Sutskever was one of the OpenAI board members who voted to remove co-founder Sam Altman as chief executive in November. Altman was reinstated days later, and Sutskever and the other directors who voted against Altman resigned from the board.

On Tuesday, Google announced its own new tools, including a conversational assistant called Gemini Live, which can do many of the same things. It also revealed that it's building a sort of "do-everything" AI agent, which is currently in development but will not be released until later this year. Soon you'll be able to explore for yourself to gauge whether you'll turn to these tools in your daily routine as much as their makers hope, or whether they're more like a sci-fi party trick that eventually loses its charm. Here's what you should know about how to access these new tools, what you might use them for, and how much it will cost. What it's capable of: The model can talk with you in real time, with a response delay of about 320 milliseconds, which OpenAI says is on par with natural human conversation.

When OpenAI released its ChatGPT tool in November 2022, it was a shot across the bows of Google, with generative artificial intelligence promising a new way to access the world's information beyond search engines. Since then, the rivalry between these firms has only grown, with both announcing new services this week. While there are signs that OpenAI is winning this race, is either company aiming for a future anyone actually wants?

OpenAI's co-founder and chief scientist, Ilya Sutskever, is leaving the startup at the center of today's artificial intelligence boom. "After almost a decade, I have made the decision to leave OpenAI," Sutskever said in a post on X. Sutskever played a key role in the dramatic firing and rehiring in November last year of OpenAI's CEO, Sam Altman. At the time, Sutskever was on the board of OpenAI and helped to orchestrate Altman's firing. Days later, he reversed course, signing on to an employee letter demanding Altman's return and expressing regret for his "participation in the board's actions". After Altman returned, Sutskever was removed from the board, and his position at the company became unclear.

Ilya Sutskever has announced on X, formerly known as Twitter, that he's leaving OpenAI almost a decade after he co-founded the company. He's confident that OpenAI "will build [artificial general intelligence] that is both safe and beneficial" under the leadership of CEO Sam Altman, President Greg Brockman and CTO Mira Murati, he continued. In his own post about Sutskever's departure, Altman called him "one of the greatest minds of our generation" and credited him for his work with the company. Jakub Pachocki, OpenAI's previous Director of Research who headed the development of GPT-4 and OpenAI Five, has taken Sutskever's role as Chief Scientist. After almost a decade, I have made the decision to leave OpenAI.

OpenAI Chief Scientist and co-founder Ilya Sutskever is leaving the artificial intelligence company, a departure that ends months of speculation in Silicon Valley about the future of a top AI researcher who played a key role in the brief ouster of Sam Altman last year. Sutskever will be replaced by Research Director Jakub Pachocki, OpenAI said on its blog Tuesday. In a post on X, Sutskever called trajectory of OpenAI "miraculous" and said that he was confident the company will build AI that is "both safe and beneficial" under its current leadership. The exit removes an executive and renowed researcher who has played a pivotal role in the company since its earliest days, helping guide discussions over the safety of AI technology and at times differing with Altman over strategy. When OpenAI was founded in 2015, he served as its research director after being recruited to join the company by Elon Musk.

The rapid advancement of AI technologies yields numerous future impacts on individuals and society. Policy-makers are therefore tasked to react quickly and establish policies that mitigate those impacts. However, anticipating the effectiveness of policies is a difficult task, as some impacts might only be observable in the future and respective policies might not be applicable to the future development of AI. In this work we develop a method for using large language models (LLMs) to evaluate the efficacy of a given piece of policy at mitigating specified negative impacts. We do so by using GPT-4 to generate scenarios both pre- and post-introduction of policy and translating these vivid stories into metrics based on human perceptions of impacts. We leverage an already established taxonomy of impacts of generative AI in the media environment to generate a set of scenario pairs both mitigated and non-mitigated by the transparency legislation of Article 50 of the EU AI Act. We then run a user study (n=234) to evaluate these scenarios across four risk-assessment dimensions: severity, plausibility, magnitude, and specificity to vulnerable populations. We find that this transparency legislation is perceived to be effective at mitigating harms in areas such as labor and well-being, but largely ineffective in areas such as social cohesion and security. Through this case study on generative AI harms we demonstrate the efficacy of our method as a tool to iterate on the effectiveness of policy on mitigating various negative impacts. We expect this method to be useful to researchers or other stakeholders who want to brainstorm the potential utility of different pieces of policy or other mitigation strategies.

Generative artificial intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music. Creating these advanced generative models requires significant resources, particularly large and high-quality datasets. To minimize training expenses, many algorithm developers use data created by the models themselves as a cost-effective training solution. However, not all synthetic data effectively improve model performance, necessitating a strategic balance in the use of real versus synthetic data to optimize outcomes. Currently, the previously well-controlled integration of real and synthetic data is becoming uncontrollable. The widespread and unregulated dissemination of synthetic data online leads to the contamination of datasets traditionally compiled through web scraping, now mixed with unlabeled synthetic data. This trend portends a future where generative AI systems may increasingly rely blindly on consuming self-generated data, raising concerns about model performance and ethical issues. What will happen if generative AI continuously consumes itself without discernment? What measures can we take to mitigate the potential adverse effects? There is a significant gap in the scientific literature regarding the impact of synthetic data use in generative AI, particularly in terms of the fusion of multimodal information. To address this research gap, this review investigates the consequences of integrating synthetic data blindly on training generative AI on both image and text modalities and explores strategies to mitigate these effects. The goal is to offer a comprehensive view of synthetic data's role, advocating for a balanced approach to its use and exploring practices that promote the sustainable development of generative AI technologies in the era of large models.

This study explores the integration of the ChatGPT API with GPT-4 model and Microsoft Copilot Studio on the Microsoft Teams platform to develop an intelligent tutoring system. Designed to provide instant support to students, the system dynamically adjusts educational content in response to the learners' progress and feedback. Utilizing advancements in natural language processing and machine learning, it interprets student inquiries, offers tailored feedback, and facilitates the educational journey. Initial implementation highlights the system's potential in boosting students' motivation and engagement, while equipping educators with critical insights into the learning process, thus promoting tailored educational experiences and enhancing instructional effectiveness.

Earlier today, Google presented a new vision for its flagship search engine, one that is uniquely tailored to the generative-AI moment. With advanced technology at its disposal, "Google will do the Googling for you," Liz Reid, the company's head of search, declared onstage at the company's annual software conference. Googling something rarely yields an immediate, definitive answer. You enter a query, confront a wall of blue links, open a zillion tabs, and wade through them to find the most relevant information. If that doesn't work, you refine the search and start again.