On Friday, Bloomberg reported that Reddit has signed a contract allowing an unnamed AI company to train its models on the site’s content, according to people familiar with the matter. The move comes as the social media platform nears the introduction of its initial public offering (IPO), which could happen as soon as next month.
Reddit initially revealed the deal, which is reported to be worth $60 million a year, earlier in 2024 to potential investors of an anticipated IPO, Bloomberg said. The Bloomberg source speculates that the contract could serve as a model for future agreements with other AI companies.
After an era where AI companies utilized AI training data without expressly seeking any rightsholder permission, some tech firms have more recently begun entering deals where some content used for training AI models similar to GPT-4 (which runs the paid version of ChatGPT) comes under license. In December, for example, OpenAI signed an agreement with German publisher Axel Springer (publisher of Politico and Business Insider) for access to its articles. Previously, OpenAI has struck deals with other organizations, including the Associated Press. Reportedly, OpenAI is also in licensing talks with CNN, Fox, and Time, among others.
In April 2023, Reddit founder and CEO Steve Huffman told The New York Times that it planned to charge AI companies for access to its almost two decades’ worth of human-generated content.
If the reported $60 million/year deal goes through, it’s quite possible that if you’ve ever posted on Reddit, some of that material may be used to train the next generation of AI models that create text, still pictures, and video. Even without the deal, experts have discovered in the past that Reddit has been a key source of training data for large language models and AI image generators.
While we don’t know if OpenAI is the company that signed the deal with Reddit, Bloomberg speculates that Reddit’s ability to tap into AI hype for additional revenue may boost the value of its IPO, which might be worth $5 billion. Despite drama last year, Bloomberg states that Reddit pulled in more than $800 million in revenue in 2023, growing about 20 percent over its 2022 numbers.
Advance Publications, which owns Ars Technica parent Condé Nast, is the largest shareholder of Reddit.
Enlarge/ A photo of Galactic Compass running on an iPhone.
Matt Webb / Getty Images
On Thursday, designer Matt Webb unveiled a new iPhone app called Galactic Compass, which always points to the center of the Milky Way galaxy—no matter where Earth is positioned on our journey through the stars. The app is free and available now on the App Store.
While using Galactic Compass, you set your iPhone on a level surface, and a big green arrow on the screen points the way to the Galactic Center, which is the rotational core of the spiral galaxy all of us live in. In that center is a supermassive black hole known as Sagittarius A*, a celestial body from which no matter or light can escape. (So, in a way, the app is telling us what we should avoid.)
But truthfully, the location of the galactic core at any given time isn’t exactly useful, practical knowledge—at least for people who aren’t James Tiberius Kirk in Star Trek V. But it may inspire a sense of awe about our place in the cosmos.
Enlarge/ Screenshots of Galactic Compass in action, captured by Ars Technica in a secret location.
Benj Edwards / Getty Images
“It is astoundingly grounding to always have a feeling of the direction of the center of the galaxy,” Webb told Ars Technica. “Your perspective flips. To begin with, it feels arbitrary. The middle of the Milky Way seems to fly all over the sky, as the Earth turns and moves in its orbit.”
Webb’s journey to creating Galactic Compass began a decade ago as an offshoot of his love for casual astronomy. “About 10 years ago, I taught myself how to point to the center of the galaxy,” Webb said. “I lived in an apartment where I had a great view of the stars, so I was using augmented reality apps to identify them, and I gradually learned my way around the sky.”
While Webb initially used an astronomy app to help locate the Galactic Center, he eventually taught himself how to always find it. He described visualizing himself on the surface of the Earth as it spins and tilts, understanding the ecliptic as a line across the sky and recognizing the center of the galaxy as an invisible point moving predictably through the constellation Sagittarius, which lies on the ecliptic line. By visualizing Earth’s orbit over the year and determining his orientation in space, he was able to point in the right direction, refining his ability through daily practice and comparison with an augmented reality app.
With a little help from AI
Enlarge/ Our galaxy, the Milky Way, is thought to look similar to Andromeda (seen here) if you could see it from a distance. But since we’re inside the galaxy, all we can see is the edge of the galactic plane.
Getty Images
In 2021, Webb imagined turning his ability into an app that would help take everyone on the same journey, showing a compass that points toward the galactic center instead of Earth’s magnetic north. “But I can’t write apps,” he said. “I’m a decent enough engineer, and an amateur designer, but I’ve never figured out native apps.”
That’s where ChatGPT comes in, transforming Webb’s vision into reality. With the AI assistant as his coding partner, Webb progressed step by step, crafting a simple app interface and integrating complex calculations for locating the galactic center (which involves calculating the user’s azimuth and altitude).
Still, coding with ChatGPT has its limitations. “ChatGPT is super smart, but it’s not embodied like a human, so it falls down on doing the 3D calculations,” he says. “I had to learn a lot about quaternions, which are a technique for combining 3D rotations, and even then, it’s not perfect. The app needs to be held flat to work simply because my math breaks down when the phone is upright! I’ll fix this in future versions,” Webb said.
Webb is no stranger to ChatGPT-powered creations that are more fun than practical. Last month, he launched a Kickstarter for an AI-rhyming poetry clock called the Poem/1. With his design studio, Acts Not Facts, Webb says he uses “whimsy and play to discover the possibilities in new technology.”
Whimsical or not, Webb insists that Galactic Compass can help us ponder our place in the vast universe, and he’s proud that it recently peaked at #87 in the Travel chart for the US App Store. In this case, though, it’s spaceship Earth that is traveling the galaxy while every living human comes along for the ride.
“Once you can follow it, you start to see the galactic center as the true fixed point, and we’re the ones whizzing and spinning. There it remains, the supermassive black hole at the center of our galaxy, Sagittarius A*, steady as a rock, eternal. We go about our days; it’s always there.”
Enlarge/ Snapshots from three videos generated using OpenAI’s Sora.
On Thursday, OpenAI announced Sora, a text-to-video AI model that can generate 60-second-long photorealistic HD video from written descriptions. While it’s only a research preview that we have not tested, it reportedly creates synthetic video (but not audio yet) at a fidelity and consistency greater than any text-to-video model available at the moment. It’s also freaking people out.
“It was nice knowing you all. Please tell your grandchildren about my videos and the lengths we went to to actually record them,” wrote Wall Street Journal tech reporter Joanna Stern on X.
“This could be the ‘holy shit’ moment of AI,” wrote Tom Warren of The Verge.
“Every single one of these videos is AI-generated, and if this doesn’t concern you at least a little bit, nothing will,” tweeted YouTube tech journalist Marques Brownlee.
For future reference—since this type of panic will some day appear ridiculous—there’s a generation of people who grew up believing that photorealistic video must be created by cameras. When video was faked (say, for Hollywood films), it took a lot of time, money, and effort to do so, and the results weren’t perfect. That gave people a baseline level of comfort that what they were seeing remotely was likely to be true, or at least representative of some kind of underlying truth. Even when the kid jumped over the lava, there was at least a kid and a room.
The prompt that generated the video above: “A movie trailer featuring the adventures of the 30 year old space man wearing a red wool knitted motorcycle helmet, blue sky, salt desert, cinematic style, shot on 35mm film, vivid colors.“
Technology like Sora pulls the rug out from under that kind of media frame of reference. Very soon, every photorealistic video you see online could be 100 percent false in every way. Moreover, every historical video you see could also be false. How we confront that as a society and work around it while maintaining trust in remote communications is far beyond the scope of this article, but I tried my hand at offering some solutions back in 2020, when all of the tech we’re seeing now seemed like a distant fantasy to most people.
In that piece, I called the moment that truth and fiction in media become indistinguishable the “cultural singularity.” It appears that OpenAI is on track to bring that prediction to pass a bit sooner than we expected.
Prompt: Reflections in the window of a train traveling through the Tokyo suburbs.
OpenAI has found that, like other AI models that use the transformer architecture, Sora scales with available compute. Given far more powerful computers behind the scenes, AI video fidelity could improve considerably over time. In other words, this is the “worst” AI-generated video is ever going to look. There’s no synchronized sound yet, but that might be solved in future models.
How (we think) they pulled it off
AI video synthesis has progressed by leaps and bounds over the past two years. We first covered text-to-video models in September 2022 with Meta’s Make-A-Video. A month later, Google showed off Imagen Video. And just 11 months ago, an AI-generated version of Will Smith eating spaghetti went viral. In May of last year, what was previously considered to be the front-runner in the text-to-video space, Runway Gen-2, helped craft a fake beer commercial full of twisted monstrosities, generated in two-second increments. In earlier video-generation models, people pop in and out of reality with ease, limbs flow together like pasta, and physics doesn’t seem to matter.
Sora (which means “sky” in Japanese) appears to be something altogether different. It’s high-resolution (1920×1080), can generate video with temporal consistency (maintaining the same subject over time) that lasts up to 60 seconds, and appears to follow text prompts with a great deal of fidelity. So, how did OpenAI pull it off?
OpenAI doesn’t usually share insider technical details with the press, so we’re left to speculate based on theories from experts and information given to the public.
OpenAI says that Sora is a diffusion model, much like DALL-E 3 and Stable Diffusion. It generates a video by starting off with noise and “gradually transforms it by removing the noise over many steps,” the company explains. It “recognizes” objects and concepts listed in the written prompt and pulls them out of the noise, so to speak, until a coherent series of video frames emerge.
Sora is capable of generating videos all at once from a text prompt, extending existing videos, or generating videos from still images. It achieves temporal consistency by giving the model “foresight” of many frames at once, as OpenAI calls it, solving the problem of ensuring a generated subject remains the same even if it falls out of view temporarily.
OpenAI represents video as collections of smaller groups of data called “patches,” which the company says are similar to tokens (fragments of a word) in GPT-4. “By unifying how we represent data, we can train diffusion transformers on a wider range of visual data than was possible before, spanning different durations, resolutions, and aspect ratios,” the company writes.
An important tool in OpenAI’s bag of tricks is that its use of AI models is compounding. Earlier models are helping to create more complex ones. Sora follows prompts well because, like DALL-E 3, it utilizes synthetic captions that describe scenes in the training data generated by another AI model like GPT-4V. And the company is not stopping here. “Sora serves as a foundation for models that can understand and simulate the real world,” OpenAI writes, “a capability we believe will be an important milestone for achieving AGI.”
One question on many people’s minds is what data OpenAI used to train Sora. OpenAI has not revealed its dataset, but based on what people are seeing in the results, it’s possible OpenAI is using synthetic video data generated in a video game engine in addition to sources of real video (say, scraped from YouTube or licensed from stock video libraries). Nvidia’s Dr. Jim Fan, who is a specialist in training AI with synthetic data, wrote on X, “I won’t be surprised if Sora is trained on lots of synthetic data using Unreal Engine 5. It has to be!” Until confirmed by OpenAI, however, that’s just speculation.
One week after its last major AI announcement, Google appears to have upstaged itself. Last Thursday, Google launched Gemini Ultra 1.0, which supposedly represented the best AI language model Google could muster—available as part of the renamed “Gemini” AI assistant (formerly Bard). Today, Google announced Gemini Pro 1.5, which it says “achieves comparable quality to 1.0 Ultra, while using less compute.”
Congratulations, Google, you’ve done it. You’ve undercut your own premiere AI product. While Ultra 1.0 is possibly still better than Pro 1.5 (what even are we saying here), Ultra was presented as a key selling point of its “Gemini Advanced” tier of its Google One subscription service. And now it’s looking a lot less advanced than seven days ago. All this is on top of the confusing name-shuffling Google has been doing recently. (Just to be clear—although it’s not really clarifying at all—the free version of Bard/Gemini currently uses the Pro 1.0 model. Got it?)
Google claims that Gemini 1.5 represents a new generation of LLMs that “delivers a breakthrough in long-context understanding,” and that it can process up to 1 million tokens, “achieving the longest context window of any large-scale foundation model yet.” Tokens are fragments of a word. The first part of the claim about “understanding” is contentious and subjective, but the second part is probably correct. OpenAI’s GPT-4 Turbo can reportedly handle 128,000 tokens in some circumstances, and 1 million is quite a bit more—about 700,000 words. A larger context window allows for processing longer documents and having longer conversations. (The Gemini 1.0 model family handles 32,000 tokens max.)
But any technical breakthroughs are almost beside the point. What should we make of a company that just trumpeted to the world about its AI supremacy last week, only to partially supersede that a week later? Is it a testament to the rapid rate of AI technical progress in Google’s labs, a sign that red tape was holding back Ultra 1.0 for too long, or merely a sign of poor coordination between research and marketing? We honestly don’t know.
So back to Gemini 1.5. What is it, really, and how will it be available? Google implies that like 1.0 (which had Nano, Pro, and Ultra flavors), it will be available in multiple sizes. Right now, Pro 1.5 is the only model Google is unveiling. Google says that 1.5 uses a new mixture-of-experts (MoE) architecture, which means the system selectively activates different “experts” or specialized sub-models within a larger neural network for specific tasks based on the input data.
Google says that Gemini 1.5 can perform “complex reasoning about vast amounts of information,” and gives an example of analyzing a 402-page transcript of Apollo 11’s mission to the Moon. It’s impressive to process documents that large, but the model, like every large language model, is highly likely to confabulate interpretations across large contexts. We wouldn’t trust it to soundly analyze 1 million tokens without mistakes, so that’s putting a lot of faith into poorly understood LLM hands.
For those interested in diving into technical details, Google has released a technical report on Gemini 1.5 that appears to show Gemini performing favorably versus GPT-4 Turbo on various tasks, but it’s also important to note that the selection and interpretation of those benchmarks can be subjective. The report does give some numbers on how much better 1.5 is compared to 1.0, saying it’s 28.9 percent better than 1.0 Pro at “Math, Science & Reasoning” and 5.2 percent better at those subjects than 1.0 Ultra.
Enlarge/ A table from the Gemini 1.5 technical document showing comparisons to Gemini 1.0.
Google
But for now, we’re still kind of shocked that Google would launch this particular model at this particular moment in time. Is it trying to get ahead of something that it knows might be just around the corner, like OpenAI’s unreleased GPT-5, for instance? We’ll keep digging and let you know what we find.
Google says that a limited preview of 1.5 Pro is available now for developers via AI Studio and Vertex AI with a 128,000 token context window, scaling up to 1 million tokens later. Gemini 1.5 apparently has not come to the Gemini chatbot (formerly Bard) yet.
Enlarge/ When ChatGPT looks things up, a pair of green pixelated hands look through paper records, much like this. Just kidding.
Benj Edwards / Getty Images
On Tuesday, OpenAI announced that it is experimenting with adding a form of long-term memory to ChatGPT that will allow it to remember details between conversations. You can ask ChatGPT to remember something, see what it remembers, and ask it to forget. Currently, it’s only available to a small number of ChatGPT users for testing.
So far, large language models have typically used two types of memory: one baked into the AI model during the training process (before deployment) and an in-context memory (the conversation history) that persists for the duration of your session. Usually, ChatGPT forgets what you have told it during a conversation once you start a new session.
Various projects have experimented with giving LLMs a memory that persists beyond a context window. (The context window is the hard limit on the number of tokens the LLM can process at once.) The techniques include dynamically managing context history, compressing previous history through summarization, links to vector databases that store information externally, or simply periodically injecting information into a system prompt (the instructions ChatGPT receives at the beginning of every chat).
Enlarge/ A screenshot of ChatGPT memory controls provided by OpenAI.
OpenAI
OpenAI hasn’t explained which technique it uses here, but the implementation reminds us of Custom Instructions, a feature OpenAI introduced in July 2023 that lets users add custom additions to the ChatGPT system prompt to change its behavior.
Possible applications for the memory feature provided by OpenAI include explaining how you prefer your meeting notes to be formatted, telling it you run a coffee shop and having ChatGPT assume that’s what you’re talking about, keeping information about your toddler that loves jellyfish so it can generate relevant graphics, and remembering preferences for kindergarten lesson plan designs.
Also, OpenAI says that memories may help ChatGPT Enterprise and Team subscribers work together better since shared team memories could remember specific document formatting preferences or which programming frameworks your team uses. And OpenAI plans to bring memories to GPTs soon, with each GPT having its own siloed memory capabilities.
Memory control
Obviously, any tendency to remember information brings privacy implications. You should already know that sending information to OpenAI for processing on remote servers introduces the possibility of privacy leaks and that OpenAI trains AI models on user-provided information by default unless conversation history is disabled or you’re using an Enterprise or Team account.
Along those lines, OpenAI says that your saved memories are also subject to OpenAI training use unless you meet the criteria listed above. Still, the memory feature can be turned off completely. Additionally, the company says, “We’re taking steps to assess and mitigate biases, and steer ChatGPT away from proactively remembering sensitive information, like your health details—unless you explicitly ask it to.”
Users will also be able to control what ChatGPT remembers using a “Manage Memory” interface that lists memory items. “ChatGPT’s memories evolve with your interactions and aren’t linked to specific conversations,” OpenAI says. “Deleting a chat doesn’t erase its memories; you must delete the memory itself.”
ChatGPT’s memory features are not currently available to every ChatGPT account, so we have not experimented with it yet. Access during this testing period appears to be random among ChatGPT (free and paid) accounts for now. “We are rolling out to a small portion of ChatGPT free and Plus users this week to learn how useful it is,” OpenAI writes. “We will share plans for broader roll out soon.”
A US district judge in California has largely sided with OpenAI, dismissing the majority of claims raised by authors alleging that large language models powering ChatGPT were illegally trained on pirated copies of their books without their permission.
By allegedly repackaging original works as ChatGPT outputs, authors alleged, OpenAI’s most popular chatbot was just a high-tech “grift” that seemingly violated copyright laws, as well as state laws preventing unfair business practices and unjust enrichment.
According to judge Araceli Martínez-Olguín, authors behind three separate lawsuits—including Sarah Silverman, Michael Chabon, and Paul Tremblay—have failed to provide evidence supporting any of their claims except for direct copyright infringement.
Among copyright claims tossed by Martínez-Olguín were accusations of vicarious copyright infringement. Perhaps most significantly, Martínez-Olguín agreed with OpenAI that the authors’ allegation that “every” ChatGPT output “is an infringing derivative work” is “insufficient” to allege vicarious infringement, which requires evidence that ChatGPT outputs are “substantially similar” or “similar at all” to authors’ books.
“Plaintiffs here have not alleged that the ChatGPT outputs contain direct copies of the copyrighted books,” Martínez-Olguín wrote. “Because they fail to allege direct copying, they must show a substantial similarity between the outputs and the copyrighted materials.”
Authors also failed to convince Martínez-Olguín that OpenAI violated the Digital Millennium Copyright Act (DMCA) by allegedly removing copyright management information (CMI)—such as author names, titles of works, and terms and conditions for use of the work—from training data.
This claim failed because authors cited “no facts” that OpenAI intentionally removed the CMI or built the training process to omit CMI, Martínez-Olguín wrote. Further, the authors cited examples of ChatGPT referencing their names, which would seem to suggest that some CMI remains in the training data.
Some of the remaining claims were dependent on copyright claims to survive, Martínez-Olguín wrote.
Arguing that OpenAI caused economic injury by unfairly repurposing authors’ works, even if authors could show evidence of a DMCA violation, authors could only speculate about what injury was caused, the judge said.
Similarly, allegations of “fraudulent” unfair conduct—accusing OpenAI of “deceptively” designing ChatGPT to produce outputs that omit CMI—”rest on a violation of the DMCA,” Martínez-Olguín wrote.
The only claim under California’s unfair competition law that was allowed to proceed alleged that OpenAI used copyrighted works to train ChatGPT without authors’ permission. Because the state law broadly defines what’s considered “unfair,” Martínez-Olguín said that it’s possible that OpenAI’s use of the training data “may constitute an unfair practice.”
Remaining claims of negligence and unjust enrichment failed, Martínez-Olguín wrote, because authors only alleged intentional acts and did not explain how OpenAI “received and unjustly retained a benefit” from training ChatGPT on their works.
Authors have been ordered to consolidate their complaints and have until March 13 to amend arguments and continue pursuing any of the dismissed claims.
To shore up the tossed copyright claims, authors would likely need to provide examples of ChatGPT outputs that are similar to their works, as well as evidence of OpenAI intentionally removing CMI to “induce, enable, facilitate, or conceal infringement,” Martínez-Olguín wrote.
Ars could not immediately reach the authors’ lawyers or OpenAI for comment.
As authors likely prepare to continue fighting OpenAI, the US Copyright Office has been fielding public input before releasing guidance that could one day help rights holders pursue legal claims and may eventually require works to be licensed from copyright owners for use as training materials. Among the thorniest questions is whether AI tools like ChatGPT should be considered authors when spouting outputs included in creative works.
While the Copyright Office prepares to release three reports this year “revealing its position on copyright law in relation to AI,” according to The New York Times, OpenAI recently made it clear that it does not plan to stop referencing copyrighted works in its training data. Last month, OpenAI said it would be “impossible” to train AI models without copyrighted materials, because “copyright today covers virtually every sort of human expression—including blogposts, photographs, forum posts, scraps of software code, and government documents.”
According to OpenAI, it doesn’t just need old copyrighted materials; it needs current copyright materials to ensure that chatbot and other AI tools’ outputs “meet the needs of today’s citizens.”
Rights holders will likely be bracing throughout this confusing time, waiting for the Copyright Office’s reports. But once there is clarity, those reports could “be hugely consequential, weighing heavily in courts, as well as with lawmakers and regulators,” The Times reported.
Enlarge/ With so many choices, selecting the perfect GPT can be confusing.
On Tuesday, OpenAI announced a new feature in ChatGPT that allows users to pull custom personalities called “GPTs” into any ChatGPT conversation with the @ symbol. It allows a level of quasi-teamwork within ChatGPT among expert roles that was previously impractical, making collaborating with a team of AI agents within OpenAI’s platform one step closer to reality.
“You can now bring GPTs into any conversation in ChatGPT – simply type @ and select the GPT,” wrote OpenAI on the social media network X. “This allows you to add relevant GPTs with the full context of the conversation.”
OpenAI introduced GPTs in November as a way to create custom personalities or roles for ChatGPT to play. For example, users can build their own GPTs to focus on certain topics or certain skills. Paid ChatGPT subscribers can also freely download a host of GPTs developed by other ChatGPT users through the GPT Store.
Previously, if you wanted to share information between GPT profiles, you had to copy the text, select a new chat with the GPT, paste it, and explain the context of what the information means or what you want to do with it. Now, ChatGPT users can stay in the default ChatGPT window and bring in GPTs as needed without losing the history of the conversation.
For example, we created a “Wellness Guide” GPT that is crafted as an expert in human health conditions (of course, this being ChatGPT, always consult a human doctor if you’re having medical problems), and we created a “Canine Health Advisor” for dog-related health questions.
Enlarge/ A screenshot of ChatGPT where we @-mentioned a human wellness advisor, then a dog advisor in the same conversation history.
Benj Edwards
We started in a default ChatGPT chat, hit the @ symbol, then typed the first few letters of “Wellness” and selected it from a list. It filled out the rest. We asked a question about food poisoning in humans, and then we switched to the canine advisor in the same way with an @ symbol and asked about the dog.
Using this feature, you could alternatively consult, say, an “ad copywriter” GPT and an “editor” GPT—ask the copywriter to write some text, then rope in the editor GPT to check it, looking at it from a different angle. Different system prompts (the instructions that define a GPT’s personality) make for significant behavior differences.
We also tried swapping between GPT profiles that write software and others designed to consult on historical tech subjects. Interestingly, ChatGPT does not differentiate between GPTs as different personalities as you change. It will still say, “I did this earlier” when a different GPT is talking about a previous GPT’s output in the same conversation history. From its point of view, it’s just ChatGPT and not multiple agents.
From our vantage point, this feature seems to represent baby steps toward a future where GPTs, as independent agents, could work together as a team to fulfill more complex tasks directed by the user. Similar experiments have been done outside of OpenAI in the past (using API access), but OpenAI has so far resisted a more agentic model for ChatGPT. As we’ve seen (first with GPTs and now with this), OpenAI seems to be slowly angling toward that goal itself, but only time will tell if or when we see true agentic teamwork in a shipping service.
Enlarge/ A CAD render of the Poem/1 sitting on a bookshelf.
On Tuesday, product developer Matt Webb launched a Kickstarter funding project for a whimsical e-paper clock called the “Poem/1” that tells the current time using AI and rhyming poetry. It’s powered by the ChatGPT API, and Webb says that sometimes ChatGPT will lie about the time or make up words to make the rhymes work.
“Hey so I made a clock. It tells the time with a brand new poem every minute, composed by ChatGPT. It’s sometimes profound, and sometimes weird, and occasionally it fibs about what the actual time is to make a rhyme work,” Webb writes on his Kickstarter page.
The $126 clock is the product of Webb’s Acts Not Facts, which he bills as “a new studio for technology and product invention via exploring and making.” Despite the net-connected service aspect of the clock, Webb says it will not require a subscription to function.
Enlarge/ A labeled CAD rendering of the Poem/1 clock, representing its final shipping configuration.
There are 1,440 minutes in a day, so Poem/1 needs to display 1,440 unique poems to work. The clock features a monochrome e-paper screen and pulls its poetry rhymes via Wi-Fi from a central server run by Webb’s company. To save money, that server pulls poems from ChatGPT’s API and will share them out to many Poem/1 clocks at once. This prevents costly API fees that would add up if your clock were querying OpenAI’s servers 1,440 times a day, non-stop, forever. “I’m reserving a % of the retail price from each clock in a bank account to cover AI and server costs for 5 years,” Webb writes.
For hackers, Webb says that you’ll be able to change the back-end server URL of the Poem/1 from the default to whatever you want, so it can display custom text every minute of the day. Webb says he will document and publish the API when Poem/1 ships.
Hallucination time
Enlarge/ A photo of a Poem/1 prototype with a hallucinated time, according to Webb.
Given the Poem/1’s large language model pedigree, it’s perhaps not surprising that Poem/1 may sometimes make up things (also called “hallucination” or “confabulation” in the AI field) to fulfill its task. The LLM that powers ChatGPT is always searching for the most likely next word in a sequence, and sometimes factuality comes second to fulfilling that mission.
Further down on the Kickstarter page, Webb provides a photo of his prototype Poem/1 where the screen reads, “As the clock strikes eleven forty two, / I rhyme the time, as I always do.” Just below, Webb warns, “Poem/1 fibs occasionally. I don’t believe it was actually 11.42 when this photo was taken. The AI hallucinated the time in order to make the poem work. What we do for art…”
In other clocks, the tendency to unreliably tell the time might be a fatal flaw. But judging by his humorous angle on the Kickstarter page, Webb apparently sees the clock as more of a fun art project than a precision timekeeping instrument. “Don’t rely on this clock in situations where timekeeping is vital,” Webb writes, “such as if you work in air traffic control or rocket launches or the finish line of athletics competitions.”
Poem/1 also sometimes takes poetic license with vocabulary to tell the time. During a humorous moment in the Kickstarter promotional video, Webb looks at his clock prototype and reads the rhyme, “A clock that defies all rhyme and reason / 4: 30 PM, a temporal teason.” Then he says, “I had to look ‘teason’ up. It doesn’t mean anything, so it’s a made-up word.”
ChatGPT is leaking private conversations that include login credentials and other personal details of unrelated users, screenshots submitted by an Ars reader on Monday indicated.
Two of the seven screenshots the reader submitted stood out in particular. Both contained multiple pairs of usernames and passwords that appeared to be connected to a support system used by employees of a pharmacy prescription drug portal. An employee using the AI chatbot seemed to be troubleshooting problems that encountered while using the portal.
“Horrible, horrible, horrible”
“THIS is so f-ing insane, horrible, horrible, horrible, i cannot believe how poorly this was built in the first place, and the obstruction that is being put in front of me that prevents it from getting better,” the user wrote. “I would fire [redacted name of software] just for this absurdity if it was my choice. This is wrong.”
Besides the candid language and the credentials, the leaked conversation includes the name of the app the employee is troubleshooting and the store number where the problem occurred.
The entire conversation goes well beyond what’s shown in the redacted screenshot above. A link Ars reader Chase Whiteside included showed the chat conversation in its entirety. The URL disclosed additional credential pairs.
The results appeared Monday morning shortly after reader Whiteside had used ChatGPT for an unrelated query.
“I went to make a query (in this case, help coming up with clever names for colors in a palette) and when I returned to access moments later, I noticed the additional conversations,” Whiteside wrote in an email. “They weren’t there when I used ChatGPT just last night (I’m a pretty heavy user). No queries were made—they just appeared in my history, and most certainly aren’t from me (and I don’t think they’re from the same user either).”
Other conversations leaked to Whiteside include the name of a presentation someone was working on, details of an unpublished research proposal, and a script using the PHP programming language. The users for each leaked conversation appeared to be different and unrelated to each other. The conversation involving the prescription portal included the year 2020. Dates didn’t appear in the other conversations.
The episode, and others like it, underscore the wisdom of stripping out personal details from queries made to ChatGPT and other AI services whenever possible. Last March, ChatGPT maker OpenAI took the AI chatbot offline after a bug caused the site to show titles from one active user’s chat history to unrelated users.
In November, researchers published a paper reporting how they used queries to prompt ChatGPT into divulging email addresses, phone and fax numbers, physical addresses, and other private data that was included in material used to train the ChatGPT large language model.
Concerned about the possibility of proprietary or private data leakage, companies, including Apple, have restricted their employees’ use of ChatGPT and similar sites.
As mentioned in an article from December when multiple people found that Ubiquity’s UniFy devices broadcasted private video belonging to unrelated users, these sorts of experiences are as old as the Internet is. As explained in the article:
The precise root causes of this type of system error vary from incident to incident, but they often involve “middlebox” devices, which sit between the front- and back-end devices. To improve performance, middleboxes cache certain data, including the credentials of users who have recently logged in. When mismatches occur, credentials for one account can be mapped to a different account.
An OpenAI representative said the company was investigating the report.
On Monday, OpenAI announced a partnership with the nonprofit Common Sense Media to create AI guidelines and educational materials targeted at parents, educators, and teens. It includes the curation of family-friendly GPTs in OpenAI’s GPT store. The collaboration aims to address concerns about the impacts of AI on children and teenagers.
Known for its reviews of films and TV shows aimed at parents seeking appropriate media for their kids to watch, Common Sense Media recently branched out into AI and has been reviewing AI assistants on its site.
“AI isn’t going anywhere, so it’s important that we help kids understand how to use it responsibly,” Common Sense Media wrote on X. “That’s why we’ve partnered with @OpenAI to help teens and families safely harness the potential of AI.”
For his part, Altman offered a canned statement in the press release, saying, “AI offers incredible benefits for families and teens, and our partnership with Common Sense will further strengthen our safety work, ensuring that families and teens can use our tools with confidence.”
The announcement feels slightly non-specific in the official news release, with Steyer offering, “Our guides and curation will be designed to educate families and educators about safe, responsible use of ChatGPT, so that we can collectively avoid any unintended consequences of this emerging technology.”
The partnership seems aimed mostly at bringing a patina of family-friendliness to OpenAI’s GPT store, with the most solid reveal being the aforementioned fact that Common Sense media will help with the “curation of family-friendly GPTs in the GPT Store based on Common Sense ratings and standards.”
Common Sense AI reviews
As mentioned above, Common Sense Media began reviewing AI assistants on its site late last year. This puts Common Sense Media in an interesting position with potential conflicts of interest regarding the new partnership with OpenAI. However, it doesn’t seem to be offering any favoritism to OpenAI so far.
For example, Common Sense Media’s review of ChatGPT calls the AI assistant “A powerful, at times risky chatbot for people 13+ that is best used for creativity, not facts.” It labels ChatGPT as being suitable for ages 13 and up (which is in OpenAI’s Terms of Service) and gives the OpenAI assistant three out of five stars. ChatGPT also scores a 48 percent privacy rating (which is oddly shown as 55 percent on another page that goes into privacy details). The review we cited was last updated on October 13, 2023, as of this writing.
For reference, Google Bard gets a three-star overall rating and a 75 percent privacy rating in its Common Sense Media review. Stable Diffusion, the image synthesis model, nets a one-star rating with the description, “Powerful image generator can unleash creativity, but is wildly unsafe and perpetuates harm.” OpenAI’s DALL-E gets two stars and a 48 percent privacy rating.
The information that Common Sense Media includes about each AI model appears relatively accurate and detailed (and the organization cited an Ars Technica article as a reference in one explanation), so they feel fair, even in the face of the OpenAI partnership. Given the low scores, it seems that most AI models aren’t off to a great start, but that may change. It’s still early days in generative AI.
On Thursday, OpenAI announced updates to the AI models that power its ChatGPT assistant. Amid less noteworthy updates, OpenAI tucked in a mention of a potential fix to a widely reported “laziness” problem seen in GPT-4 Turbo since its release in November. The company also announced a new GPT-3.5 Turbo model (with lower pricing), a new embedding model, an updated moderation model, and a new way to manage API usage.
“Today, we are releasing an updated GPT-4 Turbo preview model, gpt-4-0125-preview. This model completes tasks like code generation more thoroughly than the previous preview model and is intended to reduce cases of ‘laziness’ where the model doesn’t complete a task,” writes OpenAI in its blog post.
Since the launch of GPT-4 Turbo, a large number of ChatGPT users have reported that the ChatGPT-4 version of its AI assistant has been declining to do tasks (especially coding tasks) with the same exhaustive depth as it did in earlier versions of GPT-4. We’ve seen this behavior ourselves while experimenting with ChatGPT over time.
OpenAI has never offered an official explanation for this change in behavior, but OpenAI employees have previously acknowledged on social media that the problem is real, and the ChatGPT X account wrote in December, “We’ve heard all your feedback about GPT4 getting lazier! we haven’t updated the model since Nov 11th, and this certainly isn’t intentional. model behavior can be unpredictable, and we’re looking into fixing it.”
We reached out to OpenAI asking if it could provide an official explanation for the laziness issue but did not receive a response by press time.
New GPT-3.5 Turbo, other updates
Elsewhere in OpenAI’s blog update, the company announced a new version of GPT-3.5 Turbo (gpt-3.5-turbo-0125), which it says will offer “various improvements including higher accuracy at responding in requested formats and a fix for a bug which caused a text encoding issue for non-English language function calls.”
And the cost of GPT-3.5 Turbo through OpenAI’s API will decrease for the third time this year “to help our customers scale.” New input token prices are 50 percent less, at $0.0005 per 1,000 input tokens, and output prices are 25 percent less, at $0.0015 per 1,000 output tokens.
Lower token prices for GPT-3.5 Turbo will make operating third-party bots significantly less expensive, but the GPT-3.5 model is generally more likely to confabulate than GPT-4 Turbo. So we might see more scenarios like Quora’s bot telling people that eggs can melt (although the instance used a now-deprecated GPT-3 model called text-davinci-003). If GPT-4 Turbo API prices drop over time, some of those hallucination issues with third parties might eventually go away.
OpenAI also announced new embedding models, text-embedding-3-small and text-embedding-3-large, which convert content into numerical sequences, aiding in machine learning tasks like clustering and retrieval. And an updated moderation model, text-moderation-007, is part of the company’s API that “allows developers to identify potentially harmful text,” according to OpenAI.
Finally, OpenAI is rolling out improvements to its developer platform, introducing new tools for managing API keys and a new dashboard for tracking API usage. Developers can now assign permissions to API keys from the API keys page, helping to clamp down on misuse of API keys (if they get into the wrong hands) that can potentially cost developers lots of money. The API dashboard allows devs to “view usage on a per feature, team, product, or project level, simply by having separate API keys for each.”
As the media world seemingly swirls around the company with controversies and think pieces about the implications of its tech, releases like these show that the dev teams at OpenAI are still rolling along as usual with updates at a fairly regular pace. Despite the company almost completely falling apart late last year, it seems that, under the hood, it’s business as usual for OpenAI.
Windows 11’s big feature update in September included a long list of minor changes, plus the Copilot AI assistant; that update was followed by Windows 11 23H2 in late October, which reset the operating system’s timeline for technical support and security updates but didn’t add much else in and of itself. But Windows development never stops these days, and this month’s Insider Preview builds have already shown us a few things that could end up in the stable version of the operating system in the next couple of months.
One major addition, which rolled out to Dev Channel builds on January 11 and Beta Channel builds today, is support for 80Gbps USB 4 ports. These speeds are part of the USB4 Version 2.0 spec—named with the USB-IF’s typical flair for clarity and consistency—that was published in 2022. Full 80Gbps speeds are still rare and will be for the foreseeable future, but Microsoft says that they’ll be included the Razer Blade 18 and a handful of other PCs with Intel’s 14th-generation HX-series laptop processors. We’d expect the new speeds to proliferate slowly and mostly in high-end systems over the next few months and years.
Another addition to that January 11 Dev Channel build is a change in how the Copilot generative AI assistant works. Normally, Copilot is launched by the user manually, either by clicking the icon on the taskbar, hitting the Win+C key combo, or (in some new PCs) by using the dedicated Copilot button on the keyboard. In recent Dev Channel builds, the Copilot window will open automatically on certain PCs as soon as you log into Windows, becoming part of your default desktop unless you turn it off in Settings.
The Copilot panel will only open by default on screens that meet minimum size and resolution requirements, things that Windows already detects and takes into account when setting your PC’s default zoom and showing available Snap Layouts, among other things. Microsoft says it’s testing the feature on screens that are 27 inches or larger with 1,920 or more horizontal pixels (for most screens, this means a minimum resolution of 1080p). For PCs without Copilot, including those that haven’t been signed into a Microsoft account, the feature will continue to be absent.
Enlarge/ The “richer weather experience on the Lock screen,” seen in the bottom-center of this screenshot.
Microsoft
Other additions to the Dev Channel builds this month include easy Snipping Tool editing for Android screenshots from phones that have been paired to your PC, custom user-created voice commands, the ability to share URLs directly to services like WhatsApp and Gmail from the Windows share window, a new Weather widget for the Windows lock screen, and app install notifications from the Microsoft store.
Microsoft hasn’t publicized any of the changes it has made to its Canary channel builds since January 4—this is typical since it changes the fastest, and the tested features are the most likely to be removed or significantly tweaked before being released to the public. Most of the significant additions from that announcement have since made it out to the other channels, but there are a couple of things worth noting. First, there’s a new Energy Saver taskbar icon for desktop PCs without batteries, making it easier to tell when the feature is on without creating confusion. And the venerable WordPad app, originally marked for deletion in September, has also been removed from these builds and can’t be reinstalled.
Microsoft doesn’t publish Windows feature updates on an exact cadence beyond its commitment to deliver one with a new version number once per year in the fall. Last year’s first major batch of Windows 11 additions rolled out at the end of February, so a late winter or early spring launch window for the next batch of features could make sense.
