MIT has launched a new academia and industry partnership called the AI Hardware Program that aims to boost research and development.

“A sharp focus on AI hardware manufacturing, research, and design is critical to meet the demands of the world’s evolving devices, architectures, and systems,” says Anantha Chandrakasan, dean of the MIT School of Engineering, and Vannevar Bush Professor of Electrical Engineering and Computer Science.

Microsoft and Hewlett Packard Enterprise (HSE) are working with NASA scientists to develop an AI system for inspecting astronauts’ gloves.

Space is an unforgiving environment and equipment failures can be catastrophic. Gloves are particularly prone to wear and tear as they’re used for just about everything, including repairing equipment and installing new equipment.

Currently, astronauts will send back images of their gloves to Earth to be manually examined by NASA analysts.

SambaNova Systems, a company that builds advanced software, hardware, and services to run AI applications, announced the addition of the Generative Pre-trained Transformer (GPT) language model to its Dataflow-as-a-Service offering. This will enable greater enterprise adoption of AI, allowing organizations to launch their customized language model in much less time — less than one month, compared to nine months or a year.

“Customers face many challenges with implementing large language models, including the complexity and cost,” said R “Ray” Wang, founder and principal analyst of Constellation Research. “Leading companies seek to make AI more accessible by bringing unique large language model capabilities and automating out the need for expertise in ML models and infrastructure.”

Cybereason, a Tel Aviv-and Boston, Massachusetts-based cybersecurity company providing endpoint prevention, detection, and response, has secured a $50 million investment from Google Cloud, VentureBeat has learned. It extends the series F round that Cybereason announced in July from $275 million to $325 million, making Cybereason one of the best-funded startups in the cybersecurity industry with over $713 million in the capital.

We reached out to a Google Cloud spokesperson, but they didn’t respond by press time.

The infusion of cash comes after Cybereason and Google Cloud entered into a strategic partnership to bring to market a platform — Cybereason XDR, powered by Chronicle — that can ingest and analyze “petabyte-scale” telemetry from endpoints, networks, containers, apps, profiles, and cloud infrastructure. Combining technology from Cybereason, Google Cloud, and Chronicle, the platform scans more than 23 trillion security-related events per week and applies AI to help reveal, mitigate, and predict cyberattacks correlated across devices, users, apps, and cloud deployments.

If the properties of materials can be reliably predicted, then the process of developing new products for a huge range of industries can be streamlined and accelerated. In a study published in Advanced Intelligent Systems, researchers from The University of Tokyo Institute of Industrial Science used core-loss spectroscopy to determine the properties of organic molecules using machine learning.

The spectroscopy techniques energy loss near-edge structure (ELNES) and X-ray near-edge structure (XANES) are used to determine information about the electrons, and through that the atoms, in materials. They have high sensitivity and high resolution and have been used to investigate a range of materials from electronic devices to drug delivery systems.

However, connecting spectral data to the properties of a material—things like optical properties, electron conductivity, density, and stability—remains ambiguous. Machine learning (ML) approaches have been used to extract information for large complex sets of data. Such approaches use artificial neural networks, which are based on how our brains work, to constantly learn to solve problems. Although the group previously used ELNES/XANES spectra and ML to find out information about materials, what they found did not relate to the properties of the material itself. Therefore, the information could not be easily translated into developments.

While the pandemic is still raging, the chaos of the past 18 months has calmed a bit, and the dust is starting to settle. Now the time has come for healthcare CIOs and other health IT leaders to look forward and plan their IT investments – shaped, in no small part, by the lessons of the recent past.

It’s been a hot, hot year in the world of data, machine learning, and AI. Just when you thought it couldn’t grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc.

It has also been a year of multiple threads and stories intertwining.

One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, DataRobot, etc.) have raised very largely (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index).

Arm is releasing new chip design offerings in the internet of things (IoT), virtual hardware, and 5G sectors.

Cambridge, United Kingdom-based Arm designs the architecture that other licensed chip makers use to build their chips. Arm likes to make it easier for those licensees to come up with their applications and create a foundation for an IoT economy.

So the company said its Arm Total Solutions for IoT now delivers a full-stack solution to significantly accelerate the development and return-on-investment for IoT chip products. And Arm Virtual Hardware removes the need to develop on physical silicon, enabling software and hardware co-design and accelerating product design by up to two years, the company claimed.

A Russian actress and a film director returned to Earth Sunday after spending 12 days on the International Space Station (ISS) shooting scenes for the first movie in orbit…

Yulia Peresild and Klim Shipenko landed as scheduled on Kazakhstan’s steppe at 436 GMT, according to footage broadcast live by the Russian space agency.

They were ferried back to terra firma by cosmonaut Oleg Novitsky, who had been on the space station for the past six months.

“The descent vehicle of the crewed spacecraft Soyuz MS-18 is standing upright and is secure. The crew are feeling good!” Russian space agency Roscosmos tweeted.

A company building chips designed for AI at the edge gets $136M: There’s no shortage of funds for any chip firm building processors for AI. After a desert of chip funding in the mid-aughts, I’m grateful for it, but it’s an overwhelming amount of money…

Tiny satellites and radios made for tracking big animals: This article is really interesting and shows just how small but powerful tracking devices have become when it comes to keeping an eye on the animal population. For example, not too long ago a tracking device meant for specific sharks would cost $10,000. These days? Open source projects combined with low-cost radios drop the price to just over a tenth of that. These aren’t just for the biggest of the big, though. One researcher at Yale has attached small “backpacks” weighting just 3.5 grams to 55 American robins to follow their migration path and time. Aside from the decreased price of the radio technology, it’s impressive how this community is working together on a common problem. (Washington Post) — Kevin C. Tofel.