The hack exposed the data of 31 million users as the embattled Wayback Machine maker scrambles to stay online and contain the fallout of digital—and legal—attacks.
The US Department of Justice (DoJ) has submitted a new “Proposed Remedy Framework” to correct Google’s violation of antitrust antitrust laws in the country (h/t Mishaal Rahman). This framework seeks to remedy the harm caused by Google’s search distribution and revenue sharing, generation and display for search results, advertising scale and monetization, and accumulation and use of data.
The most drastic of the proposed solutions includes preventing Google from using its products, such as Chrome, Play, and Android, to advantage Google Search and related products. Other solutions include allowing websites to opt-out of training or appearing in Google-owned AI products, such as in AI Overviews in Google Search.
Google responded to this by asserting that “DOJ’s radical and sweeping proposals risk hurting consumers, businesses, and developers.” While the company intends to respond in detail to DoJ’s final proposals, it says that the DoJ is “already signaling requests that go far beyond the specific legal issues in this case.”
Two of San Francisco’s leading players in artificial intelligence have challenged the public to come up with questions capable of testing the capabilities of large language models (LLMs) like Google Gemini and OpenAI’s o1. Scale AI, which specializes in preparing the vast tracts of data on which the LLMs are trained, teamed up with the Center for AI Safety (CAIS) to launch the initiative, Humanity’s Last Exam.
Featuring prizes of US$5,000 (£3,800) for those who come up with the top 50 questions selected for the test, Scale and CAIS say the goal is to test how close we are to achieving “expert-level AI systems” using the “largest, broadest coalition of experts in history.”
Why do this? The leading LLMs are already acing many established tests in intelligence, mathematics and law, but it’s hard to be sure how meaningful this is. In many cases, they may have pre-learned the answers due to the gargantuan quantities of data on which they are trained, including a significant percentage of everything on the internet.
The precise geometry of the protected area encompassing an iconic New Zealand volcano, Mount Taranaki, is unmistakable from space, highlighting its status as New Zealand’s second national park.
This conical, often snow-capped volcano not only captivates with its natural beauty but also serves as a critical area for scientific research due to its unstable geological history and ongoing volcanic threats. In 2017, Mount Taranaki was granted the same legal rights as a person, emphasizing its profound cultural significance to the Indigenous Māori people.
Mount Taranaki
The large language models that have increasingly taken over the tech world are not “cheap” in many ways. The most prominent LLMs, such as GPT-4, took some $100 million to build in the form of legal costs of accessing training data, computational power costs for what could be billions or trillions of parameters, the energy and water needed to fuel computation, and the many coders developing the training algorithms that must run cycle after cycle so the machine will “learn.”
But, if a researcher needs to do a specialized task that a machine could do more efficiently and they don’t have access to a large institution that offers access to generative AI tools, what other options are available? Say, a parent wants to prep their child for a difficult test and needs to show many examples of how to solve complicated math problems.
Building their own LLM is an onerous prospect for costs mentioned above, and making direct use of the big models like GPT-4 and Llama 3.1 might not immediately be suited for the complex reasoning in logic and math their task requires.
The Pennsylvania State University in May blocked a prominent professor at the school from doing research and making presentations on its behalf, Retraction Watch has learned.
The professor, Deborah Kelly, has faced mounting scrutiny over her work since a researcher in the United Kingdom noticed apparent data manipulation in a now-retracted article she published in 2017. Kelly earned her third retraction last week following a university probe that found “serious data integrity concerns” in another paper, as we reported at the time.
In comments she made via her legal counsel for that story, Kelly, a biomedical engineer and an expert in electron microscopy, told us:
In science fiction movies like Frankenstein and Re-Animator, human bodies are revived, existing in a strange state between life and death. While this may seem like pure fantasy, a recent study suggests that a “third state” of existence might actually be present in modern biology.
According to the researchers, this third state occurs when the cells of a dead organism continue to function after its death, sometimes gaining new capabilities they never had while the organism was alive.
Amazingly, if further experiments on cells from dead animals — including humans — prove this ability, it could even challenge the definition of legal death.
AI can enhance legal efficiency and productivity, but it cannot replace the essential human skills, judgment, and personal involvement required in law practice.
Questions to inspire discussion.
Continue reading “Can Artificial Intelligence Replace Your Lawyer?” »
The Internet Archive has lost a major legal battle—in a decision that could have a significant impact on the future of internet history. Today, the US Court of Appeals for the Second Circuit ruled against the long-running digital archive, upholding an earlier ruling in Hachette v. Internet Archive that found that one of the Internet Archive’s book digitization projects violated copyright law.
Notably, the appeals court’s ruling rejects the Internet Archive’s argument that its lending practices were shielded by the fair use doctrine, which permits for copyright infringement in certain circumstances, calling it “unpersuasive.”
In March 2020, the Internet Archive, a San Francisco-based nonprofit, launched a program called the National Emergency Library, or NEL. Library closures caused by the pandemic had left students, researchers, and readers unable to access millions of books, and the Internet Archive has said it was responding to calls from regular people and other librarians to help those at home get access to the books they needed.
This article delves into a transformative shift in the criminal justice system brought on by the use of AI-assisted police reports.
Police reports play a central role in the criminal justice system. Many times, police reports exist as the only official memorialization of what happened during an incident, shaping probable cause determinations, pretrial detention decisions, motions to suppress, plea bargains, and trial strategy. For over a century, human police officers wrote the factual narratives that shaped the trajectory of individual cases and organized the entire legal system.
All that is about to change with the creation of AI-assisted police reports. Today, with the click of a button, generative AI Large Language Models (LLMS) using predictive text capabilities can turn the audio feed of a police-worn body camera into a pre-written draft police report. Police officers then fill-in-the blanks of inserts and details like a “Mad Libs” of suspicion and submit the edited version as the official narrative of an incident.
Continue reading “AI-Assisted Police Reports and the Challenge of Generative Suspicion” »
