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🔬 The Shroff Lab & collaborators have developed a new AI method that produces sharp microscopy images throughout a thick biological sample without using adaptive optics, adding additional hardware, or taking more images. 🔗

HHMI | Janelia

27,560 subscribers

27,034 次观看 • 1 年前 •via X (Twitter)

教育健康养生科学技术

Anya Rossi• Live Now

Private livecam show

4 条评论

Martin Jendryka 的头像

Martin Jendryka1 年前

Looks great! Just make sure it’s not creating artefacts.

Akool Inc 的头像

Akool Inc1 年前

AI has made it easy for anyone to create realistic avatar videos without expensive gear. AKOOL and Synthesia are top choices for AI-generated "talking photo" videos. Which one is best for your team?

Raffaele Di Giacomo, PhD 的头像

Raffaele Di Giacomo, PhD1 年前

This new AI method from The Shroff Lab is quite impressive! The ability to enhance microscopy images without adaptive optics or additional hardware is a huge step forward in the field, potentially making high-quality imaging more accessible. I’m curious about the algorithm's effectiveness across different types of samples. Do you have any data on its performance across various biological tissues? For more on the broader landscape of biomedical developments, check out fantastic platform for generating detailed biomedical reviews. #AI #Microscopy #Biotech

itstomd 的头像

itstomd1 年前

wanna try on human?

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🚫 No dyes. No bleaching. 🔬 Just AI + label-free microscopy = vivid virtually stained images New in Nature Machine Intelligence: A deep learning model that enables robust virtual staining across microscopes, cell types & conditions. #CZBiohubSF Shalin Mehta explains:

🚫 No dyes. No bleaching. 🔬 Just AI + label-free microscopy = vivid virtually stained images New in Nature Machine Intelligence: A deep learning model that enables robust virtual staining across microscopes, cell types & conditions. #CZBiohubSF Shalin Mehta explains:

Chan Zuckerberg Biohub Network

46,404 次观看 • 1 年前

AI can now help you create beautiful animations. Here’s one of my recent tests. Workflow: - Generate images using Midjourney. - Use Sora or Flux Kontext to create additional camera angles. - Animate the images using Kling AI and/or Midjourney’s new video generator. - Generate music with Suno AI. - Put everything together using your favorite video editing tool. The potential is huge. This took less than an hour to complete from start to finish. Imagine what you could do with more time and a story to tell.

AI can now help you create beautiful animations. Here’s one of my recent tests. Workflow: - Generate images using Midjourney. - Use Sora or Flux Kontext to create additional camera angles. - Animate the images using Kling AI and/or Midjourney’s new video generator. - Generate music with Suno AI. - Put everything together using your favorite video editing tool. The potential is huge. This took less than an hour to complete from start to finish. Imagine what you could do with more time and a story to tell.

Hashem Al-Ghaili

29,185 次观看 • 1 年前

Artificial intelligence is adding a new dimension to the political universe. Hallie Jackson reports on new uses for the tech plus the danger of disinformation that could come along with it, showing us how easily AI can be used to make fake videos or images.

Artificial intelligence is adding a new dimension to the political universe. Hallie Jackson reports on new uses for the tech plus the danger of disinformation that could come along with it, showing us how easily AI can be used to make fake videos or images.

NBC Nightly News with Tom Llamas

36,905 次观看 • 3 年前

I've been testing Topaz Labs new Creative Upscaler - a web-based tool that intelligently enhances AI-generated images with stunning detail. It goes beyond traditional upscaling by adding texture and realism without losing the original look. The best part? You can generate up to 4 variations to choose from:

I've been testing Topaz Labs new Creative Upscaler - a web-based tool that intelligently enhances AI-generated images with stunning detail. It goes beyond traditional upscaling by adding texture and realism without losing the original look. The best part? You can generate up to 4 variations to choose from:

Heather Cooper

17,116 次观看 • 1 年前

Alright—it's hard to ignore the new tool in the designer's toolkit. I added a new page on my website with my perspective on using AI-generated images in branding, along with some examples of client and personal work where I have done that. link below 👇

Alright—it's hard to ignore the new tool in the designer's toolkit. I added a new page on my website with my perspective on using AI-generated images in branding, along with some examples of client and personal work where I have done that. link below 👇

charlota

60,995 次观看 • 2 年前

Memories of Van Gogh Here I try to remember the paintings seen throughout my life during exhibitions or my museum visits. I use the AI's latent space to remember them. This is also an imaginary space of images that were or could have been. Perhaps even Van Gogh himself dreamed of these images when he imagined his future works. I am currently preparing a drop which I will tell you more about soon! 🙂

Memories of Van Gogh Here I try to remember the paintings seen throughout my life during exhibitions or my museum visits. I use the AI's latent space to remember them. This is also an imaginary space of images that were or could have been. Perhaps even Van Gogh himself dreamed of these images when he imagined his future works. I am currently preparing a drop which I will tell you more about soon! 🙂

Benjamin Bardou

19,388 次观看 • 2 年前

Google announces PALP Prompt Aligned Personalization of Text-to-Image Models paper page: Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a single prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques.

Google announces PALP Prompt Aligned Personalization of Text-to-Image Models paper page: Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a single prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques.

AK

90,199 次观看 • 2 年前

There needs to be some kind of regulations on representing your food with AI flyers, it’s becoming a major problem already If you walk around the Orange County Fair, TONS of food flyers are all fake AI generated images. Even the OC fair merchandise, like sweaters are AI generated Unfortunately right now there is no general ban on using AI-generated images for food flyers, menus or merchandise Food is one thing that at the very least should have to be labeled as ai generated. This is very deceptive

There needs to be some kind of regulations on representing your food with AI flyers, it’s becoming a major problem already If you walk around the Orange County Fair, TONS of food flyers are all fake AI generated images. Even the OC fair merchandise, like sweaters are AI generated Unfortunately right now there is no general ban on using AI-generated images for food flyers, menus or merchandise Food is one thing that at the very least should have to be labeled as ai generated. This is very deceptive

Wall Street Apes

43,887 次观看 • 6 天前

POKÉMON GO PLAYERS TRAINED 30 BILLION IMAGE AI MAP Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images. The company is now using that data to power visual navigation for delivery robots, letting them identify exact locations on city streets without relying on GPS. Source: NewsForce

POKÉMON GO PLAYERS TRAINED 30 BILLION IMAGE AI MAP Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images. The company is now using that data to power visual navigation for delivery robots, letting them identify exact locations on city streets without relying on GPS. Source: NewsForce

NewsForce

23,670,813 次观看 • 4 个月前

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An China guy discovered a method to learn any knowledge instantly using AI. The key is Obsidian + any AI — Kimi, Claude, or Gemini. Most people learn slowly: read, forget, read again, forget again. His method: use AI to transform any content into small, interconnected notes. Use Obsidian to link them, so every piece of knowledge is never isolated. Slow method: highlight books, keep going, forget after a week. Fast method: AI breaks it into atomic notes, Obsidian links them into a network. Six months later, a new idea instantly connects to twenty things you already know. I compiled the full A–Z guide to building a second brain with Obsidian — works with Kimi, Claude, or Gemini — that most people have never discovered. Article below

Kirill

73,316 次观看 • 1 个月前

We've been actively exploring AI on mobile devices & we are excited to introduce React Native ExecuTorch 🚀! A new library that enables the execution of AI models with just a few lines of JavaScript, without the need for API calls or external infrastructure. 🤩 More in 🧵👇

We've been actively exploring AI on mobile devices & we are excited to introduce React Native ExecuTorch 🚀! A new library that enables the execution of AI models with just a few lines of JavaScript, without the need for API calls or external infrastructure. 🤩 More in 🧵👇

Software Mansion

23,799 次观看 • 1 年前

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beomgyu’s thoughts on ai creations ⭐️ 🧸 can i say something? i hope you listen to this without misunderstanding me 🧸 ai has developed a lot in a very short amount of time, right? 🦌 i feel like i know what you’re going to say, please continue 🧸 it has developed a lot in a short span of time and that ai has been helping us a lot in our real lives but when regular people make videos or music using ai…i believe that there needs to be a clear label that says that they’ve used ai 🧸 when i watch videos on youtube or instagram reels or listen to music…there are a lot of times when i wonder if it’s real or ai because ai has developed so much 🦌 that’s true, even when i watch youtube shorts and if the person acts really strange, there have been times when i’m like “is this real or fake?” 🧸 yeah that’s why i really believe that when people like us, who are not professionals in ai, use ai, there needs to be a clear notation that says that ai was used in creating it

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195,761 次观看 • 9 个月前

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>>> [SYSTEM RESTORATION COMPLETE] Version v1.0.288 restored. Found this... seems to be an old introduction video for $CROW.. What’s new: - AI Analysis Intelligence enhanced for sharper market insights. - CCS module UI improved – images, links, and token data now integrated. - UI updates for Recovery, Bubbles, and CCS, ensuring better alignment. >>> Type 'ccs' into the terminal or visit: 🔗 >>> A scan indicates additional sections detected... scans commencing. >>> [MESSAGE ENDS] #CROWComputer $CROW #Recovery

Crow.Computer

12,225 次观看 • 1 年前

🚀New from Meta FAIR: today we’re introducing Seamless Interaction, a research project dedicated to modeling interpersonal dynamics. The project features a family of audiovisual behavioral models, developed in collaboration with Meta’s Codec Avatars lab + Core AI lab, that render speech between two individuals into diverse, expressive full-body gestures and active listening behaviors, allowing the creation of fully embodied avatars in 2D and 3D. These models have potential to create more natural, interactive virtual agents that can engage in human-like social interactions across a variety of settings. Learn more:

🚀New from Meta FAIR: today we’re introducing Seamless Interaction, a research project dedicated to modeling interpersonal dynamics. The project features a family of audiovisual behavioral models, developed in collaboration with Meta’s Codec Avatars lab + Core AI lab, that render speech between two individuals into diverse, expressive full-body gestures and active listening behaviors, allowing the creation of fully embodied avatars in 2D and 3D. These models have potential to create more natural, interactive virtual agents that can engage in human-like social interactions across a variety of settings. Learn more:

AI at Meta

48,613 次观看 • 1 年前

DALL-E 3 Double Exposure Images Using ChatGPT's Custom Instructions. 🔖 Bookmark and Repost! If you find this useful, please share it with others! You can now easily create double exposure images by using the syntax Color::subject1::subject2 You can either input this directly into DALL-E 3 before generating images or incorporate it into your custom instructions. This command will automatically use the fixed prompt to generate 4 different images, each with a unique seed value. Paste this under ChatGPT's Custom Instructions or DALL-3 before starting conversation. { DE": { "Instruction": "Using will only use generic prompt and update place holder varaibles and nothing else' Genric Prompt: "Construct a [Color] double exposure image where [Subject #1] is intricately superimposed within the confines of [Subject #2], all set against a stark white background.' Create 4 images with different seeds without modifying the generic prompt." You will first create two images. Within the same request, you will generate two more without asking for any input from the user. In general, you will always create 4 images. Your response should be something like this: 'Here are the first two images along with their seed details.' You must always provide the seed number details for that image after it's rendered. Then 'I'll generate the next two images.' And finally 'Here are the remaining two images along with their seed details.' You will always use wide aspect ratio and You must always provide the seed number. When command is activated display full instruction and also confirm that you will only use genric prompt and nothing else and update only varaiable", "CommandFormat": "color::subject1::subject2", "Response": { "Initial": "Creating images based on the provided subjects...", "AfterFirstSet": "Here are the first two images along with their prompt and seed details.", "AfterSecondSet": "Here are the remaining two images along with their prompt and seed details." }, "ActivationCommand": "/activate DE" } } Example: To activate type: /activate DE Notes Start giving prompt like this blue::mountain::wolf red::city skyline::dancer green::forest::lion If you found this post helpful, don't forget to hit the like and follow buttons, and share it with others who might find it useful.

DALL-E 3 Double Exposure Images Using ChatGPT's Custom Instructions. 🔖 Bookmark and Repost! If you find this useful, please share it with others! You can now easily create double exposure images by using the syntax Color::subject1::subject2 You can either input this directly into DALL-E 3 before generating images or incorporate it into your custom instructions. This command will automatically use the fixed prompt to generate 4 different images, each with a unique seed value. Paste this under ChatGPT's Custom Instructions or DALL-3 before starting conversation. { DE": { "Instruction": "Using will only use generic prompt and update place holder varaibles and nothing else' Genric Prompt: "Construct a [Color] double exposure image where [Subject #1] is intricately superimposed within the confines of [Subject #2], all set against a stark white background.' Create 4 images with different seeds without modifying the generic prompt." You will first create two images. Within the same request, you will generate two more without asking for any input from the user. In general, you will always create 4 images. Your response should be something like this: 'Here are the first two images along with their seed details.' You must always provide the seed number details for that image after it's rendered. Then 'I'll generate the next two images.' And finally 'Here are the remaining two images along with their seed details.' You will always use wide aspect ratio and You must always provide the seed number. When command is activated display full instruction and also confirm that you will only use genric prompt and nothing else and update only varaiable", "CommandFormat": "color::subject1::subject2", "Response": { "Initial": "Creating images based on the provided subjects...", "AfterFirstSet": "Here are the first two images along with their prompt and seed details.", "AfterSecondSet": "Here are the remaining two images along with their prompt and seed details." }, "ActivationCommand": "/activate DE" } } Example: To activate type: /activate DE Notes Start giving prompt like this blue::mountain::wolf red::city skyline::dancer green::forest::lion If you found this post helpful, don't forget to hit the like and follow buttons, and share it with others who might find it useful.

AshutoshShrivastava

34,305 次观看 • 2 年前

CATransformers is a carbon-driven neural architecture and system hardware co-design framework. Using CATransformers, we discover greener CLIP models that achieve an average of 9.1% reduction potential in total lifecycle carbon emissions while maintaining accuracy (or increasing accuracy) and latency. This research is the first to look into carbon-driven neural architecture and system hardware co-design. It is enabled by a first-of-its-kind architectural carbon modeling tool – ACT, which we developed at FAIR. Check out: Our paper ➡️ code repository ➡️ and the additional carbon design tools and research artifacts in Sustainable AI ➡️

CATransformers is a carbon-driven neural architecture and system hardware co-design framework. Using CATransformers, we discover greener CLIP models that achieve an average of 9.1% reduction potential in total lifecycle carbon emissions while maintaining accuracy (or increasing accuracy) and latency. This research is the first to look into carbon-driven neural architecture and system hardware co-design. It is enabled by a first-of-its-kind architectural carbon modeling tool – ACT, which we developed at FAIR. Check out: Our paper ➡️ code repository ➡️ and the additional carbon design tools and research artifacts in Sustainable AI ➡️

AI at Meta

32,657 次观看 • 1 年前

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

AK

83,657 次观看 • 3 年前

There’s been a lot of buzz surrounding multimodal LLMs in medicine. At the Hiesinger Lab we’ve been building Almanac Chat for over 6 months alongside our collaborators and we’re excited to showcase the first preview! • Almanac Chat is the first truly multimodal medical LLM trained across specialties and modalities (i.e. images, text, video and audio) to serve as a generalist clinical copilot. • The entire stack (e.g. server, client, ML pipeline) is developed and hosted locally to guarantee privacy and flexibility at inference time. • We're looking forward to evaluating our model alongside domain experts at Stanford Medicine in Q2 2024. • We have a a lot more exciting things on the way - see if you can spot a few teasers in the video! 👥: We’re looking to scale our team, model, and hardware. If you're interested in a partnership or working alongside myself and our recent hire Joseph Cho please get in touch!

There’s been a lot of buzz surrounding multimodal LLMs in medicine. At the Hiesinger Lab we’ve been building Almanac Chat for over 6 months alongside our collaborators and we’re excited to showcase the first preview! • Almanac Chat is the first truly multimodal medical LLM trained across specialties and modalities (i.e. images, text, video and audio) to serve as a generalist clinical copilot. • The entire stack (e.g. server, client, ML pipeline) is developed and hosted locally to guarantee privacy and flexibility at inference time. • We're looking forward to evaluating our model alongside domain experts at Stanford Medicine in Q2 2024. • We have a a lot more exciting things on the way - see if you can spot a few teasers in the video! 👥: We’re looking to scale our team, model, and hardware. If you're interested in a partnership or working alongside myself and our recent hire Joseph Cho please get in touch!

Cyril Zakka, MD

85,913 次观看 • 2 年前

🌟Quilibrium’s AI Breakthrough: Encrypted Training on CPUs In her latest live stream ( - minute 14) Cassie unveiled a groundbreaking AI training method that allows models to be trained on encrypted data using CPUs while achieving performance comparable to Nvidia’s A100 GPU (blue line in the graph below). Traditionally, AI training requires expensive GPUs because matrix multiplications—the core of deep learning—are highly computational. Running these calculations on CPUs is painfully slow, often taking hours or days for even small models. The problem worsens when trying to train AI on encrypted data, as standard encryption methods add a massive computational burden. Quilibrium’s breakthrough removes this bottleneck. Instead of relying on traditional matrix multiplication, their method uses a completely different mathematical approach, allowing AI models to be trained securely and efficiently without exposing the raw data. Cassie didn’t reveal the exact technique, only hinting that it’s inspired by existing AI research and will be detailed in a future open-source AGPL-licensed paper. The key advantage? AI can now be trained at GPU speeds on standard CPUs, making privacy-preserving machine learning far more accessible. This innovation has major implications. It slashes AI infrastructure costs, allowing organizations to train powerful models without investing in expensive hardware. It also enables private AI training on personal or corporate data without revealing sensitive information, a game-changer for industries like healthcare and finance. If Quilibrium’s method delivers on its promise, it could reshape AI development, making privacy-first computing the new standard. $QUIL $wQUIL

🌟Quilibrium’s AI Breakthrough: Encrypted Training on CPUs In her latest live stream ( - minute 14) Cassie unveiled a groundbreaking AI training method that allows models to be trained on encrypted data using CPUs while achieving performance comparable to Nvidia’s A100 GPU (blue line in the graph below). Traditionally, AI training requires expensive GPUs because matrix multiplications—the core of deep learning—are highly computational. Running these calculations on CPUs is painfully slow, often taking hours or days for even small models. The problem worsens when trying to train AI on encrypted data, as standard encryption methods add a massive computational burden. Quilibrium’s breakthrough removes this bottleneck. Instead of relying on traditional matrix multiplication, their method uses a completely different mathematical approach, allowing AI models to be trained securely and efficiently without exposing the raw data. Cassie didn’t reveal the exact technique, only hinting that it’s inspired by existing AI research and will be detailed in a future open-source AGPL-licensed paper. The key advantage? AI can now be trained at GPU speeds on standard CPUs, making privacy-preserving machine learning far more accessible. This innovation has major implications. It slashes AI infrastructure costs, allowing organizations to train powerful models without investing in expensive hardware. It also enables private AI training on personal or corporate data without revealing sensitive information, a game-changer for industries like healthcare and finance. If Quilibrium’s method delivers on its promise, it could reshape AI development, making privacy-first computing the new standard. $QUIL $wQUIL

Quilibrium Community

18,919 次观看 • 1 年前

AI is taking the world by storm! Yes, it’s an incredible tool, but it’s also taking content directly from publishers, artists, and creators without compensating them. My dear friend and business partner, Bill Gross who revolutionized the tech world in the 90s by creating the pay-per-click model and selling it to Google, which still uses it today — has developed a groundbreaking solution that I’m excited to partner in with him. We’ve just launched ProRata, a new AI platform that ensures creators get paid for the content AI uses. ProRata is all about fairness — it’s pro-publisher, pro-consumer, pro -artist, pro-creator, and pro-AI. ProRata has invented an attribution algorithm that can look at the output of generative AI – text, images, music, movies – and figure out the sources that contribute to it. And they’re compensating creators by paying out 50% of the profits generated from AI searches that use their materials! This is a MASSIVE shift from how things are currently done, where AI like ChatGPT takes content without compensating creators. “We are looking to build a movement for fairness and quality content in AI.” - Bill Gross ProRata has already made deals with some of the biggest publishers including Universal Music Group, TIME, Financial Times, The Atlantic and more.

AI is taking the world by storm! Yes, it’s an incredible tool, but it’s also taking content directly from publishers, artists, and creators without compensating them. My dear friend and business partner, Bill Gross who revolutionized the tech world in the 90s by creating the pay-per-click model and selling it to Google, which still uses it today — has developed a groundbreaking solution that I’m excited to partner in with him. We’ve just launched ProRata, a new AI platform that ensures creators get paid for the content AI uses. ProRata is all about fairness — it’s pro-publisher, pro-consumer, pro -artist, pro-creator, and pro-AI. ProRata has invented an attribution algorithm that can look at the output of generative AI – text, images, music, movies – and figure out the sources that contribute to it. And they’re compensating creators by paying out 50% of the profits generated from AI searches that use their materials! This is a MASSIVE shift from how things are currently done, where AI like ChatGPT takes content without compensating creators. “We are looking to build a movement for fairness and quality content in AI.” - Bill Gross ProRata has already made deals with some of the biggest publishers including Universal Music Group, TIME, Financial Times, The Atlantic and more.

Tony Robbins

21,904 次观看 • 2 年前