The White House has struck a deal with major AI developers--including Amazon, Google, Meta, Microsoft, and OpenAI--that commits them to take action to prevent harmful AI models from being released into the world. Under the agreement, which the White House calls a "voluntary commitment," the companies pledge to carry out internal tests and permit external testing of new AI models before they are publicly released. The test will look for problems including biased or discriminatory output, cybersecurity flaws, and risks of broader societal harm. Startups Anthropic and Inflection, both developers of notable rivals to OpenAI's ChatGPT, also participated in the agreement. "Companies have a duty to ensure that their products are safe before introducing them to the public by testing the safety and capability of their AI systems," White House special adviser for AI Ben Buchanan told reporters in a briefing yesterday.

We aim to determine the extent and content of guidance for authors regarding the use of generative-AI (GAI), Generative Pretrained models (GPTs) and Large Language Models (LLMs) powered tools among the top 100 academic publishers and journals in science. The websites of these publishers and journals were screened from between 19th and 20th May 2023. Among the largest 100 publishers, 17% provided guidance on the use of GAI, of which 12 (70.6%) were among the top 25 publishers. Among the top 100 journals, 70% have provided guidance on GAI. Of those with guidance, 94.1% of publishers and 95.7% of journals prohibited the inclusion of GAI as an author. Four journals (5.7%) explicitly prohibit the use of GAI in the generation of a manuscript, while 3 (17.6%) publishers and 15 (21.4%) journals indicated their guidance exclusively applies to the writing process. When disclosing the use of GAI, 42.8% of publishers and 44.3% of journals included specific disclosure criteria. There was variability in guidance of where to disclose the use of GAI, including in the methods, acknowledgments, cover letter, or a new section. There was also variability in how to access GAI guidance and the linking of journal and publisher instructions to authors. There is a lack of guidance by some top publishers and journals on the use of GAI by authors. Among those publishers and journals that provide guidance, there is substantial heterogeneity in the allowable uses of GAI and in how it should be disclosed, with this heterogeneity persisting among affiliated publishers and journals in some instances. The lack of standardization burdens authors and threatens to limit the effectiveness of these regulations. There is a need for standardized guidelines in order to protect the integrity of scientific output as GAI continues to grow in popularity.

Microsoft, Google and OpenAI are among the leaders in the US artificial intelligence space that will reportedly commit to certain safeguards for their technology on Friday, following a push from the White House. The companies will voluntarily agree to abide by a number of principles though the agreement will expire when Congress passes legislation to regulate AI, according to Bloomberg. The Biden administration has placed a focus on making sure that AI companies develop the technology responsibly. Officials want to make sure tech firms can innovate in generative AI in a way that benefits society without negatively impacting the safety, rights and democratic values of the public. In May, Vice President Kamala Harris met with the CEOs of OpenAI, Microsoft, Alphabet and Anthropic, and told them they had a responsibility to make sure their AI products are safe and secure.

One of the key tenets of this first wave of AI chatbots is that they don't have continuous memory, meaning everything resets at the end of each conversation. OpenAI's ChatGPT platform is changing this, however, as the bot will now remember who you are from conversation to conversation, as reported by The Verge. This is both a tantalizing and risky prospect. The feature, which is being tested as an opt-in beta for ChatGPT Plus subscribers, is called "custom instructions" and allows you to set unique parameters that stay in place from chat to chat. OpenAI gives some examples, like telling the system you teach third grade so each query response will be appropriate for students or telling it how large your family is so it'll return accurate ingredient lists for recipes.

There is little doubt that generative AI will affect the economy--but how, exactly, remains an open question. Despite fears that these AI tools will upend jobs and exacerbate wealth inequality, early evidence suggests the technology could help level the playing field--but only if we deploy it in the right ways. Likewise, the Inflation Reduction Act and the Chips Act both have huge implications for the economy, and for efforts to revive America's high-tech manufacturing base. Rotman and Honan will look at who stands to benefit from these transformative economic events, and what the risks are. Then, on September 12, our next edition of Roundtables will tackle another important question: How should we regulate AI? Charlotte Jee, news editor, and Melissa Heikkilä, senior reporter for AI, will discuss the state of AI regulation today and what to watch for in the months ahead.

In the early weeks of 2023, as worry about ChatGPT and other artificial intelligence tools was ratcheting up dramatically in the public conversation, a tweet passed through the many interlocking corners of Book Twitter. "Imagine if every Book is converted into an Animated Book and made 10x more engaging," it read. Huge opportunity here to disrupt Kindle and Audible." The tweet's author, Gaurav Munjal, cofounded Unacademy, which bills itself as "India's largest learning platform"--and within the edtech context, where digitally animated books can be effective teaching tools, his suggestion might read a certain way. But to a broader audience, the sweeping proclamation that AI will make "every" book "10x more engaging" seemed absurd, a solution in search of a problem, and one predicated on the idea that people who choose to read narrative prose (instead of, say, watching a film or playing a game) were somehow bored or not engaged with their unanimated tomes.

Apple is working on artificial intelligence (AI) offerings similar to OpenAI's ChatGPT and Google's Bard, Bloomberg News has reported, sending its shares up as much as 2 percent to a record high. The iPhone maker has built its own framework, known as Ajax, to create large language models (LLMs) and is also testing a chatbot that some engineers call "Apple GPT", the report said on Wednesday, quoting people with knowledge of the matter. The company did not respond to a request for comment from the Reuters news agency. Apple has so far held back from any big moves in AI and even avoided mentioning the buzzword at its developer conference in June – in stark contrast to other tech giants such as Alphabet and Microsoft, which have made bold moves to incorporate the breakthrough technology. Shares of Microsoft, Nvidia and Alphabet dropped more than 1 percent after the report.

Telescopes capture images with a particular point spread function (PSF). Inferring what an image would have looked like with a much sharper PSF, a problem known as PSF deconvolution, is ill-posed because PSF convolution is not an invertible transformation. Deep generative models are appealing for PSF deconvolution because they can infer a posterior distribution over candidate images that, if convolved with the PSF, could have generated the observation. However, classical deep generative models such as VAEs and GANs often provide inadequate sample diversity. As an alternative, we propose a classifier-free conditional diffusion model for PSF deconvolution of galaxy images. We demonstrate that this diffusion model captures a greater diversity of possible deconvolutions compared to a conditional VAE.

In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions. Among those models, variational autoencoders have gained popularity as they have proven both to be computationally efficient and yield impressive results in multiple fields. Following this breakthrough, extensive research has been done in order to improve the original publication, resulting in a variety of different VAE models in response to different tasks. In this paper we present Pythae, a versatile open-source Python library providing both a unified implementation and a dedicated framework allowing straightforward, reproducible and reliable use of generative autoencoder models. As an example of application, we propose to use this library to perform a case study benchmark where we present and compare 19 generative autoencoder models representative of some of the main improvements on downstream tasks such as image reconstruction, generation, classification, clustering and interpolation.

Throughout the burgeoning "AI wars", Apple has remained suspiciously silent, until now. The company is creating its very own chatbot, as originally reported by Bloomberg. Engineers have cheekily named the toolset "AppleGPT," but it's actually called Ajax, as the large language model (LLM) was built using Google's JAX framework. Sources indicate that Apple has multiple teams working on the project, with one team devoted to addressing potential privacy concerns. What will Apple actually do with the bot?