In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have typically failed to reconstruct these properties together for complex scene images. Generative AI has recently made a leap forward with latent diffusion models capable of generating high-complexity images. Here, we investigate how to take advantage of this innovative technology for brain decoding. We present a two-stage scene reconstruction framework called ``Brain-Diffuser''. In the first stage, starting from fMRI signals, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Variational Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images. On the publicly available Natural Scenes Dataset benchmark, our method outperforms previous models both qualitatively and quantitatively. When applied to synthetic fMRI patterns generated from individual ROI (region-of-interest) masks, our trained model creates compelling ``ROI-optimal'' scenes consistent with neuroscientific knowledge. Thus, the proposed methodology can have an impact on both applied (e.g. brain-computer interface) and fundamental neuroscience.

Microsoft, Google, OpenAI and other major tech companies are rushing to develop new AI tools and push them out to millions of people. The companies have been lobbying in Washington and to other governments around the world, suggesting potential regulation while stressing the importance of allowing them to continue develop the tech. Critics have warned that the companies are focused on profit, and are trying to head-off strict government controls, or have a hand in shaping them to their own benefit.

While lawmakers in the House (and soon, the Senate) call for a "blue-ribbon commission" to study the potential impacts of AI on American society, President Biden on Tuesday met with leaders in the emerging field to discuss and debate the issue directly. The President met with Tristan Harris, executive director of the Center for Human Technology; Fei-Fei Li, Co-Director of Stanford's Human-Centered AI Institute; and Jennifer Doudna, Professor of Chemistry at UC Berkeley, among others, at the Fairmont hotel in San Francisco. Staying atop the growing swell of AI technology advancements in recent months and years, specifically the emergence of generative AI systems, has become a focal point for the Biden administration. Generative AI systems hold the promise to revolutionize many sectors of the economy and drastically reimagine the nature of modern office work. However, those same systems could just as likely wipe out entire professions, as the fields of digital art and journalism are now experiencing.

The wheels of government have finally begun to turn on the issue of generative AI regulation. US Representatives Ted Lieu (D-CA) and Ken Buck (R-CO) introduced legislation on Monday that would establish a 20-person commission to study ways to "mitigate the risks and possible harms" of AI while "protecting" America's position as a global technology power. The bill would require the Executive branch to appoint experts from throughout government, academia and industry to conduct the study over the course of two years, producing three reports during that period. The president would appoint eight members of the committee, while Congress, in an effort "to ensure bipartisanship," would split the remaining 12 positions evenly between the two parties (thereby ensuring the entire process devolves into a partisan circus). "[Generative AI] can be disruptive to society, from the arts to medicine to architecture to so many different fields, and it could also potentially harm us and that's why I think we need to take a somewhat different approach," Lieu told the Washington Post.

Over 60% of companies in Japan have a positive stance on using generative artificial intelligence in their operations, according to a survey by credit research company Teikoku Databank published Tuesday. In the survey, 9.1% of companies polled said that they are utilizing generative AI in their operations, and 52.0% said that they are considering using the technology. Meanwhile, 23.3% said that they are not considering the use of generative AI in their operations, reflecting concerns over data breaches, according to the online survey, conducted for four days through Thursday. Valid responses came from 1,380 companies. This could be due to a conflict with your ad-blocking or security software.

Opera says its generative AI-infused browser is ready for public consumption. Opera One is now out of early access. Opera features an integrated AI called Aria that you can access from the sidebar. You can use a keyboard shortcut (CTRL or Command and /) to start using Aria as well. The AI is also available in Opera's Android browser starting today.

The CEO of OpenAI, Sam Altman, has spent the last month touring world capitals where, at talks to sold-out crowds and in meetings with heads of governments, he has repeatedly spoken of the need for global AI regulation. But behind the scenes, OpenAI has lobbied for significant elements of the most comprehensive AI legislation in the world--the E.U.'s AI Act--to be watered down in ways that would reduce the regulatory burden on the company, according to documents about OpenAI's engagement with E.U. officials obtained by TIME from the European Commission via freedom of information requests. In several cases, OpenAI proposed amendments that were later made to the final text of the E.U. law--which was approved by the European Parliament on June 14, and will now proceed to a final round of negotiations before being finalized as soon as January. In 2022, OpenAI repeatedly argued to European officials that the forthcoming AI Act should not consider its general purpose AI systems--including GPT-3, the precursor to ChatGPT, and the image generator Dall-E 2--to be "high risk," a designation that would subject them to stringent legal requirements including transparency, traceability, and human oversight. That argument brought OpenAI in line with Microsoft, which has invested $13 billion into the AI lab, and Google, both of which have previously lobbied E.U. officials in favor of loosening the Act's regulatory burden on large AI providers.

The legal profession necessitates a multidimensional approach that involves synthesizing an in-depth comprehension of a legal issue with insightful commentary based on personal experience, combined with a comprehensive understanding of pertinent legislation, regulation, and case law, in order to deliver an informed legal solution. The present offering with generative AI presents major obstacles in replicating this, as current models struggle to integrate and navigate such a complex interplay of understanding, experience, and fact-checking procedures. It is noteworthy that where generative AI outputs understanding and experience, which reflect the aggregate of various subjective views on similar topics, this often deflects the model's attention from the crucial legal facts, thereby resulting in hallucination. Hence, this paper delves into the feasibility of three independent LLMs, each focused on understanding, experience, and facts, synthesising as one single ensemble model to effectively counteract the current challenges posed by the existing monolithic generative AI models. We introduce an idea of mutli-length tokenisation to protect key information assets like common law judgements, and finally we interrogate the most advanced publicly available models for legal hallucination, with some interesting results.

Humankind is entering a novel creative era in which anybody can synthesize digital information using generative artificial intelligence (AI). Text-to-image generation, in particular, has become vastly popular and millions of practitioners produce AI-generated images and AI art online. This chapter first gives an overview of the key developments that enabled a healthy co-creative online ecosystem around text-to-image generation to rapidly emerge, followed by a high-level description of key elements in this ecosystem. A particular focus is placed on prompt engineering, a creative practice that has been embraced by the AI art community. It is then argued that the emerging co-creative ecosystem constitutes an intelligent system on its own - a system that both supports human creativity, but also potentially entraps future generations and limits future development efforts in AI. The chapter discusses the potential risks and dangers of cultivating this co-creative ecosystem, such as the bias inherent in today's training data, potential quality degradation in future image generation systems due to synthetic data becoming common place, and the potential long-term effects of text-to-image generation on people's imagination, ambitions, and development.

The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL-E 2, auto-regressive and diffusion models became the new standard for large-scale generative models overnight. This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that na\"Ively increasing the capacity of the StyleGAN architecture quickly becomes unstable. We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers three major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel pixels in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.