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Free MIT course breaks down deep learning: Here, MIT ass't prof. Sara Beery discusses how it's driven breakthroughs in areas like image generation, coding, & playing games (Lecture 1).

MIT CSAIL

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20,456 次观看 • 3 个月前 •via X (Twitter)

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Here's my conversation with Edward Gibson (Ted Gibson, Language Lab MIT), a linguist and psychologist at MIT, heading the MIT Language Lab. We talk all about the human language: syntax, grammar, structure, theories of language, evolution of language, how it reflects culture, and of course LLMs, both their amazing power and their limitations. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 1:13 - Human language 5:19 - Generalizations in language 11:06 - Dependency grammar 21:05 - Morphology 29:40 - Evolution of languages 33:00 - Noam Chomsky 1:17:06 - Thinking and language 1:30:36 - LLMs 1:43:35 - Center embedding 2:10:02 - Learning a new language 2:13:54 - Nature vs nurture 2:20:30 - Culture and language 2:34:58 - Universal language 2:39:21 - Language translation 2:42:36 - Animal communication

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340,395 次观看 • 2 年前

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slash1s

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124,049 次观看 • 14 天前

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My friend applied to 200 tech jobs in two years. No MIT. No Stanford. Last month SpaceXAI offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. A 1 hour course on "Complete AI Engineering in 2026". Matt Pocock shows you how to architect & build AI agents like Grok Bot from scratch. The exact AI-assisted coding workflow that lets agents ship code while you sleep. I watched it last night. Halfway through, I realized I could break into an AI lab in days, not years. Bookmark this and read the article below.

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My friend applied to 450 tech job applications over three years. No MIT. No Stanford. Last week Anthropic offered him $750,000. I asked him how he broke in with zero background. He sent me the exact video that helped him to get in. Anthropic's 2 hour course on landing an AI career in 2026. Anthropic's core team breaks down exactly how to build AI agents from scratch. I watched it last night. Halfway through, I realized breaking into an AI lab takes weeks, not years. Bookmark this and read the article below. • 00:00 - building AI agents • 28:51 - AI agent workflow • 54:04 - memory for AI agents • 1:28:40 - production AI agent • 1:55:30 - anthropic hiring process

My friend applied to 450 tech job applications over three years. No MIT. No Stanford. Last week Anthropic offered him $750,000. I asked him how he broke in with zero background. He sent me the exact video that helped him to get in. Anthropic's 2 hour course on landing an AI career in 2026. Anthropic's core team breaks down exactly how to build AI agents from scratch. I watched it last night. Halfway through, I realized breaking into an AI lab takes weeks, not years. Bookmark this and read the article below. • 00:00 - building AI agents • 28:51 - AI agent workflow • 54:04 - memory for AI agents • 1:28:40 - production AI agent • 1:55:30 - anthropic hiring process

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13,799 次观看 • 1 个月前

Ten million people have watched an MIT professor teach a course whose first lecture is literally titled "What is a Derivative?" Almost none have written down the two-line answer. He filmed the lecture once in the fall of 2007 and it has been on YouTube ever since. Math tutors charge $200 an hour to teach a diluted version of what he covered in 50 minutes for free. His name is David Jerison. He is a professor of mathematics at MIT and the instructor of 18.01 Single Variable Calculus, one of the most-watched math courses in the history of the internet. The 50-minute clip in this video is Lecture 1, filmed at MIT in the fall of 2007. Jerison is deriving the definition of a derivative from a single tangent line. The whole framework fits on a napkin. A derivative is just how much y changes when x moves a tiny bit. Draw a tangent line to any curve at any point. The slope of that line is the derivative. Memorize one formula, the power rule, and you can differentiate every polynomial on earth in your head. Chain that with a handful of exceptions and you can differentiate almost every function humanity has ever written down. That single set of rules is what every neural network runs on gradient descent, what every rocket landing at SpaceX solves in real time, and what every options desk at Goldman Sachs is running behind every quote you see on the screen. "In mathematics you don't understand things. You just get used to them." That is John von Neumann, the mathematician who helped design the atomic bomb and invent the modern computer. Jerison returns to the same idea in every lecture. Almost no student giving up on calculus has heard von Neumann say it out loud. Every quant fund on Wall Street pays entry-level analysts $250,000 to know the same power rule Jerison derives on the board. Every AI bootcamp charges tens of thousands to teach a diluted version of the same equation on a laptop. The lecture is free on MIT OpenCourseWare. The textbook is under sixty dollars. Almost none of the millions who watched have ever taken the power rule and applied it to their own numbers on their own paper. The math is free. The willingness to actually take one derivative before your next model, trade, or engineering trade-off is the entire edge.

Ten million people have watched an MIT professor teach a course whose first lecture is literally titled "What is a Derivative?" Almost none have written down the two-line answer. He filmed the lecture once in the fall of 2007 and it has been on YouTube ever since. Math tutors charge $200 an hour to teach a diluted version of what he covered in 50 minutes for free. His name is David Jerison. He is a professor of mathematics at MIT and the instructor of 18.01 Single Variable Calculus, one of the most-watched math courses in the history of the internet. The 50-minute clip in this video is Lecture 1, filmed at MIT in the fall of 2007. Jerison is deriving the definition of a derivative from a single tangent line. The whole framework fits on a napkin. A derivative is just how much y changes when x moves a tiny bit. Draw a tangent line to any curve at any point. The slope of that line is the derivative. Memorize one formula, the power rule, and you can differentiate every polynomial on earth in your head. Chain that with a handful of exceptions and you can differentiate almost every function humanity has ever written down. That single set of rules is what every neural network runs on gradient descent, what every rocket landing at SpaceX solves in real time, and what every options desk at Goldman Sachs is running behind every quote you see on the screen. "In mathematics you don't understand things. You just get used to them." That is John von Neumann, the mathematician who helped design the atomic bomb and invent the modern computer. Jerison returns to the same idea in every lecture. Almost no student giving up on calculus has heard von Neumann say it out loud. Every quant fund on Wall Street pays entry-level analysts $250,000 to know the same power rule Jerison derives on the board. Every AI bootcamp charges tens of thousands to teach a diluted version of the same equation on a laptop. The lecture is free on MIT OpenCourseWare. The textbook is under sixty dollars. Almost none of the millions who watched have ever taken the power rule and applied it to their own numbers on their own paper. The math is free. The willingness to actually take one derivative before your next model, trade, or engineering trade-off is the entire edge.

Lumen

24,218 次观看 • 18 天前

The smartest man at Bell Labs quietly turned math into a millionaire's fortune in the market. one of his tricks: make money off a stock that goes nowhere. up 40%, down 40%, flat on the year, and you still walk away richer. it survived in a grainy black-and-white MIT lecture from 1970. free ever since, and almost nobody plays something this old. the man at the board is Herb Gross, MIT, whose 1970 course taught more traders their own math than most finance degrees ever will. here is the part that pays. one rebalance earns almost nothing. a thousand earn a little. that is still just "many." take it to infinity and it turns into something else, a premium no single trade ever held. the wiggle stops being noise and becomes the return. no ticker. no forecast. one tiny gain, taken forever. the stock did nothing. the math did everything. it has been free since 1970, and almost no one learned the one idea that turns motion into money.

The smartest man at Bell Labs quietly turned math into a millionaire's fortune in the market. one of his tricks: make money off a stock that goes nowhere. up 40%, down 40%, flat on the year, and you still walk away richer. it survived in a grainy black-and-white MIT lecture from 1970. free ever since, and almost nobody plays something this old. the man at the board is Herb Gross, MIT, whose 1970 course taught more traders their own math than most finance degrees ever will. here is the part that pays. one rebalance earns almost nothing. a thousand earn a little. that is still just "many." take it to infinity and it turns into something else, a premium no single trade ever held. the wiggle stops being noise and becomes the return. no ticker. no forecast. one tiny gain, taken forever. the stock did nothing. the math did everything. it has been free since 1970, and almost no one learned the one idea that turns motion into money.

Loran

24,688 次观看 • 1 个月前

🌟Our latest LangChain Academy course – Deep Agents with LangGraph – is now live!🌟 Many agents today follow the same simple pattern: run in a loop, call tools. That architecture works well enough, but it breaks down as tasks get more complex. Today, companies of all sizes – from startups to large enterprises – are building their own Deep Agents. These agents dive deeper. They’re able to plan complex tasks and carry them out over longer time horizons. There are four key features that set Deep Agents apart from regular agents: 1. Planning – keeps agents on track 2. File system – allows agents to offload context 3. Sub-agents – act as focused specialists 4. Prompting – provides agents with detailed instructions Our latest LangChain Academy course, Deep Agents with LangGraph, shows you how to combine these pieces with LangGraph to orchestrate long-running, multi-agent workflows. Big thanks to community member Dmitry Labazkin for helping us shape this course with his contributions! Enroll for free ➡️

🌟Our latest LangChain Academy course – Deep Agents with LangGraph – is now live!🌟 Many agents today follow the same simple pattern: run in a loop, call tools. That architecture works well enough, but it breaks down as tasks get more complex. Today, companies of all sizes – from startups to large enterprises – are building their own Deep Agents. These agents dive deeper. They’re able to plan complex tasks and carry them out over longer time horizons. There are four key features that set Deep Agents apart from regular agents: 1. Planning – keeps agents on track 2. File system – allows agents to offload context 3. Sub-agents – act as focused specialists 4. Prompting – provides agents with detailed instructions Our latest LangChain Academy course, Deep Agents with LangGraph, shows you how to combine these pieces with LangGraph to orchestrate long-running, multi-agent workflows. Big thanks to community member Dmitry Labazkin for helping us shape this course with his contributions! Enroll for free ➡️

LangChain

63,349 次观看 • 1 年前

Millions have watched an MIT professor accidentally destroy the American sports betting industry in a free 12-lecture undergraduate poker course. MIT charges $85,000 a year to sit in that classroom. He posted every lecture on OpenCourseWare for nothing. Almost no one who has ever placed a DraftKings same-game parlay has finished all twelve. His name is Kevin Desmond. He is an MIT alum, a professional poker player, and the instructor of 15.S50 Poker Theory and Analytics, which MIT gave undergraduates college credit for taking during January of 2015. The 43-minute clip in this video is one lecture from that course, filmed at MIT that same month. The chart on the screen behind him looks like a poker graph. It is the exact math that decides whether a Wall Street quant clears $500,000 a year, whether a FanDuel bettor loses their rent money on a Sunday afternoon, and whether a Silicon Valley founder can walk into a term sheet negotiation without being taken apart in the room. Desmond compresses the mathematical foundation of every adversarial decision on earth into five ideas. Ranges. You never know your opponent's exact hand. You know a distribution of hands weighted by probability. Every FanDuel bettor picking a parlay on a hunch is playing without a range. Every retail trader guessing a competitor's next move is guessing blind. Pot odds. The equation that tells you when a call has positive expected value. Every VC term sheet and every insurance premium reduces to it. Every same-game parlay on DraftKings violates it in ways the app is legally allowed to hide from you. Expected value. Sum every outcome weighted by probability. Casinos are built on it. Poker pros live on it. Sports bettors violate it every time they chase a loss hoping for a hot Sunday. Game theory optimal. The Nash equilibrium of poker. The strategy no opponent can exploit no matter how well they read you. Quant funds pay $500,000 bonuses for one senior who can solve for it under pressure. Exploitative play. When to deviate from GTO to punish a specific mistake. What every senior desk on Wall Street does against retail order flow, every trading session, every day. Every quant fund on Wall Street runs a hiring pipeline that starts with this material. Every prop trading desk drills it into juniors before their first live session. The MIT professor who filmed the whole course posted it on OpenCourseWare for the price of an internet connection. "Every time you play a hand differently from the way you would have played it if you could see all your opponent's cards, they gain." That is David Sklansky's Fundamental Theorem of Poker. Desmond opens the course with it. It is also the exact statement of information asymmetry that every trading floor, casino, and DraftKings promo card on earth is built to exploit. The lectures are free on MIT OpenCourseWare. The problem sets are online. Every equation Desmond derives fits on one page. The math is free. The willingness to spend 43 minutes on one lecture before opening a sportsbook app, placing a parlay, or entering a negotiation is a much rarer commodity than the confidence to walk in without it.

Millions have watched an MIT professor accidentally destroy the American sports betting industry in a free 12-lecture undergraduate poker course. MIT charges $85,000 a year to sit in that classroom. He posted every lecture on OpenCourseWare for nothing. Almost no one who has ever placed a DraftKings same-game parlay has finished all twelve. His name is Kevin Desmond. He is an MIT alum, a professional poker player, and the instructor of 15.S50 Poker Theory and Analytics, which MIT gave undergraduates college credit for taking during January of 2015. The 43-minute clip in this video is one lecture from that course, filmed at MIT that same month. The chart on the screen behind him looks like a poker graph. It is the exact math that decides whether a Wall Street quant clears $500,000 a year, whether a FanDuel bettor loses their rent money on a Sunday afternoon, and whether a Silicon Valley founder can walk into a term sheet negotiation without being taken apart in the room. Desmond compresses the mathematical foundation of every adversarial decision on earth into five ideas. Ranges. You never know your opponent's exact hand. You know a distribution of hands weighted by probability. Every FanDuel bettor picking a parlay on a hunch is playing without a range. Every retail trader guessing a competitor's next move is guessing blind. Pot odds. The equation that tells you when a call has positive expected value. Every VC term sheet and every insurance premium reduces to it. Every same-game parlay on DraftKings violates it in ways the app is legally allowed to hide from you. Expected value. Sum every outcome weighted by probability. Casinos are built on it. Poker pros live on it. Sports bettors violate it every time they chase a loss hoping for a hot Sunday. Game theory optimal. The Nash equilibrium of poker. The strategy no opponent can exploit no matter how well they read you. Quant funds pay $500,000 bonuses for one senior who can solve for it under pressure. Exploitative play. When to deviate from GTO to punish a specific mistake. What every senior desk on Wall Street does against retail order flow, every trading session, every day. Every quant fund on Wall Street runs a hiring pipeline that starts with this material. Every prop trading desk drills it into juniors before their first live session. The MIT professor who filmed the whole course posted it on OpenCourseWare for the price of an internet connection. "Every time you play a hand differently from the way you would have played it if you could see all your opponent's cards, they gain." That is David Sklansky's Fundamental Theorem of Poker. Desmond opens the course with it. It is also the exact statement of information asymmetry that every trading floor, casino, and DraftKings promo card on earth is built to exploit. The lectures are free on MIT OpenCourseWare. The problem sets are online. Every equation Desmond derives fits on one page. The math is free. The willingness to spend 43 minutes on one lecture before opening a sportsbook app, placing a parlay, or entering a negotiation is a much rarer commodity than the confidence to walk in without it.

Lumen

58,397 次观看 • 28 天前

I just talked to a 17-year-old who made $3,500 in two months flipping couches He picks up couches for $60-100 and sells them for $400-1200. Puts maybe an hour of work into each flip. Here's the playbook: 1/ Only uses KSL Classifieds. 2/ He filters for listings within 10 miles, looks for poor quality photos and "moving sale" urgency language, 3/Messages sellers and asks: "What's your lowest price?" 4/ He snaps a photo in the driveway, runs it through ChatGPT image generation, and lists it same day with AI-generated backgrounds that make it look like it's sitting in a $2 million home. In this episode he: - Breaks down his exact negotiation script to talk sellers down hundreds of dollars - Shows how he uses ChatGPT image generation to skip the staging step entirely - Gives the three red flags he looks for in listings that signal a deal - Tells me why poor quality pictures are his biggest buying signal - Gives his math on why he won't drive more than 10 miles to pick up a couch anymore Bentley taught a full masterclass in this episode! Check it out

I just talked to a 17-year-old who made $3,500 in two months flipping couches He picks up couches for $60-100 and sells them for $400-1200. Puts maybe an hour of work into each flip. Here's the playbook: 1/ Only uses KSL Classifieds. 2/ He filters for listings within 10 miles, looks for poor quality photos and "moving sale" urgency language, 3/Messages sellers and asks: "What's your lowest price?" 4/ He snaps a photo in the driveway, runs it through ChatGPT image generation, and lists it same day with AI-generated backgrounds that make it look like it's sitting in a $2 million home. In this episode he: - Breaks down his exact negotiation script to talk sellers down hundreds of dollars - Shows how he uses ChatGPT image generation to skip the staging step entirely - Gives the three red flags he looks for in listings that signal a deal - Tells me why poor quality pictures are his biggest buying signal - Gives his math on why he won't drive more than 10 miles to pick up a couch anymore Bentley taught a full masterclass in this episode! Check it out

Chris Koerner

76,438 次观看 • 15 天前