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 ...

Generative AI models continue to become more powerful. The launch of ChatGPT in November 2022 has ushered in a new era of AI. ChatGPT and other similar chatbots have a range of capabilities, from answering student homework questions to creating music and art. There are already concerns that humans may be replaced by chatbots for a variety of jobs. Because of the wide spectrum of data chatbots are built on, we know that they will have human errors and human biases built into them. These biases may cause significant harm and/or inequity toward different subpopulations. To understand the strengths and weakness of chatbot responses, we present a position paper that explores different use cases of ChatGPT to determine the types of questions that are answered fairly and the types that still need improvement. We find that ChatGPT is a fair search engine for the tasks we tested; however, it has biases on both text generation and code generation. We find that ChatGPT is very sensitive to changes in the prompt, where small changes lead to different levels of fairness. This suggests that we need to immediately implement "corrections" or mitigation strategies in order to improve fairness of these systems. We suggest different strategies to improve chatbots and also advocate for an impartial review panel that has access to the model parameters to measure the levels of different types of biases and then recommends safeguards that move toward responses that are less discriminatory and more accurate.

Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techniques, such as in writing examples, explanations, instructions, etc. further limits their adoption among non-expert users. In this paper, we propose a simple prompting strategy HELP ME THINK where we encourage GPT3 to help non-expert users by asking a set of relevant questions and leveraging user answers to execute the task. We demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks. Specifically, we focus on tasks that are hard for average humans and require significant thinking to perform. We hope our work will encourage the development of unconventional ways to harness the power of large language models.