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Hiring Machine Learning Engineers🚀 I'm hiring for 10+ AI/ML roles, check them out here!!! 1. Machine Learning Engineer (NLP) - Mumbai Details: Onsite, Mumbai Budget: INR 25 to 35lpa Application link: 2. Senior Machine Learning Engineer (NLP) - Mumbai Details: Onsite, Mumbai Budget: INR 35 to 45lpa Application link:... 3. Senior Machine Learning Engineer - Remote Details: FTE, Remote Budget: INR 40 to 60lpa Application link: 4. Senior Machine Learning Engineer Details: Onsite, Bangalore Budget: INR 40 to 55lpa Application link: 5. Machine Learning Research Scientist - Remote Details: FTE, Remote Budget: INR 30 to 50lpa Application link: Do apply if this seems like you or refer someone you know. DM me for more, thanksshow more

Manali Mango

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21,201 Aufrufe • vor 7 Monaten •via X (Twitter)

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Hiring Ai Engineer - Bangalore 🚀 > Must have: Worked on Chat based agent like claude code and cursor. > Budget: INR 40 to 60LPA + ESOPs Application link: Do apply if this sounds like you or refer someone you know, thanksss :)

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Manali Mango

20,256 Aufrufe • vor 7 Monaten

Every wondered if we can model motion as a language? can we tokenize this new language? is it useful? Turns out tremendously! 🚀 In out latest #NeurIPS2024 paper on QueST: Self-Supervised Skill Abstractions for Learning Continuous Control, we find that action tokenization matters a lot! We can learn skill encodings by representing temporal action abstractions with a discrete codebook. This enables 2 things 1. Better Behaviour Cloning: we can better assimilate multi-task data (>9%) over best paper. This is currently best in class BC method! 2. generalization of this language to represent new tasks in 5-shot transfer to longer horizon tasks! Check out the thread by Atharva Mete for more details. And check out more details at: Joint work with Atharva Mete Albert Wilcox Haotian Xue Yongxin Chen Georgia Tech School of Interactive Computing Machine Learning at Georgia Tech Robotics@GT NVIDIA Robotics

Every wondered if we can model motion as a language? can we tokenize this new language? is it useful? Turns out tremendously! 🚀 In out latest #NeurIPS2024 paper on QueST: Self-Supervised Skill Abstractions for Learning Continuous Control, we find that action tokenization matters a lot! We can learn skill encodings by representing temporal action abstractions with a discrete codebook. This enables 2 things 1. Better Behaviour Cloning: we can better assimilate multi-task data (>9%) over best paper. This is currently best in class BC method! 2. generalization of this language to represent new tasks in 5-shot transfer to longer horizon tasks! Check out the thread by Atharva Mete for more details. And check out more details at: Joint work with Atharva Mete Albert Wilcox Haotian Xue Yongxin Chen Georgia Tech School of Interactive Computing Machine Learning at Georgia Tech Robotics@GT NVIDIA Robotics

Animesh Garg

26,218 Aufrufe • vor 1 Jahr

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

Gaurav Sen

80,282 Aufrufe • vor 11 Monaten

This is where a lot of tech professionals landing 6-figure roles actually get hired from. Not random job boards. Not even the usual “Easy Apply.” Here are 8 platforms that are genuinely high-signal for UX designers and tech professionals looking for fully remote roles. I also broke down what makes each one stand out, and how you should use them properly. 1. Wellfound (AngelList) URL: How to win here: Build a profile like a landing page (2 to 3 outcomes + 1 niche). Filter by salary range + stage. Apply to roles where your portfolio matches the product type. Message founder/recruiter with 2 lines: relevant proof + quick question. You will also need a USD account to receive salary, go to Cleva (YC W24) and open one. Best level: mid to senior (junior can still get something here with strong case studies). 2. Otta URL: How to win here: Set preferences tightly (role, level, industry, remote rules). Treat it like “high quality, low volume”: 3–5 strong apps/week. Tailor your first line of CV to match the job’s problem space. Best level: junior-mid to senior (works for all, but best when your profile is clear). 3. Y Combinator Jobs (Work at a Startup) URL: How to win here: Apply to roles where you can show 0→1 or growth-stage wins. Add a short “Operating style” section in your profile (collaboration, scope). Follow up off-platform (LinkedIn/email) with a 3-sentence note. You may also need a USD account to receive salary, go to Cleva (YC W24) and open one. Best level: mid to senior (but juniors can land roles in smaller teams with strong proof). 4. Himalayas URL: How to win here: Set location/timezone filters correctly. Save searches + alerts for your niche (e.g., B2B SaaS, fintech). Apply within 24 - 48 hours of posting when possible. Best level: all levels. 5. Remotive URL: How to win here: Filter to “worldwide” only if you truly can work globally. Don’t apply without rewriting your top 3 bullets to match role keywords. Pair every application with a short “proof note” (1 case study link + why). Best level: mid-level + seniors, but juniors can win with tailored apps. 6. We Work Remotely URL: How to win here: Apply fast (same day if possible). Use a “1-minute cover letter”: 3 bullets (domain match, proof, link). Only apply when you match 70%+ of requirements. Best level: mid to senior. 7. Remote OK URL: How to win here: Use strict filters (role + seniority + benefits). Ignore anything vague (“rockstar”, no salary, unclear company). Treat it like lead gen: apply + then research and follow up elsewhere. Best level: mid to senior. 8. FlexJobs (paid, but filtered) URL: How to win here: Only pay if you’ll apply consistently for 30 days. Use advanced filters and avoid anything without clear employer info. Cross-check listings on the company’s careers page. Best level: junior to mid (also useful for career switchers). You will need a USD account to receive salary, go to Cleva (YC W24) and open one. If you find breakdowns like this useful, Follow for more, I share more of them here. Don't mention.

designwithkingsley

15,036 Aufrufe • vor 6 Monaten

Star Wars cost billions to make. But today, you can create videos like this for just a few dollars. Filmmaking is going to be disrupted by AI sooner than we thought. If I were to take a shot at it, here's what I'd use: - o3-mini-high for writing the script - Imagen 3 or Flux Pro for images - Kling 1.6 for the video There are also excellent unified platforms like ChatLLM, where you can access all these resources in one place. For developers like us, there's more under the same offering: CodeLLM as a coding assistant, and RouteLLM automatically selects the best model for your chat queries. The company claims that they see thousands of humans becoming AI creators every day using their platform! Maybe it's a new role in the making! I have shared a link to ChatLLM in the next tweet! ______ Find me → Akshay 🚀 ✔️ For more insights and tutorials on AI and Machine Learning.

Star Wars cost billions to make. But today, you can create videos like this for just a few dollars. Filmmaking is going to be disrupted by AI sooner than we thought. If I were to take a shot at it, here's what I'd use: - o3-mini-high for writing the script - Imagen 3 or Flux Pro for images - Kling 1.6 for the video There are also excellent unified platforms like ChatLLM, where you can access all these resources in one place. For developers like us, there's more under the same offering: CodeLLM as a coding assistant, and RouteLLM automatically selects the best model for your chat queries. The company claims that they see thousands of humans becoming AI creators every day using their platform! Maybe it's a new role in the making! I have shared a link to ChatLLM in the next tweet! ______ Find me → Akshay 🚀 ✔️ For more insights and tutorials on AI and Machine Learning.

Akshay 🚀

52,457 Aufrufe • vor 1 Jahr

Almost 20 years later, AWS is still the most popular cloud in the world. The reason is simple: it just works! They have four services focused on Generative AI: 1. Amazon Q 2. Amazon Bedrock 3. SageMaker JumpStart 4. PartyRock I've been using AWS for around 15 years (honestly, I don't remember well), and their Developer Center is a gold mine. If you open their Developer Center, you'll find a new learning path, "Generative AI for Developers." I'm linking to it below. This is not just a course. This is a collection of courses, examples, videos, tutorials, and code walkthroughs. They will teach you how to use Generative AI on AWS using the four services above. ↑ That right there is a huge selling point: These classes aren't just theoretical. You'll have a chance to learn using the same professional tools everyone else uses. By the way, there are many more resources in the Developer Center: • Machine Learning • Data Operations • DevOps All of these are free. Click, click, and start learning right away. One more thing before I forget: If you are building anything with AWS, check out Amazon Q, their coding assistant. This is the *best* coding assistant for AWS-related work, and it's not even close. It's a Visual Studio Code extension. Install it, and it works like any other code assistant, except this one knows a lot about AWS. Thanks to AWS for sponsoring a post about their free courses and learning resources. There's a special place in Developer Heaven for you.

Almost 20 years later, AWS is still the most popular cloud in the world. The reason is simple: it just works! They have four services focused on Generative AI: 1. Amazon Q 2. Amazon Bedrock 3. SageMaker JumpStart 4. PartyRock I've been using AWS for around 15 years (honestly, I don't remember well), and their Developer Center is a gold mine. If you open their Developer Center, you'll find a new learning path, "Generative AI for Developers." I'm linking to it below. This is not just a course. This is a collection of courses, examples, videos, tutorials, and code walkthroughs. They will teach you how to use Generative AI on AWS using the four services above. ↑ That right there is a huge selling point: These classes aren't just theoretical. You'll have a chance to learn using the same professional tools everyone else uses. By the way, there are many more resources in the Developer Center: • Machine Learning • Data Operations • DevOps All of these are free. Click, click, and start learning right away. One more thing before I forget: If you are building anything with AWS, check out Amazon Q, their coding assistant. This is the best coding assistant for AWS-related work, and it's not even close. It's a Visual Studio Code extension. Install it, and it works like any other code assistant, except this one knows a lot about AWS. Thanks to AWS for sponsoring a post about their free courses and learning resources. There's a special place in Developer Heaven for you.

Santiago

22,104 Aufrufe • vor 1 Jahr

Chamath: AI advantage may come less from models than from private inputs. "When labs can build similar models, the real win comes from one unique ingredient in order to monetize it well. Here is a basic thing about machine learning that is worth knowing: if you take 1,000 of the same inputs and give them to Facebook, Microsoft, Google, and Amazon, they will all come up with the same machine learning model. But if you have one extra thing, one little ingredient that all of those other companies do not have, your output can be markedly different. It is like giving two great chefs three ingredients, but giving the third chef one extra ingredient. That person has the ability to do something very special. Right now, we are in a world where everybody is crawling the open web. We are going to move to a world where, as everybody gets sophisticated enough and information is widely available, somebody is going to say, “You know what? This site, I am not going to allow anybody else to access. It is only for me, only for my models.” Those models will become better. So we have to let that play out a little bit. It is going to be a really interesting arms race. The next wave of M&A, for example, could be companies like Google, Microsoft, and Facebook looking at these companies and saying, “Can they be viable inputs to my large language models or to my other machine learning and AI models?” --- A company with unique workflows, transactions, medical records, industrial logs, legal archives, design files, or user behavior can turn boring private data into a compounding advantage. Some startups may never become great public companies on their own, yet still become valuable because they own a data stream that makes a larger AI system sharper, more differentiated, or harder to copy. That turns acquisition strategy upside down: the buyer may not be purchasing revenue, brand, or even software, but a private ingredient for intelligence. ---- From "iConnections" YouTube channel, (link in comment)

Rohan Paul

143,134 Aufrufe • vor 3 Monaten

Quit my Job at Microsoft, and back to the Classroom as a Student - My Short Story. Till today, some say quitting my job at Microsoft doesn't make logical sense. To be serious, it doesn't make any logical sense especially when I have to return to the classroom and back to the job market. This was however necessary for me to pursue a new path for my family. When I was at Microsoft, on several occasions, I felt Microsoft was God's sent to Africa and I am the Evangelist. Explain to me why a company is investing so much in capacity development in Africa like Microsoft. Through relevant skills, we can bring many out of poverty, save them from the after-effects of unemployment, make them dream big and achieve it, and keep alive hopes in the hearts of millions of others with similar backgrounds like mine. So, I took my work with passion and purpose. I was allowed to support academic institutes in several countries and I appeared as a Guest Lecturer for Masters Courses on Advance Data Analytics. I relocated to the US and everything changed, back to the classroom but now as a Student. Indeed a humbling experience for me. I love every opportunity I have to make complex topics simple. You wouldn't know how much I know about Data Management, Data Science, Data Analytics, and Power Platform until you give me a Mic or Opportunity to lead (Yeah, kindly reach out if you would like to have such a passionate young man like myself in your team). I am deeply passionate about these fields and my passion is validated by years of work that I have invested in building up my skills. I have been opportune to lead diverse teams in my career and both my listening skill and ability to break down complex tasks made it easier to bring out the best in my team members. In this presentation, I explained what Supervised Machine Learning is, the data science workflow, and evaluating machine learning models, and I did a live demo in class - built a Supervised Machine Learning model to drive home full comprehension. I am looking for a Remote Internship for Summer 2024 in Data Analytics and I need your help to Like and Repost this. Who knows, I can be lucky enough to find a team that will give me a chance. Thank you in advance 🙏 #intern

Quit my Job at Microsoft, and back to the Classroom as a Student - My Short Story. Till today, some say quitting my job at Microsoft doesn't make logical sense. To be serious, it doesn't make any logical sense especially when I have to return to the classroom and back to the job market. This was however necessary for me to pursue a new path for my family. When I was at Microsoft, on several occasions, I felt Microsoft was God's sent to Africa and I am the Evangelist. Explain to me why a company is investing so much in capacity development in Africa like Microsoft. Through relevant skills, we can bring many out of poverty, save them from the after-effects of unemployment, make them dream big and achieve it, and keep alive hopes in the hearts of millions of others with similar backgrounds like mine. So, I took my work with passion and purpose. I was allowed to support academic institutes in several countries and I appeared as a Guest Lecturer for Masters Courses on Advance Data Analytics. I relocated to the US and everything changed, back to the classroom but now as a Student. Indeed a humbling experience for me. I love every opportunity I have to make complex topics simple. You wouldn't know how much I know about Data Management, Data Science, Data Analytics, and Power Platform until you give me a Mic or Opportunity to lead (Yeah, kindly reach out if you would like to have such a passionate young man like myself in your team). I am deeply passionate about these fields and my passion is validated by years of work that I have invested in building up my skills. I have been opportune to lead diverse teams in my career and both my listening skill and ability to break down complex tasks made it easier to bring out the best in my team members. In this presentation, I explained what Supervised Machine Learning is, the data science workflow, and evaluating machine learning models, and I did a live demo in class - built a Supervised Machine Learning model to drive home full comprehension. I am looking for a Remote Internship for Summer 2024 in Data Analytics and I need your help to Like and Repost this. Who knows, I can be lucky enough to find a team that will give me a chance. Thank you in advance 🙏 #intern

TheOyinbooke

216,055 Aufrufe • vor 2 Jahren

BREAKING: Remote Viewer Just Saw The Future of AI, and it's worrying... Remote Viewer, Edward Riordan, looked at the future of what Joe Rogan would talk about in terms of world events, and what he got back wasn't Joe's podcast... He saw a reality where humans become more and more obsolete to AI: "...they handed over the quote unquote keys to AI... There is... less need for humans is what I was feeling here... The intelligence knows more than you. What can you do... It can do better, faster, cheaper, easier... Billions or trillions of data inputs. Continuous input in real time. You can't keep up." "...there's debate. Debate about it, debate about whether or not this is good. So some are saying yes, some say no. It's underground, the full extent of it, the technological take over a long time in the making. And this was a stage seven...." "...machine learning. But why, I wonder? You don't need humans anymore. The machine AI does. It cheaper, faster and more efficient and most people won't know the difference. This was one of the most important things in this session right here. But you can see the artificial. This was my statement here. It's artificial...." "...people are being conditioned to accept artificial everything to not recognize or consider that what is being presented to them is artificially generated. That's how you that's how you that's how you, douse the spark... It's diabolical..." "...my basic data on this movement order was hive mind. Maybe a event in the sky that every everybody goes. Oh, and it brings the whole world into one mindset."

BREAKING: Remote Viewer Just Saw The Future of AI, and it's worrying... Remote Viewer, Edward Riordan, looked at the future of what Joe Rogan would talk about in terms of world events, and what he got back wasn't Joe's podcast... He saw a reality where humans become more and more obsolete to AI: "...they handed over the quote unquote keys to AI... There is... less need for humans is what I was feeling here... The intelligence knows more than you. What can you do... It can do better, faster, cheaper, easier... Billions or trillions of data inputs. Continuous input in real time. You can't keep up." "...there's debate. Debate about it, debate about whether or not this is good. So some are saying yes, some say no. It's underground, the full extent of it, the technological take over a long time in the making. And this was a stage seven...." "...machine learning. But why, I wonder? You don't need humans anymore. The machine AI does. It cheaper, faster and more efficient and most people won't know the difference. This was one of the most important things in this session right here. But you can see the artificial. This was my statement here. It's artificial...." "...people are being conditioned to accept artificial everything to not recognize or consider that what is being presented to them is artificially generated. That's how you that's how you that's how you, douse the spark... It's diabolical..." "...my basic data on this movement order was hive mind. Maybe a event in the sky that every everybody goes. Oh, and it brings the whole world into one mindset."

Future Forecasting Group

22,032 Aufrufe • vor 5 Monaten

If you could only learn one thing that will be relevant for the next 10-20 years, focus on learning how to deal with data. The future is not about faster hardware, smarter algorithms, or better ideas. The future is about DATA, and those who know how to deal with it will stay relevant much longer than anyone else. I recorded a video to show you how easy it is to get started. In the video, I'm using Kestra. For a long time, I was a fan of AirFlow. Then, I moved to AWS Step Functions. Today, I only use Kestra. Kestra is open-source (repo link below) and kind enough to sponsor my work. The video will show you how easy it is to do the following: 1. Run Kestra locally (literally, one command) 2. Build a simple flow 3. Run Python scripts as part of your flow 4. Connect to HuggingFace models If you have never built a data pipeline, open Kestra's Quick Start Guide and follow their examples. (I think it will take you one weekend to feel comfortable with the application and build the courage you need to get into more serious work.)

If you could only learn one thing that will be relevant for the next 10-20 years, focus on learning how to deal with data. The future is not about faster hardware, smarter algorithms, or better ideas. The future is about DATA, and those who know how to deal with it will stay relevant much longer than anyone else. I recorded a video to show you how easy it is to get started. In the video, I'm using Kestra. For a long time, I was a fan of AirFlow. Then, I moved to AWS Step Functions. Today, I only use Kestra. Kestra is open-source (repo link below) and kind enough to sponsor my work. The video will show you how easy it is to do the following: 1. Run Kestra locally (literally, one command) 2. Build a simple flow 3. Run Python scripts as part of your flow 4. Connect to HuggingFace models If you have never built a data pipeline, open Kestra's Quick Start Guide and follow their examples. (I think it will take you one weekend to feel comfortable with the application and build the courage you need to get into more serious work.)

Santiago

51,012 Aufrufe • vor 1 Jahr

Engineers, creatives, designers - lend me your ears, and eyes, to check my🥇1st tech demo of 2025: A #WebAI Agent running entirely client side in the browser, that's capable of controlling a flight search webpage, to get the job done! I'm using Google's Gemma 2 (2B) model in #JavaScript via #WebGPU thanks to the MediaPipe Web LLM library, combined with some extra function calling logic to enable advanced user experiences. If you like what you see, give this post a share, as I'm just one guy trying to show the world the power of Web AI to do great things. Try it for yourself using the link below after watching the video, but do so on a modernish machine (I have verified it runs on my 6 year old Dell XPS with only an Intel integrated GPU) but use a modern browser like Chrome as it needs WebGPU to run. Want me to make a demo for your industry? Tell me what you need / want to see next! Let me know what you think, and if you have any questions, then drop them in the comments below! YouTube Video Link:

Engineers, creatives, designers - lend me your ears, and eyes, to check my🥇1st tech demo of 2025: A #WebAI Agent running entirely client side in the browser, that's capable of controlling a flight search webpage, to get the job done! I'm using Google's Gemma 2 (2B) model in #JavaScript via #WebGPU thanks to the MediaPipe Web LLM library, combined with some extra function calling logic to enable advanced user experiences. If you like what you see, give this post a share, as I'm just one guy trying to show the world the power of Web AI to do great things. Try it for yourself using the link below after watching the video, but do so on a modernish machine (I have verified it runs on my 6 year old Dell XPS with only an Intel integrated GPU) but use a modern browser like Chrome as it needs WebGPU to run. Want me to make a demo for your industry? Tell me what you need / want to see next! Let me know what you think, and if you have any questions, then drop them in the comments below! YouTube Video Link:

Jason Mayes

42,895 Aufrufe • vor 1 Jahr

Here's my conversation all about AI in 2026, including technical breakthroughs, scaling laws, closed & open LLMs, programming & dev tooling (Claude Code, Cursor, etc), China vs US competition, training pipeline details (pre-, mid-, post-training), rapid evolution of LLMs, work culture, diffusion, robotics, tool use, compute (GPUs, TPUs, clusters), continual learning, long context, AGI timelines (including how stuff might go wrong), advice for beginners, education, a LOT of discussion about the future, and other topics. It's a great honor and pleasure for me to be able to do this kind of episode with two of my favorite people in the AI community: 1. Sebastian Raschka (Sebastian Raschka) 2. Nathan Lambert (Nathan Lambert) They are both widely-respected machine learning researchers & engineers who also happen to be great communicators, educators, writers, and X posters. This was a whirlwind conversation: everything from the super-technical to the super-fun. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 1:57 - China vs US: Who wins the AI race? 10:38 - ChatGPT vs Claude vs Gemini vs Grok: Who is winning? 21:38 - Best AI for coding 28:29 - Open Source vs Closed Source LLMs 40:08 - Transformers: Evolution of LLMs since 2019 48:05 - AI Scaling Laws: Are they dead or still holding? 1:04:12 - How AI is trained: Pre-training, Mid-training, and Post-training 1:37:18 - Post-training explained: Exciting new research directions in LLMs 1:58:11 - Advice for beginners on how to get into AI development & research 2:21:03 - Work culture in AI (72+ hour weeks) 2:24:49 - Silicon Valley bubble 2:28:46 - Text diffusion models and other new research directions 2:34:28 - Tool use 2:38:44 - Continual learning 2:44:06 - Long context 2:50:21 - Robotics 2:59:31 - Timeline to AGI 3:06:47 - Will AI replace programmers? 3:25:18 - Is the dream of AGI dying? 3:32:07 - How AI will make money? 3:36:29 - Big acquisitions in 2026 3:41:01 - Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta 3:53:35 - Manhattan Project for AI 4:00:10 - Future of NVIDIA, GPUs, and AI compute clusters 4:08:15 - Future of human civilization

Here's my conversation all about AI in 2026, including technical breakthroughs, scaling laws, closed & open LLMs, programming & dev tooling (Claude Code, Cursor, etc), China vs US competition, training pipeline details (pre-, mid-, post-training), rapid evolution of LLMs, work culture, diffusion, robotics, tool use, compute (GPUs, TPUs, clusters), continual learning, long context, AGI timelines (including how stuff might go wrong), advice for beginners, education, a LOT of discussion about the future, and other topics. It's a great honor and pleasure for me to be able to do this kind of episode with two of my favorite people in the AI community: 1. Sebastian Raschka (Sebastian Raschka) 2. Nathan Lambert (Nathan Lambert) They are both widely-respected machine learning researchers & engineers who also happen to be great communicators, educators, writers, and X posters. This was a whirlwind conversation: everything from the super-technical to the super-fun. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 1:57 - China vs US: Who wins the AI race? 10:38 - ChatGPT vs Claude vs Gemini vs Grok: Who is winning? 21:38 - Best AI for coding 28:29 - Open Source vs Closed Source LLMs 40:08 - Transformers: Evolution of LLMs since 2019 48:05 - AI Scaling Laws: Are they dead or still holding? 1:04:12 - How AI is trained: Pre-training, Mid-training, and Post-training 1:37:18 - Post-training explained: Exciting new research directions in LLMs 1:58:11 - Advice for beginners on how to get into AI development & research 2:21:03 - Work culture in AI (72+ hour weeks) 2:24:49 - Silicon Valley bubble 2:28:46 - Text diffusion models and other new research directions 2:34:28 - Tool use 2:38:44 - Continual learning 2:44:06 - Long context 2:50:21 - Robotics 2:59:31 - Timeline to AGI 3:06:47 - Will AI replace programmers? 3:25:18 - Is the dream of AGI dying? 3:32:07 - How AI will make money? 3:36:29 - Big acquisitions in 2026 3:41:01 - Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta 3:53:35 - Manhattan Project for AI 4:00:10 - Future of NVIDIA, GPUs, and AI compute clusters 4:08:15 - Future of human civilization

Lex Fridman

914,670 Aufrufe • vor 7 Monaten

25 algorithms every programmer should know: Let's start with my top favorite 10. If nothing else, you should read about these algorithms and have a good idea of how they work: 1. Linear search to find an element in a list 2. Binary search to find an element on a sorted list 3. Bubble sort to sort a list 4. Merge sort will also sort lists 5. Quicksort to sort the list and do it fast 6. Dijkstra to find the shortest path in a graph 7. Breadth-first Search (BFS) for trees or graphs 8. Depth-first search (DFS) for trees or graphs 9. Huffman for doing data compression 10. Anything related to dynamic programming Learning about algorithms is like getting tattoos: you never have enough. Here are another 5 algorithms that will help you go beyond the basics: 11. Kruskal for the finding minimum spanning tree 12. Floyd Warshall, shortest paths in a graph 13. Union Find to detect cycles in a graph 14. Bellman-Ford, shortest path in a graph 15. Lee for finding the shortest path in a maze If you are serious about this topic, I recommend learning about algorithms' space and time complexity. People usually refer to this topic as "Big O" notation. You should build a good intuition about the performance of different algorithms and learn how to evaluate them. Machine Learning will rule the next 50 years, so the next 10 algorithms you can't ignore are the following: 16. Linear Regression 17. Logistic Regression 18. Decision Trees 19. Bayes' theorem 20. k-Nearest Neighbors (kNN) 21. Every algorithm related to neural networks 22. K-means 23. Random forest 24. Gradient boosting algorithms 25. Any dimensionality reduction algorithm (PCA, for instance) There are many more mind-blowing algorithms! I haven't found a better way to understand how computers work from a first-principles point of view than reading about different algorithms. Take a look at the attached video.

25 algorithms every programmer should know: Let's start with my top favorite 10. If nothing else, you should read about these algorithms and have a good idea of how they work: 1. Linear search to find an element in a list 2. Binary search to find an element on a sorted list 3. Bubble sort to sort a list 4. Merge sort will also sort lists 5. Quicksort to sort the list and do it fast 6. Dijkstra to find the shortest path in a graph 7. Breadth-first Search (BFS) for trees or graphs 8. Depth-first search (DFS) for trees or graphs 9. Huffman for doing data compression 10. Anything related to dynamic programming Learning about algorithms is like getting tattoos: you never have enough. Here are another 5 algorithms that will help you go beyond the basics: 11. Kruskal for the finding minimum spanning tree 12. Floyd Warshall, shortest paths in a graph 13. Union Find to detect cycles in a graph 14. Bellman-Ford, shortest path in a graph 15. Lee for finding the shortest path in a maze If you are serious about this topic, I recommend learning about algorithms' space and time complexity. People usually refer to this topic as "Big O" notation. You should build a good intuition about the performance of different algorithms and learn how to evaluate them. Machine Learning will rule the next 50 years, so the next 10 algorithms you can't ignore are the following: 16. Linear Regression 17. Logistic Regression 18. Decision Trees 19. Bayes' theorem 20. k-Nearest Neighbors (kNN) 21. Every algorithm related to neural networks 22. K-means 23. Random forest 24. Gradient boosting algorithms 25. Any dimensionality reduction algorithm (PCA, for instance) There are many more mind-blowing algorithms! I haven't found a better way to understand how computers work from a first-principles point of view than reading about different algorithms. Take a look at the attached video.

Santiago

274,092 Aufrufe • vor 2 Jahren

Manish Gupta, Senior Director at Google DeepMind India, sits down with Aakrit Vaish and Pratyush Choudhury at Mumbai Tech Week for a rare on-record conversation about the frontier AI research happening out of Bangalore. Gupta makes a pointed case against the narrative that India lacks AI research talent: a team of roughly 75 researchers, a third of them fresh out of college, producing work on par with the best in the world and feeding directly into Gemini. The conversation goes deep on what DeepMind India actually builds, why Gemini is considered the most efficient model on the planet, and what India needs to become a research leader rather than a fast follower. In this conversation, they go deep on: 0:00 Intro: Manish Gupta of DeepMind India at Mumbai Tech Week 1:23 What DeepMind India does, and why it's a "mystery" 1:59 The three roles: languages, efficiency, continual learning 2:19 The Haryanvi demo at Google I/O and the cultural playbook 2:38 Making models efficient: from mobile to servers 3:25 Matryoshka transformers and why nested models win 4:20 Why Gemini is the most efficient model on the planet 5:11 Continual learning: using Gemini to improve Gemini 5:50 Google's India plans: consumers, agents, enterprise, government 7:44 Government officials live-coding with AI Studio and NotebookLM 8:30 The India AI talent debate and how the team is structured 10:25 Why India lacks courage and R&D investment, not talent 11:16 DeepMind's global labs and India's outsized impact 13:15 On-device AI: Gemma 3n and 4n 14:08 The headline: 75 world-class researchers in Bangalore 14:58 25 of the 75 are fresh out of college If you're a founder, builder or researcher thinking about AI, frontier models, or India's place in global AI research, this one's for you. Aakrit Vaish Pratyush Choudhury (PC) Google DeepMind Google India Manish Gupta

Manish Gupta, Senior Director at Google DeepMind India, sits down with Aakrit Vaish and Pratyush Choudhury at Mumbai Tech Week for a rare on-record conversation about the frontier AI research happening out of Bangalore. Gupta makes a pointed case against the narrative that India lacks AI research talent: a team of roughly 75 researchers, a third of them fresh out of college, producing work on par with the best in the world and feeding directly into Gemini. The conversation goes deep on what DeepMind India actually builds, why Gemini is considered the most efficient model on the planet, and what India needs to become a research leader rather than a fast follower. In this conversation, they go deep on: 0:00 Intro: Manish Gupta of DeepMind India at Mumbai Tech Week 1:23 What DeepMind India does, and why it's a "mystery" 1:59 The three roles: languages, efficiency, continual learning 2:19 The Haryanvi demo at Google I/O and the cultural playbook 2:38 Making models efficient: from mobile to servers 3:25 Matryoshka transformers and why nested models win 4:20 Why Gemini is the most efficient model on the planet 5:11 Continual learning: using Gemini to improve Gemini 5:50 Google's India plans: consumers, agents, enterprise, government 7:44 Government officials live-coding with AI Studio and NotebookLM 8:30 The India AI talent debate and how the team is structured 10:25 Why India lacks courage and R&D investment, not talent 11:16 DeepMind's global labs and India's outsized impact 13:15 On-device AI: Gemma 3n and 4n 14:08 The headline: 75 world-class researchers in Bangalore 14:58 25 of the 75 are fresh out of college If you're a founder, builder or researcher thinking about AI, frontier models, or India's place in global AI research, this one's for you. Aakrit Vaish Pratyush Choudhury (PC) Google DeepMind Google India Manish Gupta

Activate

14,371 Aufrufe • vor 3 Monaten

This is how ALOHA's "teleoperation" system works - a fancy word for "remote control". Training robots will be more and more like playing games in the physical world. A human operates a "joystick++" to perform tasks and collect data, or intervene if there's any safety concern. There's actually a learning curve to master the controller, much like practicing gaming skills. Teleoperation can be done in many different ways. ALOHA is an impressive custom-built system with very low cost. Here're a few alternatives: (1) Motion Capture (MoCap): apply the MoCap systems used for Hollywood movies to capture the fine-grained motions of hand joints. There would be no "embodiment gap" if the robot hand has 5 fingers. For instance, a demonstrator can wear a CyberGlove ( and manipulate the objects. CyberGlove will capture the motion signals & haptic feedback in real-time, which can be re-targeted onto the humanoid. (2) Wearing gloves & markers can be clumsy. An alternative way to do MoCap is through computer vision. DexPilot from NVIDIA enables marker-less and glove-free data collection. The human operator simply uses their bare hands to perform the tasks. 4 Intel RealSense depth cameras and 2 NVIDIA Titan XP GPUs (yeah, 2019 work) translate the pixels to precise motion signals for robot learning. (3) VR Headset: turn the training room into a VR game and "role play" the robot. This has the advantage of scalable remote data collection - annotators from around the world can contribute without coming onsite. VR demonstration technique appeared in research projects like the iGibson home robot simulator, an initiative that I participated in at Stanford: Behind-the-scene video by Litian Liang

This is how ALOHA's "teleoperation" system works - a fancy word for "remote control". Training robots will be more and more like playing games in the physical world. A human operates a "joystick++" to perform tasks and collect data, or intervene if there's any safety concern. There's actually a learning curve to master the controller, much like practicing gaming skills. Teleoperation can be done in many different ways. ALOHA is an impressive custom-built system with very low cost. Here're a few alternatives: (1) Motion Capture (MoCap): apply the MoCap systems used for Hollywood movies to capture the fine-grained motions of hand joints. There would be no "embodiment gap" if the robot hand has 5 fingers. For instance, a demonstrator can wear a CyberGlove ( and manipulate the objects. CyberGlove will capture the motion signals & haptic feedback in real-time, which can be re-targeted onto the humanoid. (2) Wearing gloves & markers can be clumsy. An alternative way to do MoCap is through computer vision. DexPilot from NVIDIA enables marker-less and glove-free data collection. The human operator simply uses their bare hands to perform the tasks. 4 Intel RealSense depth cameras and 2 NVIDIA Titan XP GPUs (yeah, 2019 work) translate the pixels to precise motion signals for robot learning. (3) VR Headset: turn the training room into a VR game and "role play" the robot. This has the advantage of scalable remote data collection - annotators from around the world can contribute without coming onsite. VR demonstration technique appeared in research projects like the iGibson home robot simulator, an initiative that I participated in at Stanford: Behind-the-scene video by Litian Liang

Jim Fan

124,783 Aufrufe • vor 2 Jahren