Despite efforts to remove overt racial prejudice, language models using artificial intelligence still show covert racism against speakers of African American English that is triggered by features of the dialect.
HTTPS://WWW.NATURE.COM/ARTICLES/S41586-024-07856-5Tags: ai, supervised datasets, supervised parenting, algorithmic inequality, artificial intelligence, racist ai
Researchers are among those who feel uneasy about the unrestrained use of their intellectual property in training commercial large language models. Firms and regulators need to agree the rules of engagement.
HTTPS://WWW.NATURE.COM/ARTICLES/D41586-024-02757-ZTags: ai, Data Rights, intellectual property, nvidia wont care until amd makes an ai that makes chips using active nvidia patents, expert systems
Tags: ai, consciousness, regulations, accountability, responsibility, allowing industry to flourish, if it's wrong for a person to do it it's wrong for AI to do it
Woebot, a mental-health chatbot, is testing it out
HTTPS://SPECTRUM.IEEE.ORG/WOEBOTTags: If all you have is a hammer everything starts to look like a nail, ai, wisdom, how can you be so smart yet so stupid
Tags: smartphones and dumbpeople, ai, outsourcing, capitalism, laziness, zedtopia, why Zedtopia has no AI or adtech business model
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. ### Competing Interest Statement The authors have declared no competing interest.
HTTPS://WWW.BIORXIV.ORG/CONTENT/10.1101/2022.11.18.517004V1Tags: brain imaging, fmri, stable diffusion, ai, cognitive neuroscience, Freedom of thought, privacy law
Tags: ai, autonomous weapons, boston dynamics, military industrial complex, what the police will use on the hood in a few years, Israel tests these weapons on Palestinans
if you'd like to read my earlier writing on this topic:
https://www.zedtopia.com/ideas/racist-ai