The American tech industry has long paid reverence to its monolithic slate of founders and visionaries: Meta's Mark Zuckerberg; Google's Larry Page and Sergey Brin; Apple's Steve Jobs and Tim Cook. But where the other firms sold phones and search engines, Musk and Altman championed their work as a public mission for protecting mankind, with a for-profit business attached. It is notable that as private companies, they don't have to report to federal regulators or to shareholders, who can vote down proposals or push back against their work.

Sam Altman is back at the helm of OpenAI, days after the board abruptly ousted him. Almost everything else is still in flux. The deal struck Tuesday night to restore Altman as CEO is a long way from the ultimate goal he wanted to achieve heading into a weekend of intense negotiations. He had lobbied for an entirely new slate of directors--built on the ashes of the directors who fired him--and wanted to rejoin the board again himself, according to people familiar with the matter.

This week, Emily Bazelon, John Dickerson, and David Plotz discuss the problems with issue polling and issues with political journalism; the chaos and conflict of Sam Altman and OpenAI; and the failure of the Oslo Accords and perpetual struggle between Israel and Palestine. Send us your Conundrums: submit them at slate.com/conundrum. And join us in-person or online with our special guest – The Late Show's Steven Colbert – for Gabfest Live: The Conundrums Edition! December 7 at The 92nd Street Y, New York City. Here are some notes and references from this week's show: Nate Cohn for The New York Times: The Crisis in Issue Polling, and What We're Doing About It and We Did an Experiment to See How Much Democracy and Abortion Matter to Voters Eli Saslow for The New York Times: A Jan. 6 Defendant Pleads His Case to the Son Who Turned Him In John Dickerson and Jo Ling Kent for CBS News Prime Time: What Sam Altman's ouster from OpenAI could mean for the tech world Emily Bazelon for The New York Times Magazine: Was Peace Ever Possible? Ezra Klein for The New York Times's The Ezra Klein Show podcast: The Best Primer I've Heard on Israeli-Palestinian Peace Efforts John Dickerson for CBS Mornings: Former President Jimmy Carter: "America will learn from its mistakes" Here are this week's chatters: John: Julia Simon for NPR: 'It feels like I'm not crazy.'

Ahead of OpenAI CEO Sam Altman's four days in exile, several staff researchers wrote a letter to the board of directors warning of a powerful artificial intelligence discovery that they said could threaten humanity, two people familiar with the matter said. The previously unreported letter and AI algorithm were key developments before the board's ouster of Altman, the poster child of generative AI, the two sources said. Prior to his triumphant return late Tuesday, more than 700 employees had threatened to quit and join backer Microsoft in solidarity with their fired leader. The sources cited the letter as one factor among a longer list of grievances by the board leading to Altman's firing, among which were concerns over commercializing advances before understanding the consequences. A copy of the letter was unable to be reviewed for this report.

Telecom giant Nippon Telegraph and Telephone will launch a business-use generative artificial intelligence platform in March, in an effort to catch up with foreign rivals in the fast-expanding market. The AI platform has higher Japanese language processing capabilities than ChatGPT, a widely used AI chatbot developed by U.S.-based OpenAI, NTT said earlier in the month. The new AI model, called tsuzumi, named after a Japanese hand drum used in traditional events, can read documents containing charts and diagrams. NTT said it aims for annual sales of over ¥100 billion ($670 million) in this AI platform business by 2027. "The market size will grow bigger and bigger as many companies compete with each other," NTT President Akira Shimada said during a news conference in early November.

Sam Altman is returning to lead OpenAI less than five days after his surprise dismissal, which kicked off a tug of war for his talent, left the company in disarray and laid bare deep board divisions over the mission of one of the world's most valuable startups. OpenAI's new interim board, which won't include Altman at the outset, will be led by Bret Taylor, a former co-CEO of Salesforce. The other directors are Larry Summers, the former U.S. treasury secretary, and existing member Adam D'Angelo, the co-founder and CEO of Quora. Altman had been fired Friday after clashing with the board over his drive to transform OpenAI from a nonprofit organization focused on the scientific exploration of artificial intelligence into a business that builds products, attracts customers and lines up the funding needed to power AI tools. Members of the former board harbored concerns about the potential harms done by powerful, unchecked AI.

Culture fundamentally shapes people's reasoning, behavior, and communication. Generative artificial intelligence (AI) technologies may cause a shift towards a dominant culture. As people increasingly use AI to expedite and even automate various professional and personal tasks, cultural values embedded in AI models may bias authentic expression. We audit large language models for cultural bias, comparing their responses to nationally representative survey data, and evaluate country-specific prompting as a mitigation strategy. We find that GPT-4, 3.5 and 3 exhibit cultural values resembling English-speaking and Protestant European countries. Our mitigation strategy reduces cultural bias in recent models but not for all countries/territories. To avoid cultural bias in generative AI, especially in high-stakes contexts, we suggest using culture matching and ongoing cultural audits.

Digital art portfolios serve as impactful mediums for artists to convey their visions, weaving together visuals, audio, interactions, and narratives. However, without technical backgrounds, design students often find it challenging to translate creative ideas into tangible codes and designs, given the lack of tailored resources for the non-technical, academic support in art schools, and a comprehensive guiding tool throughout the mentally demanding process. Recognizing the role of companionship in code learning and leveraging generative AI models' capabilities in supporting creative tasks, we present PortfolioMentor, a coding companion chatbot for IDEs. This tool guides and collaborates with students through proactive suggestions and responsible Q&As for learning, inspiration, and support. In detail, the system starts with the understanding of the task and artist's visions, follows the co-creation of visual illustrations, audio or music suggestions and files, click-scroll effects for interactions, and creative vision conceptualization, and finally synthesizes these facets into a polished interactive digital portfolio.

Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. Even state-of-the-art AI models like ChatGPT depend on fine-tuning through human demonstrations, demanding extensive human input and domain expertise. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. To address this limitation, we propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data. Drawing inspiration from unsupervised reinforcement learning (RL) pretraining, EAI achieves exploration within the natural language space. We accomplish this by harnessing large language models to assess the novelty of generated content. Our approach employs two key components: an actor that generates novel content following exploration principles and a critic that evaluates the generated content, offering critiques to guide the actor. Empirical evaluations demonstrate that EAI significantly boosts model performance on complex reasoning tasks, addressing the limitations of human-intensive supervision.

In recent years, Text-to-Image (T2I) models have been extensively studied, especially with the emergence of diffusion models that achieve state-of-the-art results on T2I synthesis tasks. However, existing benchmarks heavily rely on subjective human evaluation, limiting their ability to holistically assess the model's capabilities. Furthermore, there is a significant gap between efforts in developing new T2I architectures and those in evaluation. To address this, we introduce HRS-Bench, a concrete evaluation benchmark for T2I models that is Holistic, Reliable, and Scalable. Unlike existing bench-marks that focus on limited aspects, HRS-Bench measures 13 skills that can be categorized into five major categories: accuracy, robustness, generalization, fairness, and bias. In addition, HRS-Bench covers 50 scenarios, including fashion, animals, transportation, food, and clothes. We evaluate nine recent large-scale T2I models using metrics that cover a wide range of skills. A human evaluation aligned with 95% of our evaluations on average was conducted to probe the effectiveness of HRS-Bench. Our experiments demonstrate that existing models often struggle to generate images with the desired count of objects, visual text, or grounded emotions. We hope that our benchmark help ease future text-to-image generation research. The code and data are available at https://eslambakr.github.io/hrsbench.github.io