Étiquette : deep learning (Page 1 of 7)

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“This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology. It has occurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research”.

Professor Venki Ramakrishnan – Nobel Laureate and President of the Royal Society

“We trained this system on publicly available data consisting of ~170,000 protein structures from the protein data bank together with large databases containing protein sequences of unknown structure. It uses approximately 16 TPUv3s (which is 128 TPUv3 cores or roughly equivalent to ~100-200 GPUs) run over a few weeks, a relatively modest amount of compute in the context of most large state-of-the-art models used in machine learning today.”

Source : AlphaFold: a solution to a 50-year-old grand challenge in biology | DeepMind

Image GPT

“We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples. By establishing a correlation between sample quality and image classification accuracy, we show that our best generative model also contains features competitive with top convolutional nets in the unsupervised setting.”

Source : Image GPT

“NVIDIA Maxine is a fully accelerated platform SDK for developers of video conferencing services to build and deploy AI-powered features that use state-of-the-art models in their cloud. Video conferencing applications based on Maxine can reduce video bandwidth usage down to one-tenth of H.264 using AI video compression, dramatically reducing costs.”

Source : NVIDIA Maxine Video Conferencing Platform | NVIDIA Developer

“Twitter it was looking into why the neural network it uses to generate photo previews apparently chooses to show white people’s faces more frequently than Black faces. Several Twitter users demonstrated the issue over the weekend, posting examples of posts that had a Black person’s face and a white person’s face. Twitter’s preview showed the white faces more often.”

Source : Twitter is looking into why its photo preview appears to favor white faces over Black faces – The Verge

Open AI - Jukebox

“We’re introducing Jukebox, a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles. We’re releasing the model weights and code, along with a tool to explore the generated samples.”

Source : Jukebox

Google Vision API

“Google notes in its own AI principles that algorithms and datasets can reinforce bias: ‘We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief.’ Google invited affected developers to comment on its discussion forums. Only one developer had commented at the time of writing, and complained the change was down to ‘political correctness.’ ‘I don’t think political correctness has room in APIs,’ the person wrote. ‘If I can 99% of the times identify if someone is a man or woman, then so can the algorithm. You don’t want to do it? Companies will go to other services.’”

Source : Google AI will no longer use gender labels like ‘woman’ or ‘man’ on images of people to avoid bias

“NOTE: The audio quality demonstrated here was additionally degraded since we want to avoid improper use of this technology. The purpose of this video is to excite the class about the potential of deep learning, not to deceive anyone. Thus, we purposely lowered the audio quality before publishing to make the synthetic aspect of this video clearer.”

via Alexander Amini

“Python code to submit rotated images to the Cloud Vision API + R code for visualizing it. This repository was used to create this animation.”

Source : GitHub – minimaxir/optillusion-animation: Python code to submit rotated images to the Cloud Vision API + R code for visualizing it

0602095339508_web_tete.jpg

“Pour moi, il ne fait aucun doute que les machines arriveront tôt ou tard à des niveaux d’intelligence aussi performante et générale que les humains et, probablement, nous dépasseront assez vite. […]
Facebook est une entreprise qui n’est pas agressive au niveau commercial ou concurrentiel. C’est une entreprise qui donne l’impression de travailler comme pour une grande famille, les gens qui sont à la direction sont à la fois intelligents et sympathiques. Ils essaient de faire les choses correctement, mais il y a des erreurs dues au fait qu’aucun service de ce type n’existait auparavant, et aussi au fait que des individus, des partenaires ou des gouvernements ont pu abuser de notre confiance.”

Source : Yann Le Cun : « Les machines vont arriver à une intelligence de niveau humain » | Les Echos

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