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Lemurian Labs is building a new compute paradigm to reduce cost of running AI models

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It’s fair to say that Nvidia has found itself in the right place at the right time with demand for its GPU chips at an all time high, thanks to the resource demands of generative AI models, but what if there were a chip that provided similar power at a lower cost? That’s what Lemurian Labs, an early stage startup from Google, Intel and Nvidia alumni, is trying to build.

To be sure, it’s a kind of moonshot idea, and it takes a lot of time and money to get a chip idea to market, but it’s the kind of idea when it comes from founders with a certain pedigree that investors are willing to take a chance on. Today, the startup announced a $9 million seed investment.

“Fundamentally, at Lemurian, our goal is to reimagine accelerated computing. And the reason we want to do that is because the existing way we have done computing is starting to come to an end. And it’s not so much that it’s not a great architecture or paradigm, it is that the physics of semiconductors is pushing back against that paradigm,” Jay Dawani, co-founder and CEO at Lemurian, told TechCrunch.

The company’s goal is to build a new chip along with software to make processing AI workloads more accessible, efficient, cheaper, and ultimately more environmentally friendly.

As though holding a master class in computer architecture, Lemurian explains that computing comes down to three things: “There’s math, there’s memory, and then there’s movement. The goal is interconnects. So data gets stored in memories that gets moved through an interconnect into a math unit where it gets manipulated, then it gets written back in memory. So that is the traditional point in architecture: data has to travel,” Dawani explained.

Lemurian wants to flip that approach. Instead of making the data travel to the compute resources, it wants the compute to move to the data. “What we’re saying is we need to essentially minimize that distance, so that we aren’t really moving data, we’re moving around compute,” he said.

He says that GPUs were essentially created for graphics-related tasks, but over time have taken on a variety of other roles because of their pure processing capabilities. “Because you’re designing for something, but also trying to do something else, and when you’re trying to do everything, you’re not really that great at doing everything. And that’s really the achilles heel of a GPU. And that’s what we’re trying to fix,” Dawani said.

The way Lemurian wants to answer this is to change the math on the chip, a huge undertaking, no doubt. As Dawani tells it, in the early days of chip development, engineers made a decision to go with a floating point approach because nobody could get a logarithmic approach working. He claims that his company has solved that problem.

“And the beauty of a log number system is that it turns all those expensive multiplies and divides into adds and subtractions, which are very free operations in hardware. So you save on area and energy and you gain speed. And you also gain a bit on exactness or precision,” all of which are quite attractive when trying to bring down the cost of processing on large language models.

How did they do this? “We actually stumbled across the realization that by constructing in a certain way, and extending the definition of a large number system, you can actually create an exact solution, which ends up being smaller and more accurate than floating point for the same number of bits,” he said.

“And as you increase the number of bits, it grows better and better in dynamic range for the same number of bits, which is really, really fascinating. Now, that is a big part of what allows us to explore the architecture we did because without the number system you succumb to the same limitations.”

They are taking a go-slow approach, releasing the software part of the stack first, which they hope to have generally available in Q3 next year. The hardware is much more challenging and will take time and money to develop, manufacture and test in production, but the goal is for that to follow in the coming years.

The company currently has 24 employees, mostly highly skilled technical engineers with a background in this kind of project. That’s a limited pool of people, but his goal is to hire six more people over the next several months, and if all goes well, and they get a Series A, another 35 in the next year.

The $9 million investment was led by Oval Park Capital with participation from Good Growth Capital, Raptor Group and Alumni Ventures, among others.

Building a company like this and getting the chip to market represents a huge and expensive challenge, but if they can pull off what they describe, it could make building generative AI models (and whatever comes next) much cheaper and more efficient.



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Webb Telescope Just Found The Holy Grail In A Famous Supernova

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At long last scientists believe they have the answer to what happened to a star that died in a famous supernova explosion not far from home.

The James Webb Space Telescope detected strong evidence supporting the existence of a neutron star, one of the densest objects in space, in its infancy. While some supernovas result in a new black hole, others create neutron stars when the core of a massive star collapses.

Though astronomers have known about neutron stars for decades, no one had actually seen one of these objects being formed before. The hunt for a neutron star within this close supernova remnant has been regarded as a holy grail quest.

“With this observatory, we have now found direct evidence for emission triggered by the newborn compact object, most likely a neutron star,” said Claes Fransson of Stockholm University, the lead author of the study, in a statement released by NASA.

Scientists first saw this stellar explosion — dubbed SN 1987A — with the naked eye nearly 40 years ago in the Large Magellanic Cloud, a small satellite galaxy of the Milky Way about 160,000 light-years away. Since then, they’ve investigated it at radio, gamma-ray, and X-ray wavelengths — searching for clues among the ashes for what came of the deceased star.

But supernovas, by their very nature, churn out a lot of dust, clouding the view. Stars on the verge of dying and supernovas are element factories: They make carbon, for instance, the same chemical on which humans and much of life on Earth are based. Then they spread elements like calcium found in bones and iron in blood across interstellar space.

This dispersal seeds new generations of stars and planets, but scientists admit they have much to learn about the early stages of the process.

The James Webb Space Telescope has observed the best evidence yet for emissions from a neutron star in supernova remnant SN 1987A.
Credit: NASA / ESA / CSA / STScI / Claes Fransson / Mikako Matsuura / M. Barlow / Patrick Kavanagh / Josefin Larsson

Webb, the leading infrared telescope, was finally able to “see” what other telescopes couldn’t in the aftermath. The new study, published this week in the journal Science, found evidence of heavily ionized argon (meaning argon atoms that had become electrically charged) in the center of the exploded material. Researchers think the most likely explanation for the changed argon is ionizing radiation from a neutron star.

“To create these ions that we observed in the ejecta, it was clear that there had to be a source of high-energy radiation in the center of the SN 1987A remnant,” Fransson said in a statement. “Only a few scenarios are likely, and all of these involve a newly born neutron star.”

Solving this mystery may help scientists better understand how stellar corpses evolve over time.





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NASA Spacecraft Spots Dramatic View Of New Impact Crater On Mars

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There’s a fresh crater on Mars, a reminder of our still-dynamic solar system.

NASA‘s Mars Reconnaissance Orbiter, a spacecraft orbiting Mars since 2006, uses an extremely powerful camera to observe the Martian surface. The team running the aptly named High Resolution Imaging Experiment, or HIRISE camera, recently released a detailed image of this impact crater.

“A Small, Very Recent Impact Crater,” they succinctly posted online. “That’s it. That’s the whole caption.”

It’s not that small. Maybe small compared to the Martian behemoths. The image above is 1 kilometer (0.6 miles) across, while the zoomed-out view below shows a Martian scene 5 km (3.1 miles) wide.

It’s unclear when such a recent object, likely an asteroid, crashed into Mars, leaving a sizable dent in the equatorial region of the Red Planet. But you can see markings from ejecta strewn around the impact basin.

The “very recent” impact crater spotted in the equatorial region of Mars.
Credit: NASA / JPL-Caltech / UArizona

Mars is absolutely covered in craters. NASA estimates there are over a quarter-million impact craters about the size of Arizona’s famous Barringer Crater, which is some 4,000 feet across. And there are over 43,000 Martian craters larger than three miles wide.

The Red Planet is much closer to our solar system’s asteroid belt, a region teeming with millions of asteroids. When they do collide with Mars, the Martian atmosphere is just 1 percent the volume of Earth’s, meaning these space rocks are less likely to heat up and disintegrate. What’s more, Mars isn’t nearly geologically dead — marsquakes frequently occur there — but it’s not nearly as active as Earth, a water-blanketed planet teeming with erupting volcanoes. On Mars today, there’s no geologic activity or volcanism to wash away, or cover up, new craters.

(Meanwhile, Earth has just around 120 known impact craters. That’s because over hundreds of millions of years, different parts of Earth’s surface have both been covered in lava or recycled as the giant plates that compose Earth’s crust, tectonic plates, continually move rock below and back up to the surface.)

As for us Earthlings, significant strikes from asteroids are rare:

– Every single day, about 100 tons of dust and sand-sized particles fall through Earth’s atmosphere and promptly burn up.

– Every year, on average, an “automobile-sized asteroid” plummets through our sky and explodes, explains NASA.

– Impacts by objects around 460 feet in diameter occur every 10,000 to 20,000 years.

– And a “dinosaur-killing” impact from a rock perhaps a half-mile across or larger happens on 100-million-year timescales.

So there’s no reason to live in fear — but it’s reasonable to have a healthy level of respect for the big space rocks out there. After all, with the asteroid deflection technology being created and tested today, we might be able to nudge a menacing asteroid off its course, should one ever barrel toward our humble blue planet.



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Miranda Bogen is creating solutions to help govern AI

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To give AI-focused women academics and others their well-deserved — and overdue — time in the spotlight, TechCrunch is launching a series of interviews focusing on remarkable women who’ve contributed to the AI revolution. We’ll publish several pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here.

Miranda Bogen is the founding director of the Center of Democracy and Technology’s AI Governance Lab, where she works to help create solutions that can effectively regulate and govern AI systems. She helped guide responsible AI strategies at Meta and previously worked as a senior policy analyst at the organization Uptown, which seeks to use tech to advance equity and justice.

Briefly, how did you get your start in AI? What attracted you to the field?

I was drawn to work on machine learning and AI by seeing the way these technologies were colliding with fundamental conversations about society — values, rights, and which communities get left behind. My early work exploring the intersection of AI and civil rights reinforced for me that AI systems are far more than technical artifacts; they are systems that both shape and are shaped by their interaction with people, bureaucracies, and policies. I’ve always been adept at translating between technical and non-technical contexts, and I was energized by the opportunity to help break through the appearance of technical complexity to help communities with different kinds of expertise shape the way AI is built from the ground up.

What work are you most proud of (in the AI field)?

When I first started working in this space, many folks still needed to be convinced AI systems could result in discriminatory impact for marginalized populations, let alone that anything needed to be done about those harms. While there is still too wide a gap between the status quo and a future where biases and other harms are tackled systematically, I’m gratified that the research my collaborators and I conducted on discrimination in personalized online advertising and my work within the industry on algorithmic fairness helped lead to meaningful changes to Meta’s ad delivery system and progress toward reducing disparities in access to important economic opportunities.

How do you navigate the challenges of the male-dominated tech industry and, by extension, the male-dominated AI industry?

I’ve been lucky to work with phenomenal colleagues and teams who have been generous with both opportunities and sincere support, and we tried to bring that energy into any room we found ourselves in. In my most recent career transition, I was delighted that nearly all of my options involved working on teams or within organizations led by phenomenal women, and I hope the field continues to lift up the voices of those who haven’t traditionally been centered in technology-oriented conversations.

What advice would you give to women seeking to enter the AI field?

The same advice I give to anyone who asks: find supportive managers, advisors, and teams who energize and inspire you, who value your opinion and perspective, and who put themselves on the line to stand up for you and your work.

What are some of the most pressing issues facing AI as it evolves?

The impacts and harms AI systems are already having on people are well-known at this point, and one of the biggest pressing challenges is moving beyond describing the problem to developing robust approaches for systematically addressing those harms and incentivizing their adoption. We launched the AI Governance Lab at CDT to drive progress in both directions.

What are some issues AI users should be aware of?

For the most part, AI systems are still missing seat belts, airbags, and traffic signs, so proceed with caution before using them for consequential tasks.

What is the best way to responsibly build AI?

The best way to responsibly build AI is with humility. Consider how the success of the AI system you are working on has been defined, who that definition serves, and what context may be missing. Think about for whom the system might fail and what will happen if it does. And build systems not just with the people who will use them but with the communities who will be subject to them.

How can investors better push for responsible AI?

Investors need to create room for technology builders to move more deliberately before rushing half-baked technologies to market. Intense competitive pressure to release the newest, biggest, and shiniest new AI models is leading to concerning underinvestment in responsible practices. While uninhibited innovation sings a tempting siren song, it is a mirage that will leave everyone worse off.

AI is not magic; it’s just a mirror that is being held up to society. If we want it to reflect something different, we’ve got work to do.



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