Astronaut Sally Ride became the first American woman in space on this day in history, June 18, 1983. Born on May 26, 1951, in Los Angeles, Ride earned bachelor's degrees in English and physics from Stanford University in California before staying at Stanford and earning a PhD in physics in 1978. Shortly before earning her doctorate, Ride saw an ad for a newspaper that piqued her interest. NASA was recruiting for astronauts -- and, for the first time, the agency would include women in its astronaut class. "Over 8,000 men and women applied to the space program that year. Of the 35 individuals accepted, six were women, and I was one of them. This was in January 1978," said Ride in quotes listed on a tribute page on NASA's website.

In recent years, discussions of responsible AI practices have seen growing support for "participatory AI" approaches, intended to involve members of the public in the design and development of AI systems. Prior research has identified a lack of standardised methods or approaches for how to use participatory approaches in the AI development process. At present, there is a dearth of evidence on attitudes to and approaches for participation in the sites driving major AI developments: commercial AI labs. Through 12 semi-structured interviews with industry practitioners and subject-matter experts, this paper explores how commercial AI labs understand participatory AI approaches and the obstacles they have faced implementing these practices in the development of AI systems and research. We find that while interviewees view participation as a normative project that helps achieve "societally beneficial" AI systems, practitioners face numerous barriers to embedding participatory approaches in their companies: participation is expensive and resource intensive, it is "atomised" within companies, there is concern about exploitation, there is no incentive to be transparent about its adoption, and it is complicated by a lack of clear context. These barriers result in a piecemeal approach to participation that confers no decision-making power to participants and has little ongoing impact for AI labs. This papers contribution is to provide novel empirical research on the implementation of public participation in commercial AI labs, and shed light on the current challenges of using participatory approaches in this context.

A Russian soldier was seen surrendering to a Ukrainian drone May 9 in edited video released by Ukraine's 92nd Mechanized Brigade. A Russian soldier whose surrender to Ukrainian forces was captured on drone camera, spoke for the first time about his experience. Ruslan Anitin, a draftee who was cornered alone by the Ukrainian military near the city of Bakhmut, surrendered by communicating via an aerial drone's camera. "It felt like it was never going to involve us at all," Anitin said of the conflict during an interview with the Wall Street Journal about his experience. A Russian soldier was seen surrendering to a Ukrainian drone May 9 in edited video released by Ukraine's 92nd Mechanized Brigade.

There's no question that artificial intelligence is changing our lives. A bot that sounds almost human can author your emails, teach you a new language, book your trip,s or even be your friend. Check out direct links to try those out here. One woman I spoke with on my national radio show even married her AI companion. No kidding, she says he's the perfect partner.

John Rich shared his unfiltered opinions on the state of country music today and if AI would make it better or worse. John Rich is less concerned with the advancements in artificial intelligence than he is with the expansion of country music, questioning if the technology could produce better quality music than what country artists are releasing now. "Could AI do any worse than some of the country singers that are out there right now?" Rich wondered during an interview with Fox News Digital, without naming names. One thing's for sure, according to Rich: AI has nothing on the legends. "Listen, you can't replicate the great songwriters. I mean, you're talking about Albert Einstein honky-tonk songwriters."

Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs. Specifically, we adopt a set of learnable adaption prompts, and prepend them to the word tokens at higher transformer layers. Then, a zero-initialized attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge. With our efficient training, LLaMA-Adapter can generate high-quality responses, comparable to Alpaca with fully fine-tuned 7B parameters. Besides language commands, our approach can be simply extended to multi-modal instructions for learning image-conditioned LLaMA model, which achieves superior reasoning performance on ScienceQA and COCO Caption benchmarks. Furthermore, we also evaluate the zero-initialized attention mechanism for fine-tuning other pre-trained models (ViT, RoBERTa) on traditional vision and language tasks, demonstrating the superior generalization capacity of our approach.

We propose the Detailed Outline Control (DOC) framework for improving long-range plot coherence when automatically generating several-thousand-word-long stories. DOC consists of two complementary components: a detailed outliner and a detailed controller. The detailed outliner creates a more detailed, hierarchically structured outline, shifting creative burden from the main drafting procedure to the planning stage. The detailed controller ensures the more detailed outline is still respected during generation by controlling story passages to align with outline details. In human evaluations of automatically generated stories, DOC substantially outperforms a strong Re3 baseline (Yang et al., 2022) on plot coherence (22.5% absolute gain), outline relevance (28.2%), and interestingness (20.7%). Humans also judged DOC to be much more controllable in an interactive generation setting.

New York City musician Jules Avalon reflects on the power of John Lennon and the Beatles at Strawberry Fields in Central Park, located across the street from where Lennon was murdered on Dec. 8, 1980. Paul McCartney announced Tuesday that artificial intelligence has been used to help create the "last" ever Beatles song, featuring the voice of John Lennon. In an interview with BBC Radio, McCartney, speaking about AI, said "we were able to use that kind of thing when Peter Jackson did the film'Get Back' where it was us making the Let It Be album." "And he was able to extricate John's voice from a ropey little bit of cassette where it had John's voice and a piano – he could separate them with AI. They tell the machine'that is a voice, this is a guitar, lose the guitar.' And he did that," McCartney continue.

I never thought I'd write these words, but here goes. Satya Nadella--and Microsoft, the company he runs--are riding high on the buzz from its search engine. That's quite a contrast from the first time I spoke with Nadella, in 2009. Back then, he was not so well known, and he made a point of telling me about his origins. Born in Hyderabad, India, he attended grad school in the US and joined Microsoft in 1992, just as the firm was rising to power.

Abstract: This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AIsimulator, and AI-student, each with distinct pedagogical benefits and risks. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI's output, errors, and biases. These strategies promote active oversight, critical assessment of AI outputs, and complementation of AI's capabilities with the students' unique insights. By challenging students to remain the "human in the loop", the authors aim to enhance learning outcomes while ensuring that AI serves as a supportive tool rather than a replacement. The proposed framework offers a guide for educators navigating the integration of AI-assisted learning in ...