Recently, a leaked document, allegedly from Google, claimed that open-source AI will outcompete Google and OpenAI. The leak brought to the fore ongoing conversations in the AI community about how an AI system and its many components should be shared with researchers and the public. Even with the slew of recent generative AI system releases, this issue remains unresolved. Irene Solaiman is policy director at Hugging Face, where she leads policy and conducts social impact research. Many people think of this as a binary question: Systems can either be open source or closed source.

Here's the thing about podcasts: There are too many of them. More than 4 million, to be precise, according to the database Podcast Index. In the past three days alone, nearly 103,000 individual podcast episodes were published online, a deluge of audio content so voluminous that listeners need never run out of options. You could spend the rest of your life working through the existing true crime catalog on Apple Podcasts or the sports chat shows on Spotify and end up dying of old age in 2070 while Michael Barbaro reads an ad for Mailchimp to your corpse. In the ongoing generative AI gold rush, though, opportunistic entrepreneurs are looking for entry into even the most saturated markets.

The leaders of the ChatGPT developer OpenAI have called for the regulation of "superintelligent" AIs, arguing that an equivalent to the International Atomic Energy Agency is needed to protect humanity from the risk of accidentally creating something with the power to destroy it. In a short note published to the company's website, co-founders Greg Brockman and Ilya Sutskever and the chief executive, Sam Altman, call for an international regulator to begin working on how to "inspect systems, require audits, test for compliance with safety standards, [and] place restrictions on degrees of deployment and levels of security" in order to reduce the "existential risk" such systems could pose. "It's conceivable that within the next 10 years, AI systems will exceed expert skill level in most domains, and carry out as much productive activity as one of today's largest corporations," they write. "In terms of both potential upsides and downsides, superintelligence will be more powerful than other technologies humanity has had to contend with in the past. We can have a dramatically more prosperous future; but we have to manage risk to get there. Given the possibility of existential risk, we can't just be reactive."

Generative AI tools such as ChatGPT have recently gained significant attention in higher education. This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions. Our study examines ChatGPT policies implemented at universities around the world, including their existence, content, and issuance dates. Specifically, we analyzed the top 500 universities according to the 2022 QS World University Rankings. Our findings indicate that there is significant variation in university policies. Less than one-third of the universities included in the study had implemented ChatGPT policies. Of the universities with ChatGPT policies, approximately 67 percent embraced ChatGPT in teaching and learning, more than twice the number of universities that banned it. The majority of the universities that ban the use of ChatGPT in assessments allow individual instructors to deviate from this restrictive policy. Our empirical analysis identifies several factors that are significantly and positively correlated with a university's likelihood of having a ChatGPT policy, including the university's academic reputation score, being in an English-speaking country, and the general public attitudes toward ChatGPT. In addition, we found that a university's likelihood of having a ban policy is positively associated with faculty student ratio, citations, and the English-speaking country dummy, while negatively associated with the number of peer universities within the same country that have banned ChatGPT. We discuss the challenges faced by universities based our empirical findings.

The impact of machine learning (ML) in many fields of application is constrained by lack of annotated data. Among existing tools for ML-assisted data annotation, one little explored tool type relies on an analogy between the coordinates of a graphical user interface and the latent space of a neural network for interaction through direct manipulation. In the present work, we 1) expand the paradigm by proposing two new analogies: time and force as reflecting iterations and gradients of network training; 2) propose a network model for learning a compact graphical representation of the data that takes into account both its internal structure and user provided annotations; and 3) investigate the impact of model hyperparameters on the learned graphical representations of the data, identifying candidate model variants for a future user study.

Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI. However, such models often fail to connect the generated outputs and desired target concepts/preferences implied by the prompts. Current research addressing this limitation has largely focused on enhancing the prompts before output generation or improving the model's performance up front. In contrast, this paper conceptualizes prompt evolution, imparting evolutionary selection pressure and variation during the generative process to produce multiple outputs that satisfy the target concepts/preferences better. We propose a multi-objective instantiation of this broader idea that uses a multi-label image classifier-guided approach. The predicted labels from the classifiers serve as multiple objectives to optimize, with the aim of producing diversified images that meet user preferences. A novelty of our evolutionary algorithm is that the pre-trained generative model gives us implicit mutation operations, leveraging the model's stochastic generative capability to automate the creation of Pareto-optimized images more faithful to user preferences.

Google has spent the past few weeks promoting generative AI tools that can summarize search results for users, help them draft essays, and swap out overcast skies for sunshine in otherwise perfect family photos. Today it's showing off what similar tools could do for its core business--selling ads. New generative AI systems for advertising clients will compose text on the fly to play off what a person is searching for, and they'll whip up product images to save them time and money on design work. The features add to the swelling ranks of AI-based text and image generators that have been introduced to online services over the past few months, since the abilities of ChatGPT and its image counterpart DALL-E inspired global excitement about generative AI. As the world's top seller of online ads by revenue, Google has been using AI programs for years to help clients target users, as well as helping them design ads, like by automatically editing the size of images.

OpenAI Chief Executive Officer Sam Altman said part of the reason for his current tour of European cities is to discover a suitable location for a new office. "Poland would be an interesting place," Altman said in an interview Tuesday when asked about European offices. "We want to do a research and engineering office in Europe, not a regulatory one. We are trying to figure it out. This is part of the goal of this trip."

Rapid advances in artificial intelligence (AI) such as Microsoft-backed OpenAI's ChatGPT are complicating governments' efforts to agree to laws governing the use of the technology. The government is consulting Australia's main science advisory body and is considering the next steps, a spokesperson for the industry and science minister said in April. The Financial Conduct Authority, one of several state regulators tasked with drawing up new guidelines covering AI, is consulting with the Alan Turing Institute and other legal and academic institutions to improve its understanding of the technology, a spokesperson said. Britain's competition regulator said on May 4 it would start examining the effect of AI on consumers, businesses and the economy, and whether new controls were needed. Britain said in March it planned to split responsibility for governing AI between its regulators for human rights, health and safety, and competition, rather than creating a new body. China's cyberspace regulator in April unveiled draft measures to manage generative AI services, saying it wanted firms to submit security assessments to authorities before they launch offerings to the public.

Software giant Adobe has announced it will integrate generative AI into its widely used Photoshop program, while downplaying fears the move will lead to job losses and mass fakes. The brand most associated with image editing will incorporate the generative AI product Adobe Firefly, which launched as a beta six weeks ago, creating a tool the company says will become a "co-pilot" to graphic design rather than a replacement for humans. Using the "generative fill" feature, Photoshop users will be able to add to, expand or remove unwanted items from images using a text prompt similar to those used by Dall-E and Midjourney, such as "long haired dachshund with long flowing rainbow hair". The generative fill feature will be available in the desktop beta from Tuesday, with a wider release set for later in 2023. Adobe has been using AI in its tools for over a decade, such as the background replacement tool in Photoshop.