Lifeboat Foundation

The feature image you see above was generated by an AI text-to-image rendering model called Stable Diffusion typically runs in the cloud via a web browser, and is driven by data center servers with big power budgets and a ton of silicon horsepower. However, the image above was generated by Stable Diffusion running on a smartphone, without a connection to that cloud data center and running in airplane mode, with no connectivity whatsoever. And the AI model rendering it was powered by a Qualcomm Snapdragon 8 Gen 2 mobile chip on a device that operates at under 7 watts or so.

It took Stable Diffusion only a few short phrases and 14.47 seconds to render this image.

This is an example of a 540p pixel input resolution image being scaled up to 4K resolution, which results in much cleaner lines, sharper textures, and a better overall experience. Though Qualcomm has a non-algorithmic version of this available today, called Snapdragon GSR, someday in the future, mobile enthusiast gamers are going to be treated to even better levels of image quality without sacrificing battery life and with even higher frame rates.

Continue reading “Powering AI On Mobile Devices Requires New Math And Qualcomm Is Pioneering It” »

Update: The image for the ChatGPT 3.5 and vicuna-13B comparison has been updated for readability.

With the launch of Large Language Models (LLMs) for Generative Artificial Intelligence (GenAI), the world has become both enamored and concerned with the potential for AI. The ability to hold a conversation, pass a test, develop a research paper, or write software code are tremendous feats of AI, but they are only the beginning to what GenAI will be able to accomplish over the next few years. All this innovative capability comes at a high cost in terms of processing performance and power consumption. So, while the potential for AI may be limitless, physics and costs may ultimately be the boundaries.

Tirias Research forecasts that on the current course, generative AI data center server infrastructure plus operating costs will exceed $76 billion by 2028, with growth challenging the business models and profitability of emergent services such as search, content creation, and business automation incorporating GenAI. For perspective, this cost is more than twice the estimated annual operating cost of Amazon’s cloud service AWS, which today holds one third of the cloud infrastructure services market according to Tirias Research estimates. This forecast incorporates an aggressive 4X improvement in hardware compute performance, but this gain is overrun by a 50X increase in processing workloads, even with a rapid rate of innovation around inference algorithms and their efficiency. Neural Networks (NNs) designed to run at scale will be even more highly optimized and will continue to improve over time, which will increase each server’s capacity. However, this improvement is countered by increasing usage, more demanding use cases, and more sophisticated models with orders of magnitude more parameters. The cost and scale of GenAI will demand innovation in optimizing NNs and is likely to push the computational load out from data centers to client devices like PCs and smartphones.

Technology As A Force For Good In People’s Lives — Dr. Emre Ozcan, PhD, VP, Global Head of Digital Health & Walid Mehanna, Group Data Officer And Senior Vice President, Merck KGaA, Darmstadt, Germany.

EPISODE DISCLAIMER — At any time during this episode when anyone says Merck, in any context, it shall always be referring to Merck KGaA, Darmstadt, Germany.

Continue reading “Dr. Emre Ozcan & Walid Mehanna — Merck KGaA, Darmstadt, Germany — Tech As A Force For Good In Health” »

A novel protocol for quantum computers could reproduce the complex dynamics of quantum materials.

RIKEN researchers have created a hybrid quantum-computational algorithm that can efficiently calculate atomic-level interactions in complex materials. This innovation enables the use of smaller quantum computers or conventional ones to study condensed-matter physics and quantum chemistry, paving the way for new discoveries in these fields.

A quantum-computational algorithm that could be used to efficiently and accurately calculate atomic-level interactions in complex materials has been developed by RIKEN researchers. It has the potential to bring an unprecedented level of understanding to condensed-matter physics and quantum chemistry—an application of quantum computers first proposed by the brilliant physicist Richard Feynman in 1981.

During its ongoing Think 2023 conference, IBM today announced an end-to-end solution to prepare organisations to adopt quantum-safe cryptography. Called Quantum Safe technology, it is a set of tools and capabilities that integrates IBM’s deep security expertise. Quantum-safe cryptography is a technique to identify algorithms that are resistant to attacks by both classical and quantum computers.

Under Quantum Safe technology, IBM is offering three capabilities. First is the Quantum Safe Explorer to locate cryptographic assets, dependencies, and vulnerabilities and aggregate all potential risks in one central location. Next is the Quantum Safe Advisor which allows the creation of a cryptographic inventory to prioritise risks. Lastly, the Quantum Safe Remidiator lets organisations test quantum-safe remediation patterns and deploy quantum-safe solutions.

In addition, the company has also announced IBM Safe Roadmap, which will serve as the guide for industries to adopt quantum technology. IBM Quantum Safe Roadmap is the company’s first blueprint to help companies in dealing with anticipated cryptographic standards and requirements and protect systems from vulnerabilities.

An algorithm that allows more precise forecasts of the positions and velocities of a beam’s distribution of particles as it passes through an accelerator has been developed by researchers with the Department of Energy (DOE) and the University of Chicago.

Traveling at nearly light speed, the linear accelerator at the DOE’s SLAC National Accelerator Laboratory fires bursts of close to one billion electrons through long metallic pipes to generate its particle beam. Located in Menlo Park, California, the facility, originally called the Stanford Linear Accelerator Center, has used its 3.2-kilometer accelerator since its construction in 1962 to propel electrons to energies as great as 50 gigaelectronvolts (GeV).

The powerful particle beam generated by SLAC’s linear accelerator is used in the study of everything from innovative materials to the behavior of molecules on the atomic scale, despite how the beam itself remains somewhat mysterious since researchers have a hard time gauging its appearance as it passes through an accelerator.

Did humanity miss the party? Are SETI, the Drake Equation, and the Fermi Paradox all just artifacts of our ignorance about Advanced Life in the Universe? And if we are wrong, how would we know?

A new study focusing on black holes and their powerful effect on star formation suggests that we, as advanced life, might be relics from a bygone age in the Universe.

Universe Today readers are familiar with SETI, the Drake Equation, and the Fermi Paradox. All three are different ways that humanity grapples with its situation. They’re all related to the Great Question: Are We Alone? We ask these questions as if humanity woke up on this planet, looked around the neighbourhood, and wondered where everyone else was. Which is kind of what has happened.

Microsoft will also contribute its algorithm knowledge to make Builder.ai’s Natasha, an AI assistant, sound more human.

Microsoft Corporation has invested an undisclosed amount in Builder.ai, a no-code builder startup, as it looks to diversify its bets in the artificial intelligence (AI) game. Builder.ai lets users with no technical knowledge or experience in coding build their own apps and manage them.

Microsoft is already ahead in the AI game thanks to its partnership with OpenAI, the maker of the popular chatbot ChatGPT.

Continue reading “Builder.ai: Microsoft invests in no-code builder startup to boost AI game” »

Summary: Researchers have created a device that emulates the human eye’s ability to see color by using narrowband perovskite photodetectors and a neuromorphic algorithm.

The photodetectors, sensitive to red, green, and blue light, mimic our cone cells, while the neuromorphic algorithm simulates our neural network to process information into high-quality images. Unlike modern cameras that require external filters, this technology could improve resolution and reduce manufacturing costs.

The device also generates electricity as it absorbs light, potentially leading to battery-free camera technology.