Oleksandr Maksymets

🚀🎙 Episode 25 of #PeymanFakharianPodcast and this time with Kayvan Mohit Mafi, CEO of Diginext.ir, the innovation center of #digikala. Kayvan truely is one of the most active supporters of startup innovation in Iran and in this episode he shares his insightful views on B2B tech and AI startups. 🔍 Topics We cover: - Foundation of Diginext - Story of Diginext and its investment portfolio - Investment on B2B Startups - AI Startups - Inviting Mentors to help startup founders 🎧 Listen to the episode on your favorite podcast app. Just search for "Peyman Fakharian" or "پیمان فخاریان" 🎧 Or listen on Castbox https://lnkd.in/eRQ6AmzA 🎧 Or Subscribe to our Youtube Channel https://lnkd.in/ePx6cfS7 📌 For more updates and exclusive content, follow my Telegram Channel: https://lnkd.in/dQJVz2Z9 hashtag #Peymanfakharian #پیمان_فخاریان #digikala #diginext #دیجیکالا #دیجی‌کالا #PeymanFakharianPodcast

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TRIPLE BAM! If you missed it, here's the recording of our live Q/A with Jay Alammar and Joshua Starmer PhD! We talked about many AI topics, answered lots of great questions, and talked about two great books: Hands-On Large Language Models: Language Understanding and Generation, and The Illustrated Guide to Neural Networks and AI!!! https://lnkd.in/dcPHs7YQ

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You don’t normally see LLM researchers and engineers talking about their day-to-day work. Big shoutout to this YouTube video from the Anthropic LLM Interpretability team, the team behind the Golden Gate Claude. It’s amazing to see that most of the team members have worked for the team for only one year, and yet they have made such a big breakthrough in interpretability research. Another thing I want to highlight is the unique challenges facing the team and how they approach these challenges, that is, through fast trial-and-error iterations and constant evaluation of what may work and what to prioritize next. #artificialintelligence #machinelearning #deeplearning https://lnkd.in/eUaPAYch

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Sharing my thoughts on How Waymo (formerly known as the Google Self-Driving Car Project) is Pioneering Autonomous Driving with AI. Waymo has become a trailblazer in self-driving technology, and at the core of it all is AI. Leveraging advanced machine learning and deep neural networks, Waymo’s AI-powered system continuously improves through real-world data, making autonomous vehicles smarter, safer, and more reliable. Perception: AI allows Waymo’s cars to "see" and understand their surroundings by processing data from sensors like cameras, LIDAR, and radar. This helps detect pedestrians, other vehicles, and objects in real time. Prediction: The AI system predicts the behavior of everyone around the vehicle—whether it’s cars changing lanes or pedestrians crossing the street—ensuring smooth and safe driving decisions. Planning & Control: AI makes split-second decisions about accelerating, braking, and steering based on the analysis of complex traffic patterns, enabling Waymo vehicles to safely navigate roads. With millions of autonomous miles driven, Waymo’s AI is constantly learning and improving, helping create the future of safer and smarter transportation. The road ahead is AI-driven! www.rxsmeta.com #AI #AutonomousVehicles #Waymo #SelfDrivingCars #MachineLearning #SmartMobility #TransportationTech #FutureOfTransport #AIinAction #Innovation

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How should developers think about the emerging AI stack? At Data Council '24, I interviewed three AI pioneers: → Ce Zhang, CTO of Together AI → Ram Sriharsha, CTO of Pinecone → Soheil Koushan, Member of Technical Staff at Anthropic Highlights: − AIOps vs MLOps: Similarities and differences, design patterns, opportunities − Evaluating Non-Deterministic LLMs: Challenges and strategies for assessment − Retrieval Augmented Generation (RAG): Vector databases, RAG strategies, context window expansion, fine-tuning vs RAG − Deployment Scenarios: Open-source vs proprietary models, mixed-model workflows − Leveraging Tabular Data: How to think about working with LLMs and your tabular data Full conversation linked in comments.

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Enums enhance code readability and clarity. They define named values for model attributes, making them ideal for representing statuses, stages, and states. An article from the team at reinteractive explores the powerful features of enums, particularly how they make code readable and self-explanatory … https://buff.ly/4cKGWiu #RubyOnRails #Programming #Enums #reinteractive

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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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The cost of training top models is increasing 2-3X per year, and at this rate will reach $1 billion per model by 2027. The findings are from Epoch AI and follow their recent analysis of increases in compute for foundation models running at 4-5X per year. They've also provided some interesting data on the relative breakdown of cost components like staff, hardware, and energy. A few thoughts after reading this: - I would assume staff costs won't increase as much as hardware and energy costs, so the relative contribution of human labor to future AI models will decrease, in yet another sign of capital's increasing dominance over labor in the industry (just look at NVIDIA's ever-rising stock price). - If compute is increasing 4-5X per year but cost is increasing 2-3X, is it safe to assume that training runs are getting more efficient? - Some people, like Anthropic CEO Dario Amodei, have said we'll see $1 billion training runs in the next year. Does this mean the 2-3X pace is about to increase? That Epoch is wrong in its estimate? Or that Amodei is overstating the cost, possibly to put fear into competitors who can't keep pace? - Only a few companies are going to be able to train future models. We're likely going to see further consolidation. Perhaps we get to a future with only two large general purpose models, like the Windows/MacOS and Android/iOS duopolies we've seen in the past. Article here: https://lnkd.in/g5SejXen

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Short video: brief summaries of several key issues in ML history and modern ML.

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Increasingly AI applications invoke tools, leverage resources/data and have multiple steps and models involved in a complex pipeline. The boundaries between "RAG", "agents" and other names we have given these compound AI systems are blurring. For example, one thing that would be super useful is hyperparameter-style optimization for RAG pipelines. You define the knobs of your system, and the search space, and we run many sweeps to find the best configuration. Some knobs could be document chunking, embedding model or retrieval-time LLM used. But other knobs could be whole algorithms or techniques, such as contextual retrieval (https://lnkd.in/ejH9vCXZ), graph RAG, etc. What's clear is there are so many permutations for increasingly complex AI systems that we need tools to help systematically measure what works and doesn't work for *your* application. Evals is just the starting point -- to take those learnings and improve your applications with it requires methodical experimentation.

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$2 H100s: How the GPU Bubble Burst Last year, H100s were $8/hr if you could get them. Today, there's 7 different resale markets selling them under $2. What happened? The market has flipped from shortage ($8/hr) to oversupplied ($2/hr), because of reserved compute resales, open model finetuning, and decline in new foundation model co’s. Rent instead. https://lnkd.in/gpQEidmC

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In this episode, we discuss GenHMR: Generative Human Mesh Recovery by Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Pu Wang, Hongfei Xue, Srijan Das, Chen Chen. The paper introduces GenHMR, a novel generative framework for human mesh recovery (HMR) that addresses uncertainties in converting 2D images to 3D mesh. It employs a pose tokenizer and an image-conditional masked transformer to learn distributions of pose tokens, improving upon deterministic and probabilistic approaches. The model also includes a 2D pose-guided refinement technique and demonstrates superior performance compared to current methods.

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Over the past period I had an immense privilege of working with Andrej Karpathy (ex director of AI at Tesla, founding member of OpenAI, and our AI sensei :)), and a few cracked GPU engineers (Arun Demeure ex GPU Platform Architect at Apple, ngc92 et al) on the llm.c project. And I must admit I was pleasantly surprised yesterday getting a shout-out from Andrej and seeing my Twitter blow up with notifications 😅 (see the attached screen shot) As someone I've been looking up to since i started my ML journey up to a point where i'm casually chatting with him on a daily basis it all feels very unreal. Arun & ngc92 are extremely knowledgeable, and i learned a ton from them building llm.c. They're our CUDA whisperers. :) But, before continuing, what is llm.c? from Andrej: "...Because llm.c is a direct implementation of GPT training in C/CUDA, the requirements are minimal - there is no need for conda environments, Python interpreters, pip installs, etc. You spin up a cloud GPU node (e.g. on Lambda), optionally install NVIDIA cuDNN, NCCL/MPI, download the .bin data shards, compile and run, and you're stepping in minutes. You then wait 24 hours and enjoy samples about English-speaking Unicorns in the Andes." original tweet here: https://lnkd.in/dU93zyZU I've quickly contributed 113 commits (+ 40 pending once i merge my muP PR), among those: * I added multi-node support with NCCL initialization going via MPI/TCP/FS so that we can support both slurm and non-slurm scheduling envs * Helped make our code bitwise deterministic - so that after the training crashes we have guarantees that starting from the last checkpoing will give **precisely** the same loss & gradient numbers! * Implemented our random shuffling logic in the data loader, added various lr schedulers, and finally caught many bugs (e.g. periodic loss spikes, distributed norm computation bug, memory leaks), etc. I have learned a ton about CUDA, contributed to our layernorm kernel, and finally I now feel comfortable with basically every single part of LLM training since llm.c is such a "bare metal" project. I really feel i can build anything at this point. But I also learned you can't do it without a cracked team. :)) And, of course, it's more fun, and you make friends along the way. Over the past 2 weeks I mostly worked on adding maximum update parametrization support to llm.c: https://lnkd.in/dB2MuCH3 ran a ton of experiments & ablations and we're getting close to merging it in. The issue is that mup's benefits will become more apparent at bigger scales, while currently we're mostly in the ~1B regime. Today we celebrate llm.c milestone: 5 years after OpenAI announced GPT-2, with all the surrounding safety talks and staged release -> today you can now train your own GPT-2 in ~650$ on a single 8xH100 nodes in 24 hours. See this post for more info: https://lnkd.in/dD6Du4se Extremely excited for the future!

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LLM powered AI agents have been a hot topic this year. Inspired by John Yang's SWE-agent paper which framed agents as their own class of end users and showed a path to improving agent performance through agent-computer interfaces (ACI), I wanted to explore the parallels between AI agents and consumers. In my latest article, I lay out my vision of Web 4.0--a web characterized by AI operability and agentic markets, and how ACIs will enable these. Through examining some recent LLM research papers, I outline: - Why some AI agents are consumers - How AI agents being consumers is currently a problem - Why allowing AI agents to spend will lead to new economies and define Web 4.0 - Why agent-computer interfaces (ACI) is needed for Web 4.0 to come to life Check out the piece below and let me know your thoughts! Huge thanks to my friends Kevin Zhang, Hanyi . , Robin Huang, 🐙 Marc Mengler, Sheng Ho, Andrew Tan, Phillip An , Nadja Carina Reischel, and Luc de Leyritz for reviewing this piece and valuable suggestions! #AI #Agents #LLM #ACI #Web4

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「Sum Day News 6.08 - 6.14」☀️ Weekly wrap-up on Tech/Health Giant AI, products, finance, web3, data, partnership: - FDA Receives Supplemental Biologics License Application for Eisai's Alzheimer's Treatment (00:00:00) - Rivian Electric Vehicles Enhanced with Cutting-Edge Nvidia Technology (00:02:02) - Google Ad Manager Unveils Innovative Programmatic Email Advertising Solution (00:03:29) - Pinterest's New Campaign Targets Potential Advertisers (00:04:51) - FDA Panel Endorses Alzheimer's Drug Showing Promise in Slowing Cognitive Decline (00:06:18) - Data Breach at Snowflake: Hackers Access Extensive Information from Customers (00:07:29) - Jabra Launches Enhanced Elite 8 Active and Elite 10 Earbuds, Unveiling the Next Generation of True Wireless Audio (00:08:51) - Apple Unveils AI Initiatives and ChatGPT Partnership (00:10:22) - Oracle and Google Cloud Announce Multicloud Partnership (00:11:42) ---------------------- 👍Follow our Linkedin Page  #EASY2DIGITAL  https://lnkd.in/gUXCft_T to explore trending lifestyle upgrade with leveraging AI, RPA, AI agent, Web3, Finance that help offload repetitive tasks --------- AI Agent --------- 🙋If you are interested in having your own AI agent to automatically complete content research tasks based on your personal/business strategies, preference, goals on behalf of you, please select one of purpose as follows, send us a message "AI agent + purpose you select", we’ll come back to you! ☑️Blog content marketing's AI Agent ☑️Stock equity investment news AI agent Contact: 🇺🇸English: https://lnkd.in/gXkEAtUe 🇯🇵日本語: https://lnkd.in/ggVPVPD5 🇨🇳中文: WeChat@easy2digital --------- 🔧Free Low/No code AI tools & APIs ---------: 🍑Buyfromlo: 1⃣️AI Image Prompt Generator https://lnkd.in/gQ6B8Pby 2⃣️Image Scraper https://lnkd.in/gGwefGHx 3⃣️Image Reader https://lnkd.in/gGwefGHx 4⃣️AI Video Subtitle Generator  https://lnkd.in/gQ-eFVWd 5⃣️AI Voiceover Generator https://lnkd.in/geEC6wXT 6⃣️AI Content Translator  https://lnkd.in/gg88KBCY 🍎LeiaPix AI 2D to 3D Converter https://lnkd.in/gDqRZPQy 🍊DeepAI Image Generator https://lnkd.in/gM4JmCPr 🍉Flair AI Product Image Generator  https://lnkd.in/gCa_PzMd 🌰Pictory AI Video Generator https://lnkd.in/dc86avmA 🍈Visla AI Video Generator https://lnkd.in/dStaMtwX 🍌AI Home Interior Designer https://lnkd.in/gCh8sfXN 🍣GravityWrite AI Content https://lnkd.in/gCf6Xueq 🍔Sonix AI Transcriptor https://lnkd.in/gaPd3p6e 🎨Artist AI Art https://lnkd.in/gSCeJ2xP 🍚More Options from full automatic content collection: https://lnkd.in/gAG4WzQA ---------  P.S: This post is blasted automatically. If you are interested in the script, please check out here: 📗Linkedin Post Automation EN: https://lnkd.in/gSCCuNRx JP: https://lnkd.in/gWBpiANa --------- さっき、すべての英語情報とお勧めには 日本語バージョンがあります。詳細については、次のリンクをご覧ください。 https://lnkd.in/gvGD7REt #AI #Web3 #Financial #sumdaynews #sundaynews #buyfromlo

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What happens when you feed 13th-century Persian poetry to modern AI? Just experimented with Anthropic Claude 3.5's Style feature using Rumi's timeless Persian verses, and the results were intriguing. The AI not only comprehended the text but created a "Spiritual Wanderer" style that brought mystical wisdom to modern communication. The fascinating part? I used this style to transform both customer service responses and social media posts. Key discoveries that showcase AI's evolving capabilities 🔔 1. 💡 Cross-Cultural Understanding: Claude seamlessly interpreted Rumi's Persian poetry and distilled its essence into a usable writing style. 2. 🖋️ Style adaptability: Taking this poetic style and applying it to modern business scenarios from customer complaints to social media posts 3. 🎯 Multi-Platform Adaptability: The ultimate test was creating an Instagram post with this style - bridging 13th-century Persian mysticism with 21st-century social media. Content that felt both timeless and perfectly platform-appropriate. 🌟 #AIInnovation #FutureOfTech #CulturalAI #GenAI

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Google had nine of my fellow Googlers *arrested* last night. Their "crime"? Peacefully protesting Google's Project Nimbus contract with the Israeli government, and recent revelations that, despite repeated explicit claims by Google to the contrary, this contract includes providing discounted cloud computing and consulting services to the Israeli Defense Force (IDF). You can agree or not with the overall message and goals of #NoTechForApartheid. You can debate whether it's appropriate to call what's happening in #Gaza a #genocide. But you can't deny that this was an act of pure #retaliation against these Googlers. Google claims that the removal of these Googlers was a matter of "workplace safety". Whose safety, exactly, was being compromised by nine peaceful employees inside the workspace of their own employer? You can say they should have used official channels - they did! We have! Hundreds of Googlers have spoken out about Project Nimbus internally through all manner of official channels. We have been consistently ignored, lied to, and given nonsense non-responses. Google culture explicitly encourages escalation - what is the next step when you've been ignored through all official channels but to make yourselves impossible to ignore? We do not consent to the output of our labor being used, directly and indirectly, to fight an unjust war, kill innocent civilians, and commit war crimes. We will not be silenced through illegal retaliation for speaking up about the conditions of our workplace. We will not be used. What can one person do? Use your voice. Speak up. And join with others. In collective action, we have power. Join the Alphabet Workers Union (AWU). Sign the petitions to end Project Nimbus, restore Eddie Hatfield's job, and more (internally or the external petition on notechforapartheid.com ). If you live in the US, contact your representatives in government and tell them that you want to stop funding this unjust war, and that you want clear violations of labor law such as Google's punished, and worker protections enforced and improved. Alone, it can seem like it's impossible. But together, we can make change. #Solidarity #Labor #Union #AWU

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