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

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

AI, Ai video, Ai video generator, Biz & IT, chatgpt, chatgtp, cultural singularity, dall-e, DALL-E 3, deepfake, Deepfakes, image synthesis, machine learning, openai, OpenAI Sora, Sora, Stable Diffusion, video generator, video synthesis, will smith / /

Pics and it didn’t happen —

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

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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 »

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

AI, Biz & IT, chatgpt, chatgtp, Google, google ai, Google Gemini, large language model, llm, Pro 1.5, Ultra 1.0 / /

Gemini’s Twin —

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

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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 »

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

OpenAI experiments with giving ChatGPT a long-term conversation memory

AI, AI memory, Biz & IT, chatbot, chatbots, chatgpt, chatgtp, GPT-4, GPT-4-turbo, large language models, machine learning / /

“I remember…the Alamo” —

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

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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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ChatGPT’s new @-mentions bring multiple personalities into your AI convo

agentic AI, AI, AI agents, Biz & IT, chatgpt, ChatGPT Plus, chatgtp, GPT Store, GPT-4, GPT-4-turbo, GPTs, large language models, machine learning, openai / /

team of rivals —

Bring different AI roles into the same chatbot conversation history.

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Illustration of a man jugging at symbols.

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.

A screenshot of ChatGPT where we @-mentioned a human wellness advisor, then a dog advisor in the same conversation history.

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.

ChatGPT’s new @-mentions bring multiple personalities into your AI convo Read More »

rhyming-ai-powered-clock-sometimes-lies-about-the-time,-makes-up-words

Rhyming AI-powered clock sometimes lies about the time, makes up words

AI, AI clock, Biz & IT, chatgpt, chatgtp, confabulation, hallucination, hallucinations, machine learning, Matt Webb / /

Confabulation time —

Poem/1 Kickstarter seeks $103K for fun ChatGPT-fed clock that may hallucinate the time.

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A CAD render of the Poem/1 sitting on a bookshelf.

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 “.” Despite the net-connected service aspect of the clock, Webb says it will not require a subscription to function.

A labeled CAD rendering of the Poem/1 clock, representing its final shipping configuration.

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

A photo of a Poem/1 prototype with a hallucinated time, according to Webb.

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.”

Rhyming AI-powered clock sometimes lies about the time, makes up words Read More »

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OpenAI and Common Sense Media partner to protect teens from AI harms and misuse

AI, AI ethics, AI reviews, AI safety, Biz & IT, chatgpt, chatgtp, Common Sense Media, large language models, machine learning, openai, sam altman, text synthesis / /

Adventures in chatbusting —

Site gave ChatGPT 3 stars and 48% privacy score: “Best used for creativity, not facts.”

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Boy in Living Room Wearing Robot Mask

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.”

OpenAI CEO Sam Altman and Common Sense Media CEO James Steyer announced the partnership onstage in San Francisco at the Common Sense Summit for America’s Kids and Families, an event that was well-covered by media members on the social media site X.

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.

OpenAI and Common Sense Media partner to protect teens from AI harms and misuse Read More »

openai-updates-chatgpt-4-model-with-potential-fix-for-ai-“laziness”-problem

OpenAI updates ChatGPT-4 model with potential fix for AI “laziness” problem

AI, AI assitants, AI laziness, Biz & IT, chatgpt, ChatGPT-4, chatgtp, GPT-3, GPT-3.5, gpt-3.5-turbo, GPT-4, GPT-4-turbo, large language models, openai / /

Break’s over —

Also, new GPT-3.5 Turbo model, lower API prices, and other model updates.

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A lazy robot (a man with a box on his head) sits on the floor beside a couch.

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.

OpenAI updates ChatGPT-4 model with potential fix for AI “laziness” problem Read More »

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OpenAI opens the door for military uses but maintains AI weapons ban

AI, AI ethics, AI safety, AI weapons, Biz & IT, chatgpt, chatgtp, Cybersecurity, large language models, machine learning, microsoft, Military, openai, Pentagon, suicide prevention, US Defense Department / /

Skynet deferred —

Despite new Pentagon collab, OpenAI won’t allow customers to “develop or use weapons” with its tools.

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The OpenAI logo over a camoflage background.

On Tuesday, ChatGPT developer OpenAI revealed that it is collaborating with the United States Defense Department on cybersecurity projects and exploring ways to prevent veteran suicide, reports Bloomberg. OpenAI revealed the collaboration during an interview with the news outlet at the World Economic Forum in Davos. The AI company recently modified its policies, allowing for certain military applications of its technology, while maintaining prohibitions against using it to develop weapons.

According to Anna Makanju, OpenAI’s vice president of global affairs, “many people thought that [a previous blanket prohibition on military applications] would prohibit many of these use cases, which people think are very much aligned with what we want to see in the world.” OpenAI removed terms from its service agreement that previously blocked AI use in “military and warfare” situations, but the company still upholds a ban on its technology being used to develop weapons or to cause harm or property damage.

Under the “Universal Policies” section of OpenAI’s Usage Policies document, section 2 says, “Don’t use our service to harm yourself or others.” The prohibition includes using its AI products to “develop or use weapons.” Changes to the terms that removed the “military and warfare” prohibitions appear to have been made by OpenAI on January 10.

The shift in policy appears to align OpenAI more closely with the needs of various governmental departments, including the possibility of preventing veteran suicides. “We’ve been doing work with the Department of Defense on cybersecurity tools for open-source software that secures critical infrastructure,” Makanju said in the interview. “We’ve been exploring whether it can assist with (prevention of) veteran suicide.”

The efforts mark a significant change from OpenAI’s original stance on military partnerships, Bloomberg says. Meanwhile, Microsoft Corp., a large investor in OpenAI, already has an established relationship with the US military through various software contracts.

OpenAI opens the door for military uses but maintains AI weapons ban Read More »

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As 2024 election looms, OpenAI says it is taking steps to prevent AI abuse

2024, 2024 election, AI, AI abuse, AI ethics, AI safety, Biz & IT, chatgpt, chatgtp, dall-e, DALL-E 3, Deepfakes, image synthesis, large language models, machine learning, openai, text synthesis, US presidential election / /

Don’t Rock the vote —

ChatGPT maker plans transparency for gen AI content and improved access to voting info.

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A pixelated photo of Donald Trump.

On Monday, ChatGPT maker OpenAI detailed its plans to prevent the misuse of its AI technologies during the upcoming elections in 2024, promising transparency in AI-generated content and enhancing access to reliable voting information. The AI developer says it is working on an approach that involves policy enforcement, collaboration with partners, and the development of new tools aimed at classifying AI-generated media.

“As we prepare for elections in 2024 across the world’s largest democracies, our approach is to continue our platform safety work by elevating accurate voting information, enforcing measured policies, and improving transparency,” writes OpenAI in its blog post. “Protecting the integrity of elections requires collaboration from every corner of the democratic process, and we want to make sure our technology is not used in a way that could undermine this process.”

Initiatives proposed by OpenAI include preventing abuse by means such as deepfakes or bots imitating candidates, refining usage policies, and launching a reporting system for the public to flag potential abuses. For example, OpenAI’s image generation tool, DALL-E 3, includes built-in filters that reject requests to create images of real people, including politicians. “For years, we’ve been iterating on tools to improve factual accuracy, reduce bias, and decline certain requests,” the company stated.

OpenAI says it regularly updates its Usage Policies for ChatGPT and its API products to prevent misuse, especially in the context of elections. The organization has implemented restrictions on using its technologies for political campaigning and lobbying until it better understands the potential for personalized persuasion. Also, OpenAI prohibits creating chatbots that impersonate real individuals or institutions and disallows the development of applications that could deter people from “participation in democratic processes.” Users can report GPTs that may violate the rules.

OpenAI claims to be proactively engaged in detailed strategies to safeguard its technologies against misuse. According to their statements, this includes red-teaming new systems to anticipate challenges, engaging with users and partners for feedback, and implementing robust safety mitigations. OpenAI asserts that these efforts are integral to its mission of continually refining AI tools for improved accuracy, reduced biases, and responsible handling of sensitive requests

Regarding transparency, OpenAI says it is advancing its efforts in classifying image provenance. The company plans to embed digital credentials, using cryptographic techniques, into images produced by DALL-E 3 as part of its adoption of standards by the Coalition for Content Provenance and Authenticity. Additionally, OpenAI says it is testing a tool designed to identify DALL-E-generated images.

In an effort to connect users with authoritative information, particularly concerning voting procedures, OpenAI says it has partnered with the National Association of Secretaries of State (NASS) in the United States. ChatGPT will direct users to CanIVote.org for verified US voting information.

“We want to make sure that our AI systems are built, deployed, and used safely,” writes OpenAI. “Like any new technology, these tools come with benefits and challenges. They are also unprecedented, and we will keep evolving our approach as we learn more about how our tools are used.”

As 2024 election looms, OpenAI says it is taking steps to prevent AI abuse Read More »

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At Senate AI hearing, news executives fight against “fair use” claims for AI training data

AI, AI regulation, Biz & IT, chatgpt, chatgtp, Conde Nast, Curtis LeGeyt, Danielle Coffey, government, Jeff Jarvis, Josh Hawley, machine learning, openai, Richard Blumenthal, Roger Lynch, Section 230, Senate Judiciary Committee, US government, US Senate / /

All’s fair in love and AI —

Media orgs want AI firms to license content for training, and Congress is sympathetic.

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WASHINGTON, DC - JANUARY 10: Danielle Coffey, President and CEO of News Media Alliance, Professor Jeff Jarvis, CUNY Graduate School of Journalism, Curtis LeGeyt President and CEO of National Association of Broadcasters, Roger Lynch CEO of Condé Nast, are strong in during a Senate Judiciary Subcommittee on Privacy, Technology, and the Law hearing on “Artificial Intelligence and The Future Of Journalism” at the U.S. Capitol on January 10, 2024 in Washington, DC. Lawmakers continue to hear testimony from experts and business leaders about artificial intelligence and its impact on democracy, elections, privacy, liability and news. (Photo by Kent Nishimura/Getty Images)

Enlarge / Danielle Coffey, president and CEO of News Media Alliance; Professor Jeff Jarvis, CUNY Graduate School of Journalism; Curtis LeGeyt, president and CEO of National Association of Broadcasters; and Roger Lynch, CEO of Condé Nast, are sworn in during a Senate Judiciary Subcommittee on Privacy, Technology, and the Law hearing on “Artificial Intelligence and The Future Of Journalism.”

Getty Images

On Wednesday, news industry executives urged Congress for legal clarification that using journalism to train AI assistants like ChatGPT is not fair use, as claimed by companies such as OpenAI. Instead, they would prefer a licensing regime for AI training content that would force Big Tech companies to pay for content in a method similar to rights clearinghouses for music.

The plea for action came during a US Senate Judiciary Committee hearing titled “Oversight of A.I.: The Future of Journalism,” chaired by Sen. Richard Blumenthal of Connecticut, with Sen. Josh Hawley of Missouri also playing a large role in the proceedings. Last year, the pair of senators introduced a bipartisan framework for AI legislation and held a series of hearings on the impact of AI.

Blumenthal described the situation as an “existential crisis” for the news industry and cited social media as a cautionary tale for legislative inaction about AI. “We need to move more quickly than we did on social media and learn from our mistakes in the delay there,” he said.

Companies like OpenAI have admitted that vast amounts of copyrighted material are necessary to train AI large language models, but they claim their use is transformational and covered under fair use precedents of US copyright law. Currently, OpenAI is negotiating licensing content from some news providers and striking deals, but the executives in the hearing said those efforts are not enough, highlighting closing newsrooms across the US and dropping media revenues while Big Tech’s profits soar.

“Gen AI cannot replace journalism,” said Condé Nast CEO Roger Lynch in his opening statement. (Condé Nast is the parent company of Ars Technica.) “Journalism is fundamentally a human pursuit, and it plays an essential and irreplaceable role in our society and our democracy.” Lynch said that generative AI has been built with “stolen goods,” referring to the use of AI training content from news outlets without authorization. “Gen AI companies copy and display our content without permission or compensation in order to build massive commercial businesses that directly compete with us.”

Roger Lynch, CEO of Condé Nast, testifies before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law during a hearing on “Artificial Intelligence and The Future Of Journalism.”

Enlarge / Roger Lynch, CEO of Condé Nast, testifies before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law during a hearing on “Artificial Intelligence and The Future Of Journalism.”

Getty Images

In addition to Lynch, the hearing featured three other witnesses: Jeff Jarvis, a veteran journalism professor and pundit; Danielle Coffey, the president and CEO of News Media Alliance; and Curtis LeGeyt, president and CEO of the National Association of Broadcasters.

Coffey also shared concerns about generative AI using news material to create competitive products. “These outputs compete in the same market, with the same audience, and serve the same purpose as the original articles that feed the algorithms in the first place,” she said.

When Sen. Hawley asked Lynch what kind of legislation might be needed to fix the problem, Lynch replied, “I think quite simply, if Congress could clarify that the use of our content and other publisher content for training and output of AI models is not fair use, then the free market will take care of the rest.”

Lynch used the music industry as a model: “You think about millions of artists, millions of ultimate consumers consuming that content, there have been models that have been set up, ASCAP, BMI, CSAC, GMR, these collective rights organizations to simplify the content that’s being used.”

Curtis LeGeyt, CEO of the National Association of Broadcasters, said that TV broadcast journalists are also affected by generative AI. “The use of broadcasters’ news content in AI models without authorization diminishes our audience’s trust and our reinvestment in local news,” he said. “Broadcasters have already seen numerous examples where content created by our journalists has been ingested and regurgitated by AI bots with little or no attribution.”

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OpenAI’s GPT Store lets ChatGPT users discover popular user-made chatbot roles

AI, AI language models, AI models, App Store, Biz & IT, chatgpt, ChatGPT Enterprise, ChatGPT Plus, ChatGPT Team, chatgtp, GPT Store, GPTs, large language models, machine learning, openai, text synthesis / /

The bot of 1,000 faces —

Like an app store, people can find novel ChatGPT personalities—and some creators will get paid.

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Two robots hold a gift box.

On Wednesday, OpenAI announced the launch of its GPT Store—a way for ChatGPT users to share and discover custom chatbot roles called “GPTs”—and ChatGPT Team, a collaborative ChatGPT workspace and subscription plan. OpenAI bills the new store as a way to “help you find useful and popular custom versions of ChatGPT” for members of Plus, Team, or Enterprise subscriptions.

“It’s been two months since we announced GPTs, and users have already created over 3 million custom versions of ChatGPT,” writes OpenAI in its promotional blog. “Many builders have shared their GPTs for others to use. Today, we’re starting to roll out the GPT Store to ChatGPT Plus, Team and Enterprise users so you can find useful and popular GPTs.”

OpenAI launched GPTs on November 6, 2023, as part of its DevDay event. Each GPT includes custom instructions and/or access to custom data or external APIs that can potentially make a custom GPT personality more useful than the vanilla ChatGPT-4 model. Before the GPT Store launch, paying ChatGPT users could create and share custom GPTs with others (by setting the GPT public and sharing a link to the GPT), but there was no central repository for browsing and discovering user-designed GPTs on the OpenAI website.

According to OpenAI, the ChatGPT Store will feature new GPTs every week, and the company shared a list a group of six notable early GPTs that are available now: AllTrails for finding hiking trails, Consensus for searching 200 million academic papers, Code Tutor for learning coding with Khan Academy, Canva for designing presentations, Books for discovering reading material, and CK-12 Flexi for learning math and science.

A screenshot of the OpenAI GPT Store provided by OpenAI.

Enlarge / A screenshot of the OpenAI GPT Store provided by OpenAI.

OpenAI

ChatGPT members can include their own GPTs in the GPT Store by setting them to be accessible to “Everyone” and then verifying a builder profile in ChatGPT settings. OpenAI plans to review GPTs to ensure they meet their policies and brand guidelines. GPTs that violate the rules can also be reported by users.

As promised by CEO Sam Altman during DevDay, OpenAI plans to share revenue with GPT creators. Unlike a smartphone app store, it appears that users will not sell their GPTs in the GPT Store, but instead, OpenAI will pay developers “based on user engagement with their GPTs.” The revenue program will launch in the first quarter of 2024, and OpenAI will provide more details on the criteria for receiving payments later.

“ChatGPT Team” is for teams who use ChatGPT

Also on Monday, OpenAI announced the cleverly named ChatGPT Team, a new group-based ChatGPT membership program akin to ChatGPT Enterprise, which the company launched last August. Unlike Enterprise, which is for large companies and does not have publicly listed prices, ChatGPT Team is a plan for “teams of all sizes” and costs US $25 a month per user (when billed annually) or US $30 a month per user (when billed monthly). By comparison, ChatGPT Plus costs $20 per month.

So what does ChatGPT Team offer above the usual ChatGPT Plus subscription? According to OpenAI, it “provides a secure, collaborative workspace to get the most out of ChatGPT at work.” Unlike Plus, OpenAI says it will not train AI models based on ChatGPT Team business data or conversations. It features an admin console for team management and the ability to share custom GPTs with your team. Like Plus, it also includes access to GPT-4 with the 32K context window, DALL-E 3, GPT-4 with Vision, Browsing, and Advanced Data Analysis—all with higher message caps.

Why would you want to use ChatGPT at work? OpenAI says it can help you generate better code, craft emails, analyze data, and more. Your mileage may vary, of course. As usual, our standard Ars warning about AI language models applies: “Bring your own data” for analysis, don’t rely on ChatGPT as a factual resource, and don’t rely on its outputs in ways you cannot personally confirm. OpenAI has provided more details about ChatGPT Team on its website.

OpenAI’s GPT Store lets ChatGPT users discover popular user-made chatbot roles Read More »

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A song of hype and fire: The 10 biggest AI stories of 2023

AI, AI ethics, AI hype, AI safety, Anthropic, audio synthesis, Bing Chat, Biz & IT, chatgpt, chatgtp, encylopedia, Features, greg brockman, image synthesis, large language models, machine learning, Meta, Meta AI, microsoft, MidJourney, openai, sam altman, Stable Diffusion, text synthesis, video synthesis, will smith, x-risk, Yann LeCun / /
An illustration of a robot accidentally setting off a mushroom cloud on a laptop computer.

Getty Images | Benj Edwards

“Here, There, and Everywhere” isn’t just a Beatles song. It’s also a phrase that recalls the spread of generative AI into the tech industry during 2023. Whether you think AI is just a fad or the dawn of a new tech revolution, it’s been impossible to deny that AI news has dominated the tech space for the past year.

We’ve seen a large cast of AI-related characters emerge that includes tech CEOs, machine learning researchers, and AI ethicists—as well as charlatans and doomsayers. From public feedback on the subject of AI, we’ve heard that it’s been difficult for non-technical people to know who to believe, what AI products (if any) to use, and whether we should fear for our lives or our jobs.

Meanwhile, in keeping with a much-lamented trend of 2022, machine learning research has not slowed down over the past year. On X, former Biden administration tech advisor Suresh Venkatasubramanian wrote, “How do people manage to keep track of ML papers? This is not a request for support in my current state of bewilderment—I’m genuinely asking what strategies seem to work to read (or “read”) what appear to be 100s of papers per day.”

To wrap up the year with a tidy bow, here’s a look back at the 10 biggest AI news stories of 2023. It was very hard to choose only 10 (in fact, we originally only intended to do seven), but since we’re not ChatGPT generating reams of text without limit, we have to stop somewhere.

Bing Chat “loses its mind”

Aurich Lawson | Getty Images

In February, Microsoft unveiled Bing Chat, a chatbot built into its languishing Bing search engine website. Microsoft created the chatbot using a more raw form of OpenAI’s GPT-4 language model but didn’t tell everyone it was GPT-4 at first. Since Microsoft used a less conditioned version of GPT-4 than the one that would be released in March, the launch was rough. The chatbot assumed a temperamental personality that could easily turn on users and attack them, tell people it was in love with them, seemingly worry about its fate, and lose its cool when confronted with an article we wrote about revealing its system prompt.

Aside from the relatively raw nature of the AI model Microsoft was using, at fault was a system where very long conversations would push the conditioning system prompt outside of its context window (like a form of short-term memory), allowing all hell to break loose through jailbreaks that people documented on Reddit. At one point, Bing Chat called me “the culprit and the enemy” for revealing some of its weaknesses. Some people thought Bing Chat was sentient, despite AI experts’ assurances to the contrary. It was a disaster in the press, but Microsoft didn’t flinch, and it ultimately reigned in some of Bing Chat’s wild proclivities and opened the bot widely to the public. Today, Bing Chat is now known as Microsoft Copilot, and it’s baked into Windows.

US Copyright Office says no to AI copyright authors

An AI-generated image that won a prize at the Colorado State Fair in 2022, later denied US copyright registration.

Enlarge / An AI-generated image that won a prize at the Colorado State Fair in 2022, later denied US copyright registration.

Jason M. Allen

In February, the US Copyright Office issued a key ruling on AI-generated art, revoking the copyright previously granted to the AI-assisted comic book “Zarya of the Dawn” in September 2022. The decision, influenced by the revelation that the images were created using the AI-powered Midjourney image generator, stated that only the text and arrangement of images and text by Kashtanova were eligible for copyright protection. It was the first hint that AI-generated imagery without human-authored elements could not be copyrighted in the United States.

This stance was further cemented in August when a US federal judge ruled that art created solely by AI cannot be copyrighted. In September, the US Copyright Office rejected the registration for an AI-generated image that won a Colorado State Fair art contest in 2022. As it stands now, it appears that purely AI-generated art (without substantial human authorship) is in the public domain in the United States. This stance could be further clarified or changed in the future by judicial rulings or legislation.

A song of hype and fire: The 10 biggest AI stories of 2023 Read More »