analog neural computer. The neural circuits perform spatiotemporal feature extraction based on the cortical motion detection model of Adelson and Bergen. The neural computer provides the neurons, synapses and synaptic time-constants required to realize the model in VLSI hardware. Results show that visual motion estimation can be implemented with simple sum-andthreshold neural hardware with temporal computational capabilities. The neural circuits compute general 20 visual motion in real-time.

Two dimensional image motion detection neural networks have been implemented using a general purpose analog neural computer. The neural circuits perform spatiotemporal feature extraction based on the cortical motion detection model of Adelson and Bergen. The neural computer provides the neurons, synapses and synaptic time-constants required to realize the model in VLSI hardware. Results show that visual motion estimation can be implemented with simple sum-andthreshold neural hardware with temporal computational capabilities. The neural circuits compute general 20 visual motion in real-time.

Two dimensional image motion detection neural networks have been implemented using a general purpose analog neural computer. The neural circuits perform spatiotemporal feature extraction based on the cortical motion detection model of Adelson and Bergen. The neural computer provides the neurons, synapses and synaptic time-constants required to realize the model in VLSI hardware. Results show that visual motion estimation can be implemented with simple sum-andthreshold neuralhardware with temporal computational capabilities. The neural circuits compute general 20 visual motion in real-time.