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Can someone with access to latest AI radiology foundation models submit this case and reply with model used and answer generated? Dr. Ameya Kawthalkar Pranav Rajpurkar JunMa Bo Wang Weidi Xie Tugba Akinci D'Antonoli Hoifung Poon Dr Amine Korchi Woojin Kim

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We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

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Today Mirror Mirror AI is launching the marketplace where fashion models license their likeness and brands get stunning AI-generated imagery featuring real people. Commercially licensed, model-approved. Try our platform: As a fashion model I used to spend hours on fashion photoshoot sets. I later did my PhD in CS and became a Research Scientist on AI for fashion. I can see clearly that AI image generation is replacing a large portion of my old job. But brands that use AI recklessly have already paid the price. It damages reputations and hurts the bottom line. Putting real people at the core of AI-generated imagery isn't just about avoiding backlash. It's better business. That's what Mirror Mirror AI is built for. Right now, Mirror Mirror AI houses agency-signed models who have graced the covers of Vogue and Harper's Bazaar. You can digitally book them using our fashion-centric AI software, get your campaign done in hours instead of weeks, and never have to fly anyone in. You purchase a license for commercial use upon approval, and the models get paid. Mirror Mirror AI is also opening a global call for independent models from anywhere in the world to apply to be featured on the platform. Work with fashion brands internationally, choose the projects you take on, and earn from your own likeness on your own terms. Selected models will be announced at an exclusive event in New York during Tech Week this June. Apply for the open call: A huge thank you to our incredible team for pouring their hearts into this launch, and to a16z speedrun for believing in our vision from the start. We're just getting started.

Today Mirror Mirror AI is launching the marketplace where fashion models license their likeness and brands get stunning AI-generated imagery featuring real people. Commercially licensed, model-approved. Try our platform: As a fashion model I used to spend hours on fashion photoshoot sets. I later did my PhD in CS and became a Research Scientist on AI for fashion. I can see clearly that AI image generation is replacing a large portion of my old job. But brands that use AI recklessly have already paid the price. It damages reputations and hurts the bottom line. Putting real people at the core of AI-generated imagery isn't just about avoiding backlash. It's better business. That's what Mirror Mirror AI is built for. Right now, Mirror Mirror AI houses agency-signed models who have graced the covers of Vogue and Harper's Bazaar. You can digitally book them using our fashion-centric AI software, get your campaign done in hours instead of weeks, and never have to fly anyone in. You purchase a license for commercial use upon approval, and the models get paid. Mirror Mirror AI is also opening a global call for independent models from anywhere in the world to apply to be featured on the platform. Work with fashion brands internationally, choose the projects you take on, and earn from your own likeness on your own terms. Selected models will be announced at an exclusive event in New York during Tech Week this June. Apply for the open call: A huge thank you to our incredible team for pouring their hearts into this launch, and to a16z speedrun for believing in our vision from the start. We're just getting started.

Yusan Lin

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Really happy to see the interest around our “Hands-on with Gemini” video. In our developer blog yesterday, we broke down how Gemini was used to create it. We gave Gemini sequences of different modalities — image and text in this case — and had it respond by predicting what might come next. Devs can try similar things when access to Pro opens on 12/13 🚀. The knitting demo used Ultra⚡ All the user prompts and outputs in the video are real, shortened for brevity. The video illustrates what the multimodal user experiences built with Gemini could look like. We made it to inspire developers. When you’re building an app, you can get similar results (there’s always some variability with LLMs) by prompting Gemini with an instruction that allows the user to "configure" the behavior of the model, like inputting “you are an expert in science …” before a user can engage in the same kind of back and forth dialogue. Here’s a clip of what this looks like in AI Studio with Gemini Pro. We’ve come a long way since Flamingo 🦩 & PALI, looking forward to seeing what people build with it!

Really happy to see the interest around our “Hands-on with Gemini” video. In our developer blog yesterday, we broke down how Gemini was used to create it. We gave Gemini sequences of different modalities — image and text in this case — and had it respond by predicting what might come next. Devs can try similar things when access to Pro opens on 12/13 🚀. The knitting demo used Ultra⚡ All the user prompts and outputs in the video are real, shortened for brevity. The video illustrates what the multimodal user experiences built with Gemini could look like. We made it to inspire developers. When you’re building an app, you can get similar results (there’s always some variability with LLMs) by prompting Gemini with an instruction that allows the user to "configure" the behavior of the model, like inputting “you are an expert in science …” before a user can engage in the same kind of back and forth dialogue. Here’s a clip of what this looks like in AI Studio with Gemini Pro. We’ve come a long way since Flamingo 🦩 & PALI, looking forward to seeing what people build with it!

Oriol Vinyals

180,991 Aufrufe • vor 2 Jahren

i just trained an AI on every alex hormozi book, playbook, blackbook, and podcast episode... he charges $5000 for his AI assistant and people pay it, i'm giving you the same thing for free this isn't some shitty GPT with 3 pages of info that hallucinates answers, NotebookLM is the best AI for consuming and recalling information right now, i fed it EVERYTHING: - $100M offers, leads, money models - the black books (given to people who donated 200 books) - all the playbooks and lost chapters - his best podcast breakdowns and frameworks the information inside is worth thousands it can answer ANY business problem using hormozi's exact frameworks it pulls from the exact books and gives you page-specific answers... no generic advice, no made-up bullshit i should NEVER be sharing this for free, that's why i'll delete this in 24hrs reply 'HORMOZI' + RT and i'll give you access for free (must follow me so i can dm)

i just trained an AI on every alex hormozi book, playbook, blackbook, and podcast episode... he charges $5000 for his AI assistant and people pay it, i'm giving you the same thing for free this isn't some shitty GPT with 3 pages of info that hallucinates answers, NotebookLM is the best AI for consuming and recalling information right now, i fed it EVERYTHING: - $100M offers, leads, money models - the black books (given to people who donated 200 books) - all the playbooks and lost chapters - his best podcast breakdowns and frameworks the information inside is worth thousands it can answer ANY business problem using hormozi's exact frameworks it pulls from the exact books and gives you page-specific answers... no generic advice, no made-up bullshit i should NEVER be sharing this for free, that's why i'll delete this in 24hrs reply 'HORMOZI' + RT and i'll give you access for free (must follow me so i can dm)

Miko

523,099 Aufrufe • vor 10 Monaten

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

Santiago

197,702 Aufrufe • vor 2 Jahren

A synthetic biologist just designed an RNA vaccine for canine cancer with just $100 in AI credits. His tech stack: an OpenClaw agent running Claude Sonnet 4.6 as the workhorse. BIOS by Bio Protocol as the AI scientist for literature search and data analysis. A minimal cloud setup with no GPUs and no model training. His words: "Could this design really work? Did we actually design a medicine which usually takes many years and many millions of dollars in three days and with a hundred dollars in AI credits?" The answer is closer than you'd think. He ran a full bioinformatics workflow and came away with what he calls a "bonafide, totally plausible neoantigen vaccine candidate." He also said something that matters more than the result: "If we fuck this up, people can die. So I need to say that in all humility. There is no way that I got this perfect. There is a mistake in here for sure." That's the difference between someone doing real science with AI tools and someone doing a demo. He knows where the limits are because he has the domain expertise to see them. This is what BIOS was built for. A callable AI scientist that any agent or researcher can plug into their workflow for deep literature search and analysis. Jake Wintermute 🧬/acc used it alongside OpenClaw and got from hypothesis to vaccine design in a weekend. Three days. One hundred dollars. One real candidate.

A synthetic biologist just designed an RNA vaccine for canine cancer with just $100 in AI credits. His tech stack: an OpenClaw agent running Claude Sonnet 4.6 as the workhorse. BIOS by Bio Protocol as the AI scientist for literature search and data analysis. A minimal cloud setup with no GPUs and no model training. His words: "Could this design really work? Did we actually design a medicine which usually takes many years and many millions of dollars in three days and with a hundred dollars in AI credits?" The answer is closer than you'd think. He ran a full bioinformatics workflow and came away with what he calls a "bonafide, totally plausible neoantigen vaccine candidate." He also said something that matters more than the result: "If we fuck this up, people can die. So I need to say that in all humility. There is no way that I got this perfect. There is a mistake in here for sure." That's the difference between someone doing real science with AI tools and someone doing a demo. He knows where the limits are because he has the domain expertise to see them. This is what BIOS was built for. A callable AI scientist that any agent or researcher can plug into their workflow for deep literature search and analysis. Jake Wintermute 🧬/acc used it alongside OpenClaw and got from hypothesis to vaccine design in a weekend. Three days. One hundred dollars. One real candidate.

Bio Protocol

10,742 Aufrufe • vor 5 Monaten

New short course: Build Long-Context AI Apps with Jamba. Learn about state space models (SSMs), which have emerged as an alternative to transformers! Specifically, Jamba is a hybrid transformer-Mamba architecture that combines strengths of the transformer with ideas from SSMs. This course is built with AI21 Labs and taught by Chen Wang and Chen Almagor. The transformer architecture is computationally expensive when handling very long input contexts. But there's an alternative called Mamba, a selective state space model that can process very long contexts with a much lower computational cost. However, researchers found that the pure Mamba architecture underperforms in understanding the context, and gives lower-quality responses. To overcome this, AI21 developed the Jamba model, which combines Mamba's computational efficiency with the transformer's attention mechanism to help with the output quality. In this course, you’ll learn about how state space models, and Jamba, work. You’ll also learn how to prompt Jamba, use it to process long documents, and build long-context RAG apps. - Learn how Jamba combines transformer and state space model architectures to achieve high performance and quality - Use the AI21 SDK, with an example of prompting over a large 200k-token annual financial report of Nvidia - Use Jamba for tool-calling, with hands-on examples from calling simple arithmetic calculations to a function that returns quarterly company financial reports. - Learn how training for long context is done, and the metrics used for its evaluation - Create a RAG app using the AI21 Conversational RAG tool and build your own RAG pipeline that uses Jamba and LangChain. By the end of this course, you'll learn how to build applications that can handle context as long as an entire book. Please sign up here:

New short course: Build Long-Context AI Apps with Jamba. Learn about state space models (SSMs), which have emerged as an alternative to transformers! Specifically, Jamba is a hybrid transformer-Mamba architecture that combines strengths of the transformer with ideas from SSMs. This course is built with AI21 Labs and taught by Chen Wang and Chen Almagor. The transformer architecture is computationally expensive when handling very long input contexts. But there's an alternative called Mamba, a selective state space model that can process very long contexts with a much lower computational cost. However, researchers found that the pure Mamba architecture underperforms in understanding the context, and gives lower-quality responses. To overcome this, AI21 developed the Jamba model, which combines Mamba's computational efficiency with the transformer's attention mechanism to help with the output quality. In this course, you’ll learn about how state space models, and Jamba, work. You’ll also learn how to prompt Jamba, use it to process long documents, and build long-context RAG apps. - Learn how Jamba combines transformer and state space model architectures to achieve high performance and quality - Use the AI21 SDK, with an example of prompting over a large 200k-token annual financial report of Nvidia - Use Jamba for tool-calling, with hands-on examples from calling simple arithmetic calculations to a function that returns quarterly company financial reports. - Learn how training for long context is done, and the metrics used for its evaluation - Create a RAG app using the AI21 Conversational RAG tool and build your own RAG pipeline that uses Jamba and LangChain. By the end of this course, you'll learn how to build applications that can handle context as long as an entire book. Please sign up here:

Andrew Ng

77,792 Aufrufe • vor 1 Jahr

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.

Andy Hall

33,905 Aufrufe • vor 4 Monaten

Chamath: US AI Startups Can Learn A Lot from DeepSeek "This is a case where necessity was the mother of invention." On E213, Chamath Palihapitiya explained how, even if the $6M number is inaccurate, DeepSeek still had some impressive breakthroughs: GRPO > PPO for reinforcement learning "These guys were like, 'Well, how am I going to do this reinforcement learning thing?' They invented a totally different algorithm." "It uses a lot less computer memory and it's highly performant." PTX > CUDA to build the model "And then the second thing that was crazy is everybody is used to building models and compiling through CUDA, which is NVIDIA's proprietary language." "And these guys worked totally around CUDA and they did something called PTX, which goes right to the bare metal." What US AI startups can learn from this: "We, meaning the West, with all the money that we've had, didn't come up with these ideas." "And I think part of why we didn't come up is not that we're not smart enough to do it, but we weren't forced to because the constraints didn't exist." "And so I just wonder how we make sure we learn this principle, meaning, when the AI company wakes up and rolls out of bed and some VC gives them $200M, maybe that's not the right answer for a Series A or a Seed." "And maybe the right answer is $2M so that they do these DeepSeek-like innovations."

Chamath: US AI Startups Can Learn A Lot from DeepSeek "This is a case where necessity was the mother of invention." On E213, Chamath Palihapitiya explained how, even if the $6M number is inaccurate, DeepSeek still had some impressive breakthroughs: GRPO > PPO for reinforcement learning "These guys were like, 'Well, how am I going to do this reinforcement learning thing?' They invented a totally different algorithm." "It uses a lot less computer memory and it's highly performant." PTX > CUDA to build the model "And then the second thing that was crazy is everybody is used to building models and compiling through CUDA, which is NVIDIA's proprietary language." "And these guys worked totally around CUDA and they did something called PTX, which goes right to the bare metal." What US AI startups can learn from this: "We, meaning the West, with all the money that we've had, didn't come up with these ideas." "And I think part of why we didn't come up is not that we're not smart enough to do it, but we weren't forced to because the constraints didn't exist." "And so I just wonder how we make sure we learn this principle, meaning, when the AI company wakes up and rolls out of bed and some VC gives them $200M, maybe that's not the right answer for a Series A or a Seed." "And maybe the right answer is $2M so that they do these DeepSeek-like innovations."

The All-In Podcast

108,815 Aufrufe • vor 1 Jahr

Before agents can participate in the economy, someone has to answer the trust question: who authorized this agent, and within what limits? On Monday evening in Seoul, that question filled a room. Thank you to everyone who joined Proof of AI: Korea Special Edition, and to our partners SOOHO.IO, Avalanche🔺, #Hashed, Fabric Foundation, and Tiger Research for building the evening with us. Our Cindy Shi opened with a keynote on Kite Passport and the state of agentic payments: where Kite Passport sits in the stack, why identity, permission, and trust come before scale, and live demos of what agents can already do today. Then the panel dug into the agent economy in Korea, moderated by 𝐘𝐮𝐦𝐢 🥀🦋 (SOOHO IO), with Eugene🔺 (Avalanche Team1), Jun Kim (Hashed), kyle.eo (Fabric Foundation), and ekko.o (Tiger Research): ▷ What infrastructure agents need as they evolve from tools into economic actors ▷ How authorization should be designed: budget limits, permission controls, and security verification ▷ Which agent use cases will land first in the Korean market Seoul is taking the agent economy seriously. Thank you, and see you at the next one. 🪁

Before agents can participate in the economy, someone has to answer the trust question: who authorized this agent, and within what limits? On Monday evening in Seoul, that question filled a room. Thank you to everyone who joined Proof of AI: Korea Special Edition, and to our partners SOOHO.IO, Avalanche🔺, #Hashed, Fabric Foundation, and Tiger Research for building the evening with us. Our Cindy Shi opened with a keynote on Kite Passport and the state of agentic payments: where Kite Passport sits in the stack, why identity, permission, and trust come before scale, and live demos of what agents can already do today. Then the panel dug into the agent economy in Korea, moderated by 𝐘𝐮𝐦𝐢 🥀🦋 (SOOHO IO), with Eugene🔺 (Avalanche Team1), Jun Kim (Hashed), kyle.eo (Fabric Foundation), and ekko.o (Tiger Research): ▷ What infrastructure agents need as they evolve from tools into economic actors ▷ How authorization should be designed: budget limits, permission controls, and security verification ▷ Which agent use cases will land first in the Korean market Seoul is taking the agent economy seriously. Thank you, and see you at the next one. 🪁

KITE AI📍Coinfest Asia🌴

20,737 Aufrufe • vor 1 Monat