The top U.S. artificial intelligence companies will participate in a government-led effort intended to craft federal standards on the technology to ensure that it's deployed safely and responsibly, the Commerce Department said Thursday. OpenAI, Anthropic, Microsoft Corp., Meta Platforms Inc. and Alphabet Inc.'s Google are among more than 200 members of a newly established AI Safety Institute Consortium under the department, Commerce Secretary Gina Raimondo said. Also on the list are Apple Inc., Amazon Inc., Hugging Face Inc. and IBM. The top industry players will work with the National Institute of Standards and Technology, a body within Commerce, along with other technology companies, civil society groups, academics, and state and local government officials to establish safety standards regarding AI. "President Biden directed us to pull every lever to accomplish two key goals: set safety standards and protect our innovation ecosystem," Raimondo said in a statement. Major tech companies have been engaging with the Biden administration and policymakers in Washington on regulating AI as the technology rapidly advances and is poised to disrupt industries.

Many people--like, say, journalists--are understandably antsy about what generative artificial intelligence might mean for the future of their profession. Some workers are already living in one potential version of the generative AI future, though: computer programmers. "Developers have arrived in the age of AI," says Thomas Dohmke, CEO of GitHub. "The only question is, how fast do you get on board? Or are you going to be stuck in the past, on the wrong side of the'productivity polarity'?"

If you asked a hundred people on the street what Google's generative artificial intelligence product is called, I'm betting that 98 of them wouldn't know, unless that street happens to be in San Jose. It's "Bard," for the record, unconsciously associating generative AI with British playwrights and D&D players who failed their sexual harassment prevention training. Now it's called "Google Gemini," after a brief period of rebranding, and it's debuting in Google's workplace products. Gemini is an umbrella term for Google's generative AI (a separate app on Android, embedded within the Google search app on iOS) and its integration into existing services like Gmail, Docs, Sheets, Slides, and Meet, where it was sometimes referred to as "Duet" before. Some advanced capabilities, like being a "personal tutor" or assistive writing in "more advanced coding scenarios," will be behind a Gemini Advanced paywall.

When OpenAI's ChatGPT opened a new era in tech, the industry's former AI champ, Google, responded by reorganizing its labs and launching a profusion of sometimes overlapping AI services. This included the Bard chatbot, workplace helper Duet AI, and a chatbot-style version of search. Now Google is consolidating many of its generative AI products under the banner of its latest AI model Gemini--and taking direct aim at OpenAI's subscription service ChatGPT Plus. Google announced today that Bard, its experimental chatbot hurriedly launched last March, is now called Gemini--taking the same name of the text, voice, and image capable AI model that started powering the Bard chatbot back in December. Gemini is also getting more prominent positioning among Google's services.

He wakes up every morning and reads Techmeme, a news aggregator resplendent with links, accessible only via the web. The web is dynamic and resilient, he says, and can still--with help from a search engine--provide whatever information a person is looking for. Yet the web and its critical search layer are changing. We can all see it happening: Social media apps, short-form video, and generative AI are challenging our outdated ideals of what it means to find information online. But he has more power than most to steer it.

Fika Juliana Putri, a 19-year-old shopkeeper in East Jakarta, plans to vote in Indonesia's presidential election next week for a once-feared former special forces commander. She likes him, she says, because he's cuddly. A doe-eyed cartoon version of former Gen. Prabowo Subianto -- produced using generative artificial intelligence -- is emblazoned on billboards across Indonesia. It's reproduced on sweatshirts and stickers, and featured prominently on #Prabowo-tagged posts that have some 19 billion views on TikTok. Prabowo is Indonesia's defense minister.

The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of many social media platforms. While this can bring promising opportunities, it also raises many threats, such as biases and privacy concerns, and may contribute to the spread of propaganda by malicious actors. We developed the "LLMs Among Us" experimental framework on top of the Mastodon social media platform for bot and human participants to communicate without knowing the ratio or nature of bot and human participants. We built 10 personas with three different LLMs, GPT-4, LLama 2 Chat, and Claude. We conducted three rounds of the experiment and surveyed participants after each round to measure the ability of LLMs to pose as human participants without human detection. We found that participants correctly identified the nature of other users in the experiment only 42% of the time despite knowing the presence of both bots and humans. We also found that the choice of persona had substantially more impact on human perception than the choice of mainstream LLMs.

Large Language Models (LLMs) play a pivotal role in generating vast arrays of narratives, facilitating a systematic exploration of their effectiveness for communicating life events in narrative form. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 narratives using OpenAI's GPT-4. From this dataset, we manually classify 2,880 narratives and evaluate their validity in conveying birth, death, hiring, and firing events. Remarkably, 87.43% of the narratives sufficiently convey the intention of the structured prompt. To automate the identification of valid and invalid narratives, we train and validate nine Machine Learning models on the classified datasets. Leveraging these models, we extend our analysis to predict the classifications of the remaining 21,120 narratives. All the ML models excelled at classifying valid narratives as valid, but experienced challenges at simultaneously classifying invalid narratives as invalid. Our findings not only advance the study of LLM capabilities, limitations, and validity but also offer practical insights for narrative generation and natural language processing applications.

Generative Artificial Intelligence (AI), because of its emergent abilities, has empowered various fields, one typical of which is large language models (LLMs). One of the typical application fields of Generative AI is large language models (LLMs), and the natural language understanding capability of LLM is dramatically improved when compared with conventional AI-based methods. The natural language understanding capability has always been a barrier to the intent recognition performance of the Knowledge-Based-Question-and-Answer (KBQA) system, which arises from linguistic diversity and the newly appeared intent. Conventional AI-based methods for intent recognition can be divided into semantic parsing-based and model-based approaches. However, both of the methods suffer from limited resources in intent recognition. To address this issue, we propose a novel KBQA system based on a Large Language Model(LLM) and BERT (LB-KBQA). With the help of generative AI, our proposed method could detect newly appeared intent and acquire new knowledge. In experiments on financial domain question answering, our model has demonstrated superior effectiveness.

Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has emerged as a very successful paradigm for a variety of machine learning tasks. In this survey, we discuss the state of the art, opportunities and open research questions in applying RL to generative AI. In particular, we will discuss three types of applications, namely, RL as an alternative way for generation without specified objectives; as a way for generating outputs while concurrently maximizing an objective function; and, finally, as a way of embedding desired characteristics, which cannot be easily captured by means of an objective function, into the generative process. We conclude the survey with an in-depth discussion of the opportunities and challenges in this fascinating emerging area.