Pawel Bulowski的动态

???????? ?????????????? ????????????: ?????? ???????????? ???? ???? ???????? ??????????-???????? ?????????????? ????????????! ?? What if an AI could understand 100 million tokens at once? That’s the equivalent of reading ???? ?????????????? ?????????? ???? ???????? ???? ?????? ???????????? in real time! ???? Meet ??????-??-????????, Magic’s breakthrough language model, designed to process vast amounts of information in a single context—taking AI’s understanding and reasoning to a whole new level. ?? Here’s why it’s revolutionary: ?? ????????-???????? ??????????????????: Imagine having your entire codebase, documentation, and libraries in context—always! This opens up new possibilities for ???????? ??????????????????, making it smarter, faster, and more reliable. ?? ???????????????? ????????????????: With a ?????????? ???? ?????????????? approach, LTM-2-mini handles reasoning tasks step by step, achieving near-perfect accuracy (95%-100%) on simpler tasks and excelling in handling complex context. ?? ?????????? ???????? ??????????????????: Compared to traditional model like Llama 3.1, LTM-2-mini is 1000x cheaper to decode each token and requires only a fraction of the memory. While running Llama 3.1 405B with a 100M token context needs ?????? ?????????? ?????? ????????, LTM-2-mini makes it work with a single H100. ?? Curious to learn more? Read the full article on this exciting development and explore how it’s reshaping the future of AI and software development! ?? https://lnkd.in/dJCitU8t #AI #MachineLearning #DataScience #LLM #GenerativeAi #Innovation #Trends

Unlock the full potential of your data with Cybertrend & Graphite Note's innovative no-code solutions. Build machine learning models in minutes, not weeks, and turn raw data into actionable insights—no coding skills required. Empower your business to make smarter, data-driven decisions today! ?? #DataAnalytics #NoCode #MachineLearning #DigitalTransformation #Cybertrend #GraphiteNote #AI #DataDriven"

?????????????????? ???????????????????? ????: ?? ???????????????? ?????????????? ???????????????????? ???? can is transforming industries, and mastering it requires a structured understanding of its key components. Here’s a comprehensive list of topics that cover 100% of Generative AI knowledge: ?? Core Concepts 1. AI Agents 2. Multi-Modality 3. Retrieval-Augmented Generation (RAG) 4. Fine-Tuning 5. Prompt Engineering ?? Model Architectures 6. Transformers (GPT, BERT, etc.) 7. Diffusion Models (e.g., Stable Diffusion) 8. Generative Adversarial Networks (GANs) 9. Variational Autoencoders (VAEs) ?? Training Paradigms 10. Pretraining & Fine-Tuning 11. Self-Supervised Learning 12. Reinforcement Learning from Human Feedback (RLHF) ??? Data and Preprocessing 13. Data Collection 14. Data Cleaning & Annotation 15. Tokenization ?? Evaluation Metrics 16. Output Quality Metrics (BLEU, ROUGE, FID, Perplexity) 17. Human Evaluation (coherence, relevance, creativity) ? Scalability and Optimization 18. Hardware Acceleration (GPUs, TPUs) 19. Distributed Training 20. Quantization & Pruning ??? Ethics and Governance 21. Bias Mitigation 22. Fairness and Safety 23. AI Ethics and Responsible Governance ?? Advanced Applications 24. Text-to-Image & Image-to-Text (DALL·E, captioning) 25. Text-to-Speech (TTS models) 26. Video and Music Generation 27. Code Generation (GitHub Copilot, Codex) ?? Deployment & Integration 28. Real-World Deployment 29. API Integration 30. Latency & Throughput Optimization ?? Supporting Areas 31. Evaluation of Outputs 32. Model Interpretability 33. Scalability Challenges 34. Knowledge Distillation Mastering these ???? ???????????? will give you a 100% understanding of Generative AI—from core concepts to advanced deployment. #GenerativeAI #ArtificialIntelligence #MachineLearning #AI #TechRoadmap #AIEngineering #DataScience #Innovation

?????? ?????? ?????????? ???? ???? ???????????? ???????? ?????? ???????? ?????????? ??????????? The AI world just got a whole lot more interesting with the launch of ???????????????? ????, a groundbreaking open-source large language model (LLM). Developed by DeepSeek AI, this 671-billion parameter model is making waves with its impressive ?????????????????? ???????????????????????? and ????????-??????????????????????????. DeepSeek R1 offers a unique "thinking out loud" approach, providing unprecedented transparency into the AI's decision-making process. ?????? ???????????????? ???? ?????????????? -> ?????????????????? ??????????????????: DeepSeek R1 goes beyond simply generating text, allowing users to see its reasoning process step-by-step. This transparency is invaluable for debugging, problem-solving, and education.? -> ????????-???????????? ??????????????????: Unlike many proprietary models, DeepSeek R1 is fully open-source under the MIT license. This means developers can freely use, modify, and commercialize it, fostering greater innovation and collaboration.? -> ????????-??????????????????: DeepSeek R1 offers competitive pricing, especially when compared to OpenAI's o1. With options for self-hosting and distilled models, it provides flexibility for different budgets and hardware setups.? -> ?????????????????? ????????????????????: DeepSeek R1 offers distilled versions with varying parameter sizes (1.5B to 70B), making it accessible for those with less powerful hardware. These smaller models still pack a punch, outperforming some larger models in certain tasks.? -> ????????-?????????? ????????????????????????: DeepSeek R1 excels in various domains, including Coding, Education, Scientific Research, and Finance. ?????????? ???? ??????????????? Dive into DeepSeek R1 and unlock new possibilities for your projects. Visit chat.deepseek.com to experience its capabilities firsthand. DeepSeek R1 is poised to democratize access to powerful AI and drive a new wave of innovation. #AI #OpenSource #DeepLearning #Innovation #DeepSeekR1

Revolutionizing AI: InstructLab Challenges Big Tech’s Monopoly on Model Development InstructLab is a community-driven project from Red Hat and IBM allowing developers to enhance LLMs used in GenAI applications—lowering costs, time to deployment, and increasing security and reliability. Check out this article from Forbes for more. #AIRevolution #InstructLab #BigTechChallenge #AIInnovation #OpenSourceAI #AICommunity #TechDisruption #ModelDevelopment #AIControversy #FutureOfAI #IBMandRedHat #GenerativeAI #LLM #SyntheticData #AIStartups #TechNews

MethodHub partners with Promptora, an innovative and highly secure generative AI platform exclusively designed for business applications. With advanced AI and ML algorithms, Promptora tailors prompt-based outputs to match clients' needs. ?? Unparalleled Customization: Choose from established LLMs like OpenAI or create custom LLMs with managed infrastructure incorporating optimizations like FlashAttention-2.? ?? Vast Model Repository: Access 150+ pre-trained models such as RedPajama, Llama2, and cloud-based options like Gemini, Grok, and more.? ??? Advanced Functionality: Multi-language interaction, AI analytics, and Python libraries offer real-time insights and streamline processes. With MethodHub's expertise and Promptora's AI capabilities, businesses can tap into the potential of AI-driven insights to streamline operations and enhance efficiency. . #AI #GenerativeAI?#Innovation #methodhub #Promptora

??My take on Prompt Engineering: Treat Prompts Like Any Other ML Model! ?? Too often, we approach Large Language Model (LLM) prompting as an art rather than a science. ?????? But what if we applied the same systematic, data-driven approaches we use in traditional machine learning? ?? Imagine treating prompts as: - ?? Versioned artifacts with clear tracking - ?? Performance metrics that can be quantitatively evaluated - ?? Subjects of systematic experimentation Key techniques we can borrow from data science: - ?? Performance calculations (ROC curves, F1 scores, recall) - ??? Versioning of prompt iterations - ?? Grid search for optimal prompt configurations - ?? Rigorous evaluation frameworks By bringing date a science discipline to prompt engineering, we can: - ?? Increase predictability - ?? Improve model performance - ?? Create more reproducible AI interactions This isn't just tweaking—it's a fundamental shift in how we think about human-AI collaboration! ???? #AIInnovation #MachineLearning #PromptEngineering #DataScience ???

?? ?????????????????? ???? ???? ?????????????????? ?? I’m excited to share my experience from the ?????????????????? ????????????????, organized by the ???????? ?????????????? ???? ??????. During this workshop, we got hands-on with ????????????????, a powerful tool that allows you to create and integrate machine learning models without any coding—a perfect way to get started with AI! We explored the ??????????????????-?????????????????? ???????????????????? (??????) model, enhancing large language models with?????????-???????? ???????? ??????????????????, and learned how to seamlessly integrate conversational AI into websites and apps. LangFlow made it all easy, bringing advanced AI tools to life in just a few steps. A huge thanks to the IEEE Society for organizing such a valuable and hands-on workshop. Looking forward to using these skills in my future projects! #LangFlow #Workshop #FlowForge #RAG #MachineLearning #ConversationalAI #IEEESociety #LearningJourney #TechInnovation

To build scalable, agentic AI that maximizes ROI by enabling/accelerating crucial decisions, a comprehensive, sound strategy is required: Centralize. Standardize. Democratize. Centralize the sources of truth, logic/reasoning, and action, each of which depends on the preceding foundational level. Standardize how business decisions are made, which requires conversations with collaborators who understand the importance of ongoing partnership and responsibility of working together to align on how the AI should do what it does and where humans should be able to step in and govern its behavior. Democratize access to the solution, while maintaining awareness of opportunities to continuously integrate improvements into the deployment. For more, see my talk on how I led a team to do this and build an AI recommendation engine that accelerated key business processes at a Fortune 500 company, Regeneron Pharmaceuticals, by more than an order of magnitude: https://lnkd.in/g6BDfeF4 #regeneron #ai #aigovernance #datagovernance #informationquality #aistrategy #airoadmap #llm #datascience #r #python #shiny #knowledgegraph #aiengineering #autonomoussystems #neo4j #training #continuingeducation #matchmaking #doingwellbydoinggood #dgiq #aigov #dataversity #aws #amazonwebservices #rstudio #positconnect #positworkbench #positpackagemanager #positconnect #posit

?? AI Arsenal 2024: Your Complete Tools & Platforms Guide After months of testing and implementing various AI tools, I'm excited to share this comprehensive guide to help you navigate the AI landscape. Here's your roadmap to the most powerful AI tools across key domains: ?? Large Language Models (LLMs) Enterprise Power Players: GPT-4, Claude 3 Opus, Gemini Ultra Daily Workhorses: GPT-3.5, Claude Sonnet Development Must-Haves: LangChain, LlamaIndex Pro tip: Mix and match based on your specific use case - not every task needs the most powerful model! ?? Image Generation Creative Champions: DALL-E 3, Midjourney Professional Suite: Adobe Firefly, Runway Quick insight: Keep an eye on Stable Diffusion - the open-source community is innovating rapidly! ???? AI-Powered Development Code Companions: GitHub Copilot, Amazon CodeWhisperer Quality Guardians: DeepCode, SonarQube AI Game-changer: These tools can cut development time by up to 40%! ??? Audio & Speech Voice Masters: ElevenLabs, Whisper Enterprise Solutions: Azure Speech Services Hidden gem: AssemblyAI is making waves with their accuracy rates! ?? Data & ML Operations AutoML Leaders: H2O.ai, DataRobot MLOps Essentials: MLflow, Weights & Biases Key learning: Start with experiment tracking early - you'll thank yourself later! ?? Infrastructure & Deployment Model Hubs: Hugging Face, AWS SageMaker Vector Databases: Pinecone, Weaviate Cost-saving tip: Many of these platforms offer generous free tiers for experimentation! ?? Pro Tips for Tool Selection: Start small, scale smart Always run proof-of-concept tests Consider total cost of ownership Monitor usage patterns religiously ?? Remember: The best tool isn't always the most advanced or expensive - it's the one that fits your specific needs and workflow. #ArtificialIntelligence #AITools #TechStack #Innovation #MachineLearning #SoftwareEngineering #AI #Technology