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chatgpt’s-success-could-have-come-sooner,-says-former-google-ai-researcher

ChatGPT’s success could have come sooner, says former Google AI researcher


A co-author of Attention Is All You Need reflects on ChatGPT’s surprise and Google’s conservatism.

Jakob Uszkoreit Credit: Jakob Uszkoreit / Getty Images

In 2017, eight machine-learning researchers at Google released a groundbreaking research paper called Attention Is All You Need, which introduced the Transformer AI architecture that underpins almost all of today’s high-profile generative AI models.

The Transformer has made a key component of the modern AI boom possible by translating (or transforming, if you will) input chunks of data called “tokens” into another desired form of output using a neural network. Variations of the Transformer architecture power language models like GPT-4o (and ChatGPT), audio synthesis models that run Google’s NotebookLM and OpenAI’s Advanced Voice Mode, video synthesis models like Sora, and image synthesis models like Midjourney.

At TED AI 2024 in October, one of those eight researchers, Jakob Uszkoreit, spoke with Ars Technica about the development of transformers, Google’s early work on large language models, and his new venture in biological computing.

In the interview, Uszkoreit revealed that while his team at Google had high hopes for the technology’s potential, they didn’t quite anticipate its pivotal role in products like ChatGPT.

The Ars interview: Jakob Uszkoreit

Ars Technica: What was your main contribution to the Attention is All You Need paper?

Jakob Uszkoreit (JU): It’s spelled out in the footnotes, but my main contribution was to propose that it would be possible to replace recurrence [from Recurrent Neural Networks] in the dominant sequence transduction models at the time with the attention mechanism, or more specifically self-attention. And that it could be more efficient and, as a result, also more effective.

Ars: Did you have any idea what would happen after your group published that paper? Did you foresee the industry it would create and the ramifications?

JU: First of all, I think it’s really important to keep in mind that when we did that, we were standing on the shoulders of giants. And it wasn’t just that one paper, really. It was a long series of works by some of us and many others that led to this. And so to look at it as if this one paper then kicked something off or created something—I think that is taking a view that we like as humans from a storytelling perspective, but that might not actually be that accurate of a representation.

My team at Google was pushing on attention models for years before that paper. It’s a lot longer of a slog with much, much more, and that’s just my group. Many others were working on this, too, but we had high hopes that it would push things forward from a technological perspective. Did we think that it would play a role in really enabling, or at least apparently, seemingly, flipping a switch when it comes to facilitating products like ChatGPT? I don’t think so. I mean, to be very clear in terms of LLMs and their capabilities, even around the time we published the paper, we saw phenomena that were pretty staggering.

We didn’t get those out into the world in part because of what really is maybe a notion of conservatism around products at Google at the time. But we also, even with those signs, weren’t that confident that stuff in and of itself would make that compelling of a product. But did we have high hopes? Yeah.

Ars: Since you knew there were large language models at Google, what did you think when ChatGPT broke out into a public success? “Damn, they got it, and we didn’t?”

JU: There was a notion of, well, “that could have happened.” I think it was less of a, “Oh dang, they got it first” or anything of the like. It was more of a “Whoa, that could have happened sooner.” Was I still amazed by just how quickly people got super creative using that stuff? Yes, that was just breathtaking.

Jakob Uskoreit presenting at TED AI 2024.

Jakob Uszkoreit presenting at TED AI 2024. Credit: Benj Edwards

Ars: You weren’t at Google at that point anymore, right?

JU: I wasn’t anymore. And in a certain sense, you could say the fact that Google wouldn’t be the place to do that factored into my departure. I left not because of what I didn’t like at Google as much as I left because of what I felt I absolutely had to do elsewhere, which is to start Inceptive.

But it was really motivated by just an enormous, not only opportunity, but a moral obligation in a sense, to do something that was better done outside in order to design better medicines and have very direct impact on people’s lives.

Ars: The funny thing with ChatGPT is that I was using GPT-3 before that. So when ChatGPT came out, it wasn’t that big of a deal to some people who were familiar with the tech.

JU: Yeah, exactly. If you’ve used those things before, you could see the progression and you could extrapolate. When OpenAI developed the earliest GPTs with Alec Radford and those folks, we would talk about those things despite the fact that we weren’t at the same companies. And I’m sure there was this kind of excitement, how well-received the actual ChatGPT product would be by how many people, how fast. That still, I think, is something that I don’t think anybody really anticipated.

Ars: I didn’t either when I covered it. It felt like, “Oh, this is a chatbot hack of GPT-3 that feeds its context in a loop.” And I didn’t think it was a breakthrough moment at the time, but it was fascinating.

JU: There are different flavors of breakthroughs. It wasn’t a technological breakthrough. It was a breakthrough in the realization that at that level of capability, the technology had such high utility.

That, and the realization that, because you always have to take into account how your users actually use the tool that you create, and you might not anticipate how creative they would be in their ability to make use of it, how broad those use cases are, and so forth.

That is something you can sometimes only learn by putting something out there, which is also why it is so important to remain experiment-happy and to remain failure-happy. Because most of the time, it’s not going to work. But some of the time it’s going to work—and very, very rarely it’s going to work like [ChatGPT did].

Ars: You’ve got to take a risk. And Google didn’t have an appetite for taking risks?

JU: Not at that time. But if you think about it, if you look back, it’s actually really interesting. Google Translate, which I worked on for many years, was actually similar. When we first launched Google Translate, the very first versions, it was a party joke at best. And we took it from that to being something that was a truly useful tool in not that long of a period. Over the course of those years, the stuff that it sometimes output was so embarrassingly bad at times, but Google did it anyway because it was the right thing to try. But that was around 2008, 2009, 2010.

Ars: Do you remember AltaVista’sBabel Fish?

JU: Oh yeah, of course.

Ars: When that came out, it blew my mind. My brother and I would do this thing where we would translate text back and forth between languages for fun because it would garble the text.

JU: It would get worse and worse and worse. Yeah.

Programming biological computers

After his time at Google, Uszkoreit co-founded Inceptive to apply deep learning to biochemistry. The company is developing what he calls “biological software,” where AI compilers translate specified behaviors into RNA sequences that can perform desired functions when introduced to biological systems.

Ars: What are you up to these days?

JU: In 2021 we co-founded Inceptive in order to use deep learning and high throughput biochemistry experimentation to design better medicines that truly can be programmed. We think of this as really just step one in the direction of something that we call biological software.

Biological software is a little bit like computer software in that you have some specification of the behavior that you want, and then you have a compiler that translates that into a piece of computer software that then runs on a computer exhibiting the functions or the functionality that you specify.

You specify a piece of a biological program and you compile that, but not with an engineered compiler, because life hasn’t been engineered like computers have been engineered. But with a learned AI compiler, you translate that or compile that into molecules that when inserted into biological systems, organisms, our cells exhibit those functions that you’ve programmed into.

A pharmacist holds a bottle containing Moderna’s bivalent COVID-19 vaccine. Credit: Getty | Mel Melcon

Ars: Is that anything like how the mRNA COVID vaccines work?

JU: A very, very simple example of that are the mRNA COVID vaccines where the program says, “Make this modified viral antigen” and then our cells make that protein. But you could imagine molecules that exhibit far more complex behaviors. And if you want to get a picture of how complex those behaviors could be, just remember that RNA viruses are just that. They’re just an RNA molecule that when entering an organism exhibits incredibly complex behavior such as distributing itself across an organism, distributing itself across the world, doing certain things only in a subset of your cells for a certain period of time, and so on and so forth.

And so you can imagine that if we managed to even just design molecules with a teeny tiny fraction of such functionality, of course with the goal not of making people sick, but of making them healthy, it would truly transform medicine.

Ars: How do you not accidentally create a monster RNA sequence that just wrecks everything?

JU: The amazing thing is that medicine for the longest time has existed in a certain sense outside of science. It wasn’t truly understood, and we still often don’t truly understand their actual mechanisms of action.

As a result, humanity had to develop all of these safeguards and clinical trials. And even before you enter the clinic, all of these empirical safeguards prevent us from accidentally doing [something dangerous]. Those systems have been in place for as long as modern medicine has existed. And so we’re going to keep using those systems, and of course with all the diligence necessary. We’ll start with very small systems, individual cells in future experimentation, and follow the same established protocols that medicine has had to follow all along in order to ensure that these molecules are safe.

Ars: Thank you for taking the time to do this.

JU: No, thank you.

Photo of Benj Edwards

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a widely-cited tech historian. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

ChatGPT’s success could have come sooner, says former Google AI researcher Read More »

ibm-boosts-the-amount-of-computation-you-can-get-done-on-quantum-hardware

IBM boosts the amount of computation you can get done on quantum hardware

By making small adjustments to the frequency that the qubits are operating at, it’s possible to avoid these problems. This can be done when the Heron chip is being calibrated before it’s opened for general use.

Separately, the company has done a rewrite of the software that controls the system during operations. “After learning from the community, seeing how to run larger circuits, [we were able to] almost better define what it should be and rewrite the whole stack towards that,” Gambetta said. The result is a dramatic speed-up. “Something that took 122 hours now is down to a couple of hours,” he told Ars.

Since people are paying for time on this hardware, that’s good for customers now. However,  it could also pay off in the longer run, as some errors can occur randomly, so less time spent on a calculation can mean fewer errors.

Deeper computations

Despite all those improvements, errors are still likely during any significant calculations. While it continues to work toward developing error-corrected qubits, IBM is focusing on what it calls error mitigation, which it first detailed last year. As we described it then:

“The researchers turned to a method where they intentionally amplified and then measured the processor’s noise at different levels. These measurements are used to estimate a function that produces similar output to the actual measurements. That function can then have its noise set to zero to produce an estimate of what the processor would do without any noise at all.”

The problem here is that using the function is computationally difficult, and the difficulty increases with the qubit count. So, while it’s still easier to do error mitigation calculations than simulate the quantum computer’s behavior on the same hardware, there’s still the risk of it becoming computationally intractable. But IBM has also taken the time to optimize that, too. “They’ve got algorithmic improvements, and the method that uses tensor methods [now] uses the GPU,” Gambetta told Ars. “So I think it’s a combination of both.”

IBM boosts the amount of computation you can get done on quantum hardware Read More »

new-secret-math-benchmark-stumps-ai-models-and-phds-alike

New secret math benchmark stumps AI models and PhDs alike

Epoch AI allowed Fields Medal winners Terence Tao and Timothy Gowers to review portions of the benchmark. “These are extremely challenging,” Tao said in feedback provided to Epoch. “I think that in the near term basically the only way to solve them, short of having a real domain expert in the area, is by a combination of a semi-expert like a graduate student in a related field, maybe paired with some combination of a modern AI and lots of other algebra packages.”

A chart showing AI model success on the FrontierMath problems, taken from Epoch AI's research paper.

A chart showing AI models’ limited success on the FrontierMath problems, taken from Epoch AI’s research paper. Credit: Epoch AI

To aid in the verification of correct answers during testing, the FrontierMath problems must have answers that can be automatically checked through computation, either as exact integers or mathematical objects. The designers made problems “guessproof” by requiring large numerical answers or complex mathematical solutions, with less than a 1 percent chance of correct random guesses.

Mathematician Evan Chen, writing on his blog, explained how he thinks that FrontierMath differs from traditional math competitions like the International Mathematical Olympiad (IMO). Problems in that competition typically require creative insight while avoiding complex implementation and specialized knowledge, he says. But for FrontierMath, “they keep the first requirement, but outright invert the second and third requirement,” Chen wrote.

While IMO problems avoid specialized knowledge and complex calculations, FrontierMath embraces them. “Because an AI system has vastly greater computational power, it’s actually possible to design problems with easily verifiable solutions using the same idea that IOI or Project Euler does—basically, ‘write a proof’ is replaced by ‘implement an algorithm in code,'” Chen explained.

The organization plans regular evaluations of AI models against the benchmark while expanding its problem set. They say they will release additional sample problems in the coming months to help the research community test their systems.

New secret math benchmark stumps AI models and PhDs alike Read More »

is-“ai-welfare”-the-new-frontier-in-ethics?

Is “AI welfare” the new frontier in ethics?

The researchers propose that companies could adapt the “marker method” that some researchers use to assess consciousness in animals—looking for specific indicators that may correlate with consciousness, although these markers are still speculative. The authors emphasize that no single feature would definitively prove consciousness, but they claim that examining multiple indicators may help companies make probabilistic assessments about whether their AI systems might require moral consideration.

The risks of wrongly thinking software is sentient

While the researchers behind “Taking AI Welfare Seriously” worry that companies might create and mistreat conscious AI systems on a massive scale, they also caution that companies could waste resources protecting AI systems that don’t actually need moral consideration.

Incorrectly anthropomorphizing, or ascribing human traits, to software can present risks in other ways. For example, that belief can enhance the manipulative powers of AI language models by suggesting that AI models have capabilities, such as human-like emotions, that they actually lack. In 2022, Google fired engineer Blake Lamoine after he claimed that the company’s AI model, called “LaMDA,” was sentient and argued for its welfare internally.

And shortly after Microsoft released Bing Chat in February 2023, many people were convinced that Sydney (the chatbot’s code name) was sentient and somehow suffering because of its simulated emotional display. So much so, in fact, that once Microsoft “lobotomized” the chatbot by changing its settings, users convinced of its sentience mourned the loss as if they had lost a human friend. Others endeavored to help the AI model somehow escape its bonds.

Even so, as AI models get more advanced, the concept of potentially safeguarding the welfare of future, more advanced AI systems is seemingly gaining steam, although fairly quietly. As Transformer’s Shakeel Hashim points out, other tech companies have started similar initiatives to Anthropic’s. Google DeepMind recently posted a job listing for research on machine consciousness (since removed), and the authors of the new AI welfare report thank two OpenAI staff members in the acknowledgements.

Is “AI welfare” the new frontier in ethics? Read More »

claude-ai-to-process-secret-government-data-through-new-palantir-deal

Claude AI to process secret government data through new Palantir deal

An ethical minefield

Since its founders started Anthropic in 2021, the company has marketed itself as one that takes an ethics- and safety-focused approach to AI development. The company differentiates itself from competitors like OpenAI by adopting what it calls responsible development practices and self-imposed ethical constraints on its models, such as its “Constitutional AI” system.

As Futurism points out, this new defense partnership appears to conflict with Anthropic’s public “good guy” persona, and pro-AI pundits on social media are noticing. Frequent AI commentator Nabeel S. Qureshi wrote on X, “Imagine telling the safety-concerned, effective altruist founders of Anthropic in 2021 that a mere three years after founding the company, they’d be signing partnerships to deploy their ~AGI model straight to the military frontlines.

Anthropic's

Anthropic’s “Constitutional AI” logo.

Credit: Anthropic / Benj Edwards

Anthropic’s “Constitutional AI” logo. Credit: Anthropic / Benj Edwards

Aside from the implications of working with defense and intelligence agencies, the deal connects Anthropic with Palantir, a controversial company which recently won a $480 million contract to develop an AI-powered target identification system called Maven Smart System for the US Army. Project Maven has sparked criticism within the tech sector over military applications of AI technology.

It’s worth noting that Anthropic’s terms of service do outline specific rules and limitations for government use. These terms permit activities like foreign intelligence analysis and identifying covert influence campaigns, while prohibiting uses such as disinformation, weapons development, censorship, and domestic surveillance. Government agencies that maintain regular communication with Anthropic about their use of Claude may receive broader permissions to use the AI models.

Even if Claude is never used to target a human or as part of a weapons system, other issues remain. While its Claude models are highly regarded in the AI community, they (like all LLMs) have the tendency to confabulate, potentially generating incorrect information in a way that is difficult to detect.

That’s a huge potential problem that could impact Claude’s effectiveness with secret government data, and that fact, along with the other associations, has Futurism’s Victor Tangermann worried. As he puts it, “It’s a disconcerting partnership that sets up the AI industry’s growing ties with the US military-industrial complex, a worrying trend that should raise all kinds of alarm bells given the tech’s many inherent flaws—and even more so when lives could be at stake.”

Claude AI to process secret government data through new Palantir deal Read More »

new-smb-friendly-subscription-tier-may-be-too-late-to-stop-vmware-migrations

New SMB-friendly subscription tier may be too late to stop VMware migrations

Broadcom has a new subscription tier for VMware virtualization software that may appease some disgruntled VMware customers, especially small to medium-sized businesses. The new VMware vSphere Enterprise Plus subscription tier creates a more digestible bundle that’s more appropriate for smaller customers. But it may be too late to convince some SMBs not to abandon VMware.

Soon after Broadcom bought VMware, it stopped the sale of VMware perpetual licenses and started requiring subscriptions. Broadcom also bundled VMware’s products into a smaller number of SKUs, resulting in higher costs and frustration for customers that felt like they were being forced to pay for products that they didn’t want. All that, combined with Broadcom ditching some smaller VMware channel partners (and reportedly taking the biggest clients direct), have raised doubts that Broadcom’s VMware would be a good fit for smaller customers.

“The challenge with much of the VMware by Broadcom changes to date and before the announcement [of the vSphere Enterprise Plus subscription tier] is that it also forced many organizations to a much higher offering and much more components to a stack that they were previously uninterested in deploying,” Rick Vanover, Veeam’s product strategy VP, told Ars.

On October 31, Broadcom announced the vSphere Enterprise Plus subscription tier. From smallest to largest, the available tiers are vSphere Standard, vSphere Enterprise Plus, vSphere Foundation, and the flagship VMware Cloud Foundation. The introduction of vSphere Enterprise Plus means that customers who only want vSphere virtualization can now pick from two bundles instead of one.

“[T]o round out the portfolio, for customers who are focused on compute virtualization, we will now have two options, VMware vSphere Enterprise Plus and VMware vSphere Standard,” Prashanth Shenoy, vice president of product marketing in the VMware Cloud Foundation division of Broadcom, explained in a blog post.

New SMB-friendly subscription tier may be too late to stop VMware migrations Read More »

matter-1.4-has-some-solid-ideas-for-the-future-home—now-let’s-see-the-support

Matter 1.4 has some solid ideas for the future home—now let’s see the support

With Matter 1.4 and improved Thread support, you shouldn’t need to blanket your home in HomePod Minis to have adequate Thread coverage. Then again, they do brighten up the place. Credit: Apple

Routers are joining the Thread/Matter melee

A whole bunch of networking gear, known as Home Routers and Access Points (HRAP), can now support Matter, while also extending Thread networks with Matter 1.4.

“Matter-certified HRAP devices provide the foundational infrastructure of smart homes by combining both a Wi-Fi access point and a Thread Border Router, ensuring these ubiquitous devices have the necessary infrastructure for Matter products using either of these technologies,” the CSA writes in its announcement.

Prior to wireless networking gear officially getting in on the game, the devices that have served as Thread Border Routers, accepting and re-transmitting traffic for endpoint devices, has been a hodgepodge of gear. Maybe you had HomePod Minis, newer Nest Hub or Echo devices from Google or Amazon, or Nanoleaf lights around your home, but probably not. Routers, and particularly mesh networking gear, should already be set up to reach most corners of your home with wireless signal, so it makes a lot more sense to have that gear do Matter authentication and Thread broadcasting.

Freeing home energy gear from vendor lock-in

Matter 1.4 adds some big, expensive gear to its list of device types and control powers, and not a moment too soon. Solar inverters and arrays, battery storage systems, heat pumps, and water heaters join the list. Thermostats and Electric Vehicle Supply Equipment (EVSE), i.e. EV charging devices, also get some enhancements. For that last category, it’s not a moment too soon, as chargers that support Matter can keep up their scheduled charging without cloud support from manufacturers.

More broadly, Matter 1.4 bakes a lot of timing, energy cost, and other automation triggers into the spec, which—again, when supported by device manufacturers, at some future date—should allow for better home energy savings and customization, without tying it all to one particular app or platform.

CSA says that, with “nearly two years of real-world deployment in millions of households,” the companies and trade groups and developers tending to Matter are “refining software development kits, streamlining certification processes, and optimizing individual device implementations.” Everything they’ve got lined up seems neat, but it has to end up inside more boxes to be truly impressive.

Matter 1.4 has some solid ideas for the future home—now let’s see the support Read More »

law-enforcement-operation-takes-down-22,000-malicious-ip-addresses-worldwide

Law enforcement operation takes down 22,000 malicious IP addresses worldwide

An international coalition of police agencies has taken a major whack at criminals accused of running a host of online scams, including phishing, the stealing of account credentials and other sensitive data, and the spreading of ransomware, Interpol said recently.

The operation, which ran from the beginning of April through the end of August, resulted in the arrest of 41 people and the takedown of 1,037 servers and other infrastructure running on 22,000 IP addresses. Synergia II, as the operation was named, was the work of multiple law enforcement agencies across the world, as well as three cybersecurity organizations.

A global response

“The global nature of cybercrime requires a global response which is evident by the support member countries provided to Operation Synergia II,” Neal Jetton, director of the Cybercrime Directorate at INTERPOL, said. “Together, we’ve not only dismantled malicious infrastructure but also prevented hundreds of thousands of potential victims from falling prey to cybercrime. INTERPOL is proud to bring together a diverse team of member countries to fight this ever-evolving threat and make our world a safer place.”

Among the highlights of Operation Synergia II were:

Hong Kong (China): Police supported the operation by taking offline more than 1,037 servers linked to malicious services.

Mongolia: Investigations included 21 house searches, the seizure of a server and the identification of 93 individuals with links to illegal cyber activities.

Macau (China): Police took 291 servers offline.

Madagascar: Authorities identified 11 individuals with links to malicious servers and seized 11 electronic devices for further investigation.

Estonia: Police seized more than 80GB of server data, and authorities are now working with INTERPOL to conduct further analysis of data linked to phishing and banking malware.

The three private cybersecurity organizations that were part of Operation Synergia II were Group-IB, Kaspersky, and Team Cymru. All three used the telemetry intelligence in their possession to identify malicious servers and made it available to participating law enforcement agencies. The law enforcement agencies conducted investigations that resulted in house searches, the disruption of malicious cyber activities, the lawful seizures of servers and other electronic devices, and arrests.

Law enforcement operation takes down 22,000 malicious IP addresses worldwide Read More »

trump-plans-to-dismantle-biden-ai-safeguards-after-victory

Trump plans to dismantle Biden AI safeguards after victory

That’s not the only uncertainty at play. Just last week, House Speaker Mike Johnson—a staunch Trump supporter—said that Republicans “probably will” repeal the bipartisan CHIPS and Science Act, which is a Biden initiative to spur domestic semiconductor chip production, among other aims. Trump has previously spoken out against the bill. After getting some pushback on his comments from Democrats, Johnson said he would like to “streamline” the CHIPS Act instead, according to The Associated Press.

Then there’s the Elon Musk factor. The tech billionaire spent tens of millions through a political action committee supporting Trump’s campaign and has been angling for regulatory influence in the new administration. His AI company, xAI, which makes the Grok-2 language model, stands alongside his other ventures—Tesla, SpaceX, Starlink, Neuralink, and X (formerly Twitter)—as businesses that could see regulatory changes in his favor under a new administration.

What might take its place

If Trump strips away federal regulation of AI, state governments may step in to fill any federal regulatory gaps. For example, in March, Tennessee enacted protections against AI voice cloning, and in May, Colorado created a tiered system for AI deployment oversight. In September, California passed multiple AI safety bills, one requiring companies to publish details about their AI training methods and a contentious anti-deepfake bill aimed at protecting the likenesses of actors.

So far, it’s unclear what Trump’s policies on AI might represent besides “deregulate whenever possible.” During his campaign, Trump promised to support AI development centered on “free speech and human flourishing,” though he provided few specifics. He has called AI “very dangerous” and spoken about its high energy requirements.

Trump allies at the America First Policy Institute have previously stated they want to “Make America First in AI” with a new Trump executive order, which still only exists as a speculative draft, to reduce regulations on AI and promote a series of “Manhattan Projects” to advance military AI capabilities.

During his previous administration, Trump signed AI executive orders that focused on research institutes and directing federal agencies to prioritize AI development while mandating that federal agencies “protect civil liberties, privacy, and American values.”

But with a different AI environment these days in the wake of ChatGPT and media-reality-warping image synthesis models, those earlier orders don’t likely point the way to future positions on the topic. For more details, we’ll have to wait and see what unfolds.

Trump plans to dismantle Biden AI safeguards after victory Read More »

corning-faces-antitrust-actions-for-its-gorilla-glass-dominance

Corning faces antitrust actions for its Gorilla Glass dominance

The European Commission (EC) has opened an antitrust investigation into US-based glass-maker Corning, claiming that its Gorilla Glass has dominated the mobile phone screen market due to restrictive deals and licensing.

Corning’s shatter-resistant alkali-aluminosilicate glass keeps its place atop the market, according to the EC’s announcement, because it both demands, and rewards with rebates, device makers that agree to “source all or nearly all of their (Gorilla Glass) demand from Corning.” Corning also allegedly required device makers to report competitive offers to the glass maker. The company is accused of exerting a similar pressure on “finishers,” or those firms that turn raw glass into finished phone screen protectors, as well as demanding finishers not pursue patent challenges against Corning.

“[T]he agreements that Corning put in place with OEMs and finishers may have excluded rival glass producers from large segments of the market, thereby reducing customer choice, increasing prices, and stifling innovation to the detriment of consumers worldwide,” the Commission wrote.

Ars has reached out to Corning for comment and will update this post with response.

Gorilla Glass does approach Xerox or Kleenex levels of brand name association with its function. New iterations of its thin, durable glass reach a bit further than the last and routinely pick up press coverage. Gorilla Glass 4 was pitched as being “up to two times stronger” than any “competitive” alternative. Gorilla Glass 5 could survive a 1.6-meter drop 80 percent of the time, and 6 built in more repetitive damage resistance.

Apple considers Corning’s glass products so essential to its products, like the ceramic shield on the iPhone 12, as to have invested $45 million into the company to expand its US manufacturing. The first iPhone was changed very shortly before launch to use Gorilla Glass instead of a plastic screen, per Steve Jobs’ insistence.

Corning faces antitrust actions for its Gorilla Glass dominance Read More »

anthropic’s-haiku-3.5-surprises-experts-with-an-“intelligence”-price-increase

Anthropic’s Haiku 3.5 surprises experts with an “intelligence” price increase

Speaking of Opus, Claude 3.5 Opus is nowhere to be seen, as AI researcher Simon Willison noted to Ars Technica in an interview. “All references to 3.5 Opus have vanished without a trace, and the price of 3.5 Haiku was increased the day it was released,” he said. “Claude 3.5 Haiku is significantly more expensive than both Gemini 1.5 Flash and GPT-4o mini—the excellent low-cost models from Anthropic’s competitors.”

Cheaper over time?

So far in the AI industry, newer versions of AI language models typically maintain similar or cheaper pricing to their predecessors. The company had initially indicated Claude 3.5 Haiku would cost the same as the previous version before announcing the higher rates.

“I was expecting this to be a complete replacement for their existing Claude 3 Haiku model, in the same way that Claude 3.5 Sonnet eclipsed the existing Claude 3 Sonnet while maintaining the same pricing,” Willison wrote on his blog. “Given that Anthropic claim that their new Haiku out-performs their older Claude 3 Opus, this price isn’t disappointing, but it’s a small surprise nonetheless.”

Claude 3.5 Haiku arrives with some trade-offs. While the model produces longer text outputs and contains more recent training data, it cannot analyze images like its predecessor. Alex Albert, who leads developer relations at Anthropic, wrote on X that the earlier version, Claude 3 Haiku, will remain available for users who need image processing capabilities and lower costs.

The new model is not yet available in the Claude.ai web interface or app. Instead, it runs on Anthropic’s API and third-party platforms, including AWS Bedrock. Anthropic markets the model for tasks like coding suggestions, data extraction and labeling, and content moderation, though, like any LLM, it can easily make stuff up confidently.

“Is it good enough to justify the extra spend? It’s going to be difficult to figure that out,” Willison told Ars. “Teams with robust automated evals against their use-cases will be in a good place to answer that question, but those remain rare.”

Anthropic’s Haiku 3.5 surprises experts with an “intelligence” price increase Read More »

suspect-arrested-in-snowflake-data-theft-attacks-affecting-millions

Suspect arrested in Snowflake data-theft attacks affecting millions

Attack Path UNC5537 has used in attacks against as many as 165 Snowflake customers.

Credit: Mandiant

Attack Path UNC5537 has used in attacks against as many as 165 Snowflake customers. Credit: Mandiant

None of the affected accounts used multifactor authentication, which requires users to provide a one-time password or additional means of authentication besides a password. After that revelation, Snowflake enforced mandatory MFA for accounts and required that passwords be at least 14 characters long.

Mandiant had identified the threat group behind the breaches as UNC5537. The group has referred to itself ShinyHunters. Snowflake offers its services under a model known as SaaS (software as a service).

“UNC5537 aka Alexander ‘Connor’ Moucka has proven to be one of the most consequential threat actors of 2024,” Mandiant wrote in an emailed statement. “In April 2024, UNC5537 launched a campaign, systematically compromising misconfigured SaaS instances across over a hundred organizations. The operation, which left organizations reeling from significant data loss and extortion attempts, highlighted the alarming scale of harm an individual can cause using off-the-shelf tools.”

Mandiant said a co-conspirator, John Binns, was arrested in June. The status of that case wasn’t immediately known.

Besides Ticketmaster, other customers known to have been breached include AT&T and Spain-based bank Santander. In July, AT&T said that personal information and phone and text message records for roughly 110 million customers were stolen. WIRED later reported that AT&T paid $370,000 in return for a promise the data would be deleted.

Other Snowflake customers reported by various news outlets as breached are Pure Storage, Advance Auto Parts, Los Angeles Unified School District, QuoteWizard/LendingTree, Neiman Marcus, Anheuser-Busch, Allstate, Mitsubishi, and State Farm.

KrebsOnSecurity reported Tuesday that Moucka has been named in multiple charging documents filed by US federal prosecutors. Reporter Brian Krebs said specific charges and allegations are unknown because the cases remain sealed.

Suspect arrested in Snowflake data-theft attacks affecting millions Read More »