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- Physics - Data Science - Postgraduate Adjunct Lecturer at Pan-Atlantic University, on data science and NLP - 9 Published Papers on African NLP - 3 Papers under review - Research paper reviewer at top conferences - Founded Tonative, a community that curates African Language Datasets for AI models.

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OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

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Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

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Aksel

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Building a personal knowledge base for my agents is increasingly where I spend my time these days. Like Andrej Karpathy, I also use Obsidian for my MD vaults. What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers. I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal. You all get to benefit from that with the papers I feature in my timeline and on DAIR.AI. The papers are indexed using tobi lutke qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip. 100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close. The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to. Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them. Work in progress. More updates soon. Back to building.

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elvis

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Pablo Vela

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Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:

Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

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🔥 Nebius AI R&D is hiring AI Research Interns for short, high-impact RL projects. Exclusive to X right now — no LinkedIn mass postings yet. In 2019, I was a fresh dental grad with 3 months of runway left, begging for an AI shot. I know the grind. We’re looking for sharp early-career folks (students, grads, career-switchers) to join us and work on: > Agent trajectories analysis at scale > Long-horizon tasks for coding agents > Pushing open RL environments > Any other data / RL env / eval project that will benefit open-source community What you get: 💰 Fully paid internship (3-6 month) 📦 100% open-source shipping 📄 Co-author research papers ⚡️ Access to Nebius compute infra 🌍 Remote-friendly (EU/US) or Amsterdam/London/other office. If you’ve done any cool AI/ML/RL stuff, dm me with your most impressive project + 1-sentence summary + cv Sharing appreciated!🤝

🔥 Nebius AI R&D is hiring AI Research Interns for short, high-impact RL projects. Exclusive to X right now — no LinkedIn mass postings yet. In 2019, I was a fresh dental grad with 3 months of runway left, begging for an AI shot. I know the grind. We’re looking for sharp early-career folks (students, grads, career-switchers) to join us and work on: > Agent trajectories analysis at scale > Long-horizon tasks for coding agents > Pushing open RL environments > Any other data / RL env / eval project that will benefit open-source community What you get: 💰 Fully paid internship (3-6 month) 📦 100% open-source shipping 📄 Co-author research papers ⚡️ Access to Nebius compute infra 🌍 Remote-friendly (EU/US) or Amsterdam/London/other office. If you’ve done any cool AI/ML/RL stuff, dm me with your most impressive project + 1-sentence summary + cv Sharing appreciated!🤝

Ibragim

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This #CVPR2026 paper from our research team is trending #1 on Hugging Face 🤗 Meet LocateAnything: a vision-language detection model that rethinks bounding box prediction. For AI agents and robots, “seeing” is only useful if a model can pinpoint where something is fast enough to act. Trained on 138M high-quality samples, LocateAnything decodes bounding boxes in parallel instead of one coordinate at a time, improving localization accuracy while dramatically increasing throughput for visual grounding and detection. Project page:

This #CVPR2026 paper from our research team is trending #1 on Hugging Face 🤗 Meet LocateAnything: a vision-language detection model that rethinks bounding box prediction. For AI agents and robots, “seeing” is only useful if a model can pinpoint where something is fast enough to act. Trained on 138M high-quality samples, LocateAnything decodes bounding boxes in parallel instead of one coordinate at a time, improving localization accuracy while dramatically increasing throughput for visual grounding and detection. Project page:

NVIDIA AI

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🌍 The brand-new CV VC African Blockchain Report is now live! The report, co-published by Absa Corporate and Investment Banking, depicts a clear message: Africa’s blockchain future is already here. Download the full report now: Our annual African Blockchain Report offers a data-rich view into the continent’s rise as a global blockchain frontier. Globally, blockchain made up just 3.2% of VC funding in 2024. In Africa? 7.4%. More than double. That’s not a coincidence. Some key takeaways from the report include: 🔹 Blockchain accounted for 12.7% of all African VC deals and 7.4% of funding, a growing share despite tighter capital markets 🔹 Africa’s share of global blockchain deals rose to 2.3%, even as global venture funding became more selective 🔹 Seed rounds dominated, attracting 34% of blockchain-focused funding, a clear signal of investor faith in early-stage innovation 🔹 Centralized Blockchain Financial Services led by funding share (41%), followed by DeFi (30%), and Data Verification (20%) 🔹 Nigeria led by deal count, while Seychelles ventures secured the highest funding shares at 31.7% 🔹 Median deal size for blockchain ($2.8M) was nearly double the all-sector African median Despite tighter capital conditions driven by both global and local challenges, funding still moved. African blockchain startups focused on practical applications, sharpening their impact across finance, infrastructure, and regulatory-compliant data solutions. Discover more about the African funding landscape and Web3 ecosystem in our report:

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We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

elvis

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Every enterprise deploying AI is making a bet on where their data infrastructure lives. Regulators, customers, and compliance teams now want clear answers about where data is stored and processed before they sign. Announced today at BoxWorks London: Box Zones expands to 10 regions worldwide: • 3 new Zones: Switzerland, Singapore, Israel • In-region compute for France & Canada • Roadmap: in-region AI processing later in 2026 According to Gartner, 35% of countries will be locked into region-specific AI platforms by 2027. Organizations that build their content infrastructure with residency at the center now, will be better positioned to win regulated customers and deploy AI responsibly. Read the full announcement:

Every enterprise deploying AI is making a bet on where their data infrastructure lives. Regulators, customers, and compliance teams now want clear answers about where data is stored and processed before they sign. Announced today at BoxWorks London: Box Zones expands to 10 regions worldwide: • 3 new Zones: Switzerland, Singapore, Israel • In-region compute for France & Canada • Roadmap: in-region AI processing later in 2026 According to Gartner, 35% of countries will be locked into region-specific AI platforms by 2027. Organizations that build their content infrastructure with residency at the center now, will be better positioned to win regulated customers and deploy AI responsibly. Read the full announcement:

Box

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Since Professor V Kamakoti has been nominated in the committee to overhaul our education system, he is being mocked by opposition leaders for his beliefs. Today, I sat to check his academic credentials . Alongside serving as the Director of IIT Madras, his academic and professional accomplishments include: • 250+ Research Papers and Patents: He has published over 250 research papers in leading international journals and conferences and holds numerous national and international patents. • Development of the SHAKTI Microprocessor: He led the design and development of SHAKTI, India’s first indigenous, open-source microprocessor. Microprocessor Development Programme (MDP): He heads this flagship initiative of the Ministry of Electronics and Information Technology (MeitY), aimed at strengthening India’s semiconductor and chip-design ecosystem. • Record Patent Filings – “One Patent a Day”: As Director of IIT Madras, he introduced the vision of “One Patent a Day.” Under his leadership, the institute filed a record 417 patents during 2024–25. • Role in the National Security Advisory Board (NSAB): As an NSAB member, he provides strategic advice to the Government of India on cybersecurity, telecommunications security, and digital infrastructure. • Leadership of the AI Task Force: He chaired the Artificial Intelligence Task Force constituted by the Ministry of Commerce and Industry, helping shape India’s AI policy framework. • Information Security Education and Awareness (ISEA): He oversees initiatives under the ISEA programme to build India’s cybersecurity workforce and promote public awareness of information security. • Research in VLSI and Hardware Security: His primary academic contributions lie in VLSI design, algorithms, and strengthening cryptographic security at the silicon hardware level. • Institutional Reforms at IIT Madras: Since assuming office as Director in 2022, he has helped IIT Madras retain its top position in the NIRF rankings while launching new online degree programmes and expanding the institute’s startup ecosystem. In recognition of his outstanding contributions to science and technology, he has been awarded the Padma Shri (2026), the DRDO Academic Excellence Award, and the IBM Faculty Award. Here is in his native village, moving on roads in a Prabhat Feri invoking the Grama Devta.

Since Professor V Kamakoti has been nominated in the committee to overhaul our education system, he is being mocked by opposition leaders for his beliefs. Today, I sat to check his academic credentials . Alongside serving as the Director of IIT Madras, his academic and professional accomplishments include: • 250+ Research Papers and Patents: He has published over 250 research papers in leading international journals and conferences and holds numerous national and international patents. • Development of the SHAKTI Microprocessor: He led the design and development of SHAKTI, India’s first indigenous, open-source microprocessor. Microprocessor Development Programme (MDP): He heads this flagship initiative of the Ministry of Electronics and Information Technology (MeitY), aimed at strengthening India’s semiconductor and chip-design ecosystem. • Record Patent Filings – “One Patent a Day”: As Director of IIT Madras, he introduced the vision of “One Patent a Day.” Under his leadership, the institute filed a record 417 patents during 2024–25. • Role in the National Security Advisory Board (NSAB): As an NSAB member, he provides strategic advice to the Government of India on cybersecurity, telecommunications security, and digital infrastructure. • Leadership of the AI Task Force: He chaired the Artificial Intelligence Task Force constituted by the Ministry of Commerce and Industry, helping shape India’s AI policy framework. • Information Security Education and Awareness (ISEA): He oversees initiatives under the ISEA programme to build India’s cybersecurity workforce and promote public awareness of information security. • Research in VLSI and Hardware Security: His primary academic contributions lie in VLSI design, algorithms, and strengthening cryptographic security at the silicon hardware level. • Institutional Reforms at IIT Madras: Since assuming office as Director in 2022, he has helped IIT Madras retain its top position in the NIRF rankings while launching new online degree programmes and expanding the institute’s startup ecosystem. In recognition of his outstanding contributions to science and technology, he has been awarded the Padma Shri (2026), the DRDO Academic Excellence Award, and the IBM Faculty Award. Here is in his native village, moving on roads in a Prabhat Feri invoking the Grama Devta.

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NVIDIA AI Released DiffusionRenderer: An AI Model for Editable, Photorealistic 3D Scenes from a Single Video In a groundbreaking new paper, researchers at NVIDIA, University of Toronto, Vector Institute and the University of Illinois Urbana-Champaign have unveiled a framework that directly tackles this challenge. DiffusionRenderer represents a revolutionary leap forward, moving beyond mere generation to offer a unified solution for understanding and manipulating 3D scenes from a single video. It effectively bridges the gap between generation and editing, unlocking the true creative potential of AI-driven content. DiffusionRenderer treats the “what” (the scene’s properties) and the “how” (the rendering) in one unified framework built on the same powerful video diffusion architecture that underpins models like Stable Video Diffusion..... Read full article here: Paper: GitHub Page: NVIDIA NVIDIA AI NVIDIAnewsroom NVIDIA AIDev

NVIDIA AI Released DiffusionRenderer: An AI Model for Editable, Photorealistic 3D Scenes from a Single Video In a groundbreaking new paper, researchers at NVIDIA, University of Toronto, Vector Institute and the University of Illinois Urbana-Champaign have unveiled a framework that directly tackles this challenge. DiffusionRenderer represents a revolutionary leap forward, moving beyond mere generation to offer a unified solution for understanding and manipulating 3D scenes from a single video. It effectively bridges the gap between generation and editing, unlocking the true creative potential of AI-driven content. DiffusionRenderer treats the “what” (the scene’s properties) and the “how” (the rendering) in one unified framework built on the same powerful video diffusion architecture that underpins models like Stable Video Diffusion..... Read full article here: Paper: GitHub Page: NVIDIA NVIDIA AI NVIDIAnewsroom NVIDIA AIDev

Marktechpost AI Dev News ⚡

104,741 görüntüleme • 1 yıl önce

🌟 باحثة سعودية تطور تقنيات التشخيص المبكر لسرطان الثدي في جامعة إكستر 🇸🇦 تألقت رغد طارق باصقر، عضو هيئة التدريس بجامعة الإمام عبدالرحمن بن فيصل، بإنجاز علمي مميز في جامعة إكستر البريطانية 🇬🇧 حصدت جائزة أفضل مشروع ماجستير لعام 2024 عن بحثها في تطوير تقنيات التشخيص المبكر لسرطان الثدي باستخدام الذكاء الاصطناعي، كما كُرمت من قبل عميد كلية البيئة والعلوم والاقتصاد بجامعة إكستر كواحدة من الطلاب الأكثر تفوقاً في الأداء الأكاديمي ضمن برنامج علوم البيانات التطبيقية والإحصاء 🎓✨ #نفخر_بك #علماء_السعودية #إنجاز_سعودي #جامعة_إكستر 🌟 Saudi Researcher Advances Early Breast Cancer Diagnosis Technologies at Exeter University 🇸🇦 Raghed Tariq Basager, faculty member at Imam Abdulrahman Bin Faisal University, achieved remarkable scientific excellence at the University of Exeter, UK 🇬🇧 She won the Best Master’s Project Award 2024 for her research in advancing early diagnosis technologies for breast cancer using AI. She was also honored by the Dean of the Faculty of Environment, Science, and Economy as one of the top-performing students in the MSc Applied Data Science and Statistics programme 🎓✨ #SaudiPride #SaudiScientists #SaudiAchievement #ExeterUniversity

🌟 باحثة سعودية تطور تقنيات التشخيص المبكر لسرطان الثدي في جامعة إكستر 🇸🇦 تألقت رغد طارق باصقر، عضو هيئة التدريس بجامعة الإمام عبدالرحمن بن فيصل، بإنجاز علمي مميز في جامعة إكستر البريطانية 🇬🇧 حصدت جائزة أفضل مشروع ماجستير لعام 2024 عن بحثها في تطوير تقنيات التشخيص المبكر لسرطان الثدي باستخدام الذكاء الاصطناعي، كما كُرمت من قبل عميد كلية البيئة والعلوم والاقتصاد بجامعة إكستر كواحدة من الطلاب الأكثر تفوقاً في الأداء الأكاديمي ضمن برنامج علوم البيانات التطبيقية والإحصاء 🎓✨ #نفخر_بك #علماء_السعودية #إنجاز_سعودي #جامعة_إكستر 🌟 Saudi Researcher Advances Early Breast Cancer Diagnosis Technologies at Exeter University 🇸🇦 Raghed Tariq Basager, faculty member at Imam Abdulrahman Bin Faisal University, achieved remarkable scientific excellence at the University of Exeter, UK 🇬🇧 She won the Best Master’s Project Award 2024 for her research in advancing early diagnosis technologies for breast cancer using AI. She was also honored by the Dean of the Faculty of Environment, Science, and Economy as one of the top-performing students in the MSc Applied Data Science and Statistics programme 🎓✨ #SaudiPride #SaudiScientists #SaudiAchievement #ExeterUniversity

أول سعوديـ/ـة

46,651 görüntüleme • 1 yıl önce

In a masterclass at Sequoia Capital AI Ascent, Jim Fan laid out the "Great Parallel": how robotics is speedrunning the LLM playbook. 🔹 VLA → WAM: Moving from language-heavy models to "World Action Models" that dream in physics. 🔹 Teleop → EgoScale: Replacing manual data with human egocentric video. 🔹 Simulation 2.0: Using neural simulators like DreamDojo to turn compute into environments. "Our generation was born too late to explore the earth and too early to explore the stars. But we are born just in time to solve robotics." He believes that robots will pass the Physical Turing Test in the coming 2–3 years.

In a masterclass at Sequoia Capital AI Ascent, Jim Fan laid out the "Great Parallel": how robotics is speedrunning the LLM playbook. 🔹 VLA → WAM: Moving from language-heavy models to "World Action Models" that dream in physics. 🔹 Teleop → EgoScale: Replacing manual data with human egocentric video. 🔹 Simulation 2.0: Using neural simulators like DreamDojo to turn compute into environments. "Our generation was born too late to explore the earth and too early to explore the stars. But we are born just in time to solve robotics." He believes that robots will pass the Physical Turing Test in the coming 2–3 years.

Humanoids daily

12,123 görüntüleme • 3 ay önce

I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what Andrej Karpathy describes. It uses obsidian vaults to organize papers, retrieve LLM-generated summaries, diagrams, and other advanced views for paper exploration. When Obsidian UI is not enough, I use my own artifact generator inside my agent orchestrator (see clip for example). This allows my agents to build any kind of view or exploration feature that I need. The papers are all curated with automations and several rules/patterns I have manually built over the years. On the surface, this looks basic. But behind the scenes, there are advanced search capabilities, connections, metadata, derived data, and other interesting bits of information that are extremely useful for my research agents. This is mostly built for agents. The artifact preview is just a high-level way to validate and quickly assess the quality of the wiki, suggest improvements, and it's also great for research. I use tobi lutke's qmd for all search capabilities. Everything is markdown. The summaries and even the diagrams. The wiki updates on its own based on several automations I have optimized over the past couple of weeks. The wiki grows and self-improves based on several requirements important for my research use cases. This is as personalized as it gets. There is nothing like it out there. And I use my research expertise to continue improving it over time. This is a vanilla wiki. There are so many things I want to build on top of this. Different aggregations, views, artifacts, etc. All to help automate more of my research work and accelerate productivity. I think the biggest leverage here is how powerful this could be for discovery and experimentation. One of my goals is to use it to find deeper connections and insights that would otherwise elude the top human researchers and use those to generate interesting new hypotheses and research experiments. That way, my agents can use autoresearch to explore research ideas at the frontier. Stay tuned for more.

I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what Andrej Karpathy describes. It uses obsidian vaults to organize papers, retrieve LLM-generated summaries, diagrams, and other advanced views for paper exploration. When Obsidian UI is not enough, I use my own artifact generator inside my agent orchestrator (see clip for example). This allows my agents to build any kind of view or exploration feature that I need. The papers are all curated with automations and several rules/patterns I have manually built over the years. On the surface, this looks basic. But behind the scenes, there are advanced search capabilities, connections, metadata, derived data, and other interesting bits of information that are extremely useful for my research agents. This is mostly built for agents. The artifact preview is just a high-level way to validate and quickly assess the quality of the wiki, suggest improvements, and it's also great for research. I use tobi lutke's qmd for all search capabilities. Everything is markdown. The summaries and even the diagrams. The wiki updates on its own based on several automations I have optimized over the past couple of weeks. The wiki grows and self-improves based on several requirements important for my research use cases. This is as personalized as it gets. There is nothing like it out there. And I use my research expertise to continue improving it over time. This is a vanilla wiki. There are so many things I want to build on top of this. Different aggregations, views, artifacts, etc. All to help automate more of my research work and accelerate productivity. I think the biggest leverage here is how powerful this could be for discovery and experimentation. One of my goals is to use it to find deeper connections and insights that would otherwise elude the top human researchers and use those to generate interesting new hypotheses and research experiments. That way, my agents can use autoresearch to explore research ideas at the frontier. Stay tuned for more.

elvis

67,153 görüntüleme • 4 ay önce

Today may be the ImageNet moment for robotics. RT-X: the largest open-source robot dataset ever compiled, across 33 institutes, 22 robot hardware, 527 skills, and 1M episodes. Why is robotics lagging so far behind NLP, vision, and other AI domains? Data scarcity is the main culprit to blame, among other difficulties. Unlike text, images, and videos, you cannot download mass amounts of onboard robot control data from the internet. They simply don't exist in the wild. 11 yrs ago, ImageNet kicked off the deep learning revolution. 3-4 yrs ago, internet-scale data fueled the first GPTs and Diffusions that define this era of foundation models. I think 2023 is finally the year for robotics to scale up. Robot foundation models like VIMA ( my team's work at NVIDIA) and RT-1/2 ( Google DeepMind's effort) are extremely data hungry. While massively parallel simulations like NVIDIA IsaacGym & Omniverse can alleviate the problem to some extent, it's still not quite enough to bridge the gap to the messy, physical world. This new dataset is not just a technical contribution. I also see it as a commendable effort to overcome institutional bureaucracies and unite researchers from around the world to tackle a grand challenge together. Robotics will be the final holy grail that we capture in AI. We are not there yet, but ascending in the right gradient direction. RT-X website: Launch blog:

Today may be the ImageNet moment for robotics. RT-X: the largest open-source robot dataset ever compiled, across 33 institutes, 22 robot hardware, 527 skills, and 1M episodes. Why is robotics lagging so far behind NLP, vision, and other AI domains? Data scarcity is the main culprit to blame, among other difficulties. Unlike text, images, and videos, you cannot download mass amounts of onboard robot control data from the internet. They simply don't exist in the wild. 11 yrs ago, ImageNet kicked off the deep learning revolution. 3-4 yrs ago, internet-scale data fueled the first GPTs and Diffusions that define this era of foundation models. I think 2023 is finally the year for robotics to scale up. Robot foundation models like VIMA ( my team's work at NVIDIA) and RT-1/2 ( Google DeepMind's effort) are extremely data hungry. While massively parallel simulations like NVIDIA IsaacGym & Omniverse can alleviate the problem to some extent, it's still not quite enough to bridge the gap to the messy, physical world. This new dataset is not just a technical contribution. I also see it as a commendable effort to overcome institutional bureaucracies and unite researchers from around the world to tackle a grand challenge together. Robotics will be the final holy grail that we capture in AI. We are not there yet, but ascending in the right gradient direction. RT-X website: Launch blog:

Jim Fan

265,061 görüntüleme • 2 yıl önce