Abdulrahman Alfozan

I just published a blog on how to fine tune LLMs and create custom datasets. In this blog, I cover: - What fine-tuning is and its benefits - When to fine-tune a model - Methods for fine-tuning, including full fine-tuning and parameter-efficient fine-tuning - How to prepare your custom dataset Check it out here: https://lnkd.in/gDcrrBGP

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Just did a keynote in PyTorch conference, on LLM deployment. Thanks for the great work of PyTorch team, our partners, and the interests from the audience! Please try out our deployment ecosystem of torchchat, torch.compile, torch.export, AOT Inductor, torchao and ExecuTorch, and checkout all the amazing talks, posters and demos in the conference!

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https://lnkd.in/gbXzsqZm Very interesting research done by my mentor Semih Yagcioglu and his team showing that multimodal models are more effective at predicting unforeseen compositional sequences than their text-only counterparts. It will be presented at NAACL this month and is likely going to motivate a lot more research to be done in this field. I hope he does a post about it soon to explain better than I can! #ComputationaLinguistics #SequentialCompositionalGeneralization

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Boring AI is the best AI 😎

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I played around with Meta AI on WhatsApp. I asked it to translate a sentence in the Latin transliteration of Arabic (aka Arabizi). While this translation is correct in the literal sense, it still lacks in capturing the nuances in a better way. I wrote: ra7 rami 3al ba7er. Which translates to: Rami went to the beach. And got a rather robotic response of rami going to the sea. Regardless of this minor hiccup, I see that the integration of LLaMA 2 into WhatsApp is a big step in democratizing AI, especially chatbots. Let’s see what happens when LLaMA 3 goes live on WhatsApp #MetaAI #WhatsAppAI #Arabizi #LLaMA2 #AIChatbots #DemocratizingAI #AIIntegration #NaturalLanguageProcessing #MachineTranslation #AIExperiments #ChatbotExperience #AIAdvancements #LLaMA3WaitingGame

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OpenAI is at it again. Yesterday, it released new models that are a significant step forward, enhancing the models’ ability to “think” through complex problems in science, coding, and math. While still in the very early days, this could be a huge opportunity to fast-forward innovation with fewer resources.The video below shows how these new models can build a simple app in just a few minutes. If NGOs can handle some of their smaller projects independently, it could open up possibilities for them to explore more advanced options in their matches with tech companies. By making these tools more accessible, tech companies can also push their commitment to social responsibility even further. It’s no longer just about offering support—but rather helping nonprofits become more independent and capable. This update from OpenAI isn’t just about advanced tech; it’s about creating stronger partnerships where both sides contribute to driving innovation. It’s a step toward reshaping what social responsibility looks like—moving from temporary fixes to long-term, sustainable impact.

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This is f*cking hilarious: TuSimple's pivot from self-driving trucks to AI animation and gaming! 🤖🎮 Right now, everyone’s betting on humanoid and general-purpose robotics startups. But after 5 years in self-driving tech and building Kopernikus Auto, here’s the reality: most humanoid robotics companies will be dead. In the self-driving world, we’ve only seen a few real winners: Tesla, Waymo, and Comma.ai. Why?  - Humanoid robotics isn’t a hardware problem; it’s a software problem. - You either have infinite data, cash, talent, or a clear use case—or you won’t make it, no matter how much you raise. From over 100 companies, almost all in self-driving tech are dead or miles away from profitability. ✨ Don’t get distracted by the appeal of humanoids. Focus on real problems you can actually solve. What are you focusing on? 👇

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The zoom meeting is starting now: 🚀 Paklaunch Webinar (23rd May) Join us for a Fireside Chat between Amir Husain, Founder and CEO SparkCognition, (unicorn AI startup) and Faisal Aftab, Founder Zayn VC. We will discuss SparkCognition, AI tech, investment landscape, global macros, and more. Our guests will also take select questions from the live audience. ➖TIME: 7am (US Pacific), 7pm (Pakistan) ➖ZOOM: https://lnkd.in/dh_Jjy5J GUEST’S BIO: Amir Husain is the Founder and CEO of SparkCognition, AI based platform offering predictive analytics solution. The product offers machine learning based analytics solutions to predict defects on a combustion turbine and optimizes asset operations. It also detects and protects malware attacks, provides visibility on the process to reduce errors, and business analytics solutions. The product is used in industries such as manufacturing, defense, aviation, maritime, financial services, oil, and gas. SparkCognition has raised a total funding of $300M over 6 rounds. It's latest funding round was a Series D round in January, 2022 for $123M at post money valuation of $1.4B. Investors include March Capital, Doha Venture Capital, B Riley Financial, Temasek, Alan Howard, AEI HorizonX, JC2 Ventures, John Chambers, Dalus Capital, Founders Equity Partners, National Grid Partners, Verison, Boeing, CME Group, Pankaj Patel, and Energize Capital. Amir is the Chairman of the Board of Directors for WorldQuant Predictive that is building a portfolio of exponential technology companies underpinned by a core platform for critical business forecasts and predictions, Chairman of the Board of Directors for Navigate that is building World’s largest decentralized intelligence platform. He was also the Founding Member of Board of Advisors for IBM Watson that helped drive the new era of Cognitive Computing. Amir has been named Austin’s Top Technoloy Entrepreneur Of the Year the year, listed as an Onalytica Top 100 AI Influencer, among several other accoldaes. He serves on the Board of Advisors for the University of Texas at Austin Department of Computer Science and ont he NATO Maritime Unammed Systems Innovation Advisory Board. He is also a member of the Council on Foriegn Relations. Husain has been awarded 30+ patents and his work has featured in world-leading outlets such as Foreign Policy, Fox Business News, and Proceedings from the U.S. Naval Institutie. He is the auther of the best-selling book “The Sentient Machine: The Coming Age of the Artificial Intelligence” and a co-author of the compliation “Hyperwar: Conflict and Competition in the AI Century”.

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My wide ranging conversation on enterprise AI automation with Alp Arhan U. on the Masters of Automation is now available on YouTube, Spotify and Apple Podcasts (links in comments). Some of the things we go over are: 🧠 My journey from Turkey to Harvard to YC 🧠 How Brainbase (YC W24) decides what to automate with AI 🧠 Our unique insight at Brainbase 🧠 Why immigrant founders make some of the best entrepreneurs Enjoy our conversation, and please sound out with comments and questions below!

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Replacing an expensive generalist LLM like GPT-4 with a cheaper more specialized model like a fine-tuned Mistral 7B is a tried and true approach to really drive your GenAI expenses down. We've got a handy guide on how to start your fine LLM cost optimization journey with OpenPipe for fine tuning and OctoAI for model serving https://lnkd.in/e8udh8de

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Microsoft is training its own large language model, internally labaled MAI-1, with Mustafa Suleyman leading the effort. The model is around 500B parameters and could compete directly with LLMs from Google, OpenAI, etc. This marks the first time Microsoft has developed an in-house AI model of this magnitude since investing over $10 billion in OpenAI for the rights to reuse the startup's AI models. OpenAI's GPT-4 powers not only ChatGPT but also Microsoft Copilot. The development of MAI-1 is being led by Mustafa Suleyman, the former Google AI leader who recently served as CEO of the AI startup Inflection before Microsoft acquired the majority of the startup's staff and intellectual property for $650 million in March. Although MAI-1 may build on techniques brought over by former Inflection staff, it is reportedly an entirely new large language model (LLM) With approximately 500 billion parameters, MAI-1 will be significantly larger than Microsoft's previous open source models (such as Phi-3).

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This fresh Heavybit post can serve as a good recap for our statement articles published two years ago: - Missing piece in modern data stack: unstructured data ETL (https://lnkd.in/e8xSUJcZ) - Why Instill AI exists (https://lnkd.in/egzFBx4t) It's getting even more challenging now if we also need to consider LLMOps as a whole, despite the fact that in-context learning (i.e., prompt engineering) and the notorious LLM hallucination can be treated as a concept drift or domain drift problem, which we just don't have a quite working solution yet. I would say the unstructured data ETL stack is the key. #unstructured #data #MLOps #LLMOps #AI

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An advice that goes well with every decor, “In a time of universal deceit, telling the truth is an [evolutionary] act.”  (School's Out) 😊 "School's Out A-Team" Talha Berkay Eren [turns dreams into realities] İrem CANSU [simplifies everything] Emirhan TALA [creates systems of systems of people] Büşra Nimet Oğuz [catches murderers] Erol Bozkurt [has fun as the prime mover] "Consultants" Tolga Bildirici [turns startups to firms] Yuksel Korukcu [turns apps to pro apps] Erdinç KÜTÜK [turns apps to pro apps] Senol Dikyurt [turns fans to customers] "Information Base" Status = https://lnkd.in/dVJyYi6U Brochure = https://lnkd.in/dzntguC9 Poster = https://lnkd.in/d8hgQiBX "Contact Information" schoolsout@mikimoka.com #mikimoka #schoolsout #managementissuesincomputersciences #miics #appsfortroublemakers #a4t #a4e #somethingfromnothing #socialexperiments #culturalterraforming #alternatereality #letsgetemotional #moody #verdict #notung #emotionalagility #emotionalagilitydash #ead #maddeningmove #uili #softwareengineering #anthropology #psychology #ethics #art #literature #strategy #fvy #fvys

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Day 3/3 GenAI Summit San Francisco Causal AI, causal grounded RAG - cRAG, scaling from POC to production in AWS, a deep dive into the evolution of Llama models and what's coming next, synthetic data in the age of genAI, PII, and domain randomization. I can't count how many times I've heard the words AI, genAI, data, cloud, RAG, LLM, and multimodal in the last 3 intense days. And when I finally went to have lunch at a restaurant, at the adjacent table, a fintech AI startup founder was pitching to a VC, while the people at the table behind me were talking about data engineering. It feels so good to be in SF, where at the airport’s security check-in a big Anthropic advertisement greets you! 😊 The last 3 days were amazing, filled with inspiration and thought-provoking new tech. I can't wait to get back to building DiffuseDrive and working with our customers!

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Really excited about KIP-932 (Queues for #Kafka) and its impact on the AI world: massive worker architectures to index unstructured documents at scale, feeding LLMs. Combined to inference cost reduction, lower latency, and smaller specialized models: local #SLMs will be a game-changer for scaling AI workloads cost-effectively in the streaming paradigm. (SLM: Small Language Model)

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Get started with the most powerful LLM library: AdalFlow--The PyTorch library to build, eval, and auto-optimize any LLM tasks. Though only public for two months, AdalFlow has been widely adopted by tech companies in Silicon Valley, including LLM engineers from Meta and various top tech startups. 1️⃣ Create your first chatbot, RAG, and Agent—all in one Colab notebook https://lnkd.in/g23YDaHt 2️⃣ Deep dive on Generator https://lnkd.in/dCRhXg2U 3️⃣ LLM evaluation playbook https://lnkd.in/gTvD93mP 4️⃣ Get started with end-to-end Auto-optimization -- Question Answering: https://lnkd.in/gtECHuGi -- Classification: https://lnkd.in/g4py2e6w 👉 Github repo: https://lnkd.in/g-KN9-dV Happy building! 😊 #adalflow #artificialintelligence #machinelearning #llms

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I just published Vol. 67 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 A Comprehensive Study of Noise Types in RAG, from Wu et al. 📚 Lessons from Adobe Express’s Iterative Enhancement of Multi-Modal Search, from Adobe 📚 Dynamic Hard Negative Mining for Enhanced Text Embeddings, from Tencent 📚 Tackling the Unique Challenges of Location-Based Search, from Airbnb 📚 Jina-ColBERT-v2: Enhancing ColBERT for Multilingual Retrieval, from Jha et al. 📚 Enhancing Music Recommendation through Psychology-Informed Session Embeddings, from Deezer 📚 Analyzing [MASK] Token Behavior in ColBERTv2, from RIT 📚 A Critical Evaluation of Traditional Models vs. LLMs in Sequential Recommendation, from Xu et al. 📚 Collaborative Discourse for Complex Information Discovery, from Jiang et al. 📚 A Large-Scale Synthetic Test Collection for Advancing Passage Retrieval Research, from Rahmani et al. #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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One of the most fun parts of data/ml engineering is feature engineering; reshaping and selecting data that can improve the predictive power of a model. 🤖 AI and ML typically have a pretty simple unit of understanding -- floating point numbers between negative one and one. ⏰ How do we help the ML understand values concepts like "What time of day is it?" 📐 ⭕ We can use trigonometry to map our times onto a unit circle, then project the X and Y components to get two values between -1 and 1 that our ML can understand! For some reason, these little tricks are very satisfying to me. Note: The numbers in the image are not calculated, I just picked some numbers to demonstrate. #ai #ml #featureengineering #dataengineering #software

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Your LLMs deserve better input. Reader is a trending Github repo that converts any URL to an LLM-friendly input with a simple prefix https://r.jina.ai/. Get improved output for your agent and RAG systems.

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