Artificial Intelligence - Page 43

This is a sign of the end times or the latest example of AI moving at a pace that is almost impossible for people to predict. With the recent launch of Meta's open-source and powerful Llama 3.1 AI model, the company hasn't skipped a beat and is currently rolling out its new AI Studio tool in the US.

What is AI Studio? Well, it's described as "a place for people to create, share, and discover AIs to chat with," with no tech skills required. Integrated into Instagram, Messenger, and WhatsApp, these AIs are custom chatbots with a twist. They're your online friends, community, specialists, or even digital twins that can be trained to become you - respond to messages, post content, and even "generate memes."

"You can use a wide variety of prompt templates or start from scratch to make an AI that teaches you how to cook, helps you with your Instagram captions, or generates memes to make your friends laugh - the possibilities are endless," Meta writes in the announcement post.

"Everybody will have an AI assistant," NVIDIA CEO Jensen Huang said at SIGGRAPH 2024. "Every single company, every single job within the company, will have AI assistance." This is a bold statement, to be sure, but not a surprising one considering the state of the industry.

SIGGRAPH is a professional graphics conference, and yes, this year, AI was not only on the menu but also included in every dish. From new microservices for 3D modeling to physics, materials, and robotics, generative AI is driving innovation. At SIGGRAPH, NVIDIA also announced that the world's largest advertising company was using generative AI as part of the Omniverse to create content for Coca-Cola - arguably the gold standard for brand advertising.

So, where does the AI assistant fit in? At this year's show, NVIDIA discussed the concept of digital agents, which are digital AIs trained on specific data. For example, an AI modeled after everything you've ever written, said, or done at work (that is measurable) could then become a personal AI assistant.

Apple has said it has been using Google TPU chips to train its AI software infrastructure, which will power its upcoming suite of Apple Intelligence, AI tools, and features.

In a new research paper from Apple, the company detailed the hardware and software infrastructure of its AI tools and features without any mention of NVIDIA hardware whatsoever. Apple said in its research paper that to train its new AI models, it used two different TPUs from Google that are organized in large clusters of chips.

Apple used 2048 x TPUv5p chips from Google for the AI model that will work on the iPhone and other devices, while the company used 8192 x TPUv4 chips for its server AI model. NVIDIA doesn't design TPUs but instead makes GPUs for gaming, workstations, AI GPUs, and more.

The power required to run complex, massive clusters of high-performance AI GPUs continues to skyrocket with the power of the AI chips themselves, but new research has a reduction in energy consumption required by AI processing by at least 1000x.

In a new peer-reviewed paper, a group of engineering researchers at the University of Minnesota Twin Cities have showed an AI efficiency-boosting technology, which is in lamens terms a shortcut in the regular practice of AI computations that massively reduces energy consumption for those workloads.

AI computing sees data transferred between components processing it (logic) and where data is stored (memory and storage). The moving around of this data back and forth is the main factor in power consumption being 200x higher than the energy used in the computation, according to this research.

NVIDIA is the company powering the tech industry's massive push into artificial intelligence-powered systems, as the green team is making the incredible hardware that makes it possible for these impressive tools and features to exist.

NVIDIA's dominance in this market was achieved by providing the best hardware for training AI systems, which briefly made the green team the most valuable company on the planet, taking the crown of long-standing tech giants such as Amazon and Microsoft. NVIDIA has since moved down to third place but remains the dependent player in the world of AI-focused microprocessors. With the push into AI many developers want to continue training their creations but don't necessarily want to rely on or keep fueling the massive beast that is NVIDIA.

A new report from The Information provides an example: Microsoft and OpenAI are in talks with several chip designs to create a new AI chip to rival NVIDIA. One of those companies was Broadcom, which is ranked the 13th most valuable company in the world for its solutions in semiconductors and software infrastructure.

X, formerly known as Twitter and the digital echo chamber for Elon Musk and his politics, also has a chatbot and powerful AI called Grok. Created by xAI, alongside hundreds of thousands of high-powered NVIDIA GPUs, Grok is described as an AI with a "rebellious streak" that will deliver candid, unfiltered responses.

X recently updated its terms and settings for all users. By default, it uses all X data for training. Grok now has access to everybody's posts, including yours, if you're on X. This move follows Meta and is understandable, given that massive amounts of raw data are a key ingredient for training and creating complex AI models like Grok.

Several AI companies and models have been under fire lately, with reports indicating that some have been scraping YouTube and other public forums to train AI. According to a Microsoft executive, if it's online, it's free to scrape. So, yes, X, Elon, and Zuckerberg are not alone in looking to social media platforms for AI training. The good news is that you can opt-out.

Meta has been training on its new Llama 3 405B model on a cluster of 16,384 x NVIDIA H100 80GB AI GPUs. Half of the issues during its 54-day training run were caused by the onboard HBM3 memory.

Meta released a new study detailing its Llama 3 405B model training, which took 54 days with the 16,384 NVIDIA H100 AI GPU cluster. During that time, 419 unexpected component failures occurred, with an average of one failure every 3 hours. In half of those failures, GPUs or their onboard HBM3 memory were the blame.

In a system with truck loads of components like CPUs, motherboards, RAM, SSDs, GPUs, power systems, cooling systems, a supercomputer is exotic and ultimately powerful, but it's completely normal for issues to happen every few hours. But, it's how developers work on those issues and get the system to remain operational no matter what local breakdowns are happening.

AMD has introduced Amuse 2.0, a new AI image generation tool currently in Beta. Amuse is a fully local experience, meaning it doesn't require plugging into the cloud. It requires either an AMD Ryzen AI 300 Series processor or a Radeon RX 7000 Series graphics card to run. Thanks to their AI-based XDNA architecture, it also runs on systems with AMD's mobile Ryzen 8040 processors.

This is important because Amuse includes AMD XDNA Super Resolution integration, which upscales lower-resolution images on AMD mobile devices. According to AMD, it "increases output size by 2X at the end of the image generation stage."

As seen in other AI image generation tools, Amuse uses Stable Diffusion models from Stability AI to create its images. Amuse takes the widely used and available Stable Diffusion AI models and creates a "painless, easy to use and optimized end-user experience" for its customers. In creating Amuse, AMD partnered with the New Zealand-based TensorStack to help develop the user-friendly UI.

Amazon is currently letting around half a dozen engineers test out a "closely guarded" new server design through its paces, according to the latest reports.

In a new article from RReuters, the outlet reports that the server in question was "packed" with Amazon's artificial intelligence (AI) chips that will compete with the likes of NVIDIA and its market-leading AI GPUs. The news is coming directly from Amazon executive Rami Sinno, said on Friday to Reuters during a visit to the Amazon AI chip lab.

Amazon is developing its own AI processors to limit its future reliance on more expensive NVIDIA AI GPU offerings and the so-called NVIDIA tax. Amazon uses AI all across its Amazon Web Services (AWS) with the company planning to spend $100 billion on data centers used for AI in the future.

The US Department of Energy (DoE) is working on its next-generation Discovery supercomputer, which will be a whopping 3-5x faster than its existing Frontier supercomputer.

The DoE has put out requests for proposals for its new Discovery supercomputer, with interested parties having until August 30, 2024 to submit their proposals. The next-generation Discovery supercomputer would be delivered to the Oak Ridge Leadership Facility (OLCF) at the Oak Ridge National Laboratory (ORNL) in Tennessee, by early 2028.

Discovery will succeed the Frontier supercomputer, which is the world's fastest supercomputer on the biannual Top500 list, which lists the world's fastest supercomputers -- of which Frontier is #1 -- for the fifth consecutive time in May 2024.