OpenAI has hit back at Elon Musk's lawsuit accusing it of betraying its altruistic roots, claiming the Tesla chief executive had in fact supported the artificial intelligence company's plans to create a for-profit unit. Executives at the ChatGPT maker released a blogpost containing what it claimed was historical email correspondence with Musk in which the entrepreneur suggested merging the San Francisco-based startup with Tesla and said it should attach to the electric carmaker "as its cash cow". The blog, authored by OpenAI executives including its chief executive, Sam Altman, claims that in 2017 "we and Elon decided the next step for the mission was to create a for-profit entity". Last week Musk filed a lawsuit accusing OpenAI, where he was a founding board member, of deviating from its foundational mission by forming a for-profit unit – and putting making money before its core aim of producing technology for the benefit of humanity. "We're sad that it's come to this with someone whom we've deeply admired – someone who inspired us to aim higher, then told us we would fail, started a competitor, and then sued us when we started making meaningful progress towards OpenAI's mission without him," said OpenAI.

Elon Musk, who sued OpenAI for violating its non-profit mission and chasing profits, allegedly wanted the organization to merge with Tesla when it was starting to plan its transition into a for-profit entity in order to accomplish its goals. Well, either that or get full control of the company, OpenAI said in a blog post. The organization responded to Musk's lawsuit by publishing old emails from 2015 to 2018 when he was still involved in its operations. When OpenAI introduced itself to the world back in 2015, it announced that it had 1 billion in funding. Apparently, Musk was the one who suggested that figure, even though OpenAI had raised less than 45 million from him and around 90 million from other donors.

Extensive research has been conducted on predicting air pollution concentrations using various modelling frameworks [1, 2, 3, 4, 5, 6, 7, 8, 9]. However, leveraging air pollution concentration data should not be seen as a unilateral process where predictions are simply delivered to stakeholders without further engagement. Instead, an iterative approach that considers the practical use and outcomes of these predictions is crucial for refining and directing future research concerning air pollution. In response to this need, our work introduces Environmental Insights, an open-source Python package designed to facilitate active engagement with air pollution issues. This package enables stakeholders to download, analyse, and visualise air pollution concentration data, thereby offering a unified platform for exploring potential air pollution futures. Environmental Insights aims to disseminate and democratise access to air pollution data, breaking down barriers for individuals and communities without extensive resources or technical expertise. By empowering a broader audience to engage with air pollution data, the package also seeks to amplify public pressure on policymakers for meaningful air quality improvements in areas of significant concern to the community.

As we keep rapidly advancing toward an era where artificial intelligence is a constant and normative experience for most of us, we must also be aware of what this vision and this progress entail. By first approximating neural connections and activities in computer circuits and then creating more and more sophisticated versions of this crude approximation, we are now facing an age to come where modern deep learning-based artificial intelligence systems can rightly be called thinking machines, and they are sometimes even lauded for their emergent behavior and black-box approaches. But as we create more powerful electronic brains, with billions of neural connections and parameters, can we guarantee that these mammoths built of artificial neurons will be able to forget the data that we store in them? If they are at some level like a brain, can the right to be forgotten still be protected while dealing with these AIs? The essential gap between machine learning and the RTBF is explored in this article, with a premonition of far-reaching conclusions if the gap is not bridged or reconciled any time soon. The core argument is that deep learning models, due to their structure and size, cannot be expected to forget or delete a data as it would be expected from a tabular database, and they should be treated more like a mechanical brain, albeit still in development.

Advanced language-processing and machine-learning techniques promise massive efficiency improvements in the previously widely manual field of patent and technical knowledge management. This field presents large-scale and complex data with very precise contents and language representation of those contents. Particularly, patent texts can differ from mundane texts in various aspects, which entails significant opportunities and challenges. This paper presents a systematic overview of patent-related tasks and popular methodologies with a special focus on evolving and promising techniques. Language processing and particularly large language models as well as the recent boost of general generative methods promise to become game changers in the patent field. The patent literature and the fact-based argumentative procedures around patents appear almost as an ideal use case. However, patents entail a number of difficulties with which existing models struggle. The paper introduces fundamental aspects of patents and patent-related data that affect technology that wants to explore or manage them. It further reviews existing methods and approaches and points out how important reliable and unbiased evaluation metrics become. Although research has made substantial progress on certain tasks, the performance across many others remains suboptimal, sometimes because of either the special nature of patents and their language or inconsistencies between legal terms and the everyday meaning of terms. Moreover, yet few methods have demonstrated the ability to produce satisfactory text for specific sections of patents. By pointing out key developments, opportunities, and gaps, we aim to encourage further research and accelerate the advancement of this field.

As the world grapples with how to regulate artificial intelligence, Washington faces a unique dilemma: how to secure America's position as the global AI leader, while guarding against AI's possible risks? Although any country seeking to regulate AI must balance regulation and innovation, this task is especially hard for the United States because we have more to lose. The United Kingdom, European Union, and China all have formidable AI companies, but U.S. firms dominate the field, propelled by our uniquely open innovation ecosystem. This dominance was on display recently, which saw OpenAI release Sora, a powerful new text-to-video platform, and Google introduce Gemini 1.5, its next-generation AI model that can absorb requests more than 30 times the size of its predecessor. If these trends continue, and AI proves the game-changer that many expect--surrendering U.S. leadership is not an option.

AI expert Marva Bailer explains how Will.i.am's app, FYI, powers his AI co-host for his radio show and why the platform has different capabilities than ChatGPT While artificial intelligence made headlines with ChatGPT, behind the scenes, the technology has quietly pervaded everyday life -- screening job resumes, rental apartment applications, and even determining medical care in some cases. While a number of AI systems have been found to discriminate, tipping the scales in favor of certain races, genders or incomes, there's scant government oversight. Lawmakers in at least seven states are taking big legislative swings to regulate bias in artificial intelligence, filling a void left by Congress' inaction. These proposals are some of the first steps in a decades-long discussion over balancing the benefits of this nebulous new technology with the widely documented risks. "AI does in fact affect every part of your life whether you know it or not," said Suresh Venkatasubramanian, a Brown University professor who co-authored the White House's Blueprint for an AI Bill of Rights.

If you'll recall, The Times sued both companies for using its published articles to train their GPT large language models (LLMs) without permission and compensation. In its filing, the company has accused The Times of pushing "doomsday futurology" by claiming that AI technologies pose a threat to independent journalism. It follows OpenAI's court filing from late February that's also seeking to dismiss some important elements on the case. Like OpenAI before it, Microsoft accused The Times of crafting "unrealistic prompts" in an effort to "coax the GPT-based tools" to spit out responses matching its content. It also compared the media organization's lawsuit to Hollywood studios' efforts to " stop a groundbreaking new technology:" The VCR. Instead of destroying Hollywood, Microsoft explained, the VCR helped the entertainment industry flourish by opening up revenue streams.

CROSSING THE THRESHOLD – Super Tuesday results expected to move Biden, Trump much closer to an all-but-certain general election rematch. GET A GRIP – Speaker slams House Dems as they attempt to reverse SCOTUS order keeping Trump on ballot. 'YOU'RE LYING!' – AOC loses it on pro-Palestinian protesters confronting her at movie theater: 'It's f---ed up.' Continue reading … BORDER BATTLE – SCOTUS weighs in on Texas law that allows police to arrest, detain illegal migrants. RED TAPE – Congressman proposes using AI to cut down on government regulations. LOW BATTERY – GOP senator targets forced electric vehicle rentals.

FIRST ON FOX: House Rep. Andy Biggs is eyeing artificial intelligence (AI) technology as a way to cut unnecessary government red tape. The Arizona Republican is introducing a bill on Tuesday that would mandate federal agencies use AI to review regulations under their purview with the aim of cutting rules that fail to meet certain standards. "American businesses must be given the opportunity to thrive without overbearing, costly, contradictory, and duplicative regulations mandated by the DC Swamp," Biggs told Fox News Digital. "Federal overregulation takes a colossal toll on the U.S. economy. Thousands of new regulations go into effect every year, and there simply isn't enough manpower or existing technology to sift through previously issued regulations. AI technology is an effective tool that can save taxpayer dollars, benefit American business owners, and promote economic growth."