artificial inteligence

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If you want to satiate AI’s hunger for power, Google suggests going to space


Google engineers think they already have all the pieces needed to build a data center in orbit.

With Project Suncatcher, Google will test its Tensor Processing Units on satellites. Credit: Google

It was probably always when, not if, Google would add its name to the list of companies intrigued by the potential of orbiting data centers.

Google announced Tuesday a new initiative, named Project Suncatcher, to examine the feasibility of bringing artificial intelligence to space. The idea is to deploy swarms of satellites in low-Earth orbit, each carrying Google’s AI accelerator chips designed for training, content generation, synthetic speech and vision, and predictive modeling. Google calls these chips Tensor Processing Units, or TPUs.

“Project Suncatcher is a moonshot exploring a new frontier: equipping solar-powered satellite constellations with TPUs and free-space optical links to one day scale machine learning compute in space,” Google wrote in a blog post.

“Like any moonshot, it’s going to require us to solve a lot of complex engineering challenges,” Google’s CEO, Sundar Pichai, wrote on X. Pichai noted that Google’s early tests show the company’s TPUs can withstand the intense radiation they will encounter in space. “However, significant challenges still remain like thermal management and on-orbit system reliability.”

The why and how

Ars reported on Google’s announcement on Tuesday, and Google published a research paper outlining the motivation for such a moonshot project. One of the authors, Travis Beals, spoke with Ars about Project Suncatcher and offered his thoughts on why it just might work.

“We’re just seeing so much demand from people for AI,” said Beals, senior director of Paradigms of Intelligence, a research team within Google. “So, we wanted to figure out a solution for compute that could work no matter how large demand might grow.”

Higher demand will lead to bigger data centers consuming colossal amounts of electricity. According to the MIT Technology Review, AI alone could consume as much electricity annually as 22 percent of all US households by 2028. Cooling is also a problem, often requiring access to vast water resources, raising important questions about environmental sustainability.

Google is looking to the sky to avoid potential bottlenecks. A satellite in space can access an infinite supply of renewable energy and an entire Universe to absorb heat.

“If you think about a data center on Earth, it’s taking power in and it’s emitting heat out,” Beals said. “For us, it’s the satellite that’s doing the same. The satellite is going to have solar panels … They’re going to feed that power to the TPUs to do whatever compute we need them to do, and then the waste heat from the TPUs will be distributed out over a radiator that will then radiate that heat out into space.”

Google envisions putting a legion of satellites into a special kind of orbit that rides along the day-night terminator, where sunlight meets darkness. This north-south, or polar, orbit would be synchronized with the Sun, allowing a satellite’s power-generating solar panels to remain continuously bathed in sunshine.

“It’s much brighter even than the midday Sun on Earth because it’s not filtered by Earth’s atmosphere,” Beals said.

This means a solar panel in space can produce up to eight times more power than the same collecting area on the ground, and you don’t need a lot of batteries to reserve electricity for nighttime. This may sound like the argument for space-based solar power, an idea first described by Isaac Asimov in his short story Reason published in 1941. But instead of transmitting the electricity down to Earth for terrestrial use, orbiting data centers would tap into the power source in space.

“As with many things, the ideas originate in science fiction, but it’s had a number of challenges, and one big one is, how do you get the power down to Earth?” Beals said. “So, instead of trying to figure out that, we’re embarking on this moonshot to bring [machine learning] compute chips into space, put them on satellites that have the solar panels and the radiators for cooling, and then integrate it all together so you don’t actually have to be powered on Earth.”

SpaceX is driving down launch costs, thanks to reusable rockets and an abundant volume of Starlink satellite launches. Credit: SpaceX

Google has a mixed record with its ambitious moonshot projects. One of the most prominent moonshot graduates is the self-driving car kit developer Waymo, which spun out to form a separate company in 2016 and is now operational. The Project Loon initiative to beam Internet signals from high-altitude balloons is one of the Google moonshots that didn’t make it.

Ars published two stories last week on the promise of space-based data centers. One of the startups in this field, named Starcloud, is partnering with Nvidia, the world’s largest tech company by market capitalization, to build a 5 gigawatt orbital data center with enormous solar and cooling panels approximately 4 kilometers (2.5 miles) in width and length. In response to that story, Elon Musk said SpaceX is pursuing the same business opportunity but didn’t provide any details. It’s worth noting that Google holds an estimated 7 percent stake in SpaceX.

Strength in numbers

Google’s proposed architecture differs from that of Starcloud and Nvidia in an important way. Instead of putting up just one or a few massive computing nodes, Google wants to launch a fleet of smaller satellites that talk to one another through laser data links. Essentially, a satellite swarm would function as a single data center, using light-speed interconnectivity to aggregate computing power hundreds of miles over our heads.

If that sounds implausible, take a moment to think about what companies are already doing in space today. SpaceX routinely launches more than 100 Starlink satellites per week, each of which uses laser inter-satellite links to bounce Internet signals around the globe. Amazon’s Kuiper satellite broadband network uses similar technology, and laser communications will underpin the US Space Force’s next-generation data-relay constellation.

Artist’s illustration of laser crosslinks in space. Credit: TESAT

Autonomously constructing a miles-long structure in orbit, as Nvidia and Starcloud foresee, would unlock unimagined opportunities. The concept also relies on tech that has never been tested in space, but there are plenty of engineers and investors who want to try. Starcloud announced an agreement last week with a new in-space assembly company, Rendezvous Robotics, to explore the use of modular, autonomous assembly to build Starcloud’s data centers.

Google’s research paper describes a future computing constellation of 81 satellites flying at an altitude of some 400 miles (650 kilometers), but Beals said the company could dial the total swarm size to as many spacecraft as the market demands. This architecture could enable terawatt-class orbital data centers, according to Google.

“What we’re actually envisioning is, potentially, as you scale, you could have many clusters,” Beals said.

Whatever the number, the satellites will communicate with one another using optical inter-satellite links for high-speed, low-latency connectivity. The satellites will need to fly in tight formation, perhaps a few hundred feet apart, with a swarm diameter of a little more than a mile, or about 2 kilometers. Google says its physics-based model shows satellites can maintain stable formations at such close ranges using automation and “reasonable propulsion budgets.”

“If you’re doing something that requires a ton of tight coordination between many TPUs—training, in particular—you want links that have as low latency as possible and as high bandwidth as possible,” Beals said. “With latency, you run into the speed of light, so you need to get things close together there to reduce latency. But bandwidth is also helped by bringing things close together.”

Some machine-learning applications could be done with the TPUs on just one modestly sized satellite, while others may require the processing power of multiple spacecraft linked together.

“You might be able to fit smaller jobs into a single satellite. This is an approach where, potentially, you can tackle a lot of inference workloads with a single satellite or a small number of them, but eventually, if you want to run larger jobs, you may need a larger cluster all networked together like this,” Beals said.

Google has worked on Project Suncatcher for more than a year, according to Beals. In ground testing, engineers tested Google’s TPUs under a 67 MeV proton beam to simulate the total ionizing dose of radiation the chip would see over five years in orbit. Now, it’s time to demonstrate Google’s AI chips, and everything else needed for Project Suncatcher will actually work in the real environment.

Google is partnering with Planet, the Earth-imaging company, to develop a pair of small prototype satellites for launch in early 2027. Planet builds its own satellites, so Google has tapped it to manufacture each spacecraft, test them, and arrange for their launch. Google’s parent company, Alphabet, also has an equity stake in Planet.

“We have the TPUs and the associated hardware, the compute payload… and we’re bringing that to Planet,” Beals said. “For this prototype mission, we’re really asking them to help us do everything to get that ready to operate in space.”

Beals declined to say how much the demo slated for launch in 2027 will cost but said Google is paying Planet for its role in the mission. The goal of the demo mission is to show whether space-based computing is a viable enterprise.

“Does it really hold up in space the way we think it will, the way we’ve tested on Earth?” Beals said.

Engineers will test an inter-satellite laser link and verify Google’s AI chips can weather the rigors of spaceflight.

“We’re envisioning scaling by building lots of satellites and connecting them together with ultra-high bandwidth inter-satellite links,” Beals said. “That’s why we want to launch a pair of satellites, because then we can test the link between the satellites.”

Evolution of a free-fall (no thrust) constellation under Earth’s gravitational attraction, modeled to the level of detail required to obtain Sun-synchronous orbits, in a non-rotating coordinate system. Credit: Google

Getting all this data to users on the ground is another challenge. Optical data links could also route enormous amounts of data between the satellites in orbit and ground stations on Earth.

Aside from the technical feasibility, there have long been economic hurdles to fielding large satellite constellations. But SpaceX’s experience with its Starlink broadband network, now with more than 8,000 active satellites, is proof that times have changed.

Google believes the economic equation is about to change again when SpaceX’s Starship rocket comes online. The company’s learning curve analysis shows launch prices could fall to less than $200 per kilogram by around 2035, assuming Starship is flying about 180 times per year by then. This is far below SpaceX’s stated launch targets for Starship but comparable to SpaceX’s proven flight rate with its workhorse Falcon 9 rocket.

It’s possible there could be even more downward pressure on launch costs if SpaceX, Nvidia, and others join Google in the race for space-based computing. The demand curve for access to space may only be eclipsed by the world’s appetite for AI.

“The more people are doing interesting, exciting things in space, the more investment there is in launch, and in the long run, that could help drive down launch costs,” Beals said. “So, it’s actually great to see that investment in other parts of the space supply chain and value chain. There are a lot of different ways of doing this.”

Photo of Stephen Clark

Stephen Clark is a space reporter at Ars Technica, covering private space companies and the world’s space agencies. Stephen writes about the nexus of technology, science, policy, and business on and off the planet.

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Perplexity offers more than twice its total valuation to buy Chrome from Google

Google has strenuously objected to the government’s proposed Chrome divestment, which it calls “a radical interventionist agenda.” Chrome isn’t just a browser—it’s an open source project known as Chromium, which powers numerous non-Google browsers, including Microsoft’s Edge. Perplexity’s offer includes $3 billion to run Chromium over two years, and it allegedly vows to keep the project fully open source. Perplexity promises it also won’t enforce changes to the browser’s default search engine.

An unsolicited offer

We’re currently waiting on United States District Court Judge Amit Mehta to rule on remedies in the case. That could happen as soon as this month. Perplexity’s offer, therefore, is somewhat timely, but there could still be a long road ahead.

This is an unsolicited offer, and there’s no indication that Google will jump at the chance to sell Chrome as soon as the ruling drops. Even if the court decides that Google should sell, it can probably get much, much more than Perplexity is offering. During the trial, DuckDuckGo’s CEO suggested a price of around $50 billion, but other estimates have ranged into the hundreds of billions. However, the data that flows to Chrome’s owner could be vital in building new AI technologies—any sale price is likely to be a net loss for Google.

If Mehta decides to force a sale, there will undoubtedly be legal challenges that could take months or years to resolve. Should these maneuvers fail, there’s likely to be opposition to any potential buyer. There will be many users who don’t like the idea of an AI startup or an unholy alliance of venture capital firms owning Chrome. Google has been hoovering up user data with Chrome for years—but that’s the devil we know.

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No cloud needed: Nvidia creates gaming-centric AI chatbot that runs on your GPU

Nvidia has seen its fortunes soar in recent years as its AI-accelerating GPUs have become worth their weight in gold. Most people use their Nvidia GPUs for games, but why not both? Nvidia has a new AI you can run at the same time, having just released its experimental G-Assist AI. It runs locally on your GPU to help you optimize your PC and get the most out of your games. It can do some neat things, but Nvidia isn’t kidding when it says this tool is experimental.

G-Assist is available in the Nvidia desktop app, and it consists of a floating overlay window. After invoking the overlay, you can either type or speak to G-Assist to check system stats or make tweaks to your settings. You can ask basic questions like, “How does DLSS Frame Generation work?” but it also has control over some system-level settings.

By calling up G-Assist, you can get a rundown of how your system is running, including custom data charts created on the fly by G-Assist. You can also ask the AI to tweak your machine, for example, optimizing the settings for a particular game or toggling on or off a setting. G-Assist can even overclock your GPU if you so choose, complete with a graph of expected performance gains.

Nvidia on G-Assist.

Nvidia demoed G-Assist last year with some impressive features tied to the active game. That version of G-Assist could see what you were doing and offer suggestions about how to reach your next objective. The game integration is sadly quite limited in the public version, supporting just a few games, like Ark: Survival Evolved.

There is, however, support for a number of third-party plug-ins that give G-Assist control over Logitech G, Corsair, MSI, and Nanoleaf peripherals. So, for instance, G-Assist could talk to your MSI motherboard to control your thermal profile or ping Logitech G to change your LED settings.

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Google’s free Gemini Code Assist arrives with sky-high usage limits

Generative AI has wormed its way into myriad products and services, some of which benefit more from these tools than others. Coding with AI has proven to be a better application than most, with individual developers and big companies leaning heavily on generative tools to create and debug programs. Now, indie developers have access to a new AI coding tool free of charge—Google has announced that Gemini Code Assist is available to everyone.

Gemini Code Assist was first released late last year as an enterprise tool, and the new version has almost all the same features. While you can use the standard Gemini or another AI model like ChatGPT to work on coding questions, Gemini Code Assist was designed to fully integrate with the tools developers are already using. Thus, you can tap the power of a large language model (LLM) without jumping between windows. With Gemini Code Assist connected to your development environment, the model will remain aware of your code and ready to swoop in with suggestions. The model can also address specific challenges per your requests, and you can chat with the model about your code, provided it’s a public domain language.

At launch, Gemini Code Assist pricing started at $45 per month per user. Now, it costs nothing for individual developers, and the limits on the free tier are generous. Google says the product offers 180,000 code completions per month, which it claims is enough that even prolific professional developers won’t run out. This is in stark contrast to Microsoft’s GitHub Copilot, which offers similar features with a limit of just 2,000 code completions and 50 Copilot chat messages per month. Google did the math to point out Gemini Code Assist offers 90 times the completions of Copilot.

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School did nothing wrong when it punished student for using AI, court rules


Student “indiscriminately copied and pasted text,” including AI hallucinations.

Credit: Getty Images | Andriy Onufriyenko

A federal court yesterday ruled against parents who sued a Massachusetts school district for punishing their son who used an artificial intelligence tool to complete an assignment.

Dale and Jennifer Harris sued Hingham High School officials and the School Committee and sought a preliminary injunction requiring the school to change their son’s grade and expunge the incident from his disciplinary record before he needs to submit college applications. The parents argued that there was no rule against using AI in the student handbook, but school officials said the student violated multiple policies.

The Harris’ motion for an injunction was rejected in an order issued yesterday from US District Court for the District of Massachusetts. US Magistrate Judge Paul Levenson found that school officials “have the better of the argument on both the facts and the law.”

“On the facts, there is nothing in the preliminary factual record to suggest that HHS officials were hasty in concluding that RNH [the Harris’ son, referred to by his initials] had cheated,” Levenson wrote. “Nor were the consequences Defendants imposed so heavy-handed as to exceed Defendants’ considerable discretion in such matters.”

“On the evidence currently before the Court, I detect no wrongdoing by Defendants,” Levenson also wrote.

Students copied and pasted AI “hallucinations”

The incident occurred in December 2023 when RNH was a junior. The school determined that RNH and another student “had cheated on an AP US History project by attempting to pass off, as their own work, material that they had taken from a generative artificial intelligence (‘AI’) application,” Levenson wrote. “Although students were permitted to use AI to brainstorm topics and identify sources, in this instance the students had indiscriminately copied and pasted text from the AI application, including citations to nonexistent books (i.e., AI hallucinations).”

They received failing grades on two parts of the multi-part project but “were permitted to start from scratch, each working separately, to complete and submit the final project,” the order said. RNH’s discipline included a Saturday detention. He was also barred from selection for the National Honor Society, but he was ultimately allowed into the group after his parents filed the lawsuit.

School officials “point out that RNH was repeatedly taught the fundamentals of academic integrity, including how to use and cite AI,” Levenson wrote. The magistrate judge agreed that “school officials could reasonably conclude that RNH’s use of AI was in violation of the school’s academic integrity rules and that any student in RNH’s position would have understood as much.”

Levenson’s order described how the students used AI to generate a script for a documentary film:

The evidence reflects that the pair did not simply use AI to help formulate research topics or identify sources to review. Instead, it seems they indiscriminately copied and pasted text that had been generated by Grammarly.com (“Grammarly”), a publicly available AI tool, into their draft script. Evidently, the pair did not even review the “sources” that Grammarly provided before lifting them. The very first footnote in the submission consists of a citation to a nonexistent book: “Lee, Robert. Hoop Dreams: A Century of Basketball. Los Angeles: Courtside Publications, 2018.” The third footnote also appears wholly factitious: “Doe, Jane. Muslim Pioneers: The Spiritual Journey of American Icons. Chicago: Windy City Publishers, 2017.” Significantly, even though the script contained citations to various sources—some of which were real—there was no citation to Grammarly, and no acknowledgement that AI of any kind had been used.

Tool flagged paper as AI-generated

When the students submitted their script via Turnitin.com, the website flagged portions of it as being AI-generated. The AP US History teacher conducted further examination, finding that large portions of the script had been copied and pasted. She also found other damning details.

History teacher Susan Petrie “testified that the revision history showed that RNH had only spent approximately 52 minutes in the document, whereas other students spent between seven and nine hours. Ms. Petrie also ran the submission through ‘Draft Back’ and ‘Chat Zero,’ two additional AI detection tools, which also indicated that AI had been used to generate the document,” the order said.

School officials argued that the “case did not implicate subtle questions of acceptable practices in deploying a new technology, but rather was a straightforward case of academic dishonesty,” Levenson wrote. The magistrate judge’s order said “it is doubtful that the Court has any role in second-guessing” the school’s determination, and that RNH’s plaintiffs did not show any misconduct by school authorities.

As we previously reported, school officials told the court that the student handbook’s section on cheating and plagiarism bans “unauthorized use of technology during an assignment” and “unauthorized use or close imitation of the language and thoughts of another author and the representation of them as one’s own work.”

School officials also told the court that in fall 2023, students were given a copy of a “written policy on Academic Dishonesty and AI expectations” that said students “shall not use AI tools during in-class examinations, processed writing assignments, homework or classwork unless explicitly permitted and instructed.”

The parents’ case hangs largely on the student handbook’s lack of a specific statement about AI, even though that same handbook bans unauthorized use of technology. “They told us our son cheated on a paper, which is not what happened,” Jennifer Harris told WCVB last month. “They basically punished him for a rule that doesn’t exist.”

Parents’ other claims rejected

The Harrises also claim that school officials engaged in a “pervasive pattern of threats, intimidation, coercion, bullying, harassment, and intimation of reprisals.” But Levenson concluded that the “plaintiffs provide little in the way of factual allegations along these lines.”

While the case isn’t over, the rejection of the preliminary injunction shows that Levenson believes the defendants are likely to win. “The manner in which RNH used Grammarly—wholesale copying and pasting of language directly into the draft script that he submitted—powerfully supports Defendants’ conclusion that RNH knew that he was using AI in an impermissible fashion,” Levenson wrote.

While “the emergence of generative AI may present some nuanced challenges for educators, the issue here is not particularly nuanced, as there is no discernible pedagogical purpose in prompting Grammarly (or any other AI tool) to generate a script, regurgitating the output without citation, and claiming it as one’s own work,” the order said.

Levenson wasn’t impressed by the parents’ claim that RNH’s constitutional right to due process was violated. The defendants “took multiple steps to confirm that RNH had in fact used AI in completing the Assignment” before imposing a punishment, he wrote. The discipline imposed “did not deprive RNH of his right to a public education,” and thus “any substantive due process claim premised on RNH’s entitlement to a public education must fail.”

Levenson concluded with a quote from a 1988 Supreme Court ruling that said the education of youth “is primarily the responsibility of parents, teachers, and state and local school officials, and not of federal judges.” According to Levenson, “This case well illustrates the good sense in that division of labor. The public interest here weighs in favor of Defendants.”

Photo of Jon Brodkin

Jon is a Senior IT Reporter for Ars Technica. He covers the telecom industry, Federal Communications Commission rulemakings, broadband consumer affairs, court cases, and government regulation of the tech industry.

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Can a technology called RAG keep AI models from making stuff up?

Can a technology called RAG keep AI models from making stuff up?

Aurich Lawson | Getty Images

We’ve been living through the generative AI boom for nearly a year and a half now, following the late 2022 release of OpenAI’s ChatGPT. But despite transformative effects on companies’ share prices, generative AI tools powered by large language models (LLMs) still have major drawbacks that have kept them from being as useful as many would like them to be. Retrieval augmented generation, or RAG, aims to fix some of those drawbacks.

Perhaps the most prominent drawback of LLMs is their tendency toward confabulation (also called “hallucination”), which is a statistical gap-filling phenomenon AI language models produce when they are tasked with reproducing knowledge that wasn’t present in the training data. They generate plausible-sounding text that can veer toward accuracy when the training data is solid but otherwise may just be completely made up.

Relying on confabulating AI models gets people and companies in trouble, as we’ve covered in the past. In 2023, we saw two instances of lawyers citing legal cases, confabulated by AI, that didn’t exist. We’ve covered claims against OpenAI in which ChatGPT confabulated and accused innocent people of doing terrible things. In February, we wrote about Air Canada’s customer service chatbot inventing a refund policy, and in March, a New York City chatbot was caught confabulating city regulations.

So if generative AI aims to be the technology that propels humanity into the future, someone needs to iron out the confabulation kinks along the way. That’s where RAG comes in. Its proponents hope the technique will help turn generative AI technology into reliable assistants that can supercharge productivity without requiring a human to double-check or second-guess the answers.

“RAG is a way of improving LLM performance, in essence by blending the LLM process with a web search or other document look-up process” to help LLMs stick to the facts, according to Noah Giansiracusa, associate professor of mathematics at Bentley University.

Let’s take a closer look at how it works and what its limitations are.

A framework for enhancing AI accuracy

Although RAG is now seen as a technique to help fix issues with generative AI, it actually predates ChatGPT. Researchers coined the term in a 2020 academic paper by researchers at Facebook AI Research (FAIR, now Meta AI Research), University College London, and New York University.

As we’ve mentioned, LLMs struggle with facts. Google’s entry into the generative AI race, Bard, made an embarrassing error on its first public demonstration back in February 2023 about the James Webb Space Telescope. The error wiped around $100 billion off the value of parent company Alphabet. LLMs produce the most statistically likely response based on their training data and don’t understand anything they output, meaning they can present false information that seems accurate if you don’t have expert knowledge on a subject.

LLMs also lack up-to-date knowledge and the ability to identify gaps in their knowledge. “When a human tries to answer a question, they can rely on their memory and come up with a response on the fly, or they could do something like Google it or peruse Wikipedia and then try to piece an answer together from what they find there—still filtering that info through their internal knowledge of the matter,” said Giansiracusa.

But LLMs aren’t humans, of course. Their training data can age quickly, particularly in more time-sensitive queries. In addition, the LLM often can’t distinguish specific sources of its knowledge, as all its training data is blended together into a kind of soup.

In theory, RAG should make keeping AI models up to date far cheaper and easier. “The beauty of RAG is that when new information becomes available, rather than having to retrain the model, all that’s needed is to augment the model’s external knowledge base with the updated information,” said Peterson. “This reduces LLM development time and cost while enhancing the model’s scalability.”

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