Copilot is made by GitHub, a firm that runs an online software development platform used by more than 100 million programmers. The tool monitors every keystroke you make, predicts what you are trying to do on the fly, and offers up a nonstop stream of code snippets you could use to do it. Gift, who had been told about Copilot by someone he knew at GitHub's parent company, Microsoft, saw its potential at once. "There's no way I could have learned Rust as quickly as I did without Copilot," he says. "I basically had a supersmart assistant next to me that could answer my questions while I tried to level up. It was pretty obvious to me that we should start using it in class."

Elon Musk's artificial intelligence startup, xAI, is seeking to raise $1bn (£0.8bn) as the world's richest man tries to keep pace with rivals including OpenAI, Microsoft and Google in the race to dominate the field. The company has already raised $135m (£107m) from investors and is seeking a total of $1bn in equity financing, according to a filing with the US Securities and Exchange Commission. The race to develop generative AI – products that generate convincing text, image and audio from simple prompts – has intensified as Silicon Valley's biggest companies battle for supremacy after the release of OpenAI's ChatGPT in November last year. After the sensational impact of that chatbot, Microsoft announced a deepening of its partnership with OpenAI in January backed by a $10bn investment. Musk, the chief executive of Tesla and SpaceX and the owner of the X platform formerly known as Twitter, was one of OpenAI's co-founders in 2015 but left three years later. In July, Musk launched xAI and last month the company released its first AI model, a chatbot with a "rebellious streak" called Grok.

The European Union is set to thrash out an agreement on sweeping rules to regulate artificial intelligence on Wednesday, following months of difficult negotiations in particular on how to monitor generative AI tools like ChatGPT. The EU is racing to approve the world's first comprehensive AI law after the issue took on greater urgency when the ChatGPT bot burst onto the scene last year and highlighted AI's dizzying advances. ChatGPT wowed with its ability to produce poems and essays within seconds. AI proponents say the technology will benefit humanity, transforming everything from work to health care, but others worry about the risks it poses to society, fearing it could thrust the world into unprecedented chaos.

ABSTRACT As governments race to implement new climate adaptation policies that prepare for more frequent flooding, they must seek policies that are effective for all communities and uphold climate justice. This requires evaluating policies not only on their overall effectiveness but also on whether their benefits are felt across all communities. We illustrate the importance of considering such disparities for flood adaptation using the FEMA National Flood Insurance Program Community Rating System and its dataset of 2.5 million flood insurance claims. We use CausalFlow, a causal inference method based on deep generative models, to estimate the treatment effect of flood adaptation interventions based on a community's income, diversity, population, flood risk, educational attainment, and precipitation. We find that the program saves communities $5,000-15,000 per household. However, these savings are not evenly spread across communities. For example, for low-income communities savings sharply decline as flood-risk increases in contrast to their high-income counterparts with all else equal. Even among low-income communities, there is a gap in savings between predominantly white and non-white communities: savings of predominantly white communities can be higher by more than $6000 per household. As communities worldwide ramp up efforts to reduce losses inflicted by floods, simply prescribing a series flood adaptation measures is not enough. Programs must provide communities with the necessary technical and economic support to compensate for historical patterns of disenfranchisement, racism, and inequality. Future flood adaptation efforts should go beyond reducing losses overall and aim to close existing gaps to equitably support communities in the race for climate adaptation. INTRODUCTION Flooding constitutes nearly a third of all losses from natural disasters worldwide (Reuters 2022). By the end of the century, rising sea levels and coastal flooding are estimated to cost the global economy $14.2 trillion (a fifth of the global GDP) in damaged assets (Kirezci et al. 2020).

We introduce a system that allows users of Ableton Live to create MIDI-clips by naming them with musical descriptions. Users can compose by typing the desired musical content directly in Ableton's clip view, which is then inserted by our integrated system. This allows users to stay in the flow of their creative process while quickly generating musical ideas. The system works by prompting ChatGPT to reply using one of several text-based musical formats, such as ABC notation, chord symbols, or drum tablature. This is an important step in integrating generative AI tools into pre-existing musical workflows, and could be valuable for content makers who prefer to express their creative vision through descriptive language. Code is available at https://github.com/supersational/JAMMIN-GPT.

Recommender Systems are built to retrieve relevant items to satisfy users' information needs. The candidate corpus usually consists of a finite set of items that are ready to be served, such as videos, products, or articles. With recent advances in Generative AI such as GPT and Diffusion models, a new form of recommendation task is yet to be explored where items are to be created by generative models with personalized prompts. Taking image generation as an example, with a single prompt from the user and access to a generative model, it is possible to generate hundreds of new images in a few minutes. How shall we attain personalization in the presence of "infinite" items? In this preliminary study, we propose a two-stage framework, namely Prompt-Model Retrieval and Generated Item Ranking, to approach this new task formulation. We release GEMRec-18K, a prompt-model interaction dataset with 18K images generated by 200 publicly-available generative models paired with a diverse set of 90 textual prompts. Our findings demonstrate the promise of generative model recommendation as a novel personalization problem and the limitations of existing evaluation metrics. We highlight future directions for the RecSys community to advance towards generative recommender systems. Our code and dataset are available at https://github.com/MAPS-research/GEMRec.

The ChatGPT developer's new board of directors and its briefly fired but now-restored CEO, Sam Altman, said last week that they're trying to fix the unusual corporate structure that allowed four board members to trigger a near-death experience for the company. The startup was founded in 2015 as a nonprofit, but it develops AI inside a capped-profit subsidiary answerable to the nonprofit's board, which is charged with ensuring that the technology is "broadly beneficial" to humanity. To stabilize this unusual structure, OpenAI could take pointers from longer-lived companies with a similar arrangement--including introducing a second board to help balance its founding mission with its for-profit pursuit of returns for investors. OpenAI deferred comment for this story to new board chair Bret Taylor. The veteran tech executive told WIRED in a statement that the board is focused on overseeing an independent review of the recent crisis and enhancing governance.

Facebook parent Meta and IBM on Tuesday launched a new group called the AI Alliance advocating for an "open-science" approach to AI development that puts them at odds with rivals Google, Microsoft and ChatGPT-maker OpenAI. These two diverging camps – the open and the closed – disagree about whether to build AI in a way that makes the underlying technology widely accessible. Safety is at the heart of the debate, but so is who gets to profit from AI's advances. Open advocates favor an approach that is "not proprietary and closed", said Darío Gil, a senior vice-president at IBM who directs its research division. "So it's not like a thing that is locked in a barrel and no one knows what they are."

As artificial intelligence (AI) challenges us to reimagine new ways of doing and being, Dr Marcel O'Gorman, professor of English Language and Literature, embraces emerging technologies and applies them to his pedagogy in the classroom. O'Gorman has published widely about the impacts of technology, and his most recent research focuses on how critical and inclusive design methods might help tackle some of the moral and ethical issues faced by contemporary technoculture. O'Gorman recently wrapped up teaching a fourth-year undergraduate course on techno-critical writing and design that focused on key issues around responsible innovation, such as algorithmic bias, conflict minerals and the colonial practices of big tech on the global stage. Students applied what they learned by writing and designing projects throughout the course. "They wrote stories in ChatGPT that tested the AI for gender bias. They generated images in DALL-E 2 that traced a racist history in the AI's training data," O'Gorman says.

Microsoft just announced its Copilot AI chatbot is integrating with OpenAI's latest model, GPT-4 Turbo, and the image generator DALL-E 3, among other upgrades. This should drastically improve the overall functionality of the service, just in time for its one-year anniversary/birthday. Wait, do AI chatbots have birthdays? GPT-4 Turbo integration will allow Copilot users to tackle complex tasks that would cause previous iterations of the software to sputter into madness. The last generation allowed for just 50 pages of text as a data input, while GPT-4 Turbo accepts up to 300 pages. The integration is currently being tested by select users, with wider availability in the next few weeks.