AI

elon-musk’s-x-allows-china-based-propaganda-banned-on-other-platforms

Elon Musk’s X allows China-based propaganda banned on other platforms

Rinse-wash-repeat. —

X accused of overlooking propaganda flagged by Meta and criminal prosecutors.

Elon Musk’s X allows China-based propaganda banned on other platforms

Lax content moderation on X (aka Twitter) has disrupted coordinated efforts between social media companies and law enforcement to tamp down on “propaganda accounts controlled by foreign entities aiming to influence US politics,” The Washington Post reported.

Now propaganda is “flourishing” on X, The Post said, while other social media companies are stuck in endless cycles, watching some of the propaganda that they block proliferate on X, then inevitably spread back to their platforms.

Meta, Google, and then-Twitter began coordinating takedown efforts with law enforcement and disinformation researchers after Russian-backed influence campaigns manipulated their platforms in hopes of swaying the 2016 US presidential election.

The next year, all three companies promised Congress to work tirelessly to stop Russian-backed propaganda from spreading on their platforms. The companies created explicit election misinformation policies and began meeting biweekly to compare notes on propaganda networks each platform uncovered, according to The Post’s interviews with anonymous sources who participated in these meetings.

However, after Elon Musk purchased Twitter and rebranded the company as X, his company withdrew from the alliance in May 2023.

Sources told The Post that the last X meeting attendee was Irish intelligence expert Aaron Rodericks—who was allegedly disciplined for liking an X post calling Musk “a dipshit.” Rodericks was subsequently laid off when Musk dismissed the entire election integrity team last September, and after that, X apparently ditched the biweekly meeting entirely and “just kind of disappeared,” a source told The Post.

In 2023, for example, Meta flagged 150 “artificial influence accounts” identified on its platform, of which “136 were still present on X as of Thursday evening,” according to The Post’s analysis. X’s seeming oversight extends to all but eight of the 123 “deceptive China-based campaigns” connected to accounts that Meta flagged last May, August, and December, The Post reported.

The Post’s report also provided an exclusive analysis from the Stanford Internet Observatory (SIO), which found that 86 propaganda accounts that Meta flagged last November “are still active on X.”

The majority of these accounts—81—were China-based accounts posing as Americans, SIO reported. These accounts frequently ripped photos from Americans’ LinkedIn profiles, then changed the real Americans’ names while posting about both China and US politics, as well as people often trending on X, such as Musk and Joe Biden.

Meta has warned that China-based influence campaigns are “multiplying,” The Post noted, while X’s standards remain seemingly too relaxed. Even accounts linked to criminal investigations remain active on X. One “account that is accused of being run by the Chinese Ministry of Public Security,” The Post reported, remains on X despite its posts being cited by US prosecutors in a criminal complaint.

Prosecutors connected that account to “dozens” of X accounts attempting to “shape public perceptions” about the Chinese Communist Party, the Chinese government, and other world leaders. The accounts also comment on hot-button topics like the fentanyl problem or police brutality, seemingly to convey “a sense of dismay over the state of America without any clear partisan bent,” Elise Thomas, an analyst for a London nonprofit called the Institute for Strategic Dialogue, told The Post.

Some X accounts flagged by The Post had more than 1 million followers. Five have paid X for verification, suggesting that their disinformation campaigns—targeting hashtags to confound discourse on US politics—are seemingly being boosted by X.

SIO technical research manager Renée DiResta criticized X’s decision to stop coordinating with other platforms.

“The presence of these accounts reinforces the fact that state actors continue to try to influence US politics by masquerading as media and fellow Americans,” DiResta told The Post. “Ahead of the 2022 midterms, researchers and platform integrity teams were collaborating to disrupt foreign influence efforts. That collaboration seems to have ground to a halt, Twitter does not seem to be addressing even networks identified by its peers, and that’s not great.”

Musk shut down X’s election integrity team because he claimed that the team was actually “undermining” election integrity. But analysts are bracing for floods of misinformation to sway 2024 elections, as some major platforms have removed election misinformation policies just as rapid advances in AI technologies have made misinformation spread via text, images, audio, and video harder for the average person to detect.

In one prominent example, a fake robocaller relied on AI voice technology to pose as Biden to tell Democrats not to vote. That incident seemingly pushed the Federal Trade Commission on Thursday to propose penalizing AI impersonation.

It seems apparent that propaganda accounts from foreign entities on X will use every tool available to get eyes on their content, perhaps expecting Musk’s platform to be the slowest to police them. According to The Post, some of the X accounts spreading propaganda are using what appears to be AI-generated images of Biden and Donald Trump to garner tens of thousands of views on posts.

It’s possible that X will start tightening up on content moderation as elections draw closer. Yesterday, X joined Amazon, Google, Meta, OpenAI, TikTok, and other Big Tech companies in signing an agreement to fight “deceptive use of AI” during 2024 elections. Among the top goals identified in the “AI Elections accord” are identifying where propaganda originates, detecting how propaganda spreads across platforms, and “undertaking collective efforts to evaluate and learn from the experiences and outcomes of dealing” with propaganda.

Elon Musk’s X allows China-based propaganda banned on other platforms Read More »

openai-collapses-media-reality-with-sora,-a-photorealistic-ai-video-generator

OpenAI collapses media reality with Sora, a photorealistic AI video generator

Pics and it didn’t happen —

Hello, cultural singularity—soon, every video you see online could be completely fake.

Snapshots from three videos generated using OpenAI's Sora.

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.

OpenAI collapses media reality with Sora, a photorealistic AI video generator Read More »

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Scientists aghast at bizarre AI rat with huge genitals in peer-reviewed article

AI gone wild —

It’s unclear how such egregiously bad images made it through peer-review.

An actual laboratory rat, who is intrigued.

Enlarge / An actual laboratory rat, who is intrigued.

Appall and scorn ripped through scientists’ social media networks Thursday as several egregiously bad AI-generated figures circulated from a peer-reviewed article recently published in a reputable journal. Those figures—which the authors acknowledge in the article’s text were made by Midjourney—are all uninterpretable. They contain gibberish text and, most strikingly, one includes an image of a rat with grotesquely large and bizarre genitals, as well as a text label of “dck.”

AI-generated Figure 1 of the paper. This image is supposed to show spermatogonial stem cells isolated, purified, and cultured from rat testes.

Enlarge / AI-generated Figure 1 of the paper. This image is supposed to show spermatogonial stem cells isolated, purified, and cultured from rat testes.

On Thursday, the publisher of the review article, Frontiers, posted an “expression of concern,” noting that it is aware of concerns regarding the published piece. “An investigation is currently being conducted and this notice will be updated accordingly after the investigation concludes,” the publisher wrote.

The article in question is titled “Cellular functions of spermatogonial stem cells in relation to JAK/STAT signaling pathway,” which was authored by three researchers in China, including the corresponding author Dingjun Hao of Xi’an Honghui Hospital. It was published online Tuesday in the journal Frontiers in Cell and Developmental Biology.

Frontiers did not immediately respond to Ars’ request for comment, but we will update this post with any response.

The first figure in the paper, the one containing the rat, drew immediate attention as scientists began widely sharing it and commenting on it on social media platforms, including Bluesky and the platform formerly known as Twitter. From a distance, the anatomical image is clearly all sorts of wrong. But, looking closer only reveals more flaws, including the labels “dissilced,” Stemm cells,” “iollotte sserotgomar,” and “dck.” Many researchers expressed surprise and dismay that such a blatantly bad AI-generated image could pass through the peer-review system and whatever internal processing is in place at the journal.

Figure 2 is supposed to be a diagram of the JAK-STAT signaling pathway.

Enlarge / Figure 2 is supposed to be a diagram of the JAK-STAT signaling pathway.

But the rat’s package is far from the only problem. Figure 2 is less graphic but equally mangled. While it’s intended to be a diagram of a complex signaling pathway, it instead is a jumbled mess. One scientific integrity expert questioned whether it provide an overly complicated explanation of “how to make a donut with colorful sprinkles.” Like the first image, the diagram is rife with nonsense text and baffling images. Figure 3 is no better, offering a collage of small circular images that are densely annotated with gibberish. The image is supposed to provide visual representations of how the signaling pathway from Figure 2 regulates the biological properties of spermatogonial stem cells.

Some scientists online questioned whether the text was also AI-generated. One user noted that AI detection software determined that it was likely to be AI-generated; however, as Ars has reported previously, such software is unreliable.

Figure 3 is supposed to show the regulation of biological properties of spermatogonial stem cells by JAK/STAT signaling pathway.

Enlarge / Figure 3 is supposed to show the regulation of biological properties of spermatogonial stem cells by JAK/STAT signaling pathway.

The images, while egregious examples, highlight a growing problem in scientific publishing. A scientist’s success relies heavily on their publication record, with a large volume of publications, frequent publishing, and articles appearing in top-tier journals, all of which earn scientists more prestige. The system incentivizes less-than-scrupulous researchers to push through low-quality articles, which, in the era of AI chatbots, could potentially be generated with the help of AI. Researchers worry that the growing use of AI will make published research less trustworthy. As such, research journals have recently set new authorship guidelines for AI-generated text to try to address the problem. But for now, as the Frontiers article shows, there are clearly some gaps.

Scientists aghast at bizarre AI rat with huge genitals in peer-reviewed article Read More »

google-upstages-itself-with-gemini-15-ai-launch,-one-week-after-ultra-1.0

Google upstages itself with Gemini 1.5 AI launch, one week after Ultra 1.0

Gemini’s Twin —

Google confusingly overshadows its own pro product a week after its last major AI launch.

The Gemini 1.5 logo

Enlarge / The Gemini 1.5 logo, released by Google.

Google

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.

A table from the Gemini 1.5 technical document showing comparisons to Gemini 1.0.

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.

Google upstages itself with Gemini 1.5 AI launch, one week after Ultra 1.0 Read More »

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US says AI models can’t hold patents

Robot inventors dismayed —

Inventors must be human, but there’s still a condition where AI can officially help.

An illustrated concept of a digital brain, crossed out.

On Tuesday, the United States Patent and Trademark Office (USPTO) published guidance on inventorship for AI-assisted inventions, clarifying that while AI systems can play a role in the creative process, only natural persons (human beings) who make significant contributions to the conception of an invention can be named as inventors. It also rules out using AI models to churn out patent ideas without significant human input.

The USPTO says this position is supported by “the statutes, court decisions, and numerous policy considerations,” including the Executive Order on AI issued by President Biden. We’ve previously covered attempts, which have been repeatedly rejected by US courts, by Dr. Stephen Thaler to have an AI program called “DABUS” named as the inventor on a US patent (a process begun in 2019).

This guidance follows themes previously set by the US Copyright Office (and agreed upon by a judge) that an AI model cannot own a copyright for a piece of media and that substantial human contributions are required for copyright protection.

Even though an AI model itself cannot be named an inventor or joint inventor on a patent, using AI assistance to create an invention does not necessarily disqualify a human from holding a patent, as the USPTO explains:

“While AI systems and other non-natural persons cannot be listed as inventors on patent applications or patents, the use of an AI system by a natural person(s) does not preclude a natural person(s) from qualifying as an inventor (or joint inventors) if the natural person(s) significantly contributed to the claimed invention.”

However, the USPTO says that significant human input is required for an invention to be patentable: “Maintaining ‘intellectual domination’ over an AI system does not, on its own, make a person an inventor of any inventions created through the use of the AI system.” So a person simply overseeing an AI system isn’t suddenly an inventor. The person must make a significant contribution to the conception of the invention.

If someone does use an AI model to help create patents, the guidance describes how the application process would work. First, patent applications for AI-assisted inventions must name “the natural person(s) who significantly contributed to the invention as the inventor,” and additionally, applications must not list “any entity that is not a natural person as an inventor or joint inventor, even if an AI system may have been instrumental in the creation of the claimed invention.”

Reading between the lines, it seems the contributions made by AI systems are akin to contributions made by other tools that assist in the invention process. The document does not explicitly say that the use of AI is required to be disclosed during the application process.

Even with the published guidance, the USPTO is seeking public comment on the newly released guidelines and issues related to AI inventorship on its website.

US says AI models can’t hold patents Read More »

mozilla-lays-off-60-people,-wants-to-build-ai-into-firefox

Mozilla lays off 60 people, wants to build AI into Firefox

Please just make a browser —

Memo details layoffs, “strategic corrections,” and a desire for “trustworthy” AI.

Mozilla lays off 60 people, wants to build AI into Firefox

Mozilla got a new “interim” CEO just a few days ago, and the first order of business appears to be layoffs. Bloomberg was the first to report that the company is cutting about 60 jobs, or 5 percent of its workforce. A TechCrunch report has a company memo that followed these layoffs, detailing one product shutdown and a “scaling back” of a few others.

Mozilla started as the open source browser/email company that rose from the ashes of Netscape. Firefox and Thunderbird have kept on trucking since then, but the mozilla.org/products page is a great example of what the strategy has been lately: “Firefox is just the beginning!” reads the very top of the page; it then goes on to detail a lot of projects that aren’t in line with Mozilla’s core work of making a browser. There’s Mozilla Monitor (a data breach checker), Mozilla VPN, Pocket (a news reader app), Firefox Relay (for making burner email accounts), and Firefox Focus, a fork of Firefox with a privacy focus.

That’s not even a comprehensive list of recent Mozilla products. From 2017–2020, there was “Firefox Send,” an encrypted file transfer service, and a VR-focused “Firefox Reality” browser that lasted from 2018 to 2022. In 2022, Mozilla launched a $35 million venture capital fund called Mozilla Ventures. Not all Mozilla side-projects are losers—the memory-safe Rust programming language was spun out of Mozilla in 2020 and has seen rapid adoption in the Linux kernel and Android.

Mozilla is a tiny company that competes with some of the biggest tech companies in the world—Apple, Google, and Microsoft. It’s also very important to the web as a whole, as Firefox is the only browser that can’t trace its lineage back to Apple and WebKit (Chrome’s Blink engine is a WebKit fork. Microsoft Edge is a Chromium fork). So you would think focusing on Firefox would be a priority, but the company continually struggles with focus.

The Mozilla Corporation gets about 80 percent of its revenue from Google—also its primary browser competitor—via a search deal, so Mozilla isn’t exactly a healthy company. These non-browser projects could be seen as a search for a less vulnerable revenue stream, but none have put a huge dent in the bottom line.

TechCrunch managed to get an internal company memo that details a few “strategic corrections” for the myriad Mozilla products. Mozilla has a “mozilla.social” Mastodon instance that the memo says originally intended to “effectively shape the future of social media,” but the company now says the social group will get a “much smaller team.” Mozilla says it will also “reduce our investments” in Mozilla VPN, Firefox Relay, and something the memo calls “Online Footprint Scrubber” (that sounds like Mozilla Monitor?). It’s also shutting down “Mozilla Hubs,” which was a 3D virtual world it launched in 2018—that’s right, there was also a metaverse project! The memo says that “demand has moved away from 3D virtual worlds” and that “this is impacting all industry players.” The company is also cutting jobs at “MozProd,” its infrastructure team.

While chasing the trends of VR and metaverse didn’t work out, Mozilla now wants to chase another hot new trend: AI! The memo says: “In 2023, generative AI began rapidly shifting the industry landscape. Mozilla seized an opportunity to bring trustworthy AI into Firefox, largely driven by the Fakespot acquisition and the product integration work that followed. Additionally, finding great content is still a critical use case for the Internet. Therefore, as part of the changes today, we will be bringing together Pocket, Content, and the AI/ML teams supporting content with the Firefox Organization. More details on the specific organizational changes will follow shortly.” Mozilla paid an undisclosed sum in 2023 to buy a company called Fakespot, which uses AI to identify fake product reviews. Specifically citing “generative AI” leads us to believe the company wants to build a chatbot or webpage summarizer.

The TechCrunch report interprets the memo, saying, “It now looks like Mozilla may refocus on Firefox once more,” but the memo does not give an affirmative statement on “Firefox the browser” being important or seeing additional investments. In 2020, the company had another round of layoffs and said it wanted to “refocus the Firefox organization on core browser growth,” but nothing seems to have come of that. Firefox’s market share is about 3 percent of all browsers, and that number goes down every year.

Mozilla lays off 60 people, wants to build AI into Firefox Read More »

openai-experiments-with-giving-chatgpt-a-long-term-conversation-memory

OpenAI experiments with giving ChatGPT a long-term conversation memory

“I remember…the Alamo” —

AI chatbot “memory” will recall facts from previous conversations when enabled.

A pixelated green illustration of a pair of hands looking through file records.

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).

A screenshot of ChatGPT memory controls provided by OpenAI.

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.”

OpenAI experiments with giving ChatGPT a long-term conversation memory Read More »

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Judge rejects most ChatGPT copyright claims from book authors

Insufficient evidence —

OpenAI plans to defeat authors’ remaining claim at a “later stage” of the case.

Judge rejects most ChatGPT copyright claims from book authors

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.

OpenAI had argued as much in their promptly filed motion to dismiss these cases last August. At that time, OpenAI said that it expected to beat the direct infringement claim at a “later stage” of the proceedings.

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.

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The Super Bowl’s best and wackiest AI commercials

Superb Owl News —

It’s nothing like “crypto bowl” in 2022, but AI made a notable splash during the big game.

A still image from BodyArmor's 2024

Enlarge / A still image from BodyArmor’s 2024 “Field of Fake” Super Bowl commercial.

BodyArmor

Heavily hyped tech products have a history of appearing in Super Bowl commercials during football’s biggest game—including the Apple Macintosh in 1984, dot-com companies in 2000, and cryptocurrency firms in 2022. In 2024, the hot tech in town is artificial intelligence, and several companies showed AI-related ads at Super Bowl LVIII. Here’s a rundown of notable appearances that range from serious to wacky.

Microsoft Copilot

Microsoft Game Day Commercial | Copilot: Your everyday AI companion.

It’s been a year since Microsoft launched the AI assistant Microsoft Copilot (as “Bing Chat“), and Microsoft is leaning heavily into its AI-assistant technology, which is powered by large language models from OpenAI. In Copilot’s first-ever Super Bowl commercial, we see scenes of various people with defiant text overlaid on the screen: “They say I will never open my own business or get my degree. They say I will never make my movie or build something. They say I’m too old to learn something new. Too young to change the world. But I say watch me.”

Then the commercial shows Copilot creating solutions to some of these problems, with prompts like, “Generate storyboard images for the dragon scene in my script,” “Write code for my 3d open world game,” “Quiz me in organic chemistry,” and “Design a sign for my classic truck repair garage Mike’s.”

Of course, since generative AI is an unfinished technology, many of these solutions are more aspirational than practical at the moment. On Bluesky, writer Ed Zitron put Microsoft’s truck repair logo to the test and saw results that weren’t nearly as polished as those seen in the commercial. On X, others have criticized and poked fun at the “3d open world game” generation prompt, which is a complex task that would take far more than a single, simple prompt to produce useful code.

Google Pixel 8 “Guided Frame” feature

Javier in Frame | Google Pixel SB Commercial 2024.

Instead of focusing on generative aspects of AI, Google’s commercial showed off a feature called “Guided Frame” on the Pixel 8 phone that uses machine vision technology and a computer voice to help people with blindness or low vision to take photos by centering the frame on a face or multiple faces. Guided Frame debuted in 2022 in conjunction with the Google Pixel 7.

The commercial tells the story of a person named Javier, who says, “For many people with blindness or low vision, there hasn’t always been an easy way to capture daily life.” We see a simulated blurry first-person view of Javier holding a smartphone and hear a computer-synthesized voice describing what the AI model sees, directing the person to center on a face to snap various photos and selfies.

Considering the controversies that generative AI currently generates (pun intended), it’s refreshing to see a positive application of AI technology used as an accessibility feature. Relatedly, an app called Be My Eyes (powered by OpenAI’s GPT-4V) also aims to help low-vision people interact with the world.

Despicable Me 4

Despicable Me 4 – Minion Intelligence (Big Game Spot).

So far, we’ve covered a couple attempts to show AI-powered products as positive features. Elsewhere in Super Bowl ads, companies weren’t as generous about the technology. In an ad for the film Despicable Me 4, we see two Minions creating a series of terribly disfigured AI-generated still images reminiscent of Stable Diffusion 1.4 from 2022. There’s three-legged people doing yoga, a painting of Steve Carell and Will Ferrell as Elizabethan gentlemen, a handshake with too many fingers, people eating spaghetti in a weird way, and a pair of people riding dachshunds in a race.

The images are paired with an earnest voiceover that says, “Artificial intelligence is changing the way we see the world, showing us what we never thought possible, transforming the way we do business, and bringing family and friends closer together. With artificial intelligence, the future is in good hands.” When the voiceover ends, the camera pans out to show hundreds of Minions generating similarly twisted images on computers.

Speaking of image synthesis at the Super Bowl, people mistook a Christian commercial created by He Gets Us, LLC as having been AI-generated, likely due to its gaudy technicolor visuals. With the benefit of a YouTube replay and the ability to look at details, the “He washed feet” commercial doesn’t appear AI-generated to us, but it goes to show how the concept of image synthesis has begun to cast doubt on human-made creations.

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London Underground is testing real-time AI surveillance tools to spot crime

tube tracking —

Computer vision system tried to detect crime, weapons, people falling, and fare dodgers.

Commuters wait on the platform as a Central Line tube train arrives at Liverpool Street London Transport Tube Station in 2023.

Thousands of people using the London Underground had their movements, behavior, and body language watched by AI surveillance software designed to see if they were committing crimes or were in unsafe situations, new documents obtained by WIRED reveal. The machine-learning software was combined with live CCTV footage to try to detect aggressive behavior and guns or knives being brandished, as well as looking for people falling onto Tube tracks or dodging fares.

From October 2022 until the end of September 2023, Transport for London (TfL), which operates the city’s Tube and bus network, tested 11 algorithms to monitor people passing through Willesden Green Tube station, in the northwest of the city. The proof of concept trial is the first time the transport body has combined AI and live video footage to generate alerts that are sent to frontline staff. More than 44,000 alerts were issued during the test, with 19,000 being delivered to station staff in real time.

Documents sent to WIRED in response to a Freedom of Information Act request detail how TfL used a wide range of computer vision algorithms to track people’s behavior while they were at the station. It is the first time the full details of the trial have been reported, and it follows TfL saying, in December, that it will expand its use of AI to detect fare dodging to more stations across the British capital.

In the trial at Willesden Green—a station that had 25,000 visitors per day before the COVID-19 pandemic—the AI system was set up to detect potential safety incidents to allow staff to help people in need, but it also targeted criminal and antisocial behavior. Three documents provided to WIRED detail how AI models were used to detect wheelchairs, prams, vaping, people accessing unauthorized areas, or putting themselves in danger by getting close to the edge of the train platforms.

The documents, which are partially redacted, also show how the AI made errors during the trial, such as flagging children who were following their parents through ticket barriers as potential fare dodgers, or not being able to tell the difference between a folding bike and a non-folding bike. Police officers also assisted the trial by holding a machete and a gun in the view of CCTV cameras, while the station was closed, to help the system better detect weapons.

Privacy experts who reviewed the documents question the accuracy of object detection algorithms. They also say it is not clear how many people knew about the trial, and warn that such surveillance systems could easily be expanded in the future to include more sophisticated detection systems or face recognition software that attempts to identify specific individuals. “While this trial did not involve facial recognition, the use of AI in a public space to identify behaviors, analyze body language, and infer protected characteristics raises many of the same scientific, ethical, legal, and societal questions raised by facial recognition technologies,” says Michael Birtwistle, associate director at the independent research institute the Ada Lovelace Institute.

In response to WIRED’s Freedom of Information request, the TfL says it used existing CCTV images, AI algorithms, and “numerous detection models” to detect patterns of behavior. “By providing station staff with insights and notifications on customer movement and behaviour they will hopefully be able to respond to any situations more quickly,” the response says. It also says the trial has provided insight into fare evasion that will “assist us in our future approaches and interventions,” and the data gathered is in line with its data policies.

In a statement sent after publication of this article, Mandy McGregor, TfL’s head of policy and community safety, says the trial results are continuing to be analyzed and adds, “there was no evidence of bias” in the data collected from the trial. During the trial, McGregor says, there were no signs in place at the station that mentioned the tests of AI surveillance tools.

“We are currently considering the design and scope of a second phase of the trial. No other decisions have been taken about expanding the use of this technology, either to further stations or adding capability.” McGregor says. “Any wider roll out of the technology beyond a pilot would be dependent on a full consultation with local communities and other relevant stakeholders, including experts in the field.”

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report:-sam-altman-seeking-trillions-for-ai-chip-fabrication-from-uae,-others

Report: Sam Altman seeking trillions for AI chip fabrication from UAE, others

chips ahoy —

WSJ: Audacious $5-$7 trillion investment would aim to expand global AI chip supply.

WASHINGTON, DC - JANUARY 11: OpenAI Chief Executive Officer Sam Altman walks on the House side of the U.S. Capitol on January 11, 2024 in Washington, DC. Meanwhile, House Freedom Caucus members who left a meeting in the Speakers office say that they were talking to the Speaker about abandoning the spending agreement that Johnson announced earlier in the week. (Photo by Kent Nishimura/Getty Images)

Enlarge / OpenAI Chief Executive Officer Sam Altman walks on the House side of the US Capitol on January 11, 2024, in Washington, DC. (Photo by Kent Nishimura/Getty Images)

Getty Images

On Thursday, The Wall Street Journal reported that OpenAI CEO Sam Altman is in talks with investors to raise as much as $5 trillion to $7 trillion for AI chip manufacturing, according to people familiar with the matter. The funding seeks to address the scarcity of graphics processing units (GPUs) crucial for training and running large language models like those that power ChatGPT, Microsoft Copilot, and Google Gemini.

The high dollar amount reflects the huge amount of capital necessary to spin up new semiconductor manufacturing capability. “As part of the talks, Altman is pitching a partnership between OpenAI, various investors, chip makers and power providers, which together would put up money to build chip foundries that would then be run by existing chip makers,” writes the Wall Street Journal in its report. “OpenAI would agree to be a significant customer of the new factories.”

To hit these ambitious targets—which are larger than the entire semiconductor industry’s current $527 billion global sales combined—Altman has reportedly met with a range of potential investors worldwide, including sovereign wealth funds and government entities, notably the United Arab Emirates, SoftBank CEO Masayoshi Son, and representatives from Taiwan Semiconductor Manufacturing Co. (TSMC).

TSMC is the world’s largest dedicated independent semiconductor foundry. It’s a critical linchpin that companies such as Nvidia, Apple, Intel, and AMD rely on to fabricate SoCs, CPUs, and GPUs for various applications.

Altman reportedly seeks to expand the global capacity for semiconductor manufacturing significantly, funding the infrastructure necessary to support the growing demand for GPUs and other AI-specific chips. GPUs are excellent at parallel computation, which makes them ideal for running AI models that heavily rely on matrix multiplication to work. However, the technology sector currently faces a significant shortage of these important components, constraining the potential for AI advancements and applications.

In particular, the UAE’s involvement, led by Sheikh Tahnoun bin Zayed al Nahyan, a key security official and chair of numerous Abu Dhabi sovereign wealth vehicles, reflects global interest in AI’s potential and the strategic importance of semiconductor manufacturing. However, the prospect of substantial UAE investment in a key tech industry raises potential geopolitical concerns, particularly regarding the US government’s strategic priorities in semiconductor production and AI development.

The US has been cautious about allowing foreign control over the supply of microchips, given their importance to the digital economy and national security. Reflecting this, the Biden administration has undertaken efforts to bolster domestic chip manufacturing through subsidies and regulatory scrutiny of foreign investments in important technologies.

To put the $5 trillion to $7 trillion estimate in perspective, the White House just today announced a $5 billion investment in R&D to advance US-made semiconductor technologies. TSMC has already sunk $40 billion—one of the largest foreign investments in US history—into a US chip plant in Arizona. As of now, it’s unclear whether Altman has secured any commitments toward his fundraising goal.

Updated on February 9, 2024 at 8: 45 PM Eastern with a quote from the WSJ that clarifies the proposed relationship between OpenAI and partners in the talks.

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ai-cannot-be-used-to-deny-health-care-coverage,-feds-clarify-to-insurers

AI cannot be used to deny health care coverage, feds clarify to insurers

On Notice —

CMS worries AI could wrongfully deny care for those on Medicare Advantage plans.

A nursing home resident is pushed along a corridor by a nurse.

Enlarge / A nursing home resident is pushed along a corridor by a nurse.

Health insurance companies cannot use algorithms or artificial intelligence to determine care or deny coverage to members on Medicare Advantage plans, the Centers for Medicare & Medicaid Services (CMS) clarified in a memo sent to all Medicare Advantage insurers.

The memo—formatted like an FAQ on Medicare Advantage (MA) plan rules—comes just months after patients filed lawsuits claiming that UnitedHealth and Humana have been using a deeply flawed, AI-powered tool to deny care to elderly patients on MA plans. The lawsuits, which seek class-action status, center on the same AI tool, called nH Predict, used by both insurers and developed by NaviHealth, a UnitedHealth subsidiary.

According to the lawsuits, nH Predict produces draconian estimates for how long a patient will need post-acute care in facilities like skilled nursing homes and rehabilitation centers after an acute injury, illness, or event, like a fall or a stroke. And NaviHealth employees face discipline for deviating from the estimates, even though they often don’t match prescribing physicians’ recommendations or Medicare coverage rules. For instance, while MA plans typically provide up to 100 days of covered care in a nursing home after a three-day hospital stay, using nH Predict, patients on UnitedHealth’s MA plan rarely stay in nursing homes for more than 14 days before receiving payment denials, the lawsuits allege.

Specific warning

It’s unclear how nH Predict works exactly, but it reportedly uses a database of 6 million patients to develop its predictions. Still, according to people familiar with the software, it only accounts for a small set of patient factors, not a full look at a patient’s individual circumstances.

This is a clear no-no, according to the CMS’s memo. For coverage decisions, insurers must “base the decision on the individual patient’s circumstances, so an algorithm that determines coverage based on a larger data set instead of the individual patient’s medical history, the physician’s recommendations, or clinical notes would not be compliant,” the CMS wrote.

The CMS then provided a hypothetical that matches the circumstances laid out in the lawsuits, writing:

In an example involving a decision to terminate post-acute care services, an algorithm or software tool can be used to assist providers or MA plans in predicting a potential length of stay, but that prediction alone cannot be used as the basis to terminate post-acute care services.

Instead, the CMS wrote, in order for an insurer to end coverage, the individual patient’s condition must be reassessed, and denial must be based on coverage criteria that is publicly posted on a website that is not password protected. In addition, insurers who deny care “must supply a specific and detailed explanation why services are either no longer reasonable and necessary or are no longer covered, including a description of the applicable coverage criteria and rules.”

In the lawsuits, patients claimed that when coverage of their physician-recommended care was unexpectedly wrongfully denied, insurers didn’t give them full explanations.

Fidelity

In all, the CMS finds that AI tools can be used by insurers when evaluating coverage—but really only as a check to make sure the insurer is following the rules. An “algorithm or software tool should only be used to ensure fidelity,” with coverage criteria, the CMS wrote. And, because “publicly posted coverage criteria are static and unchanging, artificial intelligence cannot be used to shift the coverage criteria over time” or apply hidden coverage criteria.

The CMS sidesteps any debate about what qualifies as artificial intelligence by offering a broad warning about algorithms and artificial intelligence. “There are many overlapping terms used in the context of rapidly developing software tools,” the CMS wrote.

Algorithms can imply a decisional flow chart of a series of if-then statements (i.e., if the patient has a certain diagnosis, they should be able to receive a test), as well as predictive algorithms (predicting the likelihood of a future admission, for example). Artificial intelligence has been defined as a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. Artificial intelligence systems use machine- and human-based inputs to perceive real and virtual environments; abstract such perceptions into models through analysis in an automated manner; and use model inference to formulate options for information or action.

The CMS also openly worried that the use of either of these types of tools can reinforce discrimination and biases—which has already happened with racial bias. The CMS warned insurers to ensure any AI tool or algorithm they use “is not perpetuating or exacerbating existing bias, or introducing new biases.”

While the memo overall was an explicit clarification of existing MA rules, the CMS ended by putting insurers on notice that it is increasing its audit activities and “will be monitoring closely whether MA plans are utilizing and applying internal coverage criteria that are not found in Medicare laws.” Non-compliance can result in warning letters, corrective action plans, monetary penalties, and enrollment and marketing sanctions.

AI cannot be used to deny health care coverage, feds clarify to insurers Read More »