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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Scaling Edge AI in a Data-Driven World, Part One

Scaling Edge AI in a Data-Driven World, Part One Drowning in Data We live in a data-driven world where IoT devices are always on, always tracking, and always monitoring. Being perpetually connected also means that we’re producing mind-boggling amounts of data. In fact, IDC Research predicts by 2020 we will be generating 44 trillion gigabytes ...

AI’s Shocking Carbon Footprint

AI’s Shocking Carbon Footprint How Techniques First Invented in the 1920s are Changing That Introduction What do the melting glaciers of Greenland have in common with artificial intelligence? On the surface, not much. But if you dig just a bit deeper, the connection is scarier than you might imagine. Turns out that data is not ...

The Era of Commercializing AI, Part Two

Supporting Efficient Workflow for Commercializing AI  At the early stage of AI commercialization, we are faced with the “dark ages” of tools, infrastructure, and approaches. There are many analogies that we can draw upon to understand where we truly are at this moment. For example, in the early days of the Internet, everyone was building ...

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