Reuters

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Music industry giants allege mass copyright violation by AI firms

No one wants to be defeated —

Suno and Udio could face damages of up to $150,000 per song allegedly infringed.

Michael Jackson in concert, 1986. Sony Music owns a large portion of publishing rights to Jackson's music.

Enlarge / Michael Jackson in concert, 1986. Sony Music owns a large portion of publishing rights to Jackson’s music.

Universal Music Group, Sony Music, and Warner Records have sued AI music-synthesis companies Udio and Suno for allegedly committing mass copyright infringement by using recordings owned by the labels to train music-generating AI models, reports Reuters. Udio and Suno can generate novel song recordings based on text-based descriptions of music (i.e., “a dubstep song about Linus Torvalds”).

The lawsuits, filed in federal courts in New York and Massachusetts, claim that the AI companies’ use of copyrighted material to train their systems could lead to AI-generated music that directly competes with and potentially devalues the work of human artists.

Like other generative AI models, both Udio and Suno (which we covered separately in April) rely on a broad selection of existing human-created artworks that teach a neural network the relationship between words in a written prompt and styles of music. The record labels correctly note that these companies have been deliberately vague about the sources of their training data.

Until generative AI models hit the mainstream in 2022, it was common practice in machine learning to scrape and use copyrighted information without seeking permission to do so. But now that the applications of those technologies have become commercial products themselves, rightsholders have come knocking to collect. In the case of Udio and Suno, the record labels are seeking statutory damages of up to $150,000 per song used in training.

In the lawsuit, the record labels cite specific examples of AI-generated content that allegedly re-creates elements of well-known songs, including The Temptations’ “My Girl,” Mariah Carey’s “All I Want for Christmas Is You,” and James Brown’s “I Got You (I Feel Good).” It also claims the music-synthesis models can produce vocals resembling those of famous artists, such as Michael Jackson and Bruce Springsteen.

Reuters claims it’s the first instance of lawsuits specifically targeting music-generating AI, but music companies and artists alike have been gearing up to deal with challenges the technology may pose for some time.

In May, Sony Music sent warning letters to over 700 AI companies (including OpenAI, Microsoft, Google, Suno, and Udio) and music-streaming services that prohibited any AI researchers from using its music to train AI models. In April, over 200 musical artists signed an open letter that called on AI companies to stop using AI to “devalue the rights of human artists.” And last November, Universal Music filed a copyright infringement lawsuit against Anthropic for allegedly including artists’ lyrics in its Claude LLM training data.

Similar to The New York Times’ lawsuit against OpenAI over the use of training data, the outcome of the record labels’ new suit could have deep implications for the future development of generative AI in creative fields, including requiring companies to license all musical training data used in creating music-synthesis models.

Compulsory licenses for AI training data could make AI model development economically impractical for small startups like Udio and Suno—and judging by the aforementioned open letter, many musical artists may applaud that potential outcome. But such a development would not preclude major labels from eventually developing their own AI music generators themselves, allowing only large corporations with deep pockets to control generative music tools for the foreseeable future.

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Turkish student creates custom AI device for cheating university exam, gets arrested

spy hard —

Elaborate scheme involved hidden camera and an earpiece to hear answers.

A photo illustration of what a shirt-button camera <em>could</em> look like. ” src=”https://cdn.arstechnica.net/wp-content/uploads/2024/06/shirt-button-camera-800×450.jpg”></img><figcaption>
<p><a data-height=Enlarge / A photo illustration of what a shirt-button camera could look like.

Aurich Lawson | Getty Images

On Saturday, Turkish police arrested and detained a prospective university student who is accused of developing an elaborate scheme to use AI and hidden devices to help him cheat on an important entrance exam, reports Reuters and The Daily Mail.

The unnamed student is reportedly jailed pending trial after the incident, which took place in the southwestern province of Isparta, where the student was caught behaving suspiciously during the TYT. The TYT is a nationally held university aptitude exam that determines a person’s eligibility to attend a university in Turkey—and cheating on the high-stakes exam is a serious offense.

According to police reports, the student used a camera disguised as a shirt button, connected to AI software via a “router” (possibly a mistranslation of a cellular modem) hidden in the sole of their shoe. The system worked by scanning the exam questions using the button camera, which then relayed the information to an unnamed AI model. The software generated the correct answers and recited them to the student through an earpiece.

A video released by the Isparta police demonstrated how the cheating system functioned. In the video, a police officer scans a question, and the AI software provides the correct answer through the earpiece.

In addition to the student, Turkish police detained another individual for assisting the student during the exam. The police discovered a mobile phone that could allegedly relay spoken sounds to the other person, allowing for two-way communication.

A history of calling on computers for help

The recent arrest recalls other attempts to cheat using wireless communications and computers, such as the famous case of the Eudaemons in the late 1970s. The Eudaemons were a group of physics graduate students from the University of California, Santa Cruz, who developed a wearable computer device designed to predict the outcome of roulette spins in casinos.

The Eudaemons’ device consisted of a shoe with a computer built into it, connected to a timing device operated by the wearer’s big toe. The wearer would click the timer when the ball and the spinning roulette wheel were in a specific position, and the computer would calculate the most likely section of the wheel where the ball would land. This prediction would be transmitted to an earpiece worn by another team member, who would quickly place bets on the predicted section.

While the Eudaemons’ plan didn’t involve a university exam, it shows that the urge to call upon remote computational powers greater than oneself is apparently timeless.

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Deepfakes in the courtroom: US judicial panel debates new AI evidence rules

adventures in 21st-century justice —

Panel of eight judges confronts deep-faking AI tech that may undermine legal trials.

An illustration of a man with a very long nose holding up the scales of justice.

On Friday, a federal judicial panel convened in Washington, DC, to discuss the challenges of policing AI-generated evidence in court trials, according to a Reuters report. The US Judicial Conference’s Advisory Committee on Evidence Rules, an eight-member panel responsible for drafting evidence-related amendments to the Federal Rules of Evidence, heard from computer scientists and academics about the potential risks of AI being used to manipulate images and videos or create deepfakes that could disrupt a trial.

The meeting took place amid broader efforts by federal and state courts nationwide to address the rise of generative AI models (such as those that power OpenAI’s ChatGPT or Stability AI’s Stable Diffusion), which can be trained on large datasets with the aim of producing realistic text, images, audio, or videos.

In the published 358-page agenda for the meeting, the committee offers up this definition of a deepfake and the problems AI-generated media may pose in legal trials:

A deepfake is an inauthentic audiovisual presentation prepared by software programs using artificial intelligence. Of course, photos and videos have always been subject to forgery, but developments in AI make deepfakes much more difficult to detect. Software for creating deepfakes is already freely available online and fairly easy for anyone to use. As the software’s usability and the videos’ apparent genuineness keep improving over time, it will become harder for computer systems, much less lay jurors, to tell real from fake.

During Friday’s three-hour hearing, the panel wrestled with the question of whether existing rules, which predate the rise of generative AI, are sufficient to ensure the reliability and authenticity of evidence presented in court.

Some judges on the panel, such as US Circuit Judge Richard Sullivan and US District Judge Valerie Caproni, reportedly expressed skepticism about the urgency of the issue, noting that there have been few instances so far of judges being asked to exclude AI-generated evidence.

“I’m not sure that this is the crisis that it’s been painted as, and I’m not sure that judges don’t have the tools already to deal with this,” said Judge Sullivan, as quoted by Reuters.

Last year, Chief US Supreme Court Justice John Roberts acknowledged the potential benefits of AI for litigants and judges, while emphasizing the need for the judiciary to consider its proper uses in litigation. US District Judge Patrick Schiltz, the evidence committee’s chair, said that determining how the judiciary can best react to AI is one of Roberts’ priorities.

In Friday’s meeting, the committee considered several deepfake-related rule changes. In the agenda for the meeting, US District Judge Paul Grimm and attorney Maura Grossman proposed modifying Federal Rule 901(b)(9) (see page 5), which involves authenticating or identifying evidence. They also recommended the addition of a new rule, 901(c), which might read:

901(c): Potentially Fabricated or Altered Electronic Evidence. If a party challenging the authenticity of computer-generated or other electronic evidence demonstrates to the court that it is more likely than not either fabricated, or altered in whole or in part, the evidence is admissible only if the proponent demonstrates that its probative value outweighs its prejudicial effect on the party challenging the evidence.

The panel agreed during the meeting that this proposal to address concerns about litigants challenging evidence as deepfakes did not work as written and that it will be reworked before being reconsidered later.

Another proposal by Andrea Roth, a law professor at the University of California, Berkeley, suggested subjecting machine-generated evidence to the same reliability requirements as expert witnesses. However, Judge Schiltz cautioned that such a rule could hamper prosecutions by allowing defense lawyers to challenge any digital evidence without establishing a reason to question it.

For now, no definitive rule changes have been made, and the process continues. But we’re witnessing the first steps of how the US justice system will adapt to an entirely new class of media-generating technology.

Putting aside risks from AI-generated evidence, generative AI has led to embarrassing moments for lawyers in court over the past two years. In May 2023, US lawyer Steven Schwartz of the firm Levidow, Levidow, & Oberman apologized to a judge for using ChatGPT to help write court filings that inaccurately cited six nonexistent cases, leading to serious questions about the reliability of AI in legal research. Also, in November, a lawyer for Michael Cohen cited three fake cases that were potentially influenced by a confabulating AI assistant.

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World’s first global AI resolution unanimously adopted by United Nations

We hold these seeds to be self-evident —

Nonbinding agreement seeks to protect personal data and safeguard human rights.

The United Nations building in New York.

Enlarge / The United Nations building in New York.

On Thursday, the United Nations General Assembly unanimously consented to adopt what some call the first global resolution on AI, reports Reuters. The resolution aims to foster the protection of personal data, enhance privacy policies, ensure close monitoring of AI for potential risks, and uphold human rights. It emerged from a proposal by the United States and received backing from China and 121 other countries.

Being a nonbinding agreement and thus effectively toothless, the resolution seems broadly popular in the AI industry. On X, Microsoft Vice Chair and President Brad Smith wrote, “We fully support the @UN’s adoption of the comprehensive AI resolution. The consensus reached today marks a critical step towards establishing international guardrails for the ethical and sustainable development of AI, ensuring this technology serves the needs of everyone.”

The resolution, titled “Seizing the opportunities of safe, secure and trustworthy artificial intelligence systems for sustainable development,” resulted from three months of negotiation, and the stakeholders involved seem pleased at the level of international cooperation. “We’re sailing in choppy waters with the fast-changing technology, which means that it’s more important than ever to steer by the light of our values,” one senior US administration official told Reuters, highlighting the significance of this “first-ever truly global consensus document on AI.”

In the UN, adoption by consensus means that all members agree to adopt the resolution without a vote. “Consensus is reached when all Member States agree on a text, but it does not mean that they all agree on every element of a draft document,” writes the UN in a FAQ found online. “They can agree to adopt a draft resolution without a vote, but still have reservations about certain parts of the text.”

The initiative joins a series of efforts by governments worldwide to influence the trajectory of AI development following the launch of ChatGPT and GPT-4, and the enormous hype raised by certain members of the tech industry in a public worldwide campaign waged last year. Critics fear that AI may undermine democratic processes, amplify fraudulent activities, or contribute to significant job displacement, among other issues. The resolution seeks to address the dangers associated with the irresponsible or malicious application of AI systems, which the UN says could jeopardize human rights and fundamental freedoms.

Resistance from nations such as Russia and China was anticipated, and US officials acknowledged the presence of “lots of heated conversations” during the negotiation process, according to Reuters. However, they also emphasized successful engagement with these countries and others typically at odds with the US on various issues, agreeing on a draft resolution that sought to maintain a delicate balance between promoting development and safeguarding human rights.

The new UN agreement may be the first “global” agreement, in the sense of having the participation of every UN country, but it wasn’t the first multi-state international AI agreement. That honor seems to fall to the Bletchley Declaration signed in November by the 28 nations attending the UK’s first AI Summit.

Also in November, the US, Britain, and other nations unveiled an agreement focusing on the creation of AI systems that are “secure by design” to protect against misuse by rogue actors. Europe is slowly moving forward with provisional agreements to regulate AI and is close to implementing the world’s first comprehensive AI regulations. Meanwhile, the US government still lacks consensus on legislative action related to AI regulation, with the Biden administration advocating for measures to mitigate AI risks while enhancing national security.

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