As the volume of publicly available data continues to grow, researchers face the challenge of limited diversity in benchmarking machine learning tasks. Although thousands of datasets are available in public repositories, the sheer abundance often complicates the search for suitable data, leaving many valuable datasets underexplored. This situation is further amplified by the fact that, despite longstanding advocacy for improving data curation quality, current solutions remain prohibitively time-consuming and resource-intensive. In this paper, we propose a novel approach that combines intelligent agents with retrieval augmented generation to automate data analysis, dataset curation and indexing at scale. Our system leverages multiple agents to analyze raw, unstructured data across public repositories, generating dataset reports and interactive visual indexes that can be easily explored. We demonstrate that our approach results in more detailed dataset descriptions, higher hit rates and greater diversity in dataset retrieval tasks. Additionally, we show that the dataset reports generated by our method can be leveraged by other machine learning models to improve the performance on specific tasks, such as improving the accuracy and realism of synthetic data generation. By streamlining the process of transforming raw data into machine-learning-ready datasets, our approach enables researchers to better utilize existing data resources.

ChatGPT knows how to use the word "tickle" in a sentence but it cannot feel the sensation. Can it then be said to understand the meaning of the word tickle the same way we humans do? In an ongoing debate, AI researchers are teasing apart whether large language models (LLMs) like ChatGPT and Google's PaLM understand language in any humanlike sense. The relationship between embodiment and understanding is one question, along with the nature of intelligence and understanding. Should concepts of meaning, understanding, and intelligence be revisited to create a distinction between how humans and machines understand the world?

When IBM's Deep Blue first defeated Garry Kasparov in 1997, the world chess champion accused the company of cheating. There was no way, he thought, that the computer could have beaten him without direct assistance from a skilled human player. But now the situation has flipped entirely. When grandmasters find themselves at the receiving end of a few mind-blowingly brilliant moves today, they accuse their opponent of using a computer. The only worthwhile competition for top chess engines is one another. The programs have become too powerful; humankind has lost.

First, it's important to understand how the technology works to know what exactly you're doing with it. ChatGPT is essentially a more powerful, fancier version of the predictive text system on our phones, which suggests words to complete a sentence when we are typing by using what it has learned from vast amounts of data scraped off the web. It also can't check if what it's saying is true. If you use a chatbot to code a program, it looks at how the code was compiled in the past. Because code is constantly updated to address security vulnerabilities, the code written with a chatbot could be buggy or insecure, Mr. Christian said.

Margaret Mitchell had been working at Google for two years before she realized she needed a break. Only after she spoke with a therapist did she understand the problem: she was burnt out. She ended up taking medical leave because of stress. Mitchell, who now works as an AI researcher and chief ethics scientist at the AI startup Hugging Face, is far from alone in her experience. Burnout is becoming increasingly common in responsible-AI teams, who are unlikely to receive the same levels of support as colleagues who specialize in content moderation, although the work can be just as psychologically draining.

Advanced computer programs influence, and can even dictate, meaningful parts of our lives. Think of streaming services, credit scores, facial recognition software. As this technology becomes more sophisticated and more pervasive, it's important to understand the basic terminology. People often use "algorithm," "machine learning" and "artificial intelligence" interchangeably. There is some overlap, but they're not the same things.

New York (CNN)Google (GOOG) has fired the engineer who claimed an unreleased AI system had become sentient, the company confirmed, saying he violated employment and data security policies. Blake Lemoine, a software engineer for Google, claimed that a conversation technology called LaMDA had reached a level of consciousness after exchanging thousands of messages with it. Google confirmed it had first put the engineer on leave in June. The company said it dismissed Lemoine's "wholly unfounded" claims only after reviewing them extensively. He had reportedly been at Alphabet for seven years.

Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI's turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent―really―are the best AI programs? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us?

If you've spent any time on Twitter lately, you may have seen a viral black-and-white image depicting Jar Jar Binks at the Nuremberg Trials, or a courtroom sketch of Snoop Dogg being sued by Snoopy. These surreal creations are the products of Dall-E Mini, a popular web app that creates images on demand. Type in a prompt, and it will rapidly produce a handful of cartoon images depicting whatever you've asked for. More than 200,000 people are now using Dall-E Mini every day, its creator says--a number that is only growing. A Twitter account called "Weird Dall-E Generations," created in February, has more than 890,000 followers at the time of publication.

If you've spent any time on Twitter lately, you may have seen a viral black-and-white image depicting Jar Jar Binks at the Nuremberg Trials, or a courtroom sketch of Snoop Dogg being sued by Snoopy. These surreal creations are the products of Dall-E Mini, a popular web app that creates images on demand. Type in a prompt, and it will rapidly produce a handful of cartoon images depicting whatever you've asked for. More than 200,000 people are now using Dall-E Mini every day, its creator says--a number that is only growing. A Twitter account called "Weird Dall-E Generations," created in February, has more than 890,000 followers at the time of publication.