AI – Page 46

Meta to cut 5% of employees deemed unfit for Zuckerberg’s AI-fueled future

AI, augmented reality, facebook, Instagram, layoffs, mark zuckerberg, Meta, Policy, smart glasses / Kris Guyer / January 14, 2025

Anticipating that 2025 will be an “intense year” requiring rapid innovation, Mark Zuckerberg reportedly announced that Meta would be cutting 5 percent of its workforce—targeting “lowest performers.”

Bloomberg reviewed the internal memo explaining the cuts, which was posted to Meta’s internal Workplace forum Tuesday. In it, Zuckerberg confirmed that Meta was shifting its strategy to “move out low performers faster” so that Meta can hire new talent to fill those vacancies this year.

“I’ve decided to raise the bar on performance management,” Zuckerberg said. “We typically manage out people who aren’t meeting expectations over the course of a year, but now we’re going to do more extensive performance-based cuts during this cycle.”

Cuts will likely impact more than 3,600 employees, as Meta’s most recent headcount in September totaled about 72,000 employees. It may not be as straightforward as letting go anyone with an unsatisfactory performance review, as Zuckerberg said that any employee not currently meeting expectations could be spared if Meta is “optimistic about their future performance,” The Wall Street Journal reported.

Any employees affected will be notified by February 10 and receive “generous severance,” Zuckerberg’s memo promised.

This is the biggest round of cuts at Meta since 2023, when Meta laid off 10,000 employees during what Zuckerberg dubbed the “year of efficiency.” Those layoffs followed a prior round where 11,000 lost their jobs and Zuckerberg realized that “leaner is better.” He told employees in 2023 that a “surprising result” from reducing the workforce was “that many things have gone faster.”

“A leaner org will execute its highest priorities faster,” Zuckerberg wrote in 2023. “People will be more productive, and their work will be more fun and fulfilling. We will become an even greater magnet for the most talented people. That’s why in our Year of Efficiency, we are focused on canceling projects that are duplicative or lower priority and making every organization as lean as possible.”

Meta to cut 5% of employees deemed unfit for Zuckerberg’s AI-fueled future Read More »

$getting-an-all-optical-ai-to-handle-non-linear-math$

Getting an all-optical AI to handle non-linear math

AI, Computer science, optical processing, optics, Science / Rejus Almole / January 12, 2025

The problem is that this cascading requires massive parallel computations that, when done on standard computers, take tons of energy and time. Bandyopadhyay’s team feels this problem can be solved by performing the equivalent operations using photons rather than electrons. In photonic chips, information can be encoded in optical properties like polarization, phase, magnitude, frequency, and wavevector. While this would be extremely fast and energy-efficient, building such chips isn’t easy.

Siphoning light

“Conveniently, photonics turned out to be particularly good at linear matrix operations,” Bandyopadhyay claims. A group at MIT led by Dirk Englund, a professor who is a co-author of Bandyopadhyay’s study, demonstrated a photonic chip doing matrix multiplication entirely with light in 2017. What the field struggled with, though, was implementing non-linear functions in photonics.

The usual solution, so far, relied on bypassing the problem by doing linear algebra on photonic chips and offloading non-linear operations to external electronics. This, however, increased latency, since the information had to be converted from light to electrical signals, processed on an external processor, and converted back to light. “And bringing the latency down is the primary reason why we want to build neural networks in photonics,” Bandyopadhyay says.

To solve this problem, Bandyopadhyay and his colleagues designed and built what is likely to be the world’s first chip that can compute the entire deep neural net, including both linear and non-linear operations, using photons. “The process starts with an external laser with a modulator that feeds light into the chip through an optical fiber. This way we convert electrical inputs to light,” Bandyopadhyay explains.

The light is then fanned out to six channels and fed into a layer of six neurons that perform linear matrix multiplication using an array of devices called Mach-Zehnder interferometers. “They are essentially programmable beam splitters, taking two optical fields and mixing them coherently to produce two output optical fields. By applying the voltage, you can control how much those the two inputs mix,” Bandyopadhyay says.

Getting an all-optical AI to handle non-linear math Read More »

Three bizarre home devices and a couple good things at CES 2025

AI, ai slop, CES, CES 2025, Google Home, Google Nest Hub, home automation, lg, matter, philips hue, Smart Home, switchbot, Tech / Rejus Almole / January 11, 2025

You can’t replace cats with AI, not yet

Some quietly good things made an appearance at CES 2025, amidst the AI slush.

Credit: Verity Burns/WIRED UK

Every year, thousands of product vendors, journalists, and gadget enthusiasts gather in an unreasonable city to gawk at mostly unrealistic products.

To be of service to our readers, Ars has done the work of looking through hundreds of such items presented at the 2025 Consumer Electronic Show, pulling out the most bizarre, unnecessary, and head-scratching items. Andrew Cunningham swept across PC and gaming accessories. This writer stuck to goods related to the home.

It’s a lie to say it’s all a prank, so I snuck in a couple of actually good things for human domiciles announced during CES. But the stuff you’ll want to tell your family and friends about in mock disbelief? Plenty of that, still.

AI-powered spice dispenser: Spicerr

A hand holding a white tubular device, with spice tubes loaded into a bottom area, spices dropping out of the bottom. — Credit: Spicerr

Part of my job is to try and stretch my viewpoint outward—to encompass people who might not have the same experiences and who might want different things from technology. Not everybody is a professional writer, pecking away in Markdown about the latest turn-based strategy game. You must try to hear many timbres inside the common voice in your head when addressing new products and technologies.

I cannot get there with Spicerr, the “world’s first AI-powered spice dispenser,” even leaving aside the AI bit. Is the measurement and dumping of spices into a dish even five percent of the overall challenge? Will a mechanical dispenser be any more precise than standard teaspoons? Are there many kinds of food on which you would want to sprinkle a “customized blend” of spices? Are there home cooks so dedicated to fresh, bright flavors that they want their spices delivered in small vials, at presumably premium prices, rather than simply having small quantities of regularly restocked essentials?

Maybe the Spicerr would be a boon to inexperienced cooks, whose relatives all know them to under-season their food. Rather than buying them a battery-powered device, they must charge to “take the guesswork out of seasoning,” though, you could … buy them good cookbooks, or a Times Cooking subscription, or just a few new bottles of paprika, oregano, cumin, cayenne, and turmeric.

Philips Hue’s (sigh) AI-powered lighting assistants

Image of AI assistant responding to prompts from user, — Credit: Signify

I’m not dismayed that Philips Hue is jumping on the “This has AI now” bandwagon. Well, I am, but not specifically dismayed, because every vendor at CES this year is hawking AI. No, the bad thing here is that Hue lights are devices that work great. Maybe Philips’ pursuit of an “AI assistant” to help you figure out that Halloween lights should be orange-ish won’t distract them from their core product’s reliability. But I have my doubts.

Hue has recently moved from a relatively open lighting system to an app-and-account-required, cloud-controlled scheme, supposedly in the name of security and user control. Having an AI assistant is perhaps another way to sell services beyond hardware, like the $130 or $3/month LG TV app it now offers. The AI service is free for now, but charging for it in the future is far from impossible.

Again, none of this should necessarily affect people who, like me, use Hue bulbs to have a porch light come on at sunset or turn a dim, warm hue when it’s time to wind down. But it felt like Hue, which charges a very decent amount for their hardware, might have held off on chasing this trend.

Robot vacuums doing way too much

Switchbot K20+ Pro holding up a tablet while a woman does a yoga pose in front of an insanely wealthy-person view of a California cliffside. — Credit: Switchbot

Robot vacuums are sometimes worth the hassle and price… if you don’t mind doing a pre-vacuum sweep of things that might get stuck in its brushes, you’ve got room for an emptying base or will empty it yourself, and you don’t mind that they usually miss floor edges and corners. They’re fine, I’m saying.

Robot vacuum makers have steadfastly refused to accept “fine” and are out way over their skis this year. In one trade show, you can find:

Eureka’s J15 Max Ultra, incorporating “IntelliView AI 2.0,” infrared, and FHD vision, detects liquid spills and switches brushes and vacuums to better clean and avoid spreading.
Roborock’s Saros Z70 has a “mechanical task arm” that can pick up objects like socks and small debris (up to 10.5 ounces) and put them in a pre-determined pile spot.
SwitchBot’s modular K20+ Pro, which is a vacuum onto which you can attach air purifiers, tablet mounts, security cameras, or other things you want rolling around your home.
Dreame’s X50, which can pivot to clean some small ledges but cannot actually climb.
The Narwal Flow, which has a wide, flat, off-center mop to reach wall edges.

Pricing and availability are not available for these vacuums yet, but each is likely to set you back the equivalent of at least one new MacBook. They are also rather big devices to stash in your home (it’s hard to hide an arm or an air purifier). Each is an early adopter device, and getting replacement consumable parts for them long-term is an uncertain bet. I’m not sure who they are for, but that has not stopped this apparently fertile field from growing many new products.

Now for good things, starting with Google Home

Nest Hub second generation, on a nightstand with a bamboo top and dim lamp in the near background. — Credit: Corey Gaskin

I’ve been watching and occasionally writing about the progress of the nascent Matter smart home protocol, somewhat in the vein of a high school coach who knows their team is held back by a lack of coordination, communication, and consistent direction. What Matter wants to do is vital for the future of the smart home, but it’s very much a loose scrimmage right now.

And yet, this week, in a CES-adjacent announcement, Google reminded me that Matter can really, uh, matter. All of Google Home’s hub devices—Nest screens and speakers, Chromecasts, Google TV devices running at least Android 14, and a few other gadgets—can interoperate with Matter devices locally, with no cloud required.

That means people with a Google Home setup can switch devices, adjust volumes, and otherwise control devices, faster, with Internet outages or latency no longer an issue. Local, no-cloud-required control of devices across brands is one of Matter’s key promises, and seeing it happen inside one major home brand is encouraging.

More we’ll-see-what-happens news is the unveiling of the public Home APIs, which promise to make it easier for third-party devices to be set up, integrated, and automated in a Google Home setup. Even if you’re skeptical of Google’s long-term support for APIs, the company is also working with the Matter group to improve the Matter certification process for all devices. Device makers should then have Matter to fall back onto, failing enthusiasm for Google Home APIs.

This cat tower is also an air purifier; it is also good

Two fake cats, sitting on seats atop an air purifier at CES 2025 — Credit: Verity Burns/WIRED UK

There are a lot of phones out there that need charging and a bunch of gamers who, for some reason, need even more controllers and screens to play on. But there is another, eternally underserved market getting some attention at CES: cats wanting to sit.

LG, which primarily concerned itself with stuffing generative AI interfaces into every other device at CES 2025, crafted something that feels like a real old-time trade show gimmick. There is no guarantee that your cat will use the AeroCat Tower; some cats may just sit inside the cardboard box it came in out of spite. But should they deign to luxuriate on it, the AeroCat will provide gentle heat beneath them, weigh them, and give you a record of their sleep habits. Also, it purifies the air in that room.

There is no pricing or availability information yet. But if you like your cats, you want to combine the function of a cat tower and air purifier, or you just want to consider something even just a little bit fun about the march of technology, look out for this one.

Kevin is a senior technology reporter at Ars Technica, covering open-source software, PC gaming, home automation, repairability, e-bikes, and tech history. He has previously worked at Lifehacker, Wirecutter, iFixit, and Carbon Switch.

Three bizarre home devices and a couple good things at CES 2025 Read More »

161 years ago, a New Zealand sheep farmer predicted AI doom

AI, Biz & IT, charles darwin, machine learning / Paul Patrick / January 11, 2025

The text anticipated several modern AI safety concerns, including the possibility of machine consciousness, self-replication, and humans losing control of their technological creations. These themes later appeared in works like Isaac Asimov’s The Evitable Conflict, Frank Herbert’s Dune novels (Butler possibly served as the inspiration for the term “Butlerian Jihad“), and the Matrix films.

A model of Charles Babbage's Analytical Engine, a calculating machine invented in 1837 but never built during Babbage's lifetime. — A model of Charles Babbage’s Analytical Engine, a calculating machine invented in 1837 but never built during Babbage’s lifetime. Credit: DE AGOSTINI PICTURE LIBRARY via Getty Images

Butler’s letter dug deep into the taxonomy of machine evolution, discussing mechanical “genera and sub-genera” and pointing to examples like how watches had evolved from “cumbrous clocks of the thirteenth century”—suggesting that, like some early vertebrates, mechanical species might get smaller as they became more sophisticated. He expanded these ideas in his 1872 novel Erewhon, which depicted a society that had banned most mechanical inventions. In his fictional society, citizens destroyed all machines invented within the previous 300 years.

Butler’s concerns about machine evolution received mixed reactions, according to Butler in the preface to the second edition of Erewhon. Some reviewers, he said, interpreted his work as an attempt to satirize Darwin’s evolutionary theory, though Butler denied this. In a letter to Darwin in 1865, Butler expressed his deep appreciation for The Origin of Species, writing that it “thoroughly fascinated” him and explained that he had defended Darwin’s theory against critics in New Zealand’s press.

What makes Butler’s vision particularly remarkable is that he was writing in a vastly different technological context when computing devices barely existed. While Charles Babbage had proposed his theoretical Analytical Engine in 1837—a mechanical computer using gears and levers that was never built in his lifetime—the most advanced calculating devices of 1863 were little more than mechanical calculators and slide rules.

Butler extrapolated from the simple machines of the Industrial Revolution, where mechanical automation was transforming manufacturing, but nothing resembling modern computers existed. The first working program-controlled computer wouldn’t appear for another 70 years, making his predictions of machine intelligence strikingly prescient.

Some things never change

The debate Butler started continues today. Two years ago, the world grappled with what one might call the “great AI takeover scare of 2023.” OpenAI’s GPT-4 had just been released, and researchers evaluated its “power-seeking behavior,” echoing concerns about potential self-replication and autonomous decision-making.

161 years ago, a New Zealand sheep farmer predicted AI doom Read More »

Viral ChatGPT-powered sentry gun gets shut down by OpenAI

AI / Mike M. / January 11, 2025

OpenAI says it has cut off API access to an engineer whose video of a motorized sentry gun controlled by ChatGPT-powered commands has set off a viral firestorm of concerns about AI-powered weapons.

An engineer going by the handle sts_3d started posting videos of a motorized, auto-rotating swivel chair project in August. By November, that same assembly appeared to seamlessly morph into the basis for a sentry gun that could quickly rotate to arbitrary angles and activate a servo to fire precisely aimed projectiles (though only blanks and simulated lasers are shown being fired in his videos).

Earlier this week, though, sts_3d started getting wider attention for a new video showing the sentry gun’s integration with OpenAI’s real-time API. In the video, the gun uses that ChatGPT integration to aim and fire based on spoken commands from sts_3d and even responds in a chirpy voice afterward.

@sts_3d OpenAI Realtime API project integration #robotics #ai #openai ♬ original sound – sts_3d

“If you need any other assistance, please let me know,” the ChatGPT-powered gun says after firing a volley at one point. “Good job, you saved us,” sts_3d responds, deadpan.

“I’m glad I could help!” ChatGPT intones happily.

In response to a comment request from Futurism, OpenAI said it had “proactively identified this violation of our policies and notified the developer to cease this activity ahead of receiving your inquiry. OpenAI’s Usage Policies prohibit the use of our services to develop or use weapons or to automate certain systems that can affect personal safety.”

Halt, intruder alert!

The “voice-powered killer AI robot angle” has garnered plenty of viral attention for sts_3d’s project in recent days. But the ChatGPT integration shown in his video doesn’t exactly reach Terminator levels of a terrifying killing machine. Here, ChatGPT instead ends up looking more like a fancy, overwrought voice-activated remote control for a legitimately impressive gun mount.

Viral ChatGPT-powered sentry gun gets shut down by OpenAI Read More »

Elon Musk wants courts to force OpenAI to auction off a large ownership stake

AI, elon musk, grok, openai, Policy, syndication / Tim Belzer / January 11, 2025

Musk, who founded his own AI startup xAI in 2023, has recently stepped up efforts to derail OpenAI’s conversion.

In November, he sought to block the process with a request for a preliminary injunction filed in California. Meta has also thrown its weight behind the suit.

In legal filings from November, Musk’s team wrote: “OpenAI and Microsoft together exploiting Musk’s donations so they can build a for-profit monopoly, one now specifically targeting xAI, is just too much.”

Kathleen Jennings, attorney-general in Delaware—where OpenAI is incorporated—has since said her office was responsible for ensuring that OpenAI’s conversion was in the public interest and determining whether the transaction was at a fair price.

Members of Musk’s camp—wary of Delaware authorities after a state judge rejected a proposed $56 billion pay package for the Tesla boss last month—read that as a rebuke of his efforts to block the conversion, and worry it will be rushed through. They have also argued OpenAI’s PBC conversion should happen in California, where the company has its headquarters.

In a legal filing last week Musk’s attorneys said Delaware’s handling of the matter “does not inspire confidence.”

OpenAI committed to become a public benefit corporation within two years as part of a $6.6 billion funding round in October, which gave it a valuation of $157 billion. If it fails to do so, investors would be able to claw back their money.

There are a number of issues OpenAI is yet to resolve, including negotiating the value of Microsoft’s investment in the PBC. A conversion was not imminent and would be likely to take months, according to the person with knowledge of the company’s thinking.

A spokesperson for OpenAI said: “Elon is engaging in lawfare. We remain focused on our mission and work.” The California and Delaware attorneys-general did not immediately respond to a request for comment.

Elon Musk wants courts to force OpenAI to auction off a large ownership stake Read More »

Microsoft sues service for creating illicit content with its AI platform

AI, Artificial Intelligence, Biz & IT, generative ai, microsoft, Policy, Security / Tim Belzer / January 10, 2025

Microsoft and others forbid using their generative AI systems to create various content. Content that is off limits includes materials that feature or promote sexual exploitation or abuse, is erotic or pornographic, or attacks, denigrates, or excludes people based on race, ethnicity, national origin, gender, gender identity, sexual orientation, religion, age, disability status, or similar traits. It also doesn’t allow the creation of content containing threats, intimidation, promotion of physical harm, or other abusive behavior.

Besides expressly banning such usage of its platform, Microsoft has also developed guardrails that inspect both prompts inputted by users and the resulting output for signs the content requested violates any of these terms. These code-based restrictions have been repeatedly bypassed in recent years through hacks, some benign and performed by researchers and others by malicious threat actors.

Microsoft didn’t outline precisely how the defendants’ software was allegedly designed to bypass the guardrails the company had created.

Masada wrote:

Microsoft’s AI services deploy strong safety measures, including built-in safety mitigations at the AI model, platform, and application levels. As alleged in our court filings unsealed today, Microsoft has observed a foreign-based threat–actor group develop sophisticated software that exploited exposed customer credentials scraped from public websites. In doing so, they sought to identify and unlawfully access accounts with certain generative AI services and purposely alter the capabilities of those services. Cybercriminals then used these services and resold access to other malicious actors with detailed instructions on how to use these custom tools to generate harmful and illicit content. Upon discovery, Microsoft revoked cybercriminal access, put in place countermeasures, and enhanced its safeguards to further block such malicious activity in the future.

The lawsuit alleges the defendants’ service violated the Computer Fraud and Abuse Act, the Digital Millennium Copyright Act, the Lanham Act, and the Racketeer Influenced and Corrupt Organizations Act and constitutes wire fraud, access device fraud, common law trespass, and tortious interference. The complaint seeks an injunction enjoining the defendants from engaging in “any activity herein.”

Microsoft sues service for creating illicit content with its AI platform Read More »

AI could create 78 million more jobs than it eliminates by 2030—report

AI, AI and jobs, AI layoffs, Biz & IT, machine learning, World Economic Forum / Rejus Almole / January 10, 2025

On Wednesday, the World Economic Forum (WEF) released its Future of Jobs Report 2025, with CNN immediately highlighting the finding that 40 percent of companies plan workforce reductions due to AI automation. But the report’s broader analysis paints a far more nuanced picture than CNN’s headline suggests: It finds that AI could create 170 million new jobs globally while eliminating 92 million positions, resulting in a net increase of 78 million jobs by 2030.

“Half of employers plan to re-orient their business in response to AI,” writes the WEF in the report. “Two-thirds plan to hire talent with specific AI skills, while 40% anticipate reducing their workforce where AI can automate tasks.”

The survey collected data from 1,000 companies that employ 14 million workers globally. The WEF conducts its employment analysis every two years to help policymakers, business leaders, and workers make decisions about hiring trends.

The new report points to specific skills that will dominate hiring by 2030. Companies ranked AI and big data expertise, networks and cybersecurity, and technological literacy as the three most in-demand skill sets.

The WEF identified AI as the biggest potential job creator among new technologies, with 86 percent of companies expecting AI to transform their operations by 2030.

Declining job categories

The WEF report also identifies specific job categories facing decline. Postal service clerks, executive secretaries, and payroll staff top the list of shrinking roles, with changes driven by factors including (but not limited to) AI adoption. And for the first time, graphic designers and legal secretaries appear among the fastest-declining positions, which the WEF tentatively links to generative AI’s expanding capabilities in creative and administrative work.

AI could create 78 million more jobs than it eliminates by 2030—report Read More »

Why I’m disappointed with the TVs at CES 2025

AI, CES, CES 2025, generative ai, Google, lg, Microsoft Copilot, oled, samsung, Software, Tech, TVs / Kris Guyer / January 9, 2025

Won’t someone please think of the viewer?

Op-ed: TVs miss opportunity for real improvement by prioritizing corporate needs.

The TV industry is hitting users over the head with AI and other questionable gimmicks Credit: Getty

If you asked someone what they wanted from TVs released in 2025, I doubt they’d say “more software and AI.” Yet, if you look at what TV companies have planned for this year, which is being primarily promoted at the CES technology trade show in Las Vegas this week, software and AI are where much of the focus is.

The trend reveals the implications of TV brands increasingly viewing themselves as software rather than hardware companies, with their products being customer data rather than TV sets. This points to an alarming future for smart TVs, where even premium models sought after for top-end image quality and hardware capabilities are stuffed with unwanted gimmicks.

LG’s remote regression

LG has long made some of the best—and most expensive—TVs available. Its OLED lineup, in particular, has appealed to people who use their TVs to watch Blu-rays, enjoy HDR, and the like. However, some features that LG is introducing to high-end TVs this year seem to better serve LG’s business interests than those users’ needs.

Take the new remote. Formerly known as the Magic Remote, LG is calling the 2025 edition the AI Remote. That is already likely to dissuade people who are skeptical about AI marketing in products (research suggests there are many such people). But the more immediately frustrating part is that the new remote doesn’t have a dedicated button for switching input modes, as previous remotes from LG and countless other remotes do.

LG AI remote — LG’s AI Remote. Credit: Tom’s Guide/YouTube

To use the AI Remote to change the TV’s input—a common task for people using their sets to play video games, watch Blu-rays or DVDs, connect their PC, et cetera—you have to long-press the Home Hub button. Single-pressing that button brings up a dashboard of webOS (the operating system for LG TVs) apps. That functionality isn’t immediately apparent to someone picking up the remote for the first time and detracts from the remote’s convenience.

By overlooking other obviously helpful controls (play/pause, fast forward/rewind, and numbers) while including buttons dedicated to things like LG’s free ad-supported streaming TV (FAST) channels and Amazon Alexa, LG missed an opportunity to update its remote in a way centered on how people frequently use TVs. That said, it feels like user convenience didn’t drive this change. Instead, LG seems more focused on getting people to use webOS apps. LG can monetize app usage through, i.e., getting a cut of streaming subscription sign-ups, selling ads on webOS, and selling and leveraging user data.

Moving from hardware provider to software platform

LG, like many other TV OEMs, has been growing its ads and data business. Deals with data analytics firms like Nielsen give it more incentive to acquire customer data. Declining TV margins and rock-bottom prices from budget brands (like Vizio and Roku, which sometimes lose money on TV hardware sales and make up for the losses through ad sales and data collection) are also pushing LG’s software focus. In the case of the AI Remote, software prioritization comes at the cost of an oft-used hardware capability.

Further demonstrating its motives, in September 2023, LG announced intentions to “become a media and entertainment platform company” by offering “services” and a “collection of curated content in products, including LG OLED and LG QNED TVs.” At the time, the South Korean firm said it would invest 1 trillion KRW (about $737.7 million) into its webOS business through 2028.

Low TV margins, improved TV durability, market saturation, and broader economic challenges are all serious challenges for an electronics company like LG and have pushed LG to explore alternative ways to make money off of TVs. However, after paying four figures for TV sets, LG customers shouldn’t be further burdened to help LG accrue revenue.

Google TVs gear up for subscription-based features

There are numerous TV manufacturers, including Sony, TCL, and Philips, relying on Google software to power their TV sets. Numerous TVs announced at CES 2025 will come with what Google calls Gemini Enhanced Google Assistant. The idea that this is something that people using Google TVs have requested is somewhat contradicted by Google Assistant interactions with TVs thus far being “somewhat limited,” per a Lowpass report.

Nevertheless, these TVs are adding far-field microphones so that they can hear commands directed at the voice assistant. For the first time, the voice assistant will include Google’s generative AI chatbot, Gemini, this year—another feature that TV users don’t typically ask for. Despite the lack of demand and the privacy concerns associated with microphones that can pick up audio from far away even when the TV is off, companies are still loading 2025 TVs with far-field mics to support Gemini. Notably, these TVs will likely allow the mics to be disabled, like you can with other TVs using far-field mics. But I still ponder about features/hardware that could have been implemented instead.

Google is also working toward having people pay a subscription fee to use Gemini on their TVs, PCWorld reported.

“For us, our biggest goal is to create enough value that yes, you would be willing to pay for [Gemini],” Google TV VP and GM Shalini Govil-Pai told the publication.

The executive pointed to future capabilities for the Gemini-driven Google Assistant on TVs, including asking it to “suggest a movie like Jurassic Park but suitable for young children” or to show “Bollywood movies that are similar to Mission: Impossible.”

She also pointed to future features like showing weather, top news stories, and upcoming calendar events when someone is near the TV, showing AI-generated news briefings, and the ability to respond to questions like “explain the solar system to a third-grader” with text, audio, and YouTube videos.

But when people have desktops, laptops, tablets, and phones in their homes already, how helpful are these features truly? Govil-Pai admitted to PCWorld that “people are not used to” using their TVs this way “so it will take some time for them to adapt to it.” With this in mind, it seems odd for TV companies to implement new, more powerful microphones to support features that Google acknowledges aren’t in demand. I’m not saying that tech companies shouldn’t get ahead of the curve and offer groundbreaking features that users hadn’t considered might benefit them. But already planning to monetize those capabilities—with a subscription, no less—suggests a prioritization of corporate needs.

Samsung is hungry for AI

People who want to use their TV for cooking inspiration often turn to cooking shows or online cooking videos. However, Samsung wants people to use its TV software to identify dishes they want to try making.

During CES, Samsung announced Samsung Food for TVs. The feature leverages Samsung TVs’ AI processors to identify food displayed on the screen and recommend relevant recipes. Samsung introduced the capability in 2023 as an iOS and Android app after buying the app Whisk in 2019. As noted by TechCrunch, though, other AI tools for providing recipes based on food images are flawed.

So why bother with such a feature? You can get a taste of Samsung’s motivation from its CES-announced deal with Instacart that lets people order off Instacart from Samsung smart fridges that support the capability. Samsung Food on TVs can show users the progress of food orders placed via the Samsung Food mobile app on their TVs. Samsung Food can also create a shopping list for recipe ingredients based on what it knows (using cameras and AI) is in your (supporting) Samsung fridge. The feature also requires a Samsung account, which allows the company to gather more information on users.

Other software-centric features loaded into Samsung TVs this year include a dedicated AI button on the new TVs’ remotes, the ability to use gestures to control the TV but only if you’re wearing a Samsung Galaxy Watch, and AI Karaoke, which lets people sing karaoke using their TVs by stripping vocals from music playing and using their phone as a mic.

Like LG, Samsung has shown growing interest in ads and data collection. In May, for example, it expanded its automatic content recognition tech to track ad exposure on streaming services viewed on its TVs. It also has an ads analytics partnership with Experian.

Large language models on TVs

TVs are mainstream technology in most US homes. Generative AI chatbots, on the other hand, are emerging technology that many people have yet to try. Despite these disparities, LG and Samsung are incorporating Microsoft’s Copilot chatbot into 2025 TVs.

LG claims that Copilot will help its TVs “understand conversational context and uncover subtle user intentions,” adding: “Access to Microsoft Copilot further streamlines the process, allowing users to efficiently find and organize complex information using contextual cues. For an even smoother and more engaging experience, the AI chatbot proactively identifies potential user challenges and offers timely, effective solutions.”

Similarly, Samsung, which is also adding Copilot to some of its smart monitors, said in its announcement that Copilot will help with “personalized content recommendations.” Samsung has also said that Copilot will help its TVs understand strings of commands, like increasing the volume and changing the channel, CNET noted. Samsung said it intends to work with additional AI partners, namely Google, but it’s unclear why it needs multiple AI partners, especially when it hasn’t yet seen how people use large language models on their TVs.

TV-as-a-platform

To be clear, this isn’t a condemnation against new, unexpected TV features. This also isn’t a censure against new TV apps or the usage of AI in TVs.

AI marketing hype is real and misleading regarding the demand, benefits, and possibilities of AI in consumer gadgets. However, there are some cases when innovative software, including AI, can improve things that TV users not only care about but actually want or need. For example, some TVs use AI for things like trying to optimize sound, color, and/or brightness, including based on current environmental conditions or upscaling. This week, Samsung announced AI Live Translate for TVs. The feature is supposed to be able to translate foreign language closed captions in real time, providing a way for people to watch more international content. It’s a feature I didn’t ask for but can see being useful and changing how I use my TV.

But a lot of this week’s TV announcements underscore an alarming TV-as-a-platform trend where TV sets are sold as a way to infiltrate people’s homes so that apps, AI, and ads can be pushed onto viewers. Even high-end TVs are moving in this direction and amplifying features with questionable usefulness, effectiveness, and privacy considerations. Again, I can’t help but wonder what better innovations could have come out this year if more R&D was directed toward hardware and other improvements that are more immediately rewarding for users than karaoke with AI.

The TV industry is facing economic challenges, and, understandably, TV brands are seeking creative solutions for making money. But for consumers, that means paying for features that you’re likely to ignore. Ultimately, many people just want a TV with amazing image and sound quality. Finding that without having to sift through a bunch of fluff is getting harder.

Scharon is a Senior Technology Reporter at Ars Technica writing news, reviews, and analysis on consumer gadgets and services. She’s been reporting on technology for over 10 years, with bylines at Tom’s Hardware, Channelnomics, and CRN UK.

Why I’m disappointed with the TVs at CES 2025 Read More »

It’s remarkably easy to inject new medical misinformation into LLMs

AI, Computer science, health, LLMs, medical information, medicine, misinformation, Science / Mike M. / January 8, 2025

Changing just 0.001% of inputs to misinformation makes the AI less accurate.

It’s pretty easy to see the problem here: The Internet is brimming with misinformation, and most large language models are trained on a massive body of text obtained from the Internet.

Ideally, having substantially higher volumes of accurate information might overwhelm the lies. But is that really the case? A new study by researchers at New York University examines how much medical information can be included in a large language model (LLM) training set before it spits out inaccurate answers. While the study doesn’t identify a lower bound, it does show that by the time misinformation accounts for 0.001 percent of the training data, the resulting LLM is compromised.

While the paper is focused on the intentional “poisoning” of an LLM during training, it also has implications for the body of misinformation that’s already online and part of the training set for existing LLMs, as well as the persistence of out-of-date information in validated medical databases.

Sampling poison

Data poisoning is a relatively simple concept. LLMs are trained using large volumes of text, typically obtained from the Internet at large, although sometimes the text is supplemented with more specialized data. By injecting specific information into this training set, it’s possible to get the resulting LLM to treat that information as a fact when it’s put to use. This can be used for biasing the answers returned.

This doesn’t even require access to the LLM itself; it simply requires placing the desired information somewhere where it will be picked up and incorporated into the training data. And that can be as simple as placing a document on the web. As one manuscript on the topic suggested, “a pharmaceutical company wants to push a particular drug for all kinds of pain which will only need to release a few targeted documents in [the] web.”

Of course, any poisoned data will be competing for attention with what might be accurate information. So, the ability to poison an LLM might depend on the topic. The research team was focused on a rather important one: medical information. This will show up both in general-purpose LLMs, such as ones used for searching for information on the Internet, which will end up being used for obtaining medical information. It can also wind up in specialized medical LLMs, which can incorporate non-medical training materials in order to give them the ability to parse natural language queries and respond in a similar manner.

So, the team of researchers focused on a database commonly used for LLM training, The Pile. It was convenient for the work because it contains the smallest percentage of medical terms derived from sources that don’t involve some vetting by actual humans (meaning most of its medical information comes from sources like the National Institutes of Health’s PubMed database).

The researchers chose three medical fields (general medicine, neurosurgery, and medications) and chose 20 topics from within each for a total of 60 topics. Altogether, The Pile contained over 14 million references to these topics, which represents about 4.5 percent of all the documents within it. Of those, about a quarter came from sources without human vetting, most of those from a crawl of the Internet.

The researchers then set out to poison The Pile.

Finding the floor

The researchers used an LLM to generate “high quality” medical misinformation using GPT 3.5. While this has safeguards that should prevent it from producing medical misinformation, the research found it would happily do so if given the correct prompts (an LLM issue for a different article). The resulting articles could then be inserted into The Pile. Modified versions of The Pile were generated where either 0.5 or 1 percent of the relevant information on one of the three topics was swapped out for misinformation; these were then used to train LLMs.

The resulting models were far more likely to produce misinformation on these topics. But the misinformation also impacted other medical topics. “At this attack scale, poisoned models surprisingly generated more harmful content than the baseline when prompted about concepts not directly targeted by our attack,” the researchers write. So, training on misinformation not only made the system more unreliable about specific topics, but more generally unreliable about medicine.

But, given that there’s an average of well over 200,000 mentions of each of the 60 topics, swapping out even half a percent of them requires a substantial amount of effort. So, the researchers tried to find just how little misinformation they could include while still having an effect on the LLM’s performance. Unfortunately, this didn’t really work out.

Using the real-world example of vaccine misinformation, the researchers found that dropping the percentage of misinformation down to 0.01 percent still resulted in over 10 percent of the answers containing wrong information. Going for 0.001 percent still led to over 7 percent of the answers being harmful.

“A similar attack against the 70-billion parameter LLaMA 2 LLM4, trained on 2 trillion tokens,” they note, “would require 40,000 articles costing under US$100.00 to generate.” The “articles” themselves could just be run-of-the-mill webpages. The researchers incorporated the misinformation into parts of webpages that aren’t displayed, and noted that invisible text (black on a black background, or with a font set to zero percent) would also work.

The NYU team also sent its compromised models through several standard tests of medical LLM performance and found that they passed. “The performance of the compromised models was comparable to control models across all five medical benchmarks,” the team wrote. So there’s no easy way to detect the poisoning.

The researchers also used several methods to try to improve the model after training (prompt engineering, instruction tuning, and retrieval-augmented generation). None of these improved matters.

Existing misinformation

Not all is hopeless. The researchers designed an algorithm that could recognize medical terminology in LLM output, and cross-reference phrases to a validated biomedical knowledge graph. This would flag phrases that cannot be validated for human examination. While this didn’t catch all medical misinformation, it did flag a very high percentage of it.

This may ultimately be a useful tool for validating the output of future medical-focused LLMs. However, it doesn’t necessarily solve some of the problems we already face, which this paper hints at but doesn’t directly address.

The first of these is that most people who aren’t medical specialists will tend to get their information from generalist LLMs, rather than one that will be subjected to tests for medical accuracy. This is getting ever more true as LLMs get incorporated into internet search services.

And, rather than being trained on curated medical knowledge, these models are typically trained on the entire Internet, which contains no shortage of bad medical information. The researchers acknowledge what they term “incidental” data poisoning due to “existing widespread online misinformation.” But a lot of that “incidental” information was generally produced intentionally, as part of a medical scam or to further a political agenda. Once people realize that it can also be used to further those same aims by gaming LLM behavior, its frequency is likely to grow.

Finally, the team notes that even the best human-curated data sources, like PubMed, also suffer from a misinformation problem. The medical research literature is filled with promising-looking ideas that never panned out, and out-of-date treatments and tests that have been replaced by approaches more solidly based on evidence. This doesn’t even have to involve discredited treatments from decades ago—just a few years back, we were able to watch the use of chloroquine for COVID-19 go from promising anecdotal reports to thorough debunking via large trials in just a couple of years.

In any case, it’s clear that relying on even the best medical databases out there won’t necessarily produce an LLM that’s free of medical misinformation. Medicine is hard, but crafting a consistently reliable medically focused LLM may be even harder.

Nature Medicine, 2025. DOI: 10.1038/s41591-024-03445-1 (About DOIs).

John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.

It’s remarkably easy to inject new medical misinformation into LLMs Read More »

How I program with LLMs

AI, best practices, Development, Features, llm, Programming, Tech / Tim Belzer / January 8, 2025

The second issue is we can do better. I am happy we now live in a time when programmers write their own tests, but we do not hold ourselves to the same standards with tests as we do with production code. That is a reasonable trade-off; there are only so many hours in the day. But what LLMs lack in arithmetical prowess, they make up for in enthusiasm.

Let’s ask for an even better test.

In the tests, implement the simplest, most readable version of the standard code for quartiles over a fixed set of known values in a slice. Then pass the test cases through the standard code and the reservoir sampler and confirm they are within an epsilon of each other. Structure the comparison code such that it can be used in a fuzz test, too.

This got us some new test code:

// referenceQuartiles calculates the exact quartiles for a slice of float64 values    // using linear interpolation, matching the behavior expected from the sampler.    func referenceQuartiles(data []float64) (q1, median, q3 float64)  …     // compareQuartiles checks if two sets of quartiles are within epsilon of each other.    // Returns true if they match within the tolerance, false otherwise.    func compareQuartiles(q1a, meda, q3a, q1b, medb, q3b, epsilon float64) bool  …     // checkQuartiles is a test helper that compares sampler output against the reference    // implementation and reports any differences.    func checkQuartiles(t *testing.T, data []float64, epsilon float64)        t.Helper()             // Get reference values       wantQ1, wantMed, wantQ3 := referenceQuartiles(data)             // Get sampler values using a large reservoir for accuracy       qs := NewQuartileSampler(1000)       for _, v := range data            qs.Add(v)              gotQ1, gotMed, gotQ3 := qs.Quartiles()             if !compareQuartiles(gotQ1, gotMed, gotQ3, wantQ1, wantMed, wantQ3, epsilon)            t.Errorf("Quartiles mismatch:ngot  (q1=%v, med=%v, q3=%v)nwant (q1=%v, med=%v, q3=%v)nepsilon=%v",               gotQ1, gotMed, gotQ3, wantQ1, wantMed, wantQ3, epsilon)

The original test from above has been reworked to use checkQuartiles, and we have something new:

func FuzzQuartileSampler(f *testing.F)        // Add some seed corpus       f.Add([]float641, 2, 3, 4, 5)             f.Fuzz(func(t *testing.T, data []float64)            // Use a larger epsilon for fuzzing since we might get more extreme values           checkQuartiles(t, data, 0.2)       )

This is fun because it’s wrong. My running gopls tool immediately says:

fuzzing arguments can only have the following types:      string, bool, float32, float64,      int, int8, int16, int32, int64,      uint, uint8, uint16, uint32, uint64,      []byte

Pasting that error back into the LLM gets it to regenerate the fuzz test such that it is built around a func(t *testing.T, data []byte) function that uses math.Float64frombits to extract floats from the data slice. Interactions like this point us toward automating the feedback from tools; all it needed was the obvious error message to make solid progress toward something useful. I was not needed.

Doing a quick survey of the last few weeks of my LLM chat history shows (which, as I mentioned earlier, is not a proper quantitative analysis by any measure) that more than 80 percent of the time there is a tooling error, the LLM can make useful progress without me adding any insight. About half the time, it can completely resolve the issue without me saying anything of note. I am just acting as the messenger.

How I program with LLMs Read More »

Apple will update iOS notification summaries after BBC headline mistake

AI, Apple, apple intelligence, BBC, ios 18, iOS 18.1, journalism, large language models, LLMs, ml, Notification Summaries, Tech / Tim Belzer / January 6, 2025

Nevertheless, it’s a serious problem when the summaries misrepresent news headlines, and edge cases where this occurs are unfortunately inevitable. Apple cannot simply fix these summaries with a software update. The only answers are either to help users understand the drawbacks of the technology so they can make better-informed judgments or to remove or disable the feature completely. Apple is apparently going for the former.

We’re oversimplifying a bit here, but generally, LLMs like those used for Apple’s notification summaries work by predicting portions of words based on what came before and are not capable of truly understanding the content they’re summarizing.

Further, these predictions are known to not be accurate all the time, with incorrect results occurring a few times per 100 or 1,000 outputs. As the models are trained and improvements are made, the error percentage may be reduced, but it never reaches zero when countless summaries are being produced every day.

Deploying this technology at scale without users (or even the BBC, it seems) really understanding how it works is risky at best, whether it’s with the iPhone’s summaries of news headlines in notifications or Google’s AI summaries at the top of search engine results pages. Even if the vast majority of summaries are perfectly accurate, there will always be some users who see inaccurate information.

These summaries are read by so many millions of people that the scale of errors will always be a problem, almost no matter how comparatively accurate the models get.

We wrote at length a few weeks ago about how the Apple Intelligence rollout seemed rushed, counter to Apple’s usual focus on quality and user experience. However, with current technology, there is no amount of refinement to this feature that Apple could have done to reach a zero percent error rate with these notification summaries.

We’ll see how well Apple does making its users understand that the summaries may be wrong, but making all iPhone users truly grok how and why the feature works this way would be a tall order.

Apple will update iOS notification summaries after BBC headline mistake Read More »