Amazon’s AI-Powered Palm Recognition Breakthrough
Plus: Intel's diverse efforts to capture AI opportunities, introducing Refact Code LLM.
Hello, Engineering Leaders and AI Enthusiasts!
Welcome to the 99th edition of The AI Edge newsletter. This edition brings you Amazon’s generative AI-powered palm recognition device, Amazon One.
And a huge shoutout to our amazing readers. We appreciate you! 😊
In today’s edition:
🖐️ Amazon’s AI-powered palm recognition breakthrough
and
🚀 Intel is going after the AI opportunity in multiple ways
💬 Introducing Refact Code LLM, for real-time code completion and chat
📚 Knowledge Nugget: The easiest part of LLM applications is the LLM by
Let’s go!
Amazon’s AI-powered palm recognition breakthrough
Amazon One is a fast, convenient, and contactless device that lets customers use the palm of their hand for everyday activities like paying at a store, presenting a loyalty card, verifying their age, or entering a venue. No phone, no wallet.
Amazon One does this by combining generative AI, machine learning, cutting-edge biometrics, and optical engineering.
Currently, Amazon One is being rolled out to more than 500 Whole Foods Market stores and dozens of third-party locations, including travel retailers, sports and entertainment venues, convenience stores, and grocers. It can also detect fake hands and reject them. It has already been used over 3 million times with 99.9999% accuracy.
Why does it matter?
Generative AI has recently captured the world’s imagination with feats like summarizing text, writing, and composing code. But this marks how it can address complex real-world problems and reimagine the future of convenience in shopping, entertainment, access, and much more.
(Source)
Intel is going after the AI opportunity in multiple ways
Intel is aggressively pursuing opportunities in the AI space by expanding beyond data center-based AI accelerators. CEO Pat Gelsinger believes that AI will move closer to end-users due to economic, physical, and privacy considerations. They are incorporating AI into various products, including server CPUs like Sapphire Rapids, which come with built-in AI accelerators for inference tasks.
Furthermore, Intel is set to launch Meteor Lake PC CPUs with dedicated AI hardware to accelerate AI workloads directly on user devices. This approach aligns with Intel's dominant position in the CPU market, making it attractive for software providers to support their AI hardware.
Why does it matter?
Intel's multi-pronged strategy positions it as a strong contender in the AI landscape alongside other key players like Nvidia. And as the demand for AI chips continues to surge, Intel's initiatives could offer a potential solution to this industry-wide challenge and help shape the future of AI.
(Source)
Introducing Refact Code LLM, for real-time code completion and chat
Refact LLM 1.6B model is primarily for real-time code completion (infill) in multiple programming languages and works as a chat. It achieves the state-of-the-art performance among the code LLMs, coming closer to HumanEval as Starcoder while being 10x smaller in size. It also beats other code models, as shown below. First, a tl;dr
1.6b parameters
20 programming languages
4096 tokens context
code completion and chat capabilities
pre-trained on permissive licensed code and available for commercial use
Why does this matter?
While the trend recently is for the model sizes to get bigger, this lowers barriers to entry and makes it a versatile tool for developers with varying hardware setups. With the smaller size, it can run much faster and affordably.
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Knowledge Nugget: The easiest part of LLM applications is the LLM
Beyond the LLMs, you’ll also have to think about everything else that’s required to deliver a successful product. “Everything else” is what’s required once you know you can use an LLM to build an application — deploying it to the cloud, accessing the right data, tracing inputs & outputs, and so on.
The everything else turns out to be harder than you might think.
While LLMs have made it easier for developers to create AI/ML applications, this article by
and highlights the complexity that arises beyond just the model itself. It addresses several key areas of concern, outlines the various considerations, and challenges that developers face in this domain.
Why does this matter?
In a rapidly evolving AI landscape, the article sheds light on often-overlooked aspects involved in harnessing the power of LLMs for applications and the multifaceted nature of LLM-powered development.
What Else Is Happening❗
🌟OpenAI plugs ChatGPT into Canva to sharpen its competitive edge in AI (Link)
🎮Epic Games Store will now accept games created with generative AI (Link)
🚀UAE's G42 launches a powerful open-source Arabic language AI model, Jais (Link)
👃AI model predicts smell based on a molecule’s structure with human-level proficiency (Link)
💉New AI-generated COVID drug enters Phase I clinical trials, effective against all variants (Link)
⚽A startup called AI Scout is using automation to find football’s next star (Link)
😁Tickle Tuesday
Neural networks are made up of interconnected nodes, or 'neurons,' passing information like a network of friends. This allows them to learn, process data, and make predictions, just like our brains.
So, the next time you're explaining neural networks, remember, it's all about connections and a bit of 'neural' networking! 😄🧠
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