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- ⚡🔋 You Won't Believe How Tree Attention Speeds Up AI Processing!
⚡🔋 You Won't Believe How Tree Attention Speeds Up AI Processing!
And more: Zoom partners with Suki to offer AI-powered medical note-taking; Automate to Dominate: CrewAI Revolutionizes Business Tasks
Supercharged With AI
October 23, 2024
In partnership with
Good morning, aspiring leader of the next generation! ☀️
🚀 Here is the most impactful AI news today. Enjoy and we wish you an amazing day:
New Tree Attention paper drops for long-context GPU clusters. Making AI attention span longer than a goldfish! 🌳💻
Zoom and Suki team up for AI medical notes. Finally, doctors' handwriting might be readable! 👩⚕️📝
Anthropic's new AI can control your PC. HAL 9000 vibes, anyone? 🖥️😱
Highlight spins off with $10M to build desktop AI assistants. Your computer's getting a fancy new butler! 🎩💫
Marc Andreessen claims AI model makers are racing to the bottom. Tech billionaire says stop being so cheap! 📉💰
Interface.ai bags $30M to help banks with customer service. Because nothing says "personal touch" like a banking robot! 🏦🤖
Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters
Zyphra has announced Tree Attention, a revolutionary method for parallel processing in multi-GPU transformer decoding that promises significant improvements in both speed and memory efficiency. This innovative approach demonstrates an estimated 8x faster decoding capability at 1M sequence length compared to traditional Ring Attention methods, while requiring half the communication volume.
Technical Innovation and Design
The development of Tree Attention stems from a deep understanding of self-attention's energy function, bridging the gap between transformers, Hopfield Networks, and Bayesian Inference. The key breakthrough lies in recognizing that core operations in the reduction of logsumexp and max across the sequence axis are associative, enabling parallel computation through an associative scan.
This insight led to the creation of an algorithm that achieves logarithmic complexity in the number of devices, contrasting with Ring Attention's linear approach. The method is remarkably streamlined, requiring only a few lines of Jax code and utilizing existing Jax and NCCL primitives.
The timing couldn't be more crucial, as the AI industry witnesses a dramatic expansion in context length capabilities, from 8K to 128K and beyond 1M. These extended contexts enable transformative capabilities, such as processing entire textbooks or datasets in memory, enhancing in-context learning, and facilitating new modalities like native video understanding.
Performance and Practical Applications
Empirical testing has validated Tree Attention's theoretical advantages. The algorithm demonstrates superior performance in decoding latency computation, with the advantage growing more pronounced as both sequence length and GPU numbers increase. Peak memory usage tests confirm the theoretical benefits, showing significant reductions compared to Ring Attention.
The reduced memory requirements stem from Tree Attention's efficient communication architecture. While Ring Attention must communicate keys and values for entire sequence chunks across devices, Tree Attention only needs to transmit partially reduced results. These results scale with the model's hidden dimension rather than sequence length, enabling substantial latency improvements even with relatively few devices.
Traditional approaches to handling long contexts face significant challenges due to the quadratic complexity of attention and the need to store KV cache, which grows linearly with context size. The limited GPU VRAM typically requires splitting the KV cache across multiple GPUs. Tree Attention's innovative approach to this challenge represents a significant step forward in making long-context AI processing more efficient and practical.
The implications of this breakthrough extend beyond mere performance metrics. By enabling more efficient processing of extremely long contexts, Tree Attention could unlock new possibilities in AI applications, from enhanced document analysis to more sophisticated video processing capabilities. The reduced resource requirements could also make advanced AI processing more accessible to organizations with limited computing resources.
Looking ahead, Tree Attention's architecture suggests potential for even greater optimization and scaling. Its logarithmic complexity advantage over existing methods indicates that as AI systems continue to grow in size and complexity, the benefits of this approach will become even more pronounced.
This development represents a significant milestone in AI computing efficiency, offering a path forward for handling increasingly complex AI tasks with greater speed and lower resource requirements. As the field continues to evolve, Tree Attention's innovative approach may well become a standard component in the next generation of AI systems.
For those interested in implementing or studying the method, Zyphra has made both the research paper and reference code implementation publicly available, enabling broader adoption and potential further development by the AI research community.
Zoom partners with Suki to offer AI-powered medical note-taking
Zoom has announced a partnership with Suki, an AI medical scribe provider, to integrate AI-powered note-taking capabilities into telehealth visits. The collaboration aims to help doctors save time on documentation during patient consultations. Zoom currently handles 36% of U.S. telehealth visits, making it the market leader.
The partnership comes as AI medical assistants gain popularity in healthcare. Suki, which recently raised $70 million in Series D funding, was chosen after Zoom evaluated various competitors. The move aligns with Zoom CEO Eric Yuan's vision to transform the company from a video conferencing platform into an AI-focused workplace tools provider.
The medical note-taking AI market is becoming increasingly competitive, with players like Abridge, Nabla, Ambiance Healthcare, and Microsoft's Nuance offering similar services. Amazon's One Medical is also developing its own solution using AWS technology. Despite the crowded market, investors believe there's room for differentiation, as companies target different segments of the healthcare industry.
VaultCraft V2 secures $100M+ BTC from Matrixport
VaultCraft launches V2 in partnership with Safe, lands $100M+ in Bitcoin
Matrixport entrusts VaultCraft with $100M+ Bitcoin
OKX Web3 rolls out Safe Smart Vaults with $250K+ rewards
Anthropic’s new AI model can control your PC
Anthropic has released an upgraded version of its Claude 3.5 Sonnet AI model with the ability to control desktop applications through a new "Computer Use" API. The model can simulate keyboard and mouse actions by analyzing screenshots and calculating cursor movements.
The technology aims to automate various tasks across applications and websites, though current success rates vary, with the model failing about one-third to half of the time on tasks like airline bookings. Anthropic acknowledges limitations, including struggles with basic actions like scrolling and zooming.
Safety concerns are being addressed through various measures, including classifiers to prevent high-risk actions and a 30-day retention of screenshots. The company has involved the U.S. and U.K. AI Safety Institutes in testing the model before deployment.
Additionally, Anthropic announced an upcoming Claude 3.5 Haiku model, promising performance comparable to Claude 3 Opus at lower costs. This version will initially be text-only, with plans to add image analysis capabilities later.
The development represents Anthropic's move toward its vision of AI-powered virtual assistants capable of automating various office tasks, though the company emphasizes the need for careful, gradual deployment to ensure safety and reliability.
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AI is not just about creating intelligent systems, it's about developing new tools for understanding complexity. Each advance in AI gives us new insights into both the nature of intelligence and the vast landscape of problems we can solve.
Manuela Maria Veloso is the Head of J.P. Morgan AI Research & Herbert A. Simon University Professor Emeritus in the School of Computer Science at Carnegie Mellon University, where she was previously Head of the Machine Learning Department.
⬆️ ️ Trends: How Businesses Implement AI
CrewAI: Reimagining Business Automation with AI
When João Moura left his position as AI engineering director at Clearbit following its acquisition by HubSpot, he wasn't just making a career move – he was laying the groundwork for a revolution in business automation. His new venture, CrewAI, represents a fresh approach to handling the mundane yet crucial tasks that keep businesses running.
Unlike traditional robotic process automation (RPA) systems, which rely on rigid "if-then" rules and frequently break down, CrewAI takes a more flexible approach by leveraging existing AI models from companies like OpenAI and Anthropic. The platform allows businesses to automate everything from report summaries to employee onboarding, all while working with their existing software tools.
The contrast with traditional RPA is stark. While 69% of organizations using RPA report broken workflows at least weekly, CrewAI's AI-driven approach promises greater resilience and adaptability. Though AI systems aren't perfect – they can hallucinate or show bias – Moura argues they offer significant advantages over conventional automation methods.
The market has responded enthusiastically to this vision. CrewAI has secured $18 million in funding from prominent investors including Boldstart Ventures, Craft Ventures, and Insight Partners, along with individual investors like Coursera co-founder Andrew Ng and HubSpot's CTO Dharmesh Shah. The company's current valuation stands at approximately $100 million.
In its first year since launching in January, CrewAI has attracted 150 customers and processes about 100,000 multi-AI executions daily. The company is now expanding its offerings with Enterprise Cloud, a managed subscription plan built on open-source components that adds security controls, analytics, and audit capabilities for corporate clients.
However, CrewAI isn't alone in this space. Competitors like Orby, Bardeen, and Tektonic are all vying for a piece of the AI automation market, while traditional RPA vendors scramble to incorporate AI into their existing solutions. Yet CrewAI's rapid growth suggests its approach is resonating with businesses looking for more flexible automation solutions.
With a team of 16 split between San Francisco and Brazil, CrewAI appears poised for further expansion. Moura projects the company could achieve cash-flow positivity by next summer, marking a significant milestone in its journey to reshape business automation.
In a world where businesses increasingly seek to streamline their operations, CrewAI's blend of flexibility, AI capability, and practical application may well represent the future of workplace automation.
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⚡ AI Playground: Code & Craft
🛠️ Create Your Own GPT for Personal Use with ChatGPT
What is GPT?
GPT (Generative Pre-trained Transformer) is an AI that creates human-like text by predicting words from patterns it learned in training. You can train it on personal data to fit your specific needs. It’s accessible, even for beginners!
💡 How to Create Your Own GPT
Step 1: Define Your Goal
Decide what task you want your GPT to handle. Be specific!
Example: "I want to create a GPT that helps me draft professional emails."
Step 2: Access ChatGPT
Visit the ChatGPT website, then sign up or log in.
Click Create New to begin.
Step 3: Feed It Your Data
To make your GPT act like a personal copywriter, you’ll need to train it with examples of the type of text you want it to generate.
1️⃣ Collect Content:
Gather emails, articles, or documents you’ve written or like to emulate.
2️⃣ Feed the Content:
Use ChatGPT by copy-pasting your examples into the chat. Give clear instructions, like:
“Here are examples of professional emails I’ve written. Use this style when generating new emails for me.”
3️⃣ Fine-Tune It:
Ask GPT to create new samples. Provide feedback on what you like or want changed to improve its accuracy.
Step 4: Add Your Own Text
Incorporate your style by feeding it specific phrases or templates you often use.
1️⃣ Create a Template:
Provide a sample text with placeholders for customization, such as:
"Dear [Name], I hope this email finds you well. I wanted to follow up on [Topic]. Let me know if you have any questions!"
2️⃣ Set Your Preferences:
Ask GPT to include these templates automatically:
“Use the greeting ‘Hello there,’ in every email and always close with ‘Best regards.’”
3️⃣ Test & Refine:
Run the tool through different tasks—ask it to draft emails, marketing copy, or follow-up notes. Keep refining until the results match your style perfectly.
🔧 This Week’s Tool: GPT by ChatGPT
What is this tool best for?
Creating personalized tools to analyze data, write custom text, and make predictions. It acts like your personal LLM (Large Language Model)—trained on your data to serve your needs.
Why is it better than other tools?
Easy to use, adaptable to personal data, and accessible without advanced skills.
What does it do best?
Generates text, answers questions, summarizes, translates, and supports tasks like writing or coding.
Applications:
💬 Chatbots
📝 Content creation
🌐 Translation
💻 Coding assistance
📧 Personalized writing
📞 Customer support
⚡ Take the Challenge, Make Your GPT, and Win Big! Send us your GPT by Friday!
What is your outcome? |
Have an amazing day!
No more playing catch-up. It's time to GET AHEAD!!! 🚀🚀🚀
🙌🏼
Elena
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