chatgpt – Page 3

LLMs keep leaping with Llama 3, Meta’s newest open-weights AI model

AI, AI assistants, Biz & IT, chatgpt, chatgtp, copilot, Google Gemini, LLaMA, Llama 2, Llama 3, machine learning, Meta, Meta AI Assistant, microsoft, Microsoft Copilot, Open Source, open weights, openai / Mike M. / April 19, 2024

computer-powered word generator —

Zuckerberg says new AI model “was still learning” when Meta stopped training.

Benj Edwards – Apr 18, 2024 9: 04 pm UTC

A group of pink llamas on a pixelated background.

On Thursday, Meta unveiled early versions of its Llama 3 open-weights AI model that can be used to power text composition, code generation, or chatbots. It also announced that its Meta AI Assistant is now available on a website and is going to be integrated into its major social media apps, intensifying the company’s efforts to position its products against other AI assistants like OpenAI’s ChatGPT, Microsoft’s Copilot, and Google’s Gemini.

Like its predecessor, Llama 2, Llama 3 is notable for being a freely available, open-weights large language model (LLM) provided by a major AI company. Llama 3 technically does not quality as “open source” because that term has a specific meaning in software (as we have mentioned in other coverage), and the industry has not yet settled on terminology for AI model releases that ship either code or weights with restrictions (you can read Llama 3’s license here) or that ship without providing training data. We typically call these releases “open weights” instead.

At the moment, Llama 3 is available in two parameter sizes: 8 billion (8B) and 70 billion (70B), both of which are available as free downloads through Meta’s website with a sign-up. Llama 3 comes in two versions: pre-trained (basically the raw, next-token-prediction model) and instruction-tuned (fine-tuned to follow user instructions). Each has a 8,192 token context limit.

Enlarge / A screenshot of the Meta AI Assistant website on April 18, 2024.

Benj Edwards

Meta trained both models on two custom-built, 24,000-GPU clusters. In a podcast interview with Dwarkesh Patel, Meta CEO Mark Zuckerberg said that the company trained the 70B model with around 15 trillion tokens of data. Throughout the process, the model never reached “saturation” (that is, it never hit a wall in terms of capability increases). Eventually, Meta pulled the plug and moved on to training other models.

“I guess our prediction going in was that it was going to asymptote more, but even by the end it was still leaning. We probably could have fed it more tokens, and it would have gotten somewhat better,” Zuckerberg said on the podcast.

Meta also announced that it is currently training a 400B parameter version of Llama 3, which some experts like Nvidia’s Jim Fan think may perform in the same league as GPT-4 Turbo, Claude 3 Opus, and Gemini Ultra on benchmarks like MMLU, GPQA, HumanEval, and MATH.

Speaking of benchmarks, we have devoted many words in the past to explaining how frustratingly imprecise benchmarks can be when applied to large language models due to issues like training contamination (that is, including benchmark test questions in the training dataset), cherry-picking on the part of vendors, and an inability to capture AI’s general usefulness in an interactive session with chat-tuned models.

But, as expected, Meta provided some benchmarks for Llama 3 that list results from MMLU (undergraduate level knowledge), GSM-8K (grade-school math), HumanEval (coding), GPQA (graduate-level questions), and MATH (math word problems). These show the 8B model performing well compared to open-weights models like Google’s Gemma 7B and Mistral 7B Instruct, and the 70B model also held its own against Gemini Pro 1.5 and Claude 3 Sonnet.

Enlarge / A chart of instruction-tuned Llama 3 8B and 70B benchmarks provided by Meta.

Meta says that the Llama 3 model has been enhanced with capabilities to understand coding (like Llama 2) and, for the first time, has been trained with both images and text—though it currently outputs only text. According to Reuters, Meta Chief Product Officer Chris Cox noted in an interview that more complex processing abilities (like executing multi-step plans) are expected in future updates to Llama 3, which will also support multimodal outputs—that is, both text and images.

Meta plans to host the Llama 3 models on a range of cloud platforms, making them accessible through AWS, Databricks, Google Cloud, and other major providers.

Also on Thursday, Meta announced that Llama 3 will become the new basis of the Meta AI virtual assistant, which the company first announced in September. The assistant will appear prominently in search features for Facebook, Instagram, WhatsApp, Messenger, and the aforementioned dedicated website that features a design similar to ChatGPT, including the ability to generate images in the same interface. The company also announced a partnership with Google to integrate real-time search results into the Meta AI assistant, adding to an existing partnership with Microsoft’s Bing.

LLMs keep leaping with Llama 3, Meta’s newest open-weights AI model Read More »

Words are flowing out like endless rain: Recapping a busy week of LLM news

AI, Anthropic, Biz & IT, Chatbot Arena, chatgpt, chatgtp, Claude 3, Claude 3 Opus, Claude 3 Sonnet, Gemini, Gemini 1.5, Google Gemini, GPT-4, GPT-4-turbo, large language models, machine learning, Meta, Mistral, Mixtral, Mixtral 8x22B, openai, RECAP, Simon Willison, whirlwind / Ari B / April 12, 2024

many things frequently —

Gemini 1.5 Pro launch, new version of GPT-4 Turbo, new Mistral model, and more.

Benj Edwards – Apr 12, 2024 8: 31 pm UTC

Enlarge / An image of a boy amazed by flying letters.

Some weeks in AI news are eerily quiet, but during others, getting a grip on the week’s events feels like trying to hold back the tide. This week has seen three notable large language model (LLM) releases: Google Gemini Pro 1.5 hit general availability with a free tier, OpenAI shipped a new version of GPT-4 Turbo, and Mistral released a new openly licensed LLM, Mixtral 8x22B. All three of those launches happened within 24 hours starting on Tuesday.

With the help of software engineer and independent AI researcher Simon Willison (who also wrote about this week’s hectic LLM launches on his own blog), we’ll briefly cover each of the three major events in roughly chronological order, then dig into some additional AI happenings this week.

Gemini Pro 1.5 general release

On Tuesday morning Pacific time, Google announced that its Gemini 1.5 Pro model (which we first covered in February) is now available in 180-plus countries, excluding Europe, via the Gemini API in a public preview. This is Google’s most powerful public LLM so far, and it’s available in a free tier that permits up to 50 requests a day.

It supports up to 1 million tokens of input context. As Willison notes in his blog, Gemini 1.5 Pro’s API price at $7/million input tokens and $21/million output tokens costs a little less than GPT-4 Turbo (priced at $10/million in and $30/million out) and more than Claude 3 Sonnet (Anthropic’s mid-tier LLM, priced at $3/million in and $15/million out).

Notably, Gemini 1.5 Pro includes native audio (speech) input processing that allows users to upload audio or video prompts, a new File API for handling files, the ability to add custom system instructions (system prompts) for guiding model responses, and a JSON mode for structured data extraction.

“Majorly Improved” GPT-4 Turbo launch

Enlarge / A GPT-4 Turbo performance chart provided by OpenAI.

Just a bit later than Google’s 1.5 Pro launch on Tuesday, OpenAI announced that it was rolling out a “majorly improved” version of GPT-4 Turbo (a model family originally launched in November) called “gpt-4-turbo-2024-04-09.” It integrates multimodal GPT-4 Vision processing (recognizing the contents of images) directly into the model, and it initially launched through API access only.

Then on Thursday, OpenAI announced that the new GPT-4 Turbo model had just become available for paid ChatGPT users. OpenAI said that the new model improves “capabilities in writing, math, logical reasoning, and coding” and shared a chart that is not particularly useful in judging capabilities (that they later updated). The company also provided an example of an alleged improvement, saying that when writing with ChatGPT, the AI assistant will use “more direct, less verbose, and use more conversational language.”

The vague nature of OpenAI’s GPT-4 Turbo announcements attracted some confusion and criticism online. On X, Willison wrote, “Who will be the first LLM provider to publish genuinely useful release notes?” In some ways, this is a case of “AI vibes” again, as we discussed in our lament about the poor state of LLM benchmarks during the debut of Claude 3. “I’ve not actually spotted any definite differences in quality [related to GPT-4 Turbo],” Willison told us directly in an interview.

The update also expanded GPT-4’s knowledge cutoff to April 2024, although some people are reporting it achieves this through stealth web searches in the background, and others on social media have reported issues with date-related confabulations.

Mistral’s mysterious Mixtral 8x22B release

An illustration of a robot holding a French flag, figuratively reflecting the rise of AI in France due to Mistral. It's hard to draw a picture of an LLM, so a robot will have to do. — Enlarge / An illustration of a robot holding a French flag, figuratively reflecting the rise of AI in France due to Mistral. It’s hard to draw a picture of an LLM, so a robot will have to do.

Not to be outdone, on Tuesday night, French AI company Mistral launched its latest openly licensed model, Mixtral 8x22B, by tweeting a torrent link devoid of any documentation or commentary, much like it has done with previous releases.

The new mixture-of-experts (MoE) release weighs in with a larger parameter count than its previously most-capable open model, Mixtral 8x7B, which we covered in December. It’s rumored to potentially be as capable as GPT-4 (In what way, you ask? Vibes). But that has yet to be seen.

“The evals are still rolling in, but the biggest open question right now is how well Mixtral 8x22B shapes up,” Willison told Ars. “If it’s in the same quality class as GPT-4 and Claude 3 Opus, then we will finally have an openly licensed model that’s not significantly behind the best proprietary ones.”

This release has Willison most excited, saying, “If that thing really is GPT-4 class, it’s wild, because you can run that on a (very expensive) laptop. I think you need 128GB of MacBook RAM for it, twice what I have.”

The new Mixtral is not listed on Chatbot Arena yet, Willison noted, because Mistral has not released a fine-tuned model for chatting yet. It’s still a raw, predict-the-next token LLM. “There’s at least one community instruction tuned version floating around now though,” says Willison.

Chatbot Arena Leaderboard shake-ups

Enlarge / A Chatbot Arena Leaderboard screenshot taken on April 12, 2024.

Benj Edwards

This week’s LLM news isn’t limited to just the big names in the field. There have also been rumblings on social media about the rising performance of open source models like Cohere’s Command R+, which reached position 6 on the LMSYS Chatbot Arena Leaderboard—the highest-ever ranking for an open-weights model.

And for even more Chatbot Arena action, apparently the new version of GPT-4 Turbo is proving competitive with Claude 3 Opus. The two are still in a statistical tie, but GPT-4 Turbo recently pulled ahead numerically. (In March, we reported when Claude 3 first numerically pulled ahead of GPT-4 Turbo, which was then the first time another AI model had surpassed a GPT-4 family model member on the leaderboard.)

Regarding this fierce competition among LLMs—of which most of the muggle world is unaware and will likely never be—Willison told Ars, “The past two months have been a whirlwind—we finally have not just one but several models that are competitive with GPT-4.” We’ll see if OpenAI’s rumored release of GPT-5 later this year will restore the company’s technological lead, we note, which once seemed insurmountable. But for now, Willison says, “OpenAI are no longer the undisputed leaders in LLMs.”

Words are flowing out like endless rain: Recapping a busy week of LLM news Read More »

Intel’s “Gaudi 3” AI accelerator chip may give Nvidia’s H100 a run for its money

AI, AI chips, B200, Biz & IT, Blackwell, chatgpt, chatgtp, Gaudi 3, H100, H200, intel, Intel Gaudi, machine learning, NVIDIA, Nvidia Blackwell, openai / Blake Jones / April 11, 2024

Adventures in Matrix Multiplication —

Intel claims 50% more speed when running AI language models vs. the market leader.

Benj Edwards – Apr 11, 2024 8: 56 pm UTC

Enlarge / An Intel handout photo of the Gaudi 3 AI accelerator.

On Tuesday, Intel revealed a new AI accelerator chip called Gaudi 3 at its Vision 2024 event in Phoenix. With strong claimed performance while running large language models (like those that power ChatGPT), the company has positioned Gaudi 3 as an alternative to Nvidia’s H100, a popular data center GPU that has been subject to shortages, though apparently that is easing somewhat.

Compared to Nvidia’s H100 chip, Intel projects a 50 percent faster training time on Gaudi 3 for both OpenAI’s GPT-3 175B LLM and the 7-billion parameter version of Meta’s Llama 2. In terms of inference (running the trained model to get outputs), Intel claims that its new AI chip delivers 50 percent faster performance than H100 for Llama 2 and Falcon 180B, which are both relatively popular open-weights models.

Intel is targeting the H100 because of its high market share, but the chip isn’t Nvidia’s most powerful AI accelerator chip in the pipeline. Announcements of the H200 and the Blackwell B200 have since surpassed the H100 on paper, but neither of those chips is out yet (the H200 is expected in the second quarter of 2024—basically any day now).

Meanwhile, the aforementioned H100 supply issues have been a major headache for tech companies and AI researchers who have to fight for access to any chips that can train AI models. This has led several tech companies like Microsoft, Meta, and OpenAI (rumor has it) to seek their own AI-accelerator chip designs, although that custom silicon is typically manufactured by either Intel or TSMC. Google has its own line of tensor processing units (TPUs) that it has been using internally since 2015.

Given those issues, Intel’s Gaudi 3 may be a potentially attractive alternative to the H100 if Intel can hit an ideal price (which Intel has not provided, but an H100 reportedly costs around $30,000–$40,000) and maintain adequate production. AMD also manufactures a competitive range of AI chips, such as the AMD Instinct MI300 Series, that sell for around $10,000–$15,000.

Gaudi 3 performance

Enlarge / An Intel handout featuring specifications of the Gaudi 3 AI accelerator.

Intel says the new chip builds upon the architecture of its predecessor, Gaudi 2, by featuring two identical silicon dies connected by a high-bandwidth connection. Each die contains a central cache memory of 48 megabytes, surrounded by four matrix multiplication engines and 32 programmable tensor processor cores, bringing the total cores to 64.

The chipmaking giant claims that Gaudi 3 delivers double the AI compute performance of Gaudi 2 using 8-bit floating-point infrastructure, which has become crucial for training transformer models. The chip also offers a fourfold boost for computations using the BFloat 16-number format. Gaudi 3 also features 128GB of the less expensive HBMe2 memory capacity (which may contribute to price competitiveness) and features 3.7TB of memory bandwidth.

Since data centers are well-known to be power hungry, Intel emphasizes the power efficiency of Gaudi 3, claiming 40 percent greater inference power-efficiency across Llama 7B and 70B parameters, and Falcon 180B parameter models compared to Nvidia’s H100. Eitan Medina, chief operating officer of Intel’s Habana Labs, attributes this advantage to Gaudi’s large-matrix math engines, which he claims require significantly less memory bandwidth compared to other architectures.

Gaudi vs. Blackwell

Last month, we covered the splashy launch of Nvidia’s Blackwell architecture, including the B200 GPU, which Nvidia claims will be the world’s most powerful AI chip. It seems natural, then, to compare what we know about Nvidia’s highest-performing AI chip to the best of what Intel can currently produce.

For starters, Gaudi 3 is being manufactured using TSMC’s N5 process technology, according to IEEE Spectrum, narrowing the gap between Intel and Nvidia in terms of semiconductor fabrication technology. The upcoming Nvidia Blackwell chip will use a custom N4P process, which reportedly offers modest performance and efficiency improvements over N5.

Gaudi 3’s use of HBM2e memory (as we mentioned above) is notable compared to the more expensive HBM3 or HBM3e used in competing chips, offering a balance of performance and cost-efficiency. This choice seems to emphasize Intel’s strategy to compete not only on performance but also on price.

As far as raw performance comparisons between Gaudi 3 and the B200, that can’t be known until the chips have been released and benchmarked by a third party.

As the race to power the tech industry’s thirst for AI computation heats up, IEEE Spectrum notes that the next generation of Intel’s Gaudi chip, code-named Falcon Shores, remains a point of interest. It also remains to be seen whether Intel will continue to rely on TSMC’s technology or leverage its own foundry business and upcoming nanosheet transistor technology to gain a competitive edge in the AI accelerator market.

Intel’s “Gaudi 3” AI accelerator chip may give Nvidia’s H100 a run for its money Read More »

New AI music generator Udio synthesizes realistic music on demand

AI, audio synthesis, Biz & IT, chatgpt, chatgtp, machine learning, music, music synthesis, openai, Suno, Udio / Ryan Harris / April 10, 2024

Battle of the AI bands —

But it still needs trial and error to generate high-quality results.

Benj Edwards – Apr 10, 2024 9: 47 pm UTC

Enlarge / A screenshot of AI-generated songs listed on Udio on April 10, 2024.

Benj Edwards

Between 2002 and 2005, I ran a music website where visitors could submit song titles that I would write and record a silly song around. In the liner notes for my first CD release in 2003, I wrote about a day when computers would potentially put me out of business, churning out music automatically at a pace I could not match. While I don’t actively post music on that site anymore, that day is almost here.

On Wednesday, a group of ex-DeepMind employees launched Udio, a new AI music synthesis service that can create novel high-fidelity musical audio from written prompts, including user-provided lyrics. It’s similar to Suno, which we covered on Monday. With some key human input, Udio can create facsimiles of human-produced music in genres like country, barbershop quartet, German pop, classical, hard rock, hip hop, show tunes, and more. It’s currently free to use during a beta period.

Udio is also freaking out some musicians on Reddit. As we mentioned in our Suno piece, Udio is exactly the kind of AI-powered music generation service that over 200 musical artists were afraid of when they signed an open protest letter last week.

But as impressive as the Udio songs first seem from a technical AI-generation standpoint (not necessarily judging by musical merit), its generation capability isn’t perfect. We experimented with its creation tool and the results felt less impressive than those created by Suno. The high-quality musical samples showcased on Udio’s site likely resulted from a lot of creative human input (such as human-written lyrics) and cherry-picking the best compositional parts of songs out of many generations. In fact, Udio lays out a five-step workflow to build a 1.5-minute-long song in a FAQ.

For example, we created an Ars Technica “Moonshark” song on Udio using the same prompt as one we used previously with Suno. In its raw form, the results sound half-baked and almost nightmarish (here is the Suno version for comparison). It’s also a lot shorter by default at 32 seconds compared to Suno’s 1-minute and 32-second output. But Udio allows songs to be extended, or you can try generating a poor result again with different prompts for different results.

After registering a Udio account, anyone can create a track by entering a text prompt that can include lyrics, a story direction, and musical genre tags. Udio then tackles the task in two stages. First, it utilizes a large language model (LLM) similar to ChatGPT to generate lyrics (if necessary) based on the provided prompt. Next, it synthesizes music using a method that Udio does not disclose, but it’s likely a diffusion model, similar to Stability AI’s Stable Audio.

From the given prompt, Udio’s AI model generates two distinct song snippets for you to choose from. You can then publish the song for the Udio community, download the audio or video file to share on other platforms, or directly share it on social media. Other Udio users can also remix or build on existing songs. Udio’s terms of service say that the company claims no rights over the musical generations and that they can be used for commercial purposes.

Although the Udio team has not revealed the specific details of its model or training data (which is likely filled with copyrighted material), it told Tom’s Guide that the system has built-in measures to identify and block tracks that too closely resemble the work of specific artists, ensuring that the generated music remains original.

And that brings us back to humans, some of whom are not taking the onset of AI-generated music very well. “I gotta be honest, this is depressing as hell,” wrote one Reddit commenter in a thread about Udio. “I’m still broadly optimistic that music will be fine in the long run somehow. But like, why do this? Why automate art?”

We’ll hazard an answer by saying that replicating art is a key target for AI research because the results can be inaccurate and imprecise and still seem notable or gee-whiz amazing, which is a key characteristic of generative AI. It’s flashy and impressive-looking while allowing for a general lack of quantitative rigor. We’ve already seen AI come for still images, video, and text with varied results regarding representative accuracy. Fully composed musical recordings seem to be next on the list of AI hills to (approximately) conquer, and the competition is heating up.

New AI music generator Udio synthesizes realistic music on demand Read More »

MIT License text becomes viral “sad girl” piano ballad generated by AI

AI, AI-generated music, barbarians, Biz & IT, chatgpt, chatgtp, machine learning, moonshark, music synthesis, openai, Riley Goodside, sound synthesis, Suno, Suno.ai / Ari B / April 8, 2024

WARRANTIES OF MERCHANTABILITY —

“Permission is hereby granted” comes from Suno AI engine that creates new songs on demand.

Benj Edwards – Apr 8, 2024 10: 46 pm UTC

We’ve come a long way since primitive AI music generators in 2022. Today, AI tools like Suno.ai allow any series of words to become song lyrics, including inside jokes (as you’ll see below). On Wednesday, prompt engineer Riley Goodside tweeted an AI-generated song created with the prompt “sad girl with piano performs the text of the MIT License,” and it began to circulate widely in the AI community online.

The MIT License is a famous permissive software license created in the late 1980s, frequently used in open source projects. “My favorite part of this is ~1: 25 it nails ‘WARRANTIES OF MERCHANTABILITY’ with a beautiful Imogen Heap-style glissando then immediately pronounces ‘FITNESS’ as ‘fistiff,'” Goodside wrote on X.

Suno (which means “listen” in Hindi) was formed in 2023 in Cambridge, Massachusetts. It’s the brainchild of Michael Shulman, Georg Kucsko, Martin Camacho, and Keenan Freyberg, who formerly worked at companies like Meta and TikTok. Suno has already attracted big-name partners, such as Microsoft, which announced the integration of an earlier version of the Suno engine into Bing Chat last December. Today, Suno is on v3 of its model, which can create temporally coherent two-minute songs in many different genres.

The company did not reply to our request for an interview by press time. In March, Brian Hiatt of Rolling Stone wrote a profile about Suno that describes the service as a collaboration between OpenAI’s ChatGPT (for lyric writing) and Suno’s music generation model, which some experts think has likely been trained on recordings of copyrighted music without license or artist permission.

It’s exactly this kind of service that upset over 200 musical artists enough last week that they signed an Artist Rights Alliance open letter asking tech companies to stop using AI tools to generate music that could replace human artists.

Considering the unknown provenance of the training data, ownership of the generated songs seems like a complicated question. Suno’s FAQ says that music generated using its free tier remains owned by Suno and can only be used for non-commercial purposes. Paying subscribers reportedly own generated songs “while subscribed to Pro or Premier,” subject to Suno’s terms of service. However, the US Copyright Office took a stance last year that purely AI-generated visual art cannot be copyrighted, and while that standard has not yet been resolved for AI-generated music, it might eventually become official legal policy as well.

The Moonshark song

Enlarge / A screenshot of the Suno.ai website showing lyrics of an AI-generated “Moonshark” song.

Benj Edwards

While using the service, Suno appears to have no trouble creating unique lyrics based on your prompt (unless you supply your own) and sets those words to stylized genres of music it generates based on its training dataset. It dynamically generates vocals as well, although they include audible aberrations. Suno’s output is not indistinguishable from high-fidelity human-created music yet, but given the pace of progress we’ve seen, that bridge could be crossed within the next year.

To get a sense of what Suno can do, we created an account on the site and prompted the AI engine to create songs about our mascot, Moonshark, and about barbarians with CRTs, two inside jokes at Ars. What’s interesting is that although the AI model aced the task of creating an original song for each topic, both songs start with the same line, “In the depths of the digital domain.” That’s possibly an artifact of whatever hidden prompt Suno is using to instruct ChatGPT when writing the lyrics.

Suno is arguably a fun toy to experiment with and doubtless a milestone in generative AI music tools. But it’s also an achievement tainted by the unresolved ethical issues related to scraping musical work without the artist’s permission. Then there’s the issue of potentially replacing human musicians, which has not been far from the minds of people sharing their own Suno results online. On Monday, AI influencer Ethan Mollick wrote, “I’ve had a song from Suno AI stuck in my head all day. Grim milestone or good one?”

MIT License text becomes viral “sad girl” piano ballad generated by AI Read More »

Billie Eilish, Pearl Jam, 200 artists say AI poses existential threat to their livelihoods

AI, AI art, AI ethics, AI music, ara, Arists Rights Alliance, Billie Eilish, Biz & IT, chatgpt, chatgtp, culture, Google, machine learning, music, music industry, music synthesis, MusicLM, openai, Pearl Jam, Riffusion, Stable Audio / Blake Jones / April 2, 2024

artificial music —

Artists say AI will “set in motion a race to the bottom that will degrade the value of our work.”

Benj Edwards – Apr 2, 2024 8: 14 pm UTC

Billie Eilish attends the 2024 Vanity Fair Oscar Party hosted by Radhika Jones at the Wallis Annenberg Center for the Performing Arts on March 10, 2024 in Beverly Hills, California. — Enlarge / Billie Eilish attends the 2024 Vanity Fair Oscar Party hosted by Radhika Jones at the Wallis Annenberg Center for the Performing Arts on March 10, 2024, in Beverly Hills, California.

On Tuesday, the Artist Rights Alliance (ARA) announced an open letter critical of AI signed by over 200 musical artists, including Pearl Jam, Nicki Minaj, Billie Eilish, Stevie Wonder, Elvis Costello, and the estate of Frank Sinatra. In the letter, the artists call on AI developers, technology companies, platforms, and digital music services to stop using AI to “infringe upon and devalue the rights of human artists.” A tweet from the ARA added that AI poses an “existential threat” to their art.

Visual artists began protesting the advent of generative AI after the rise of the first mainstream AI image generators in 2022, and considering that generative AI research has since been undertaken for other forms of creative media, we have seen that protest extend to professionals in other creative domains, such as writers, actors, filmmakers—and now musicians.

“When used irresponsibly, AI poses enormous threats to our ability to protect our privacy, our identities, our music and our livelihoods,” the open letter states. It alleges that some of the “biggest and most powerful” companies (unnamed in the letter) are using the work of artists without permission to train AI models, with the aim of replacing human artists with AI-created content.

A list of musical artists that signed the ARA open letter against generative AI.
A list of musical artists that signed the ARA open letter against generative AI.
A list of musical artists that signed the ARA open letter against generative AI.
A list of musical artists that signed the ARA open letter against generative AI.

In January, Billboard reported that AI research taking place at Google DeepMind had trained an unnamed music-generating AI on a large dataset of copyrighted music without seeking artist permission. That report may have been referring to Google’s Lyria, an AI-generation model announced in November that the company positioned as a tool for enhancing human creativity. The tech has since powered musical experiments from YouTube.

We’ve previously covered AI music generators that seemed fairly primitive throughout 2022 and 2023, such as Riffusion, Google’s MusicLM, and Stability AI’s Stable Audio. We’ve also covered open source musical voice-cloning technology that is frequently used to make musical parodies online. While we have yet to see an AI model that can generate perfect, fully composed high-quality music on demand, the quality of outputs from music synthesis models has been steadily improving over time.

In considering AI’s potential impact on music, it’s instructive to remember historical instances where tech innovations initially sparked concern among artists. For instance, the introduction of synthesizers in the 1960s and 1970s and the advent of digital sampling in the 1980s both faced scrutiny and fear from parts of the music community, but the music industry eventually adjusted.

While we’ve seen fear of the unknown related to AI going around quite a bit for the past year, it’s possible that AI tools will be integrated into the music production process like any other music production tool or technique that came before. It’s also possible that even if that kind of integration comes to pass, some artists will still get hurt along the way—and the ARA wants to speak out about it before the technology progresses further.

“Race to the bottom”

The Artists Rights Alliance is a nonprofit advocacy group that describes itself as an “alliance of working musicians, performers, and songwriters fighting for a healthy creative economy and fair treatment for all creators in the digital world.”

The signers of the ARA’s open letter say they acknowledge the potential of AI to advance human creativity when used responsibly, but they also claim that replacing artists with generative AI would “substantially dilute the royalty pool” paid out to artists, which could be “catastrophic” for many working musicians, artists, and songwriters who are trying to make ends meet.

In the letter, the artists say that unchecked AI will set in motion a race to the bottom that will degrade the value of their work and prevent them from being fairly compensated. “This assault on human creativity must be stopped,” they write. “We must protect against the predatory use of AI to steal professional artist’ voices and likenesses, violate creators’ rights, and destroy the music ecosystem.”

The emphasis on the word “human” in the letter is notable (“human artist” was used twice and “human creativity” and “human artistry” are used once, each) because it suggests the clear distinction they are drawing between the work of human artists and the output of AI systems. It implies recognition that we’ve entered a new era where not all creative output is made by people.

The letter concludes with a call to action, urging all AI developers, technology companies, platforms, and digital music services to pledge not to develop or deploy AI music-generation technology, content, or tools that undermine or replace the human artistry of songwriters and artists or deny them fair compensation for their work.

While it’s unclear whether companies will meet those demands, so far, protests from visual artists have not stopped development of ever-more advanced image-synthesis models. On Threads, frequent AI industry commentator Dare Obasanjo wrote, “Unfortunately this will be as effective as writing an open letter to stop the sun from rising tomorrow.”

Billie Eilish, Pearl Jam, 200 artists say AI poses existential threat to their livelihoods Read More »

OpenAI drops login requirements for ChatGPT’s free version

AI, AI assistants, AI models, Anthropic, Antrhopic Claude, Biz & IT, chatgpt, chatgtp, Claude 3, Google, Google Gemini, GPT-3.5, GPT-4, GPT-4-turbo, large language models, machine learning, openai / Blake Jones / April 1, 2024

free as in beer? —

ChatGPT 3.5 still falls far short of GPT-4, and other models surpassed it long ago.

Benj Edwards – Apr 1, 2024 10: 31 pm UTC

Benj Edwards

On Monday, OpenAI announced that visitors to the ChatGPT website in some regions can now use the AI assistant without signing in. Previously, the company required that users create an account to use it, even with the free version of ChatGPT that is currently powered by the GPT-3.5 AI language model. But as we have noted in the past, GPT-3.5 is widely known to provide more inaccurate information compared to GPT-4 Turbo, available in paid versions of ChatGPT.

Since its launch in November 2022, ChatGPT has transformed over time from a tech demo to a comprehensive AI assistant, and it’s always had a free version available. The cost is free because “you’re the product,” as the old saying goes. Using ChatGPT helps OpenAI gather data that will help the company train future AI models, although free users and ChatGPT Plus subscription members can both opt out of allowing the data they input into ChatGPT to be used for AI training. (OpenAI says it never trains on inputs from ChatGPT Team and Enterprise members at all).

Opening ChatGPT to everyone could provide a frictionless on-ramp for people who might use it as a substitute for Google Search or potentially gain new customers by providing an easy way for people to use ChatGPT quickly, then offering an upsell to paid versions of the service.

“It’s core to our mission to make tools like ChatGPT broadly available so that people can experience the benefits of AI,” OpenAI says on its blog page. “For anyone that has been curious about AI’s potential but didn’t want to go through the steps to set up an account, start using ChatGPT today.”

When you visit the ChatGPT website, you're immediately presented with a chat box like this (in some regions). Screenshot captured April 1, 2024. — Enlarge / When you visit the ChatGPT website, you’re immediately presented with a chat box like this (in some regions). Screenshot captured April 1, 2024.

Benj Edwards

Since kids will also be able to use ChatGPT without an account—despite it being against the terms of service—OpenAI also says it’s introducing “additional content safeguards,” such as blocking more prompts and “generations in a wider range of categories.” What exactly that entails has not been elaborated upon by OpenAI, but we reached out to the company for comment.

There might be a few other downsides to the fully open approach. On X, AI researcher Simon Willison wrote about the potential for automated abuse as a way to get around paying for OpenAI’s services: “I wonder how their scraping prevention works? I imagine the temptation for people to abuse this as a free 3.5 API will be pretty strong.”

With fierce competition, more GPT-3.5 access may backfire

Willison also mentioned a common criticism of OpenAI (as voiced in this case by Wharton professor Ethan Mollick) that people’s ideas about what AI models can do have so far largely been influenced by GPT-3.5, which, as we mentioned, is far less capable and far more prone to making things up than the paid version of ChatGPT that uses GPT-4 Turbo.

“In every group I speak to, from business executives to scientists, including a group of very accomplished people in Silicon Valley last night, much less than 20% of the crowd has even tried a GPT-4 class model,” wrote Mollick in a tweet from early March.

With models like Google Gemini Pro 1.5 and Anthropic Claude 3 potentially surpassing OpenAI’s best proprietary model at the moment —and open weights AI models eclipsing the free version of ChatGPT—allowing people to use GPT-3.5 might not be putting OpenAI’s best foot forward. Microsoft Copilot, powered by OpenAI models, also supports a frictionless, no-login experience, but it allows access to a model based on GPT-4. But Gemini currently requires a sign-in, and Anthropic sends a login code through email.

For now, OpenAI says the login-free version of ChatGPT is not yet available to everyone, but it will be coming soon: “We’re rolling this out gradually, with the aim to make AI accessible to anyone curious about its capabilities.”

OpenAI drops login requirements for ChatGPT’s free version Read More »

OpenAI holds back wide release of voice-cloning tech due to misuse concerns

AI, AI ethics, AI privacy, AI safety, API, Biz & IT, chatgpt, chatgtp, machine learning, openai, Voice Engine, voice synthesis / Ari B / March 29, 2024

AI speaks letters, text-to-speech or TTS, text-to-voice, speech synthesis applications, generative Artificial Intelligence, futuristic technology in language and communication.

Voice synthesis has come a long way since 1978’s Speak & Spell toy, which once wowed people with its state-of-the-art ability to read words aloud using an electronic voice. Now, using deep-learning AI models, software can create not only realistic-sounding voices, but also convincingly imitate existing voices using small samples of audio.

Along those lines, OpenAI just announced Voice Engine, a text-to-speech AI model for creating synthetic voices based on a 15-second segment of recorded audio. It has provided audio samples of the Voice Engine in action on its website.

Once a voice is cloned, a user can input text into the Voice Engine and get an AI-generated voice result. But OpenAI is not ready to widely release its technology yet. The company initially planned to launch a pilot program for developers to sign up for the Voice Engine API earlier this month. But after more consideration about ethical implications, the company decided to scale back its ambitions for now.

“In line with our approach to AI safety and our voluntary commitments, we are choosing to preview but not widely release this technology at this time,” the company writes. “We hope this preview of Voice Engine both underscores its potential and also motivates the need to bolster societal resilience against the challenges brought by ever more convincing generative models.”

Voice cloning tech in general is not particularly new—we’ve covered several AI voice synthesis models since 2022, and the tech is active in the open source community with packages like OpenVoice and XTTSv2. But the idea that OpenAI is inching toward letting anyone use their particular brand of voice tech is notable. And in some ways, the company’s reticence to release it fully might be the bigger story.

OpenAI says that benefits of its voice technology include providing reading assistance through natural-sounding voices, enabling global reach for creators by translating content while preserving native accents, supporting non-verbal individuals with personalized speech options, and assisting patients in recovering their own voice after speech-impairing conditions.

But it also means that anyone with 15 seconds of someone’s recorded voice could effectively clone it, and that has obvious implications for potential misuse. Even if OpenAI never widely releases its Voice Engine, the ability to clone voices has already caused trouble in society through phone scams where someone imitates a loved one’s voice and election campaign robocalls featuring cloned voices from politicians like Joe Biden.

Also, researchers and reporters have shown that voice-cloning technology can be used to break into bank accounts that use voice authentication (such as Chase’s Voice ID), which prompted Sen. Sherrod Brown (D-Ohio), the chairman of the US Senate Committee on Banking, Housing, and Urban Affairs, to send a letter to the CEOs of several major banks in May 2023 to inquire about the security measures banks are taking to counteract AI-powered risks.

OpenAI holds back wide release of voice-cloning tech due to misuse concerns Read More »

Intel, Microsoft discuss plans to run Copilot locally on PCs instead of in the cloud

AI, chatgpt, copilot, intel, microsoft, Tech / Ari B / March 27, 2024

the ai pc —

Companies are trying to make the “AI PC” happen with new silicon and software.

Andrew Cunningham – Mar 27, 2024 6: 45 pm UTC

The basic requirements for an AI PC, at least when it's running Windows. — Enlarge / The basic requirements for an AI PC, at least when it’s running Windows.

Intel

Microsoft said in January that 2024 would be the year of the “AI PC,” and we know that AI PCs will include a few hardware components that most Windows systems currently do not include—namely, a built-in neural processing unit (NPU) and Microsoft’s new Copilot key for keyboards. But so far we haven’t heard a whole lot about what a so-called AI PC will actually do for users.

Microsoft and Intel are starting to talk about a few details as part of an announcement from Intel about a new AI PC developer program that will encourage software developers to leverage local hardware to build AI features into their apps.

The main news comes from Tom’s Hardware, confirming that AI PCs would be able to run “more elements of Copilot,” Microsoft’s AI chatbot assistant, “locally on the client.” Currently, Copilot relies on server-side processing even for small requests, introducing lag that is tolerable if you’re making a broad request for information but less so if all you want to do is change a setting or get basic answers. Running generative AI models locally could also improve user privacy, making it possible to take advantage of AI-infused software without automatically sending information to a company that will use it for further model training.

Right now, Windows doesn’t use local NPUs for much, since most current PCs don’t have them. The Surface Studio webcam features can use NPUs for power-efficient video effects and background replacement, but as of this writing that’s pretty much it. Apple’s and Google’s operating systems both use NPUs for a wider swatch of image and audio processing features, including facial recognition and object recognition, OCR, live transcription and translation, and more.

Intel also said that Microsoft would require NPUs in “next-gen AI PCs” to hit speeds of 40 trillion operations per second (TOPS) to meet its requirements. Intel, AMD, Qualcomm, and others sometimes use TOPS as a high-level performance metric when comparing their NPUs; Intel’s Meteor Lake laptop chips can run 10 TOPS, while AMD’s Ryzen 7040 and 8040 laptop chips hit 10 TOPS and 16 TOPS, respectively.

Unfortunately for Intel, the first company to put out an NPU suitable for powering Copilot locally may come from Qualcomm. The company’s upcoming Snapdragon X processors, long seen as the Windows ecosystem’s answer to Apple’s M-series Mac chips, promise up to 45 TOPS. Rumors suggest that Microsoft will shift the consumer version of its Surface tablet to Qualcomm’s chips after a few years of offering both Intel and Qualcomm options; Microsoft announced a Surface Pro update with Intel’s Meteor Lake chips last week but is only selling it to businesses.

Enlarge / Asus and Intel are offering a NUC with a Meteor Lake CPU and its built-in NPU as an AI development platform.

Intel

All of that said, TOPS are just one simplified performance metric. As when using FLOPS to compare graphics performance, it’s imprecise and won’t capture variations in how each NPU handles different tasks. And the Arm version of Windows still has software and hardware compatibility issues that could continue to hold it back.

As part of its developer program, Intel is also offering an “AI PC development kit” centered on an Asus NUC Pro 14, a mini PC built around Intel’s Meteor Lake silicon. Intel formally stopped making its NUC mini PCs last year, passing the brand and all of its designs off to Asus. Asus is also handling all remaining warranty service and software support for older NUCs designed and sold by Intel. The NUC Pro 14 is one of the first new NUCs announced since the transition, along with the ROG NUC mini gaming PC.

Intel, Microsoft discuss plans to run Copilot locally on PCs instead of in the cloud Read More »

Nvidia unveils Blackwell B200, the “world’s most powerful chip” designed for AI

AI, AI inference, Biz & IT, chatgpt, chatgtp, GPU, GPUs, Jensen Huang, large language models, LLMs, machine learning, NVIDIA, openai, supercomputers / Blake Jones / March 19, 2024

There’s no knowing where we’re rowing —

208B transistor chip can reportedly reduce AI cost and energy consumption by up to 25x.

Benj Edwards – Mar 19, 2024 3: 27 pm UTC

Enlarge / The GB200 “superchip” covered with a fanciful blue explosion.

Nvidia / Benj Edwards

On Monday, Nvidia unveiled the Blackwell B200 tensor core chip—the company’s most powerful single-chip GPU, with 208 billion transistors—which Nvidia claims can reduce AI inference operating costs (such as running ChatGPT) and energy consumption by up to 25 times compared to the H100. The company also unveiled the GB200, a “superchip” that combines two B200 chips and a Grace CPU for even more performance.

The news came as part of Nvidia’s annual GTC conference, which is taking place this week at the San Jose Convention Center. Nvidia CEO Jensen Huang delivered the keynote Monday afternoon. “We need bigger GPUs,” Huang said during his keynote. The Blackwell platform will allow the training of trillion-parameter AI models that will make today’s generative AI models look rudimentary in comparison, he said. For reference, OpenAI’s GPT-3, launched in 2020, included 175 billion parameters. Parameter count is a rough indicator of AI model complexity.

Nvidia named the Blackwell architecture after David Harold Blackwell, a mathematician who specialized in game theory and statistics and was the first Black scholar inducted into the National Academy of Sciences. The platform introduces six technologies for accelerated computing, including a second-generation Transformer Engine, fifth-generation NVLink, RAS Engine, secure AI capabilities, and a decompression engine for accelerated database queries.

Enlarge / Press photo of the Grace Blackwell GB200 chip, which combines two B200 GPUs with a Grace CPU into one chip.

Several major organizations, such as Amazon Web Services, Dell Technologies, Google, Meta, Microsoft, OpenAI, Oracle, Tesla, and xAI, are expected to adopt the Blackwell platform, and Nvidia’s press release is replete with canned quotes from tech CEOs (key Nvidia customers) like Mark Zuckerberg and Sam Altman praising the platform.

GPUs, once only designed for gaming acceleration, are especially well suited for AI tasks because their massively parallel architecture accelerates the immense number of matrix multiplication tasks necessary to run today’s neural networks. With the dawn of new deep learning architectures in the 2010s, Nvidia found itself in an ideal position to capitalize on the AI revolution and began designing specialized GPUs just for the task of accelerating AI models.

Nvidia’s data center focus has made the company wildly rich and valuable, and these new chips continue the trend. Nvidia’s gaming GPU revenue ($2.9 billion in the last quarter) is dwarfed in comparison to data center revenue (at $18.4 billion), and that shows no signs of stopping.

A beast within a beast

Enlarge / Press photo of the Nvidia GB200 NVL72 data center computer system.

The aforementioned Grace Blackwell GB200 chip arrives as a key part of the new NVIDIA GB200 NVL72, a multi-node, liquid-cooled data center computer system designed specifically for AI training and inference tasks. It combines 36 GB200s (that’s 72 B200 GPUs and 36 Grace CPUs total), interconnected by fifth-generation NVLink, which links chips together to multiply performance.

Enlarge / A specification chart for the Nvidia GB200 NVL72 system.

“The GB200 NVL72 provides up to a 30x performance increase compared to the same number of NVIDIA H100 Tensor Core GPUs for LLM inference workloads and reduces cost and energy consumption by up to 25x,” Nvidia said.

That kind of speed-up could potentially save money and time while running today’s AI models, but it will also allow for more complex AI models to be built. Generative AI models—like the kind that power Google Gemini and AI image generators—are famously computationally hungry. Shortages of compute power have widely been cited as holding back progress and research in the AI field, and the search for more compute has led to figures like OpenAI CEO Sam Altman trying to broker deals to create new chip foundries.

While Nvidia’s claims about the Blackwell platform’s capabilities are significant, it’s worth noting that its real-world performance and adoption of the technology remain to be seen as organizations begin to implement and utilize the platform themselves. Competitors like Intel and AMD are also looking to grab a piece of Nvidia’s AI pie.

Nvidia says that Blackwell-based products will be available from various partners starting later this year.

Nvidia unveils Blackwell B200, the “world’s most powerful chip” designed for AI Read More »

Apple may hire Google to power new iPhone AI features using Gemini—report

AI, Apple, Biz & IT, chatgpt, chatgtp, cloud AI, Gemini, Google, Google Gemini, GPT-4, image synthesis, iOS, large language models, llm, machine learning, openai, Siri, text synthesis / Ari B / March 18, 2024

Bake a cake as fast as you can —

With Apple’s own AI tech lagging behind, the firm looks for a fallback solution.

Benj Edwards – Mar 18, 2024 7: 56 pm UTC

On Monday, Bloomberg reported that Apple is in talks to license Google’s Gemini model to power AI features like Siri in a future iPhone software update coming later in 2024, according to people familiar with the situation. Apple has also reportedly conducted similar talks with ChatGPT maker OpenAI.

The potential integration of Google Gemini into iOS 18 could bring a range of new cloud-based (off-device) AI-powered features to Apple’s smartphone, including image creation or essay writing based on simple prompts. However, the terms and branding of the agreement have not yet been finalized, and the implementation details remain unclear. The companies are unlikely to announce any deal until Apple’s annual Worldwide Developers Conference in June.

Gemini could also bring new capabilities to Apple’s widely criticized voice assistant, Siri, which trails newer AI assistants powered by large language models (LLMs) in understanding and responding to complex questions. Rumors of Apple’s own internal frustration with Siri—and potential remedies—have been kicking around for some time. In January, 9to5Mac revealed that Apple had been conducting tests with a beta version of iOS 17.4 that used OpenAI’s ChatGPT API to power Siri.

As we have previously reported, Apple has also been developing its own AI models, including a large language model codenamed Ajax and a basic chatbot called Apple GPT. However, the company’s LLM technology is said to lag behind that of its competitors, making a partnership with Google or another AI provider a more attractive option.

Google launched Gemini, a language-based AI assistant similar to ChatGPT, in December and has updated it several times since. Many industry experts consider the larger Gemini models to be roughly as capable as OpenAI’s GPT-4 Turbo, which powers the subscription versions of ChatGPT. Until just recently, with the emergence of Gemini Ultra and Claude 3, OpenAI’s top model held a fairly wide lead in perceived LLM capability.

The potential partnership between Apple and Google could significantly impact the AI industry, as Apple’s platform represents more than 2 billion active devices worldwide. If the agreement gets finalized, it would build upon the existing search partnership between the two companies, which has seen Google pay Apple billions of dollars annually to make its search engine the default option on iPhones and other Apple devices.

However, Bloomberg reports that the potential partnership between Apple and Google is likely to draw scrutiny from regulators, as the companies’ current search deal is already the subject of a lawsuit by the US Department of Justice. The European Union is also pressuring Apple to make it easier for consumers to change their default search engine away from Google.

With so much potential money on the line, selecting Google for Apple’s cloud AI job could potentially be a major loss for OpenAI in terms of bringing its technology widely into the mainstream—with a market representing billions of users. Even so, any deal with Google or OpenAI may be a temporary fix until Apple can get its own LLM-based AI technology up to speed.

Apple may hire Google to power new iPhone AI features using Gemini—report Read More »

“Overwhelming evidence” shows Craig Wright did not create bitcoin, judge says

Bitcoin, bitcoin creator, chatgpt, Craig Wright, Cryptocurrency, fraud, perjury, Policy, satoshi nakamoto, UK high court / Mike M. / March 14, 2024

Debate closed —

Jack Dorsey posted a “W,” as judge halts Wright’s suits against developers.

Ashley Belanger – Mar 14, 2024 6: 59 pm UTC

Enlarge / Dr. Craig Wright arrives at the Rolls Building, part of the Royal Courts of Justice, on February 06, 2024, in London, England.

“Overwhelming evidence” shows that Australian computer scientist Craig Wright is not bitcoin creator Satoshi Nakamoto, a UK judge declared Thursday.

In what Wired described as a “surprise ruling” at the closing of Wright’s six-week trial, Justice James Mellor abruptly ended years of speculation by saying:

“Dr. Wright is not the author of the Bitcoin white paper. Dr. Wright is not the person that operated under the pseudonym Satoshi Nakamoto. Dr. Wright is not the person that created the Bitcoin system. Nor is Dr. Wright the author of the Bitcoin software.”

Wright was not in the courtroom for this explosive moment, Wired reported.

In 2016, Wright had claimed that he did not have the “courage” to prove that he was the creator of bitcoin, shortly after claiming that he had “extraordinary proof.” As debate swirled around his claims, Wright began filing lawsuits, alleging that many had violated his intellectual property rights.

A nonprofit called the Crypto Open Patent Alliance (COPA) sued to stop Wright from filing any more lawsuits that it alleged were based on fabricated evidence, Wired reported. They submitted hundreds of alleged instances of forgery or tampering, Wired reported, asking the UK High Court for a permanent injunction to block Wright from ever making the claim again.

As a result of Mellor’s ruling, CoinDesk reported that Wright’s lawsuits against Coinbase and Twitter founder Jack Dorsey’s Block would be halted. COPA’s lawyer, Jonathan Hough, told CoinDesk that Wright’s conduct should be considered “deadly serious.”

“On the basis of his dishonest claim to be Satoshi, he has pursued claims he puts at hundreds of billions of dollars, including against numerous private individuals,” Hough said.

On Thursday, Dorsey posted a “W” on X (formerly Twitter), marking the win and quoting Mellor’s statements clearly rejecting Wright’s claims as false. COPA similarly celebrated the victory.

“This decision is a win for developers, for the entire open source community, and for the truth,” a COPA spokesperson told CoinDesk. “For over eight years, Dr. Wright and his financial backers have lied about his identity as Satoshi Nakamoto and used that lie to bully and intimidate developers in the bitcoin community. That ends today with the court’s ruling that Craig Wright is not Satoshi Nakamoto.”

Wright’s counsel, Lord Anthony Grabiner, had argued that Mellor granting an injunction would infringe Wright’s freedom of speech. Grabiner noted that “such a prohibition is unprecedented in the UK and would prevent Wright from even casually going to the park and declaring he’s Satoshi without incurring fines or going to prison,” CoinDesk reported.

COPA thinks the injunction is necessary, though.

“We are seeking to enjoin Dr. Wright from ever claiming to be Satoshi Nakamoto again and in doing so avoid further litigation terror campaigns,” COPA’s spokesperson told Wired.

And that’s not all that COPA wants. COPA has also petitioned for Wright’s alleged forgeries—some of which Reuters reported were allegedly produced using ChatGPT—to be review by UK criminal courts, where he could face fines and/or prison time. Hough alleged at trial that Wright “has committed fraud upon the court,” Wired reported, asking Britain’s Crown Prosecution Service to consider prosecuting Wright for “perjury and perverting the course of justice,” CoinDesk reported.

Wright’s counsel argued that COPA would need more evidence to back such a claim, CoinDesk reported.

Mellor won’t issue his final judgment for a month or more, Wired reported, so it’s not clear yet if Wright will be enjoined from claiming he is bitcoin’s creator. The judgement will “be ready when it’s ready and not before,” Mellor said.

“Overwhelming evidence” shows Craig Wright did not create bitcoin, judge says Read More »