Відео

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No device such as AI or Deepmind google can beat a Pro TT Human. I strongly believe. Let us challenge. I can bet on any amount with AI or Google any time anywhere. Le me know please.

price please?

👏🏻👏🏻👏🏻👏🏻👏🏻bravo ❤️🔥🌹

Here are links to the two papers I cited in the video. Reflexion: Language Agents with Verbal Reinforcement Learning arxiv.org/pdf/2303.11366 AgentCoder: Multiagent-Code Generation with Iterative Testing and Optimisation arxiv.org/pdf/2312.13010

Good job👍🏻👍🏻

Brilliant video

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Good analysis. Talk about ttt also!

As promised in the video, here are links to the two papers I featured. Sequoia Capital Generative AI’s Act o1: The Agentic Reasoning Era Begins www.sequoiacap.com/article/generative-ais-act-o1/ OpenAI Learning to Reason with LLMs openai.com/index/learning-to-reason-with-llms/

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Here's a link to the authors' GitHub page. AgentStore: Scalable Integration Of Heterogeneous Agents As Specialized Generalist Computer Assistant. chengyou-jia.github.io/AgentStore-Home/

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

Audio Podcast on Spotify at spoti.fi/3XIf1te Audio Podcast on Apple at apple.co/48cS6eB

The AI Master Group Podcast launches this week on Friday! The show is an ideal place to meet people who are creating the future of AI in their work today. It features interviews with authors of recent papers, and with people deploying AI in real-world applications, including people in senior leadership roles. If you want a front row seat to the technology and craft of AI, this show will be perfect. You can Follow the show on Spotify at this link: spoti.fi/3XIf1te (The show will also be the other usual channels, such as Apple Podcasts.) LAUNCH SCHEDULE The first episode on Friday of this week will be a surprise guest. 😊 Following that, here’s a preview of upcoming episodes. Monday 10/7: Ramsu Sundararajan, PhD Ramsu is the Head of R&D at solus.ai, which powers Segment of One personalization. Roles prior to that included Senior Scientist at GE Global Research, and Principal at Sabre Airline Solutions, where he developed some of the original algorithms. Monday 10/7: Jodi Blomberg Jodi is the VP, Data Science at Cox Automotive. Prior to that, she’s held senior leadership roles at SAS, Charles Schwab and Waste Management. Tuesday 10/8: Shawn Goodin Shawn is the Global VP of Solutions at FirstHive, which is a customer data platform. Prior to that, he held senior leadership roles at Capgemini, Silicon Valley Bank, JPMorgan Chase, Clorox, Northwestern Mutual and SC Johnson. Shawn is also an advisory board member of the Customer Data Platform (CDP) Institute. Wednesday 10/9: Dave Stern Dave is a fractional CTO and devops engineer with over 25 years of experience in systems and software engineering. He's the President and Senior Solutions Architect of Stern Devops Group, which is a consultancy focused on early stage companies. He's also the author of a new book: Hackproof Your Startup, and that book is a key topic of the show. Thursday 10/10: Andrei Lopatenko, PhD Andrei is the Director of Search and the AI Lab at Neuron7. Prior to that, he was the VP Engineering and AI at Zillow. He’s also held key leadership roles at Google, Apple, Walmart and eBay. Friday 10/11: Celia Wanderly Celia is the Chief Innovation Officer at Bits In Glass (BIG), a top Canadian IT consulting firm. Prior to that, she held senior leadership roles at AltaML and at Deloitte Canada. Last year. Celia was recognized as the AI Innovator of the Year, by Women in AI. Monday 10/14: Sam Marks Sam is the Director of Business Strategy, Solutions & Analytics at the Boston Bruins & TD Garden. Prior to that, he directed strategy and analytics for the Arizona Coyotes, and at VaynerMedia. Tuesday 10/15: Ivan Pinto Ivan is an Associate VP of Delivery at Robosoft Technologies. His team does software development, engineering and QA for US clients. This conversation covers his experiments using Gen AI to improve the efficiency and quality of code produced by his 300-member team. Wednesday 10/16: Ray Pettit, PhD Ray is the Chief Data and Analytics Officer at Valhalla AI Solutions. Prior to that, he held senior leadership roles at the Advertising Research Foundation, at the Institute for Experiential AI, and at comScore. Thursday 10/17: Victor (Viko) Perrine Viko is the Global Director of New Growth Initiatives at Circle K. Prior to that, he’s held senior leadership roles at Delek US Holdings and at UGP Inc. The conversation covers a new initiative that he just launched last month in Europe. Friday 10/18: Nikhil Patel Nikhil is the Data Science Director at Sally Beauty Holdings. Prior to that, he held a senior leadership role at Harman International. Friday 10/25: Aida Farahani Aida is a doctoral candidate in Machine Learning at Technische Universität Chemnitz, in Germany. With a specialization in 3D deep learning, Aida is doing ground-breaking work creating simulations using implicit fields, which is a much more computationally-efficient approach than mesh. Friday 11/1: Tapan Khopkar, PhD Tapan is the Executive Director of Marketing Sciences at OMD, which is the world’s largest media agency network. Prior to that, he was VP and Head of Innovation Labs at Cartesian Consulting.

STEM, Science, Technology, Engineering, Math are at the core of hard sciences upon which systems are built. I suggest an addition: STEMAI. Adding AI where once it was a domain of art, it is now a science. Call it STEMY for short. Y meaning "Why?" Math Symbol: ? AI giving a technical hard answer to the question that was once an art in the domain of Philosophy! ...Because Intelligent Science.....?.

I want those 8 minutes back. How did this get upvotes?

Wow, that was really explanatory, thanks a lot for this

This doesn't make any sense to me. There are a bunch of options for remeshing that have been around for years. Instant Meshes comes to mind, which runs standalone free & open source. It also converts to quad typology, which is important for applying subdivisions. Of all the tools that are currently avaliable, why would you promote the one AI project that doesn't do anything new and doesn't even work?

Here’s a link to the code I ran in the video. github.com/buaacyw/MeshAnythingV2 And here’s a link to the Rodin website where I created the 3D character. hyperhuman.deemos.com/rodin

"he" this, "him" that. Is that thumbnail narrator AI-generated?

Could not deal with the massive spin of advanced players.

Here’s a link to the full paper: Achieving Human Level Competitive Robot Table Tennis arxiv.org/pdf/2408.03906

Here’s a link to the SharkEye website. sharkeye.org/#our-process And here’s a link to the documentation page for YOLO. docs.ultralytics.com/

they should use this to end world hunger and do cancer research pleaseeeeeeeeeeeeeeeeeeeeeeee

Here’s a link to the full article from Google DeepMind. AI achieves silver-medal standard solving International Mathematical Olympiad problems deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/

Here’s a link to the blog post I quoted from in the video. Open Source AI Is the Path Forward about.fb.com/news/2024/07/open-source-ai-is-the-path-forward/

If you’re interested in digger deeper into this topic, I highly recommend this excellent article by Ed Bott on ZDnet, which includes some very helpful historical context. “What caused the great CrowdStrike-Windows meltdown of 2024? History has the answer.” www.zdnet.com/article/what-caused-the-great-crowdstrike-windows-meltdown-of-2024-history-has-the-answer/

Great video! Thank you for the clear explanation. The calm tone made it easier to undertand.

Here’s a link to the paper I featured in this video: “Mixture of A Million Experts,” by Xu He at Google DeepMind arxiv.org/pdf/2407.04153

At the very end of this video there’s a snippet from a brilliant performance of the song “Daisy Bell: A Bicycle Built For Two,” by permission from Julien Neel. (Thank you Julien!) Here’s his website: julienneel.sellfy.store/ All four singers in the barbershop quartet are Julien. It’s brilliant - and amazing. Here’s a link to the full song on UA-cam. ua-cam.com/video/JmiNlZAiDqo/v-deo.html

Here’s a link to the paper this video is about: “Discovering Preference Optimization Algorithms with and for Large Language Models” arxiv.org/pdf/2406.08414

Please check your microphone volume before upload, you are DRAMATICALLY quieter than any of the sound effects or your end card. I'd hazard a guess at around 12dB quieter.

You could probably get it running at home through a smart scalar API like Ollama with 4 A6000s, 512gb of system RAM, and a pair of Intel Scalable CPUs with AVX-512 cores, it'll be around $10k to build on used parts. It won't be snappy, 2 minutes per response at least, but it's cheaper than renting a whole DGX for a month. A 22.3k entry synthetic dataset generated by continually inferring desireable results with Nemotron over a month should be good enough to start training a smaller model to a far higher standard than it's natural dataset counterpart could provide, and look at that, you'll also have hardware good enough for training 32B models already on-hand by the end.

That's mixtral though isn't it just getting memory throttled

No performance gains on CPU unless you use their models *and* you have avx2 instruct set, so meh. I'll stick to my current project of making ultra slim, CPU only mini models that challenge mainstream, thanks.

Very interesting. Is there a way to link powerinfer2 to sillytavern ?

don't make such loud noises in the video

phone farms might take this on 🍿

Here’s a link to the paper I cited in the video. PowerInfer-2: Fast Large Language Model Inference on a Smartphone arxiv.org/abs/2406.06282

Agent smith.