Reconstructing visual experiences from human brain activity offers a unique way to understand how the brain represents the world, and to interpret the connection between computer vision models and our visual system. While deep generative models have recently been employed for this task, reconstructing realistic images with high semantic fidelity is still a challenging problem. Here, we propose a new method based on a diffusion model (DM) to reconstruct images from human brain activity obtained via functional magnetic resonance imaging (fMRI). More specifically, we rely on a latent diffusion model (LDM) termed Stable Diffusion. This model reduces the computational cost of DMs, while preserving their high generative performance. We also characterize the inner mechanisms of the LDM by studying how its different components (such as the latent vector of image Z, conditioning inputs C, and different elements of the denoising U-Net) relate to distinct brain functions. We show that our proposed method can reconstruct high-resolution images with high fidelity in straight-forward fashion, without the need for any additional training and fine-tuning of complex deep-learning models. We also provide a quantitative interpretation of different LDM components from a neuroscientific perspective. Overall, our study proposes a promising method for reconstructing images from human brain activity, and provides a new framework for understanding DMs. Please check out our webpage at this https URL.

The authors have declared no competing interest.

OpenAI announced its latest language model, GPT-4, but many in the AI community were disappointed by the lack of public information. Their complaints track increasing tensions in the AI world over safety.

Yesterday, OpenAI announced GPT-4, its long-awaited next-generation AI language model.

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Should AI research be open or closed? Experts disagree.

Many in the AI community have criticized this decision, noting that it undermines the company’s founding ethos as a research org and makes it harder for others to replicate its work. Perhaps more significantly, some say it also makes it difficult to develop safeguards against the sort of threats posed by AI systems like GPT-4, with these complaints coming at a time of increasing tension and rapid progress in the AI world.

“I think we can call it shut on ‘Open’ AI: the 98 page paper introducing GPT-4 proudly declares that they’re disclosing *nothing* about the contents of their training set,” tweeted Ben Schmidt, VP of information design at Nomic AI, in a thread on the topic.

Earlier this month, when LinkedIn started seeding “AI-powered conversation starters” in people’s news feeds to boost engagement on its platform, the move saw more than little engagement of its own, none of it too positive.

But the truth of the matter with LinkedIn is that it’s been using a lot of AI and other kinds of automation across different aspects of its platform for years, primarily behind the scenes with how it builds and operates its network. Now, with its owner Microsoft going all-in on OpenAI, it looks like it’s becoming a more prominent part of the strategy for LinkedIn on the front end, too — with the latest coming today in the areas of LinkedIn profiles, recruitment and LinkedIn Learning.

The company is today introducing AI-powered writing suggestions, which will initially be offered to people to spruce up their LinkedIn profiles, and to recruiters writing job descriptions. Both are built on advanced GPT models, said Tomer Cohen, LinkedIn’s chief product officer. LinkedIn is using GPT-4 for personalized profiles, with GPT-3.5 for job descriptions. Alongside this, the company is also creating a bigger focus on AI in LinkedIn Learning, corralling 100 courses around the subject and adding 20 more focused just on generative AI.