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⚠️ POOR PROXIES FOR LEARNING! A new 6-part ⚗️DistillED series kicks off this week. Based on Professor Rob Coe's research, this series examines the classroom signals that look like evidence of learning — but don't guarantee it: busy students, engaged students, calm rooms, feedback given, a few hands up...

11,485 görüntüleme • 4 ay önce •via X (Twitter)

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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

52,110 görüntüleme • 8 ay önce

Vintage Editions - a brief history and what’s next. In 2023 I released my first 3 editions on eth. My goal was to create a collection of quality vintage animations, on par with my 1/1 artworks. The reception blew me away, over 4,000 folks registered for two artworks with a total of 175 editions. In 2025 I set a goal to go all in and released 5 more editions throughout the year. Bringing the collection to 340 editions via 8 artworks with a total of 168 holders. Each work in this series can take two weeks and even a month to create as I obsesses on each pixel. I’m very proud of how it turned out. My plan is being consistent and adding to the collection yearly, with enough love and time it will become an epic collection of animated vintage loops. Nothing quite like it in our space. At the beginning of the year I set out to make as much work as I can physically can in 2026. I find that healing. keeping my mind busy while dealing with grief. My notebook is of full of ideas and sketches and once again I’m going all in. Excited to see how this collection shapes up in 2026 along with the new distortions series. This week we kick off this year's Vintage Editions next chapter adding two new pieces and one more via a burn in July. The first piece “Mob Mentality” is a special edition of 7 for select top collectors of mine. Minting tomorrow. The second piece is “Hell of a Time”. A bigger edition that includes a free burn in July. aimed to bring more folks into the series. Minting Wednesday. Full details tomorrow. Thanks for reading! Hope to have you on this journey with me.🖤

Tony Babel

66,394 görüntüleme • 2 ay önce

All the best coaching in the world at the youngest ages is rendered useless if a child hasn’t developed the ability to focus their attention. You can bring the best coach in the world to your child’s training session and if they don’t pay full attention absolutely nothing takes place. Similar to inside a classroom where you can bring the teacher of the year to your child’s classroom and if they don’t focus and pay attention, learning does not take place. So it’s not always the case of good or bad coaching it’s often the case a child’s lack of ability to learn by not focusing on what is being taught. This is especially true for the youngest ages. This video is an example of a 5yr old child focusing their attention, trying to control an object with their feet, the ball. This becomes a mental task, married together with the action of movement, making it a physical task as well. The brain loves to learn while moving. Combining, mind and body, thinking and feeling, this allows the cerebellum, the seat of the unconscious mind, to create a chemical signature of this experience, which is emotions. Emotions are the on off switch for learning. Couple in a parent being present, this becomes a shared experience together, where the child is constantly seeking the parents approval, attention, and praise which creates a chemical electrical process in the body which is emotions. This facilitates, deep learning, and long-term memory, all disguised as playtime. A parent just being present allows for this experience to take place. A child this young rarely starts playing or exercising with a ball without someone being present. Being present and sharing this experience together is key for the learning process to take place. These movements are being stored in the non-declared memory which makes this implicit learning when you do something so many times it becomes natural and outside your conscious awareness. Like riding a bicycle or driving a car.

Tom Byerトム•バイヤー

26,691 görüntüleme • 2 yıl önce

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

103,734 görüntüleme • 1 ay önce