Yearly Archives: 2020

Gartner Recognizes Latent AI as Unique Edge AI Tech Innovator

Gartner recently published a new report on Tech Innovators in Edge AI, covering the trends and impact of Edge AI on products and services by analysts Eric Goodness, Danielle Casey, and Anthony Bradley.  Latent AI greatly appreciates Gartner’s interest in Latent AI and coverage in this analysis. The following is a summary of the report ...
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Vasu Madabushi November 18, 2020
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Edge AI Case Study: Edge AI in Access Control

This year at Edge Computing World, Latent AI was invited to participate with other industry leaders in a few relevant sessions addressing the future of Edge AI, use cases and perspectives on how to bring AI to the edge. Event:  Edge Computing World Date:  Thursday, October 15, 2020 Panel Session:  Edge AI Case Study:  Edge ...
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Latent AI November 2, 2020
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AI and the New Data Refinery for the Edge Continuum

In 2017, The Economist published an article about The Fuel of the Future:  Data Giving Rise to a New Economy.  Today, this is more true than ever, and prospecting for new data, in the form of new applications and various sensor-driven data acquisition continues at a rapid pace.  According to Cisco, in 2020, we generate ...
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Jags Kandasamy October 8, 2020
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Latent AI Wins 2020 Startup of the Year Award from IoT World

Latent AI’s Efficient Inference Platform (LEIP) recognized by leading IoT experts, analysts, and editors as key edge AI technology bringing AI to a smarter edge. Menlo Park, CA, August 14, 2020 — Latent AI, creators of the Latent AI Efficient Inference Platform (LEIP) and the latest spinout from SRI International, has been recognized as the ...
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Latent AI August 14, 2020
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Autotech Ventures Adds Latent AI to Portfolio

Autotech Ventures is excited to announce our investment and partnership with Latent AI following their recent seed round of funding.  Autotech Ventures is a venture capital firm focused on transportation tech and backed by 30+ transport corporations and financial limited partners. We are excited to partner with Latent AI to help deploy ML applications inside ...
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Latent AI July 21, 2020
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The Adaptive AI™ Approach: Run Deep Neural Networks with Optimal Performance

If you are an AI engineer dissatisfied with the speed or power consumption of your neural network inferences and believe there are no solutions available for better performance….then keep reading! I’d like to introduce a new way to run your deep neural networks at their most optimal point. It is dynamic, context-aware, and flexible. It ...
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Sek Chai May 7, 2020
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How Latent AI’s LEIP SDK Improves Deep Learning Inference Efficiency

Our Latent AI Efficient Inference Platform (LEIP) is an SDK for deep learning training and inference. LEIP provides APIs to train, quantize, and deploy edge AI models from many deep learning frameworks. It then generates optimized runtime engines deployable in embedded devices and infrastructure edge devices. Latent AI’s LEIP SDK enables you to deploy deep ...
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Sek Chai April 30, 2020
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The Quantization Myth

Demystifying Quantization Do you need high bit-precision to operate neural networks? More specifically, do you need a high bit-precision processor to run neural network inferences in the same level of fidelity that you’ve trained them? These concepts seem to be popular myths but are not true. A large part of the confusion around these myths ...
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Sek Chai April 21, 2020
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Defining the Edge

Edge Computing Started with the Abacus Edge computing and edge AI are extremely popular tech buzzwords today.  Just do a quick Google search for this term and you’ll get about 233 million results in less than a few seconds. Yes, there’s an overwhelming amount of information about edge computing and especially the importance of moving ...
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Jags Kandasamy February 14, 2020
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Scaling Edge AI in a Data Driven World, Part Two

The Essence of Edge AI Many applications today need edge computing. However, due to privacy issues, the need for real-time responses, and lack of network connectivity, applications are often compromised.  A similar position supporting edge computing comes from the argument around network scalability. The cost and latency for network backhaul can be problematic, especially when ...
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Sek Chai January 17, 2020
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