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BrainChip 2020 Predictions: Artificial Intelligence, Deep Learning, and Neural Networks in 2020 Vision

VMblog Predictions 2020 

Industry executives and experts share their predictions for 2020.  Read them in this 12th annual series exclusive.

By Roger Levinson, COO, BrainChip

Artificial Intelligence, Deep Learning, and Neural Networks in 2020 Vision

What end users, investors, and industry insiders can look forward to in 2020. 

Five predictions addressing the new and changing technologies that affect AI applications.  

1. Investment in today's Deep Learning Neural Network technologies will plateau and shift to next generation AI solutions   

Deep Learning accomplishments and advances have played center stage in stories about Artificial Intelligence in recent years.  With the availability of vast computing resources and the evolution of GPU based accelerators, Artificial Intelligence has soared to the forefront of industry attention and investment, driving new use cases across nearly every end market.  Transformational solutions have been enabled for businesses in the areas of finance, insurance, credit, security, social media, smart cities, just to name a few.  CIOs are driven to invest in AI as the next greatest thing since cloud computing as a way to drive new levels of innovation and competitiveness.  However, this has all come at a cost in terms of physical infrastructure and energy consumption and currently falls short of delivering on the promise of Artificial Intelligence by providing only classification based upon a pre-trained solution.  The intelligence in Artificial Intelligence is yet to be demonstrated.  As markets move from the data center to the edge, where data is created, a next generation of AI solutions will be required and that is where investment focus will shift in 2020.  

2. Edge Applications will change the requirements for AI solutions   

To release the full potential of AI, Edge applications, such as IoT, requires certain characteristics not available in today's deep learning infrastructures.  Edge applications include well known examples such as Smart Cameras, Smart Home environments, autonomous vehicles, robotics, drones, and many more.  Edge applications come with limited power budgets, physical space constraints, a wide variety of use cases, the need for autonomous function and the requirement for continued learning.  As we enter 2020, the growth in Edge applications will accelerate as solutions capable of achieving these requirements will begin to enter the market.  No longer will AI solutions be measured by the amount of data that can be processed in isolated data centers, but will be judged on the ability to solve challenging, real world problems at the Edge.    

3. Interest in 3rd generation Neural Networks will grow  

Given the needs of Edge applications, next generation technologies, such as Neuromorphic Computing, will emerge as the predominant solution.  At the heart of an AI solution is the neural network which is intended to mimic the human brain.  The solutions to date have shown no real relationship to the human brain but are extensions of traditional computing architectures.  The limitations of past neural network approaches, which fundamentally try to ‘shrink' the GPU-driven solution down to fit at the edge, simply do not achieve the requirements.  Third generation neural networks based upon the fundamentals of spiking neural networks (SNN) will lead the way in solutions for AI at the Edge.  Successful suppliers of the next generation solutions will provide technology which not only achieves the future needs of Edge applications but will support much of today's already established, infrastructure and ecosystem.  The ability to merge today's solutions with next generation capabilities will accelerate the proliferation of intelligent devices and will bring intelligence to artificial intelligence at the Edge.  

4. Proliferation of Smart Edge Devices  

2020 will bring a surge in Smart Devices for Edge applications.  Utilizing today's technology, early Smart Devices will continue to proliferate, leveraging deep learning infrastructure and currently available technologies.  Applications such as Smart Speakers, Video Doorbells, remote controls, hand-held devices and many more will continue to incrementally improve on the AI features in their offering.  However, 2020 will be a turning point where a new class of these devices will begin to emerge with new capabilities never seen before.  Individuals will be able to personalize their devices through human interfaces such as voice and gesture without the need for connectivity to the Internet.  Smart Cameras will be able to identify specific individuals unique to each camera.  This next step in the artificial intelligence evolution will represent a turning point in how AI systems behave.  At the heart of this change will be Neuromorphic Computing transforming the landscape.   

5. Advances in real-time learning will take root in 2020  

The structure of the brain contributes to another important factor. As we walk down stairs or listen to music, the brain predicts what happens next before sensory perception arrives. Walking down the stairs our foot ‘knows' the location of the next step. Listening to music we ‘know' what note follows next. This is because the brain predicts the next event a few milliseconds before it happens. When this same prediction system is used in artificial neural networks we will start to see better learning, and the system will learn better predictions. As opposed to today's deep learning approach of back-propagation, continuous learning with forward-propagation, reinforced through Spike-timing-dependent-plasticity (STDP) will provide the solution. Artificial Neural Networks will become increasingly capable, yielding better accuracy in predicting events, advanced episodic memory, and real-time learning. Real-time learning of complex, real-world events is the key to adaptability, and eventually, intelligent machines. As we continue to use SNNs, and we start to implement these new structures, AI will no longer be restricted to simple classification tasks.   

The era of artificial intelligence has just begun and the next decade is destined to bring dramatic and transformative change. 


About the Author

Roger Levinson 

Roger Levinson, BrainChip COO, Mr. Levinson most recently served as Vice President of Data Management at Rstor where he previously served as Vice President of ASIC Engineering. Mr. Levinson has also served as Vice President of Engineer at Rambus and Vice President/General Manager of Strategy and Innovation at Semtech. He held a variety of senior engineering positions prior to joining Semtech including at Intersil, Xicor, Analog Integration Partners, Exar ,and Micropower. Mr. Levison earned his Bachelor's Degree from the University of California, Davis in Electrical and Electronic Engineering, and also earned his Masters Degree from the University of California, Davis in Electrical and Electronic Engineering.  (

Published Friday, January 03, 2020 7:42 AM by David Marshall
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