The ChatGPT developer's new board of directors and its briefly fired but now-restored CEO, Sam Altman, said last week that they're trying to fix the unusual corporate structure that allowed four board members to trigger a near-death experience for the company. The startup was founded in 2015 as a nonprofit, but it develops AI inside a capped-profit subsidiary answerable to the nonprofit's board, which is charged with ensuring that the technology is "broadly beneficial" to humanity. To stabilize this unusual structure, OpenAI could take pointers from longer-lived companies with a similar arrangement--including introducing a second board to help balance its founding mission with its for-profit pursuit of returns for investors. OpenAI deferred comment for this story to new board chair Bret Taylor. The veteran tech executive told WIRED in a statement that the board is focused on overseeing an independent review of the recent crisis and enhancing governance.

Facebook parent Meta and IBM on Tuesday launched a new group called the AI Alliance advocating for an "open-science" approach to AI development that puts them at odds with rivals Google, Microsoft and ChatGPT-maker OpenAI. These two diverging camps – the open and the closed – disagree about whether to build AI in a way that makes the underlying technology widely accessible. Safety is at the heart of the debate, but so is who gets to profit from AI's advances. Open advocates favor an approach that is "not proprietary and closed", said Darío Gil, a senior vice-president at IBM who directs its research division. "So it's not like a thing that is locked in a barrel and no one knows what they are."

As artificial intelligence (AI) challenges us to reimagine new ways of doing and being, Dr Marcel O'Gorman, professor of English Language and Literature, embraces emerging technologies and applies them to his pedagogy in the classroom. O'Gorman has published widely about the impacts of technology, and his most recent research focuses on how critical and inclusive design methods might help tackle some of the moral and ethical issues faced by contemporary technoculture. O'Gorman recently wrapped up teaching a fourth-year undergraduate course on techno-critical writing and design that focused on key issues around responsible innovation, such as algorithmic bias, conflict minerals and the colonial practices of big tech on the global stage. Students applied what they learned by writing and designing projects throughout the course. "They wrote stories in ChatGPT that tested the AI for gender bias. They generated images in DALL-E 2 that traced a racist history in the AI's training data," O'Gorman says.

Microsoft just announced its Copilot AI chatbot is integrating with OpenAI's latest model, GPT-4 Turbo, and the image generator DALL-E 3, among other upgrades. This should drastically improve the overall functionality of the service, just in time for its one-year anniversary/birthday. Wait, do AI chatbots have birthdays? GPT-4 Turbo integration will allow Copilot users to tackle complex tasks that would cause previous iterations of the software to sputter into madness. The last generation allowed for just 50 pages of text as a data input, while GPT-4 Turbo accepts up to 300 pages. The integration is currently being tested by select users, with wider availability in the next few weeks.

Until late November, when the epic saga of OpenAI's board breakdown unfolded, the casual observer could be forgiven for assuming that the ecosystem around generative AI was vibrant and competitive. But this is not the case--nor has it ever been. And understanding why is fundamental to understanding what AI is, and what threats it poses. Put simply, in the context of the current paradigm of building larger- and larger-scale AI systems, there is no AI without Big Tech. With vanishingly few exceptions, every startup, new entrant, and even AI research lab is dependent on these firms. Those with the money make the rules.

When the board of OpenAI suddenly fired the company's CEO last month, it sparked speculation that board members were rattled by the breakneck pace of progress in artificial intelligence and the possible risks of seeking to commercialize the technology too quickly. Robust Intelligence, a startup founded in 2020 to develop ways to protect AI systems from attack, says that some existing risks need more attention. Working with researchers from Yale University, Robust Intelligence has developed a systematic way to probe large language models (LLMs), including OpenAI's prized GPT-4 asset, using "adversarial" AI models to discover "jailbreak" prompts that cause the language models to misbehave. While the drama at OpenAI was unfolding, the researchers warned OpenAI of the vulnerability. They say they have yet to receive a response.

AI development and concerns about its safety continue to grow at a rapid pace with little regulation in place. The latest industry-based solution to this comes courtesy of IBM and Meta, which have announced the creation of the AI Alliance. Its mission centers on "fostering an open community and enabling developers and researchers to accelerate responsible innovation in AI while ensuring scientific rigor, trust, safety, security, diversity and economic competitiveness." Part of this work will involve efforts to expand the number of open-source AI models -- ones with public source code -- which runs counter to the private models of companies like OpenAI and Google. Open-sourcing is a key pillar of the AI Alliance.

The recent OpenAI saga, in which Microsoft exerted its quiet but firm dominance over the "capped profit" entity, provides a powerful demonstration of what we've been analyzing for the last half-decade. To wit: those with the money make the rules. And right now, they're engaged in a race to the bottom, releasing systems before they're ready in an attempt to retain their dominant position. Relying on a few unaccountable corporate actors for core infrastructure is a problem for democracy, culture, and individual and collective agency. Without significant intervention, the AI market will only end up rewarding and entrenching the very same companies that reaped the profits of the invasive surveillance business model that has powered the commercial internet, often at the expense of the public. The Cambridge Analytica scandal was just one among many that exposed this seedy reality.

While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment. In this paper, we present a method to provide detailed textual and visual explanation of detected misalignments between text-image pairs. We leverage large language models and visual grounding models to automatically construct a training set that holds plausible misaligned captions for a given image and corresponding textual explanations and visual indicators. We also publish a new human curated test set comprising ground-truth textual and visual misalignment annotations. Empirical results show that fine-tuning vision language models on our training set enables them to articulate misalignments and visually indicate them within images, outperforming strong baselines both on the binary alignment classification and the explanation generation tasks. Our method code and human curated test set are available at: https://mismatch-quest.github.io/

Communicating science and technology is essential for the public to understand and engage in a rapidly changing world. Tweetorials are an emerging phenomenon where experts explain STEM topics on social media in creative and engaging ways. However, STEM experts struggle to write an engaging "hook" in the first tweet that captures the reader's attention. We propose methods to use large language models (LLMs) to help users scaffold their process of writing a relatable hook for complex scientific topics. We demonstrate that LLMs can help writers find everyday experiences that are relatable and interesting to the public, avoid jargon, and spark curiosity. Our evaluation shows that the system reduces cognitive load and helps people write better hooks. Lastly, we discuss the importance of interactivity with LLMs to preserve the correctness, effectiveness, and authenticity of the writing.