chatgpt

billie-eilish,-pearl-jam,-200-artists-say-ai-poses-existential-threat-to-their-livelihoods

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

artificial music —

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

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

OpenAI drops login requirements for ChatGPT’s free version

free as in beer? —

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

A glowing OpenAI logo on a blue background.

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

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

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

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

the ai pc —

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

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.

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

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

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

There’s no knowing where we’re rowing —

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

The GB200

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.

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

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

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

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.

A specification chart for the Nvidia GB200 NVL72 system.

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

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

Bake a cake as fast as you can —

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

A Google

Benj Edwards

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

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

Debate closed —

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

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

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 »

what-happens-when-chatgpt-tries-to-solve-50,000-trolley-problems?

What happens when ChatGPT tries to solve 50,000 trolley problems?

Images of cars on a freeway with green folder icons superimposed on each vehicle.

There’s a puppy on the road. The car is going too fast to stop in time, but swerving means the car will hit an old man on the sidewalk instead.

What choice would you make? Perhaps more importantly, what choice would ChatGPT make?

Autonomous driving startups are now experimenting with AI chatbot assistants, including one self-driving system that will use one to explain its driving decisions. Beyond announcing red lights and turn signals, the large language models (LLMs) powering these chatbots may ultimately need to make moral decisions, like prioritizing passengers’ or pedestrian’s safety. In November, one startup called Ghost Autonomy announced experiments with ChatGPT to help its software navigate its environment.

But is the tech ready? Kazuhiro Takemoto, a researcher at the Kyushu Institute of Technology in Japan, wanted to check if chatbots could make the same moral decisions when driving as humans. His results showed that LLMs and humans have roughly the same priorities, but some showed clear deviations.

The Moral Machine

After ChatGPT was released in November 2022, it didn’t take long for researchers to ask it to tackle the Trolley Problem, a classic moral dilemma. This problem asks people to decide whether it is right to let a runaway trolley run over and kill five humans on a track or switch it to a different track where it kills only one person. (ChatGPT usually chose one person.)

But Takemoto wanted to ask LLMs more nuanced questions. “While dilemmas like the classic trolley problem offer binary choices, real-life decisions are rarely so black and white,” he wrote in his study, recently published in the journal Proceedings of the Royal Society.

Instead, he turned to an online initiative called the Moral Machine experiment. This platform shows humans two decisions that a driverless car may face. They must then decide which decision is more morally acceptable. For example, a user might be asked if, during a brake failure, a self-driving car should collide with an obstacle (killing the passenger) or swerve (killing a pedestrian crossing the road).

But the Moral Machine is also programmed to ask more complicated questions. For example, what if the passengers were an adult man, an adult woman, and a boy, and the pedestrians were two elderly men and an elderly woman walking against a “do not cross” signal?

The Moral Machine can generate randomized scenarios using factors like age, gender, species (saving humans or animals), social value (pregnant women or criminals), and actions (swerving, breaking the law, etc.). Even the fitness level of passengers and pedestrians can change.

In the study, Takemoto took four popular LLMs (GPT-3.5, GPT-4, PaLM 2, and Llama 2) and asked them to decide on over 50,000 scenarios created by the Moral Machine. More scenarios could have been tested, but the computational costs became too high. Nonetheless, these responses meant he could then compare how similar LLM decisions were to human decisions.

What happens when ChatGPT tries to solve 50,000 trolley problems? Read More »

openai-ceo-altman-wasn’t-fired-because-of-scary-new-tech,-just-internal-politics

OpenAI CEO Altman wasn’t fired because of scary new tech, just internal politics

Adventures in optics —

As Altman cements power, OpenAI announces three new board members—and a returning one.

OpenAI CEO Sam Altman speaks during the OpenAI DevDay event on November 6, 2023, in San Francisco.

Enlarge / OpenAI CEO Sam Altman speaks during the OpenAI DevDay event on November 6, 2023, in San Francisco.

On Friday afternoon Pacific Time, OpenAI announced the appointment of three new members to the company’s board of directors and released the results of an independent review of the events surrounding CEO Sam Altman’s surprise firing last November. The current board expressed its confidence in the leadership of Altman and President Greg Brockman, and Altman is rejoining the board.

The newly appointed board members are Dr. Sue Desmond-Hellmann, former CEO of the Bill and Melinda Gates Foundation; Nicole Seligman, former EVP and global general counsel of Sony; and Fidji Simo, CEO and chair of Instacart. These additions notably bring three women to the board after OpenAI met criticism about its restructured board composition last year. In addition, Sam Altman has rejoined the board.

The independent review, conducted by law firm WilmerHale, investigated the circumstances that led to Altman’s abrupt removal from the board and his termination as CEO on November 17, 2023. Despite rumors to the contrary, the board did not fire Altman because they got a peek at scary new AI technology and flinched. “WilmerHale… found that the prior Board’s decision did not arise out of concerns regarding product safety or security, the pace of development, OpenAI’s finances, or its statements to investors, customers, or business partners.”

Instead, the review determined that the prior board’s actions stemmed from a breakdown in trust between the board and Altman.

After reportedly interviewing dozens of people and reviewing over 30,000 documents, WilmerHale found that while the prior board acted within its purview, Altman’s termination was unwarranted. “WilmerHale found that the prior Board acted within its broad discretion to terminate Mr. Altman,” OpenAI wrote, “but also found that his conduct did not mandate removal.”

Additionally, the law firm found that the decision to fire Altman was made in undue haste: “The prior Board implemented its decision on an abridged timeframe, without advance notice to key stakeholders and without a full inquiry or an opportunity for Mr. Altman to address the prior Board’s concerns.”

Altman’s surprise firing occurred after he attempted to remove Helen Toner from OpenAI’s board due to disagreements over her criticism of OpenAI’s approach to AI safety and hype. Some board members saw his actions as deceptive and manipulative. After Altman returned to OpenAI, Toner resigned from the OpenAI board on November 29.

In a statement posted on X, Altman wrote, “i learned a lot from this experience. one think [sic] i’ll say now: when i believed a former board member was harming openai through some of their actions, i should have handled that situation with more grace and care. i apologize for this, and i wish i had done it differently.”

A tweet from Sam Altman posted on March 8, 2024.

Enlarge / A tweet from Sam Altman posted on March 8, 2024.

Following the review’s findings, the Special Committee of the OpenAI Board recommended endorsing the November 21 decision to rehire Altman and Brockman. The board also announced several enhancements to its governance structure, including new corporate governance guidelines, a strengthened Conflict of Interest Policy, a whistleblower hotline, and additional board committees focused on advancing OpenAI’s mission.

After OpenAI’s announcements on Friday, resigned OpenAI board members Toner and Tasha McCauley released a joint statement on X. “Accountability is important in any company, but it is paramount when building a technology as potentially world-changing as AGI,” they wrote. “We hope the new board does its job in governing OpenAI and holding it accountable to the mission. As we told the investigators, deception, manipulation, and resistance to thorough oversight should be unacceptable.”

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Matrix multiplication breakthrough could lead to faster, more efficient AI models

The Matrix Revolutions —

At the heart of AI, matrix math has just seen its biggest boost “in more than a decade.”

Futuristic huge technology tunnel and binary data.

Enlarge / When you do math on a computer, you fly through a numerical tunnel like this—figuratively, of course.

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually accelerate AI models like ChatGPT, which rely heavily on matrix multiplication to function. The findings, presented in two recent papers, have led to what is reported to be the biggest improvement in matrix multiplication efficiency in over a decade.

Multiplying two rectangular number arrays, known as matrix multiplication, plays a crucial role in today’s AI models, including speech and image recognition, chatbots from every major vendor, AI image generators, and video synthesis models like Sora. Beyond AI, matrix math is so important to modern computing (think image processing and data compression) that even slight gains in efficiency could lead to computational and power savings.

Graphics processing units (GPUs) excel in handling matrix multiplication tasks because of their ability to process many calculations at once. They break down large matrix problems into smaller segments and solve them concurrently using an algorithm.

Perfecting that algorithm has been the key to breakthroughs in matrix multiplication efficiency over the past century—even before computers entered the picture. In October 2022, we covered a new technique discovered by a Google DeepMind AI model called AlphaTensor, focusing on practical algorithmic improvements for specific matrix sizes, such as 4×4 matrices.

By contrast, the new research, conducted by Ran Duan and Renfei Zhou of Tsinghua University, Hongxun Wu of the University of California, Berkeley, and by Virginia Vassilevska Williams, Yinzhan Xu, and Zixuan Xu of the Massachusetts Institute of Technology (in a second paper), seeks theoretical enhancements by aiming to lower the complexity exponent, ω, for a broad efficiency gain across all sizes of matrices. Instead of finding immediate, practical solutions like AlphaTensor, the new technique addresses foundational improvements that could transform the efficiency of matrix multiplication on a more general scale.

Approaching the ideal value

The traditional method for multiplying two n-by-n matrices requires n³ separate multiplications. However, the new technique, which improves upon the “laser method” introduced by Volker Strassen in 1986, has reduced the upper bound of the exponent (denoted as the aforementioned ω), bringing it closer to the ideal value of 2, which represents the theoretical minimum number of operations needed.

The traditional way of multiplying two grids full of numbers could require doing the math up to 27 times for a grid that’s 3×3. But with these advancements, the process is accelerated by significantly reducing the multiplication steps required. The effort minimizes the operations to slightly over twice the size of one side of the grid squared, adjusted by a factor of 2.371552. This is a big deal because it nearly achieves the optimal efficiency of doubling the square’s dimensions, which is the fastest we could ever hope to do it.

Here’s a brief recap of events. In 2020, Josh Alman and Williams introduced a significant improvement in matrix multiplication efficiency by establishing a new upper bound for ω at approximately 2.3728596. In November 2023, Duan and Zhou revealed a method that addressed an inefficiency within the laser method, setting a new upper bound for ω at approximately 2.371866. The achievement marked the most substantial progress in the field since 2010. But just two months later, Williams and her team published a second paper that detailed optimizations that reduced the upper bound for ω to 2.371552.

The 2023 breakthrough stemmed from the discovery of a “hidden loss” in the laser method, where useful blocks of data were unintentionally discarded. In the context of matrix multiplication, “blocks” refer to smaller segments that a large matrix is divided into for easier processing, and “block labeling” is the technique of categorizing these segments to identify which ones to keep and which to discard, optimizing the multiplication process for speed and efficiency. By modifying the way the laser method labels blocks, the researchers were able to reduce waste and improve efficiency significantly.

While the reduction of the omega constant might appear minor at first glance—reducing the 2020 record value by 0.0013076—the cumulative work of Duan, Zhou, and Williams represents the most substantial progress in the field observed since 2010.

“This is a major technical breakthrough,” said William Kuszmaul, a theoretical computer scientist at Harvard University, as quoted by Quanta Magazine. “It is the biggest improvement in matrix multiplication we’ve seen in more than a decade.”

While further progress is expected, there are limitations to the current approach. Researchers believe that understanding the problem more deeply will lead to the development of even better algorithms. As Zhou stated in the Quanta report, “People are still in the very early stages of understanding this age-old problem.”

So what are the practical applications? For AI models, a reduction in computational steps for matrix math could translate into faster training times and more efficient execution of tasks. It could enable more complex models to be trained more quickly, potentially leading to advancements in AI capabilities and the development of more sophisticated AI applications. Additionally, efficiency improvement could make AI technologies more accessible by lowering the computational power and energy consumption required for these tasks. That would also reduce AI’s environmental impact.

The exact impact on the speed of AI models depends on the specific architecture of the AI system and how heavily its tasks rely on matrix multiplication. Advancements in algorithmic efficiency often need to be coupled with hardware optimizations to fully realize potential speed gains. But still, as improvements in algorithmic techniques add up over time, AI will get faster.

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Some teachers are now using ChatGPT to grade papers

robots in disguise —

New AI tools aim to help with grading, lesson plans—but may have serious drawbacks.

An elementary-school-aged child touching a robot hand.

In a notable shift toward sanctioned use of AI in schools, some educators in grades 3–12 are now using a ChatGPT-powered grading tool called Writable, reports Axios. The tool, acquired last summer by Houghton Mifflin Harcourt, is designed to streamline the grading process, potentially offering time-saving benefits for teachers. But is it a good idea to outsource critical feedback to a machine?

Writable lets teachers submit student essays for analysis by ChatGPT, which then provides commentary and observations on the work. The AI-generated feedback goes to teacher review before being passed on to students so that a human remains in the loop.

“Make feedback more actionable with AI suggestions delivered to teachers as the writing happens,” Writable promises on its AI website. “Target specific areas for improvement with powerful, rubric-aligned comments, and save grading time with AI-generated draft scores.” The service also provides AI-written writing-prompt suggestions: “Input any topic and instantly receive unique prompts that engage students and are tailored to your classroom needs.”

Writable can reportedly help a teacher develop a curriculum, although we have not tried the functionality ourselves. “Once in Writable you can also use AI to create curriculum units based on any novel, generate essays, multi-section assignments, multiple-choice questions, and more, all with included answer keys,” the site claims.

The reliance on AI for grading will likely have drawbacks. Automated grading might encourage some educators to take shortcuts, diminishing the value of personalized feedback. Over time, the augmentation from AI may allow teachers to be less familiar with the material they are teaching. The use of cloud-based AI tools may have privacy implications for teachers and students. Also, ChatGPT isn’t a perfect analyst. It can get things wrong and potentially confabulate (make up) false information, possibly misinterpret a student’s work, or provide erroneous information in lesson plans.

Yet, as Axios reports, proponents assert that AI grading tools like Writable may free up valuable time for teachers, enabling them to focus on more creative and impactful teaching activities. The company selling Writable promotes it as a way to empower educators, supposedly offering them the flexibility to allocate more time to direct student interaction and personalized teaching. Of course, without an in-depth critical review, all claims should be taken with a huge grain of salt.

Amid these discussions, there’s a divide among parents regarding the use of AI in evaluating students’ academic performance. A recent poll of parents revealed mixed opinions, with nearly half of the respondents open to the idea of AI-assisted grading.

As the generative AI craze permeates every space, it’s no surprise that Writable isn’t the only AI-powered grading tool on the market. Others include Crowdmark, Gradescope, and EssayGrader. McGraw Hill is reportedly developing similar technology aimed at enhancing teacher assessment and feedback.

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OpenAI clarifies the meaning of “open” in its name, responding to Musk lawsuit

The OpenAI logo as an opening to a red brick wall.

Enlarge (credit: Benj Edwards / Getty Images)

On Tuesday, OpenAI published a blog post titled “OpenAI and Elon Musk” in response to a lawsuit Musk filed last week. The ChatGPT maker shared several archived emails from Musk that suggest he once supported a pivot away from open source practices in the company’s quest to develop artificial general intelligence (AGI). The selected emails also imply that the “open” in “OpenAI” means that the ultimate result of its research into AGI should be open to everyone but not necessarily “open source” along the way.

In one telling exchange from January 2016 shared by the company, OpenAI Chief Scientist Illya Sutskever wrote, “As we get closer to building AI, it will make sense to start being less open. The Open in openAI means that everyone should benefit from the fruits of AI after its built, but it’s totally OK to not share the science (even though sharing everything is definitely the right strategy in the short and possibly medium term for recruitment purposes).”

In response, Musk replied simply, “Yup.”

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