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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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I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

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New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

Andrew Ng

139,834 görüntüleme • 1 yıl önce

A common misconception in AI right now is that everything we’re excited about is new. When I graduated from MIT in 2023, only 3% of students majored in AI (Course 6-4). I wasn’t one of them...oops. What I know about models, agents, frameworks, and hardware I learned after graduating. And if you’re newer to the field like me, it’s easy to assume today’s breakthroughs are fairly recent. After all, GPT-1 shipped in 2018... so it’s tempting for us to think the important ideas are less than a decade old. They aren’t. Mixture-of-Experts, the backbone of top models from companies like OpenAI and Google was invented in 1991 by Jacobs, Jordan, and Nowlan in “Adaptive Mixtures of Local Experts.” Over 30 years ago! In this conversation with Daria Soboleva, we discuss why MoEs are resurfacing now, the rapidly emerging compute bottlenecks, and how newer hardware architectures change the equation (Cerebras). A reminder that what feels “frontier” today is often decades in the making :)

A common misconception in AI right now is that everything we’re excited about is new. When I graduated from MIT in 2023, only 3% of students majored in AI (Course 6-4). I wasn’t one of them...oops. What I know about models, agents, frameworks, and hardware I learned after graduating. And if you’re newer to the field like me, it’s easy to assume today’s breakthroughs are fairly recent. After all, GPT-1 shipped in 2018... so it’s tempting for us to think the important ideas are less than a decade old. They aren’t. Mixture-of-Experts, the backbone of top models from companies like OpenAI and Google was invented in 1991 by Jacobs, Jordan, and Nowlan in “Adaptive Mixtures of Local Experts.” Over 30 years ago! In this conversation with Daria Soboleva, we discuss why MoEs are resurfacing now, the rapidly emerging compute bottlenecks, and how newer hardware architectures change the equation (Cerebras). A reminder that what feels “frontier” today is often decades in the making :)

Sarah Chieng

360,067 görüntüleme • 6 ay önce

an MIT professor who taught both physics and finance told his class something none of them expected "finance is harder than physics" not as a joke. as a mathematical statement in physics, the laws don't change. gravity works the same today as it did a billion years ago you can run an experiment, get a result, and repeat it forever in finance, the moment you discover a law, the participants learn it too and their behavior changes the system you just measured in physics, electrons don't read your paper and start moving differently in finance, traders do. every published edge gets arbitraged away by the people who read it this is why quant models have a half-life and physics equations don't Newton's laws: 300+ years and counting Long-Term Capital Management's model: worked perfectly until it didn't, lost $4.6 billion in 4 months the system you're modeling is aware of you modeling it that's not a solvable problem. it's a permanent condition and the quants who survive are the ones who build for it instead of pretending it doesn't exist > this lecture: MIT finance series, free, public, 53 seconds > LTCM collapse: 1998, Nobel Prize winners, $4.6B loss > Renaissance's solution: never stop researching, replace signals before they decay > average lifespan of a quant signal: 2-5 years before it's crowded out retail builds one strategy and trades it until it breaks quant desks build a research engine that produces new strategies faster than old ones die that's not a difference in skill. it's a difference in understanding what game you're actually playing full breakdown in the video below

an MIT professor who taught both physics and finance told his class something none of them expected "finance is harder than physics" not as a joke. as a mathematical statement in physics, the laws don't change. gravity works the same today as it did a billion years ago you can run an experiment, get a result, and repeat it forever in finance, the moment you discover a law, the participants learn it too and their behavior changes the system you just measured in physics, electrons don't read your paper and start moving differently in finance, traders do. every published edge gets arbitraged away by the people who read it this is why quant models have a half-life and physics equations don't Newton's laws: 300+ years and counting Long-Term Capital Management's model: worked perfectly until it didn't, lost $4.6 billion in 4 months the system you're modeling is aware of you modeling it that's not a solvable problem. it's a permanent condition and the quants who survive are the ones who build for it instead of pretending it doesn't exist > this lecture: MIT finance series, free, public, 53 seconds > LTCM collapse: 1998, Nobel Prize winners, $4.6B loss > Renaissance's solution: never stop researching, replace signals before they decay > average lifespan of a quant signal: 2-5 years before it's crowded out retail builds one strategy and trades it until it breaks quant desks build a research engine that produces new strategies faster than old ones die that's not a difference in skill. it's a difference in understanding what game you're actually playing full breakdown in the video below

delost

18,961 görüntüleme • 1 ay önce

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

Andrew Ng

357,661 görüntüleme • 1 yıl önce

playing vayne is like a drug the first time you condemn someone into a wall and watch them explode you feel something no other champion in this game can give you and you spend the next 5 years chasing that feeling every single game first game you go 0/7. you tumble INTO the enemy. you condemn someone AWAY from the wall. you're fucking terrible. any normal person would uninstall. but then game 2 something happens you're losing lane. down 30 cs. support abandoned you. 1 item at 22 minutes. a dragon fight breaks out and you come in a bit late. you ult. tumble sideways. dodge a couple abilities. output some auto attacks and land a nasty condemn. triple kill. entire team pinging you. your hands are shaking. THAT'S the hit. three kills in a losing game and your brain is rewired PERMANENTLY you will spend the next thousands of games chasing that exact feeling. you will go 0/5 in lane and lock her in again. you will play into counters. into hard matchups. into unwinnable lanes. doesn't matter. you're not here for laning phase. you're here for the MOMENT. 3 items. teamfight breaks out. you walk in and the entire enemy team scatters like cockroaches. YOU are the raid boss now. 5 years deep and that rush hasn't worn off ONCE. every condemn into a perfect angle. every tumble through an ability that should've killed you. every invisible reposition they didn't see coming. no other champion does this. no other champion even comes CLOSE. that's why vayne mains never switch. it's not loyalty. it's not stubbornness. it's addiction. Study the Saskio way

playing vayne is like a drug the first time you condemn someone into a wall and watch them explode you feel something no other champion in this game can give you and you spend the next 5 years chasing that feeling every single game first game you go 0/7. you tumble INTO the enemy. you condemn someone AWAY from the wall. you're fucking terrible. any normal person would uninstall. but then game 2 something happens you're losing lane. down 30 cs. support abandoned you. 1 item at 22 minutes. a dragon fight breaks out and you come in a bit late. you ult. tumble sideways. dodge a couple abilities. output some auto attacks and land a nasty condemn. triple kill. entire team pinging you. your hands are shaking. THAT'S the hit. three kills in a losing game and your brain is rewired PERMANENTLY you will spend the next thousands of games chasing that exact feeling. you will go 0/5 in lane and lock her in again. you will play into counters. into hard matchups. into unwinnable lanes. doesn't matter. you're not here for laning phase. you're here for the MOMENT. 3 items. teamfight breaks out. you walk in and the entire enemy team scatters like cockroaches. YOU are the raid boss now. 5 years deep and that rush hasn't worn off ONCE. every condemn into a perfect angle. every tumble through an ability that should've killed you. every invisible reposition they didn't see coming. no other champion does this. no other champion even comes CLOSE. that's why vayne mains never switch. it's not loyalty. it's not stubbornness. it's addiction. Study the Saskio way

Tony Chau

81,327 görüntüleme • 3 ay önce

I've seen a lot of comments about this so I'll address it here. I will talk about it more in detail in tomorrows YT video. Context: Deciding 3rd game of a #NHL24WC qualifier playoff series. I enter the zone with 4 minutes to go in game which is a bit over minute in real time. I know Lauri Enström is playing protect net collapsing like he has the whole series. Like I always do in tournament games with this little time on the clock I start circling and killing time, seeing if Sopuli overcommits or something opens up, like I always do at the end of tournament games when I'm leading. Around a minute left in game I get full pressure on and at this stage I know the game is basically over cause Sopuli can't change his players to be more aggressive mid cycle and your players are dead during full pressure (and it lasts 30 seconds in real time). What makes this clip stand out is the 5-1 in front of his net with 30 or so seconds remaining 😅 But again why would I need to try to score here explaining everything I just did? I would just give Sopuli a free change to tie the game. With full pressure added I personally play with aggressive own zone strategies just to avoid stuff like this in 24, of course you can still try to rag the pressure on with how the AI is but it's way harder at least. I'm not saying it's nice or fun or whatever, and I totally get if people don't like it, but I would do the same thing every single time in an important tournament game.

I've seen a lot of comments about this so I'll address it here. I will talk about it more in detail in tomorrows YT video. Context: Deciding 3rd game of a #NHL24WC qualifier playoff series. I enter the zone with 4 minutes to go in game which is a bit over minute in real time. I know Lauri Enström is playing protect net collapsing like he has the whole series. Like I always do in tournament games with this little time on the clock I start circling and killing time, seeing if Sopuli overcommits or something opens up, like I always do at the end of tournament games when I'm leading. Around a minute left in game I get full pressure on and at this stage I know the game is basically over cause Sopuli can't change his players to be more aggressive mid cycle and your players are dead during full pressure (and it lasts 30 seconds in real time). What makes this clip stand out is the 5-1 in front of his net with 30 or so seconds remaining 😅 But again why would I need to try to score here explaining everything I just did? I would just give Sopuli a free change to tie the game. With full pressure added I personally play with aggressive own zone strategies just to avoid stuff like this in 24, of course you can still try to rag the pressure on with how the AI is but it's way harder at least. I'm not saying it's nice or fun or whatever, and I totally get if people don't like it, but I would do the same thing every single time in an important tournament game.

Erik "Eki" Tammenpää

291,567 görüntüleme • 2 yıl önce