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How Google Will Transform Healthcare With New Gemini Version

How Google Will Transform Healthcare With New Gemini Version

The Google Healthcare AI team has been making significant strides in applying advanced AI technologies to improve patient outcomes. In a streamed video on YouTube yesterday, Google demonstrated the transformative potential of AI in healthcare through exciting research innovations. Greg Corrado, head of the Google Health AI Research team, discussed several breakthroughs utilizing the company Gemini model. In this article, we will examine the latest findings on the new Gemini fine-tuned version and discuss how it can revolutionize healthcare.

Greg Corrado’s Vision for Health AI

Greg Corrado believes that applying AI to healthcare can deliver tremendous benefits to patients worldwide. He discusses how breakthroughs in generative AI models like MedPaLM and MedLM are already improving clinical decision-making. However, Google’s focus is on developing more advanced AI techniques that push the boundaries of what’s possible. Corrado hints that the new Gemini family of AI models may hold the key to unlocking new frontiers in healthcare innovation through their sophisticated capabilities.

Google new version of Gemini AI model is specialized for healthcare

Potential of Google Gemini in Healthcare

1. Advanced Reasoning with Gemini

One area where Gemini models excel is advanced reasoning. Google’s health AI team built a system that achieved a passing score on the medical licensing exam benchmark ‘MedQA’, demonstrating strong question-answering abilities. The newest Gemini model surpasses prior SOTA performance on MedQA at over 91%. While medical exams were an initial test, the real value is applying this reasoning ability to other contexts.

Google new version of Gemini AI model is specialized for healthcare, showing high aptitude across clinical domains with 91% on MedQA exams.

2. Long Context Understanding of EMRs

Another crucial Gemini capability is understanding long contexts like dense electronic medical records (EMRs). When physicians see new patients, they have many questions about complex medical histories documented across EMRs. Google’s AI can now directly answer physician queries about a synthetic patient’s history by comprehending full EMR context windows. This saves doctors significant time reviewing records.

3. Extracting Insights from Medical Videos

Video is another important context that unlocks knowledge, but understanding medical video content has proven difficult. Google achieved SOTA on ‘MedVidQA’ by answering questions based on educational video content, showing proficiency in extracting insights from this long-form multimedia context. Such abilities will help disseminate medical learning more efficiently.

4. The Power of Multimodality

Medicine inherently involves reasoning across multimodal data sources like text, images, records and tests. Corrado notes Gemini models were designed from the ground up for multimodality, which is critical for AI to genuinely assist caregivers. Moreover, Google achieved human-level report generation for chest X-rays and superior results for more complex 3D brain CTs, demonstrating the potential to augment radiologists’ workflows.

Conversational Diagnostics with Google AMIE

The most important element in medicine is conversations between clinical teams and patients. Google introduced ‘AMIE’, an LLM trained to engage in diagnostic dialogues humanely and empathetically. Studies found AMIE surpassed individual clinicians in differential diagnosis generation and held natural conversations with patient actors. While still in research, AMIE shows conversational AI may one day help improve diagnosis quality and health outcomes.

Conclusion: Future of Google Gemini in Healthcare

Google’s breakthroughs with Gemini affirm that large language models specialized for healthcare can attain human-level performance across diverse tasks. By leveraging the full breadth of multimodal medical data and simulating clinical dialogues, AI is gaining the versatility needed to meaningfully support clinicians and patients. Additionally, with rigorous evaluation and stakeholder collaboration, the next generation of assistive AI tools may help deliver more affordable, equitable and individualized care worldwide. 

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Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

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ByteDance Drops UI-TARS-1.5, The AI Agent That Can Control Your Screen

ByteDance Drops UI-TARS-1.5, The AI Agent That Can Control Your Screen

Have you ever wished your computer could just do things for you? Not just answer questions, but actually click buttons, type text, and navigate websites? Well, that dream just got real. ByteDance recently dropped UI-TARS-1.5, a breakthrough AI agent that can see your screen and control it just like you would, with your mouse and keyboard. Most AI assistants can chat with you and maybe set an alarm. UI-TARS-1.5 goes way beyond that; it watches your screen and takes action.

Table of Contents

What is UI-TARS-1.5

UI-TARS-1.5 is an open-source multimodal agent that can look at your screen, understand what it sees, and then take over your mouse and keyboard to get things done. What’s really cool is how it thinks before acting, it plans its moves. Let’s say you ask it to organize your messy desktop files. Instead of just giving you tips, it’ll actually create folders, drag files into them, and even rename things if needed,  all while you sit back and watch the magic happen.

How UI-TARS-1.5 AI Agent Works

The core of UI-TARS-1.5’s abilities lies in its enhanced perception system. Unlike other AI systems that require special access to understand interfaces, UI-TARS-1.5 works by looking at your screen, just like you do.

The agent has been trained on massive datasets of GUI screenshots, allowing it to recognize buttons, text fields, icons, and other interface elements across different apps and websites. It doesn’t need custom integration with each program; it can learn to use virtually any software with a visual interface.

When it looks at your screen, it’s not just seeing pixels; it understands context, identifies interactive elements, and plans how to navigate them to achieve your goals.

Example Tasks Performed by UI-TARS-1.5

The Technology Behind UI-TARS-1.5

It builds on ByteDance’s previous architecture but adds several key innovations:

1. Enhanced Perception: The AI understands context on your screen and can precisely caption what it sees

2. Unified Action Modeling: Actions are standardized across platforms for precise interaction

3. System-2 Reasoning: The agent incorporates deliberate thinking into its decision-making

4. Iterative Training: It continuously learns from mistakes and adapts to new situations

Perhaps most impressive is UI-TARS-1.5’s scaling ability; the longer it works on a task, the better it gets. This shows its ability to learn and adapt in real-time, just like humans do.

UI-TARS-1.5 vs. OpenAI CUA and Claude 3.7

ByteDance didn’t just create another AI agent; they built a record-breaker. In head-to-head tests against the OpenAI CUA and Claude 3.7, UI-TARS-1.5 came out on top:

  • In computer tests (OSworld), it scored 42.5%, while OpenAI CUA got 36.4%, and Claude 3.7 managed only 28%.
  • For browser tasks, it achieved 84.8% success in WebVoyager tests.
  • On phone interfaces, it reached 64.2% in Android World tests.
  • The secret to UI-TARS-1.5’s success? It can spot things on your screen with incredible accuracy. On the challenging ScreenSpotPro benchmark, which tests how well AI can locate specific elements, it scored 61.6%, more than double what OpenAI CUA (23.4%) and Claude 3.7 (27.7%) scored.

What makes these scores even more impressive is that the model gets better the longer it works on something. It doesn’t get tired or bored; it just keeps learning and improving with each step.

Key Tasks Performed by UI-TARS-1.5 AI Agent

1. Daily Computer Tasks

Think about all those repetitive tasks you handle daily: sorting emails, organizing files, updating spreadsheets. UI-TARS-1.5 can take these off your plate by watching and learning how you work.

In one demonstration, it was asked to transfer data from a LibreOffice Calc spreadsheet to a Writer document while keeping the original formatting. The AI handled it flawlessly.

What’s impressive isn’t just that it completed the task; it’s how it handled unexpected situations. When its first attempt to select data didn’t work perfectly, it recognized the problem, adjusted its approach, and tried again until successful.

2. Web Research

While UI-TARS-1.5 wasn’t specifically designed for deep research, it shows remarkable ability to navigate the web and find information. In SimpleQA tests, it scored 83.8, outperforming GPT-4.5’s 60.

Imagine asking, “Find me the latest research on climate change solutions and create a summary document.” It could open your browser, search for relevant information, organize findings, and even create a document with what it learns—all by controlling your computer just like you would.

3. Gaming Tasks

One of the most exciting applications for UI-TARS-1.5 is gaming. ByteDance tested the AI on 14 different games from poki.com, and the results were mind-blowing. It achieved perfect 100% scores across nearly all games tested.

Games like 2048, Snake, and various puzzle games pose no challenge for this AI. What’s even more impressive is that it gets better the longer it plays, learning from each move and refining its strategy.

The ultimate test came with Minecraft. It outperformed specialized gaming AI by a significant margin, successfully mining blocks and defeating enemies while navigating the 3D environment using only visual input and standard controls.

How to Get Started With UI-TARS-1.5

ByteDance has open-sourced this model, making it available for the research community. Developers can access it, which is trained from Qwen2.5-VL-7B. They’ve also released UI-TARS-desktop, an application that lets users experiment with the technology directly. This open approach encourages collaboration and further development from the community.

The Unlimited Benefits of UI-TARS-1.5

UI-TARS-1.5 represents a fundamental shift in human-computer interaction. Instead of you adapting to how computers work, it makes computers adapt to how humans work.

This approach makes AI immediately useful across countless applications without requiring special compatibility. You can use it to create presentations, manage email, organize photos, or fill out tax forms, all using standard software you already own.

For businesses, it could automate countless routine tasks. For individuals, it means having a digital assistant that can take real action instead of just offering advice.

With UI-TARS-1.5, ByteDance has potentially changed how we’ll interact with computers for years to come. As this technology continues to develop, the line between what humans do and what AI assistants do will continue to blur, freeing us to focus on more creative and fulfilling tasks.

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Picture of Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

Diffusion Arc, the Ultimate Open Database for AI Image Models – A Civitai Alternative

Diffusion Arc, the Ultimate Open Database for AI Image Models, A Civitai Alternative

If you’ve been creating AI art, you’re probably familiar with Civitai. For years, it’s been the go-to platform for finding AI image models. But recently, Civitai has made some controversial changes that have upset many users. Their new subscription-based access to popular models, stricter content moderation policies, and the introduction of AI compute credits have left many creators feeling priced out and restricted. Just scroll through any AI art community forum, and you’ll see countless threads from frustrated users looking for alternatives. Enter Diffusion Arc – the free, open database for AI image models that’s rapidly winning over disillusioned Civitai users. It has launched at the perfect time when the community needs it the most.

Table of Contents

What Is Diffusion Arc?

Diffusion Arc is a fresh community-driven platform where you can freely browse, upload, and download AI image generation models. It offers what many creators have been desperately seeking: a truly open platform without the paywalls and arbitrary restrictions that have recently plagued Civitai.

The platform was originally launched under a different name, Civit Arc, and has since rebranded to Diffusion Arc to better reflect its independent vision. What makes this stand out is its commitment to being completely free while offering a safe haven for models that might be removed elsewhere.

Key Features of Diffusion Arc

The platform comes packed with features designed to make sharing and discovering AI models easier than ever:

1. Easy, Restriction-Free Uploads

Unlike some other platforms that have begun implementing stricter content policies, Diffusion Arc allows you to upload your models with minimal restrictions. This is particularly valuable for creators who’ve had their content removed from other sites without clear explanations.

2. Always Free Downloads

One of Diffusion Arc’s core promises is that all models will remain free to download, without paywalls or limitations. No premium tiers, no subscription fees! Just open access for everyone in the community.

3. Wide Model Compatibility

Diffusion Arc supports models from various popular platforms, including Stable Diffusion, Flux, and others. This broad compatibility ensures that creators aren’t limited by technical constraints when sharing their work.

4. Community-First Approach

Built by AI enthusiasts for AI enthusiasts, the platform prioritizes community needs. The team is actively working on improvements based on user feedback, with plans to eventually make the platform open-source.

Explore Various AI Image Models on Diffusion Arc

When you first visit Diffusion Arc, you might be amazed by just how many AI image models are available at your fingertips. From realistic portrait generators to fantasy art creators and abstract pattern makers – there’s something for every style and need.

What makes Diffusion Arc special is how they’ve streamlined the experience of finding exactly what you need. Their search and filter options let you narrow down models by style, complexity, and even how recently they were added.

The platform already hosts many popular models that AI artists love:

  • Dreamshaper v9.0 (4.9 rating) – Specializes in realistic portraits
  • RealisticVision v5.1 (4.8 rating) – Creates photo-realistic images
  • Deliberate v3.0 (4.7 rating) – A versatile creator model
  • Anything XL v4.5 (4.9 rating) – Perfect for anime-style images
  • SDXL Turbo v1.0 (4.6 rating) – Known for fast generation
  • Juggernaut XL v8.0 (4.8 rating) – Excels at high-detail images

These models offer something for everyone, whether you’re into realistic portraits, anime, or highly detailed artistic creations. And there are many, many more!

AI Art Creation Accessible for All Users

The platform provides clear instructions for each model, explaining how to use it and what kinds of results you can expect. They even offer simple guides for getting started with the basic software you’ll need to run these models.

This approach has opened up AI art to:

  • Students exploring creative technology
  • Small business owners creating marketing materials
  • Writers who want to visualize their stories
  • Hobbyists just having fun with new tech

How to Get Started with Diffusion Arc Today

Ready to dive into this platform and see what all the buzz is about? Getting started is easier than you might think:

1. Visit the Diffusion Arc website and create a free account

2. Browse through the categories or use the search feature to find models that interest you

3. Download the models you want to try

4. Follow their beginner-friendly guides to set up the necessary software

5. Start creating!

The best part? You don’t need a super powerful computer to begin. While some advanced models do require more processing power, many entry-level models will run just fine on an average laptop. Diffusion Arc clearly marks which models are “lightweight” so beginners can start without investing in expensive hardware.

What Updates Will We Be Expecting

As AI technology continues to evolve at lightning speed, Diffusion Arc is positioning itself to grow right alongside it. The platform will regularly add new features based on user feedback and keep up with the latest developments in AI image generation.

The team behind Diffusion Arc has hinted at some exciting updates coming soon, including:

  • Torrent download functionality that will make getting large models much faster and more reliable
  • More interactive tutorials for beginners
  • Enhanced model comparison tools
  • Collaborative creation spaces
  • Mobile-friendly options for on-the-go creation

With each update, Diffusion Arc gets closer to their vision of making advanced AI creative tools as common and accessible as word processors or photo editors.

The Future of AI Image Generation With Diffusion Arc

By creating a space where advanced AI technology meets user-friendly design, Diffusion Arc is democratizing digital art creation. Whether you’re a curious beginner or a seasoned AI art creator looking for a better Civitai alternative, Diffusion Arc deserves a spot on your bookmarks bar.

The platform continues to add new models, features, and improvements almost daily, making it an exciting time to join the Diffusion Arc community. Who knows? The next amazing AI creation trending online might be yours, made with a model you discovered through Diffusion Arc.

So what are you waiting for? Jump into the world of AI image creation with Diffusion Arc – where your imagination is the only limit.

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Picture of Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

Tiny Agents, A 50-Line MCP-Powered AI Framework by HuggingFace Co-Founder for the Modern Dev

Tiny Agents, A 50-Line MCP-Powered AI Framework by HuggingFace Co-Founder for the Modern Dev

Want to build an AI agent without drowning in complexity? Tiny Agents might be exactly what you’re looking for. This minimalist, MCP-powered AI framework, created by the co-founder of HuggingFace, lets you build powerful AI agents with just 50 lines of JavaScript code.

Table of Contents

Introducing Tiny Agents

The magic behind Tiny Agents is surprisingly simple. While exploring the Model Context Protocol (MCP), the HuggingFace co-founder discovered something mind-blowing: once you have an MCP client, creating an agent is just a while loop on top of it.

“Once you have a MCP Client, an Agent is literally just a while loop on top of it,” explains the Hugging Face co-founder behind the project.

This insight led to the creation of Tiny Agents – a lightweight, powerful AI framework that makes building AI agents accessible to more developers.

Understanding MCP

MCP isn’t complicated, but it’s incredibly useful. At its core, MCP is a standard API that lets you connect tools to Large Language Models (LLMs).

Think of it as a universal adapter that helps your LLM talk to various tools and services. The beauty of MCP is that once you understand this standard, you can plug in different tools without changing your core agent code.

Modern LLMs have native support for tool calling (also known as function calling). This means they can understand when to use external tools and how to format the calls correctly. Your LLM can decide to call zero, one, or multiple tools during a conversation.

How Tiny Agents Work

The Tiny Agents framework builds on top of an MCP client, which handles all the complex parts of connecting to MCP servers and managing tools. Here’s what makes it work:

  • It connects to MCP servers to discover available tools
  • It formats these tools so the LLM can understand them
  • It handles the back-and-forth between the LLM and the tools
  • It manages the conversation flow with a simple while loop

The whole agent is built around alternating between tool calling and feeding results back to the LLM. This continues until certain exit conditions are met, like completing a task or needing user input.

The Technical Magic Behind Tiny Agents

Let’s peek under the hood at what makes Tiny Agents work:

1. The McpClient Class

This class is the foundation. It maintains an Inference Client for talking to LLMs, manages connections to MCP servers and keeps track of all available tools. When connecting to an MCP server, it gets a list of tools and formats them for the LLM.

2. The Agent Class

The Agent class extends McpClient and adds a system prompt to guide the LLM, a message history for the conversation, control flow tools like task_complete and ask_question and the all-important while loop that drives the agent.

3. The “50 Line” While Loop

The heart of Tiny Agents is its while loop. This surprisingly simple bit of code processes a single turn with the LLM, checks if any tools were called exits the loop if needed (task completed, question asked, etc.) and continues the conversation by alternating between tool results and LLM responses.

Getting Started with Tiny Agents

Want to try it out? It’s super easy to get going:

npx @huggingface/mcp-client

Or if you’re using pnpm:

pnpx @huggingface/mcp-client

This installs the package temporarily and runs it. You’ll see your agent connect to two MCP servers running locally:

  • A file system server that can access your Desktop
  • A Playwright MCP server that controls a sandboxed browser

Experiment With Tiny Agents

Now, you can ask it to do different tasks. I used it to perform the following task:

File System Operations

Web Browsing & Research

Using Different Models and Providers with Tiny Agents

By default, Tiny Agents uses “Qwen/Qwen2.5-72B-Instruct” as the model and Nebius as the inference provider.

But you can easily configure it to use different models and providers through environment variables:

javascript

const agent = new Agent({

    provider: process.env.PROVIDER ?? “nebius”,

    model: process.env.MODEL_ID ?? “Qwen/Qwen2.5-72B-Instruct”,

    apiKey: process.env.HF_TOKEN,

    servers: SERVERS,

});

Why Tiny Agents Matters

This represents a significant step forward in making AI agents more accessible. By reducing the complexity to just 50 lines of code, it opens the door for many more developers to experiment with and build AI agents.

The framework shows that you don’t need complex architectures to create useful AI agents. With modern LLMs that have native tool-calling abilities, a simple approach can be surprisingly effective.

Build Your Own Tiny Agents Today

The entire Tiny Agents codebase is open source and available in the huggingface.js mono-repo. Whether you’re new to AI agents or an experienced developer, this minimal framework offers a great way to start experimenting with MCP-powered AI agents.

With just 50 lines of code, you can have a working AI agent that connects to tools, maintains context, and solves real problems. It’s a perfect example of how sometimes the simplest solutions are the most elegant.

So why not give Tiny Agents a try? Your first MCP-powered agent is just a few lines of code away.

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Picture of Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

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