Lifeboat Foundation

The “iPhone moment for A.I.” hype takes many hues, but Nvidia is about the future of computing itself. NVIDIA DGX supercomputers, originally used as an AI research instrument, are now running 24/7 at businesses across the world to refine data and process AI.

While OpenAI gets a lot of the glory, I believe the credit should go to Nvidia. Launched late last year, ChatGPT went mainstream almost instantaneously, attracting over 100 million users, making it the fastest-growing application in history. “We are at the iPhone moment of AI,” Huang said. Nvidia makes about $6 to $7 Billion a fiscal quarter in revenue.

Continue reading “The iPhone Moment of A.I. Has Started” »

At its annual GTC event, Nvidia announced a partnership with Tel Aviv-based Quantum Machines to create a state-of-the-art architecture for quantum-classical computing.

The collaboration intends to bring about purpose-built infrastructure for quantum computing and GPU supercomputing capable of real-time quantum error correction. Known as DGX Quantum, the first system is expected to deploy to the Israel Quantum Computing Center.

Chipmaker Nvidia has announced a new service that will allow users to access supercomputer level computing power from web browsers. The service, called Nvidia DGX Cloud, is an AI supercomputing service that allows enterprises to run workloads on the company’s A100 and H100 chips remotely.

“DGX Cloud provides dedicated clusters of NVIDIA DGX AI supercomputing, paired with NVIDIA AI software. The service makes it possible for every enterprise to access its own AI supercomputer using a simple web browser, removing the complexity of acquiring, deploying and managing on-premises infrastructure,” the company said in a blog post.

As reported by Reuters, however, the service isn’t cheap. Nvidia is charging $37,000 per month for access to eight of the A100 or H100 chips — the company’s flagship chips, which are both designed for AI computing. “Each instance of DGX Cloud features eight Nvidia H100 or A100 80GB Tensor Core GPUs for a total of 640GB of GPU memory per node. A high-performance, low-latency fabric built with Nvidia Networking ensures workloads can scale across clusters of interconnected systems, allowing multiple instances to act as one massive GPU to meet the performance requirements of advanced AI training,” Nvidia’s blog post explained.

The introduction of a novel idea of quantum computing in industrial applications is the result of the slow but steady progress of computing systems and equipment. Quantum computers, which are primarily used to aid in complex computations, are anticipated to significantly progress several industries and open up new prospects.

The promotion of IBM’s supercomputers is not far behind that of other tech behemoths like Google, who claim to have a better grasp on quantum dominance. What’s more crucial, though, is that enterprises and entire sectors will undoubtedly benefit from massive automation and digital transformation thanks to the industrial applications of quantum computing development. Quantum computing in 2023 offers countless opportunities. The world will eventually learn about the actual potential of quantum computing. With each passing day, the demand for effective processing grows, and it appears that the only option is to develop quantum applications. In this article, we have enlisted the top 10 industrial applications of quantum computing.

Amid a flurry of Google and Microsoft generative AI releases last week during SXSW, Garry Kasparov, who is a chess grandmaster, Avast Security Ambassador and Chairman of the Human Rights Foundation, told me he is less concerned about ChatGPT hacking into home appliances than he is about users being duped by bad actors.

“People still have the monopoly on evil,” he warned, standing firm on thoughts he shared with me in 2019. Widely considered one of the greatest chess players of all time, Kasparov gained mythic status in the 1990s as world champion when he beat, and then was defeated by IBM’s Deep Blue supercomputer.

Despite the rapid advancement of generative AI, chess legend Garry Kasparov, now ambassador for the security firm Avast, explains why he doesn’t fear ChatGPT creating a virus to take down the Internet, but shares Gen’s CTO concerns that text-to-video deepfakes could warp our reality.

Continue reading “Google AI And Microsoft ChatGPT Are Not Our Biggest Security Risks, Warns Chess Legend Kasparov” »

The new AI strategy, which includes the construction of a supercomputer, will cost the UK £900 million ($1.2 billion).

The United Kingdom (U.K.) has announced plans to develop its own ChatGPT version, “BritGPT” as part of a new artificial intelligence (AI) strategy.

“These investments will provide scientists with access to cutting-edge computing power and bring a significant uplift in computing capacity to the AI community,” reads the Spring Budget 2023 plan.

Continue reading “‘BritGPT’: UK plans ChatGPT-like superpower to counter China’s AI influence” »

No, it’s not hypermodern art. This image, generated by NASA

Established in 1958, the National Aeronautics and Space Administration (NASA) is an independent agency of the United States Federal Government that succeeded the National Advisory Committee for Aeronautics (NACA). It is responsible for the civilian space program, as well as aeronautics and aerospace research. Its vision is “To discover and expand knowledge for the benefit of humanity.” Its core values are “safety, integrity, teamwork, excellence, and inclusion.” NASA conducts research, develops technology and launches missions to explore and study Earth, the solar system, and the universe beyond. It also works to advance the state of knowledge in a wide range of scientific fields, including Earth and space science, planetary science, astrophysics, and heliophysics, and it collaborates with private companies and international partners to achieve its goals.

Its Up!

We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

Over the past two years, we rebuilt our entire deep learning stack and, together with Azure, co-designed a supercomputer from the ground up for our workload. A year ago, we trained GPT-3.5 as a first “test run” of the system. We found and fixed some bugs and improved our theoretical foundations. As a result, our GPT-4 training run was (for us at least!) unprecedentedly stable, becoming our first large model whose training performance we were able to accurately predict ahead of time. As we continue to focus on reliable scaling, we aim to hone our methodology to help us predict and prepare for future capabilities increasingly far in advance—something we view as critical for safety.

Continue reading “GPT-4” »

Now its building one that even bigger and even more sophisticated.

Nearly five years ago, a little-known company approached Microsoft with a special request to put together computing horsepower to the scale it had never done before. Microsoft then spent millions of dollars in putting together tens of thousands of powerful chips to build a supercomputer. OpenAI used this to train its large language model, GPT, and the rest, as they say, is history.

Microsoft is no stranger to building artificial intelligence (AI) models that help users work more efficiently. The automatic spell checker that has helped millions of users is an example of an AI model trained on language.

Continue reading “Microsoft spent millions to put together a supercomputer for OpenAI” »