As the size and complexity of large language models (LLMs) continue to grow, NVIDIA is today announcing updates to the NeMo Megatron framework that provide training speed-ups of up to 30%. These updates–which include two trailblazing techniques and a hyperparameter tool to optimize and scale training of LLMs on any number of GPUs–offer new capabilities to train and deploy models using the NVIDIA AI platform. BLOOM, the world's largest open-science, open-access multilingual language model, with 176 billion parameters, was recently trained on the NVIDIA AI platform, enabling text generation in 46 languages and 13 programming languages. The NVIDIA AI platform has also powered one of the most powerful transformer language models, with 530 billion parameters, Megatron-Turing NLG model (MT-NLG). LLMs are one of today's most important advanced technologies, involving up to trillions of parameters that learn from text.

This week's highlight is surely Meta's new chatbot: BlenderBot 3. BlenderBot 3 is accessible to everyone in the U.S. to chat with in order to collect feedback on its capabilities. It seems like "Meta's new AI chatbot can't stop bashing Facebook" with some hilarious and unexpected answers. The bot has some really funny answers bashing its own company, and as they clearly say in the article: "If you're worried that artificial intelligence is getting too smart, talking to Meta's AI chatbot might make you feel better." Indeed, even though BlenderBot 3 would pass a very specific Turing test and be classified as "intelligent" by some people, it remains a machine interpolating (and not extrapolating as humans can do) from data. Data gathered from human discussions on the internet, including our biases, and some of the worst ones due to anonymity's tendency to bring out the worst in some people.

I recently got access to OpenAI's DALL-E 2 instance. It's a lot of fun, but beyond its obvious application as a cornucopia of funny cat avatars, I think it's now fit to use in certain kinds of creative work. There are already plenty of good articles out there on the model's strengths and weaknesses, so I won't go over that here other than to note that it's not a threat to high art. It's got an idea of what things look like and how they can visually fit together, but it's very vague on how they work (e.g. However, with human guidance and a carefully chosen domain, it can still do some very impressive things.

Stiles said GuessThe.Game has been played millions of times on "almost every single country on Earth" since it launched. He was deeply thankful that what started as a passion project is now being enjoyed daily by thousands of fans. In the future, Stiles is planning on releasing new feature that will allow players to retroactively solve puzzles from days that they missed (as of this article's publishing, GuessThe.Game is currently on Day 95). Stiles is also interested in finding a new home for GuessThe.Game, in the same way that Wordle and Heardle were acquired.

The book starts off with a look at what machine learning is and what sorts of things it can be used for. The actual subject of the book gets started at Chapter 3 where we meet linear regression. I'm not at all sure I class regression as ML - it is more classical statistics. What is interesting about this presentation is that the regression problem is solved using gradient descent, i.e an iterative algorithm like many others in machine learning. This is a good idea in that it is a simple way into the idea of iterative learning.

The leading company in the AR contact lens space - Mojo Vision - has developed a working prototype with very advanced features. The Mojo contact lens overlays digital information onto your field of vision and provides information to you directly through the lens so that you don't have to look away at an external object like a phone, sign, or map. The team at Mojo Lens have developed a working prototype and they are testing use cases. This is a big step closer to AR. Mojo Vision was founded in 2015 in Silicon Valley. The company has raised $204 million from top investors including Khosla Ventures, Motorola, LG, Amazon's Alexa Fund, Stanford's StartX Fund, HP Tech Ventures, 8VC, Bold Capital Partners, Gradient Ventures, AME Cloud Ventures, Advan Tech, Dolby Family Ventures, Edge, Fusion Fund, HiJoJo Partners, InFocus Capital Partners, Kakao Ventures, NEA, Numbase, TDK Ventures, Open Field Capital, and Liberty Global.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Artificial intelligence (AI) and machine learning (ML) are about more than algorithms: The right hardware to turbocharge your AI and ML computations is key. To speed up job completion, AI and ML training clusters need high bandwidth and dependable transport with predictable low-tail latency (tail latency is the 1 or 2% of a job that trails the rest of responses). A high-performance interconnection can optimize data center and high-performance computing (HPC) workloads across your portfolio of hyperconverged AI and ML training clusters, resulting in lower latency for better model training, increased data packet utilization and lower operational costs. Today, San Jose-based Broadcom announced its contribution to the need for high-performance interconnections: the StrataXGS Tomahawk 5 switch series, which offers 51.2 Tbps of Ethernet switching capacity in a single, monolithic device – more than double the bandwidth of its contemporaries, the company claims.

The fashion social network, TikTok, it will be helped by Artificial intelligence to add a new feature that increases the interactivity of its users. This video content platform adds features and effects on a regular basis, and its millionaire community turns these novelties viral in a short time, integrating them into their creations in a fairly organic way.

A team of three autonomous patrol robots are now providing security at the Tokyo metropolitan government building. The robots are SQ-2 models produced by Seqsense in Tokyo and they're equipped with multiple cameras that can broadcast video directly to human security personnel at a central location. These robots look decidedly more R2-D2 than Robocop with spinning cameras on their heads that constantly whirl around and can make 3-D maps of their surroundings. The robots are SQ-2 models (seen above) produced by Seqsense in Tokyo and they're equipped with cameras that can broadcast video directly to human security personnel They do still have artificial intelligence capabilities that can detect people and other obstacles to avoid any collisions on the predetermined patrol routes. They even have hand sensors so that people in the space can request help from human guards, but they are not meant to fulfill all the duties of a regular human security guard.

A Russian cosmonaut just dealt with a rare spacesuit problem. As CNN's Jackie Wattles observed, mission control ordered Oleg Artemyev back to the International Space Station's airlock after encountering a suit issue. While the exact nature of the trouble wasn't clear as of this writing, NASA commentators noted a "slight fluctuation" in the suit's battery power. Artemyev returned safely, plugged into the station's power supply and resumed operations. We've asked NASA for comment.