It turns out the space industry has a lot of ideas on how to improve NASA’s $11 billion, 15-year plan to collect and return samples from Mars.
Announced today, NASA has awarded $1.5 million contracts to seven companies to further develop their plans for the revamped Mars Sample Return mission.
According to the request for proposal, studies could be for complete overhauls of the mission design, or for designs that include elements of NASA’s MSR mission or NASA’s Artemis program.
NASA turned to private industry after finally admitting that its architecture for MSR is incredibly complicated.
Last year, an independent review board recommended that NASA revisit the mission design given the concerns about the technical features and the high costs.
Every company, large or small, needs to choose software, and the bigger the company, the more complex the exercise.
Some have internal tools and processes to help narrow down the list of possible vendors and eventually make a selection.
“Taloflow replaces homegrown technology and software selection processes that can last weeks or months,” the startup’s CEO and co-founder, Louis-Victor Jadavji, told TechCrunch.
“Unlike Gartner or G2, which offer mostly generic insights, Taloflow creates tailored reports for specific use cases,” he said.
Taloflow has built large language models that sift through publicly available information and speed up the time and cost of generating the base reports.
It was while sourcing manufacturing equipment for Tesla factories that Will Drewery drew inspiration for Diagon, a startup that helps manufacturers procure equipment.
Companies in fields like automotive and aerospace can identify qualified suppliers from Diagon’s network of equipment suppliers, system integrators and service providers.
East Coast originsThe journey to Diagon for Drewery, who spent most of his career as an equipment buyer, started in Pittsburgh.
“This is why I felt the market needs a Diagon,” Drewery said.
“Now we are developing tools that help customers find suppliers better or help them interpret and summarize quotes better,” Drewery said.
Just about everyone is trying to get a piece of the generative AI action these days.
While lacking the brand name recognition of some of these other players, it boasts the largest open source model API with over 12,000 users, per the company.
That kind of open source traction tends to attract investor attention, and the company has raised $25 million so far.
“It can be either off the shelf, open source models or the models we tune or the models our customer can tune by themselves.
Being an API, developers can plug it into their application, bring their model of choice trained on their data, and add generative AI capabilities like asking questions very quickly.
MIT professor Mike Stonebreaker has been at the forefront of database technology for over 50 years.
Now 80, he knows a thing or two about database technology and launching companies.
His latest project, DBOS, puts the database at the center of the software stack, reducing the operating system to a small kernel of low level functions.
“The genesis of the project was OLTP (online transaction processing) database systems have gotten a lot faster in the last 15 years.
And so the thesis was that they would be competitive as a new operating system stack,” he told TechCrunch.
After naming Phil Graves the chief financial officer of alternative protein maker Meati Foods at the beginning of February, he is now the company’s new chief executive officer.
The company also said it was reducing its workforce by 13% as Meati works to “build a financially sustainable business,” Graves told TechCrunch exclusively.
Products are now in over 3,600 retail locations nationwide, including Whole Foods Market, Meijer, Cub Foods and Sprouts Farmers Market.
Graves even said the company has a goal of expanding that to 10,000 locations before the end of 2024.
Once fully ramped up, the facility will have the capability of producing more than 45 million pounds of product, Huggins told TechCrunch at the time.
Now, before you get too excited about the “quantum” part of “Azure Quantum Elements” (and why wouldn’t you — it’s in the name, after all), let’s get this out of the way first: No quantum computer was used in this project.
Azure Quantum Elements, which launched last summer, combines AI and traditional high-performance computing (HPC) techniques into what is essentially a workbench for scientific computing, with the promise of providing access to Microsoft’s quantum supercomputer in the future.
Krysta Svore, who leads Microsoft Quantum, told me that the overall idea here was to see how far the team could push what is currently available in Azure Quantum Elements (AQE) — and especially the AI accelerator — to advance materials discovery.
After that, the researchers used existing HPC techniques to identify those 18 promising candidates to focus on.
And while the quantum computing community continues to push the state of the art ahead at a steady pace, we’re still at least a few years away from seeing a quantum computer that is actually useful.
Microsoft today announced that it has worked with the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) to use its Azure Quantum Elements service to whittle down millions of potential new battery materials to only a few — with one of them now in the prototype stage.
Now, before you get too excited about the ‘quantum’ part of ‘Azure Quantum Elements’ (and why wouldn’t you — it’s in the name, after all), let’s get this out of the way first: no quantum computer was used in this project.
Azure Quantum Elements, which launched last summer, combines AI and traditional high-performance computing (HPC) techniques into what is essentially a workbench for scientific computing, with the promise of providing access to Microsoft’s quantum supercomputer in the future.
Krysta Svore, who leads Microsoft Quantum, told me that the overall idea here was to see how far the team could push what is currently available in Azure Quantum Elements (AQE) — and especially the AI accelerator — to advance materials discovery.
And while the quantum computing community continues to push the state of the art ahead at a steady pace, we’re still at least a few years away from seeing a quantum computer that is actually useful.
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