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Andrew Ng launches $175M AI fund
- bitL 9y agoHow to apply to become a company supported by his fund?
- camone 9y agoThere's a contact link at the very end of their webpage: https://www.aifund.ai/ https://www.aifund.ai/
- oneiric 9y agohttps://www.aifund.ai/ https://www.aifund.ai/
- tylerruby 9y agoThanks for this
- debt 9y agoI really think to achieve actual AI we need a new material other than silicon but idk
- nora4 9y agoLike H2O and carbon and stuff?
- quickthrower2 9y agoNo that would not give you the A, or the I from my experience.
- matte_black 9y agoI remember when he was just a guy teaching me machine learning.
- fnord77 9y agoconcretely.
- hobls 9y agoIt turns out...
- davesque 9y agoIf this seems confusing to you, don't worry about it...
- denzil_correa 9y agoThis might help https://youtu.be/5ZNJPSe1nZs https://youtu.be/5ZNJPSe1nZs
- jtmcmc 9y agoyeah but before that he was a super fucking famous ML researcher.
- matte_black 9y ago“Our next guest needs no introduction...”
- deleted 9y ago[deleted]
- ejanus 9y agoThis guy has enormous energy . How can I copy him (1%)?
- tylerruby 9y agoRed Bull
- thisisit 9y agoI am surprised it took so long for this to happen. Ideally speaking you would want the famous ML guy to help you in vetting any startup claiming to solve the problem using AI/ML.
- igravious 9y agoI'm starting to notice that this AI malarkey is becoming a bit of a thing. Do people recommend I take a course in it?
- hobls 9y agoI'm really enjoying his ML class on Coursera. Considering taking the deep learning series next. (And I work more than full time at a tech giant. It's working into my schedule okay so far.)
- davesque 9y agoThe deep learning series is great. It's a nice start. You'll just have to do a fair bit of extra legwork on your own if you want to be solid on the theory.
- igravious 9y agoI'm at the end of a PhD. Once I've this baby wrapped up I'm jumping on that course quicker than you can say "rise of the robots". I've heard only good things about it :)
- icc97 9y agoDeep learning course is better as it uses more relevant tools. The ML course uses Matlab where as DL is in Python and Tensorflow. The ML course though is great as a precursor though.
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- make3 9y agoAndrew Ng for President!
- beambot 9y agoHe announced that he was raising the fund 5 months ago [1]... surprised it took so long, honestly. [1] https://news.ycombinator.com/item?id=15028322 https://news.ycombinator.com/item?id=15028322
- hkmurakami 9y agoIirc Saastr wrote that raising money from LPs takes months, if not a couple of years.
- KaoruAoiShiho 9y agoDid he just cop baidu's business model lmao.
- Laurentvw 9y agoSounds interesting! Does anyone have examples of AI startups that solve real world problems? Just trying to get a picture of what companies would fit their fund.
- malux85 9y agoMy startup, RAMM Science (https://ramm.science https://ramm.science) has a Deep Learning Platform, we have customers using Deep Learning for: Predicting real estate opportunities (which houses are about to sell) predicting energy usage for thousands of sites in real-time, predicting churn, predicting Ad-Tech prices and fraud in real-time, predicting anomalies in seismic radar scans, customer segmentation and recommender engines. Those are examples of real world paying customers using Deep Learning
- deepGem 9y agoI think mapping roads is a real world problem. But for a few urban areas, maps are rarely updated. Of course Google has some sort of monopoly here, but there is a LOT of work to be done. Planet labs now have the ability to map the entire planet every day at a 3m resolution. So object detection applied on these images can be a quite efficient. I still wouldn't call this AI as we are talking mostly about supervised learning but it's a highly practical real world use case. CrowdAI and the like are on this path already.
- csomar 9y agoI'm not sure if this falls (strictly speaking) under AI but lately I have been using Google Maps for Traffic almost daily. It shows the congested roads, estimates the time, suggests paths and show length, etc... It is battle tested by me and I can say it is surprisingly accurate.
- agibsonccc 9y agoWe're "horizontal". We would not fit his definition of a vertical specific startup. (We have also been around a while though) The bulk of what we do is time series. Applications we do for real paying customers include: Detecting theft of power on the raw grid Online payments fraud Detecting people stealing from the telco network Detecting faults in assembly line machines Detecting computers about to fail Detecting root cause of dropped calls Kind of researchy, but we've also done robotics with RL to teach a robot to learn an obstacle course.
- solipsism 9y agoSounds incredibly similar to Phil Libin's project: https://all-turtles.com https://all-turtles.com. Phil Libin is the ex-CEO of Evernote. Maybe there's room for multiple players in this space, but I imagine the name recognition and bona fides of Andrew Ng is going to suck the air out of All Turtles.
- shafyy 9y agoI have a feeling that Andrew's fund is much more focused on cutting-edge AI than Phil's. If you take a look at Phil's portfolio, you see can see that the companies are probably using some form of AI but that's not the core of them. Also, in my personal experience with talking to Phil, it seems that for him the "practical AI" part is more important than what kind of tech is used. So, maybe a more consumer-focused perspective (which I like). I can imagine that Andrew focuses more on companies that have or have the potential to have significant AI themselves. I doubt that Andrew would consider a bot that uses the Google Vision API and Dialogflow an "AI" company. If that makes sense. PS: I do think that the latter should be considered an AI company. Just like a company using AWS to host is called web company. In fact, probably a lot of companies will be using some sort of AI api pretty soon.
- strin 9y ago“One of my philosophies of building companies is the importance of velocity,” True. And the key to keep (increasing) velocity is momentum. I believe the fundamentals to that is infrastructure. Once you get to the "flywheel stage" of an AI company (http://nicodjimenez.github.io/2018/01/25/stages.html http://nicodjimenez.github.io/2018/01/25/stages.html), experimentation becomes so easy that new models can be rolled out nightly.
- ogdoad 9y agoActually the key to keep increasing velocity is maintaining a positive dv/dt, that is to say, acceleration. Momentum is more of a tendency to refuse to leave a state (think of `inertia-in-motion`), rather than actively seek it. If that makes any sense at all, that is.
- yeukhon 9y agoI think goal-driven is what keeps the momentum, regardless of the amount of investment and machinery available (though I have to admit a clean desk is better than a messy desk).
- xvilka 9y agoI hope this fund wont be limited by only ML, but also will work on formal logic approaches.
- gota 9y agoI don't think anything other than ML will be funded like that for a while. We are on the very top of that hype curve, a few of years from now we'll forget how absolutely stupefied we were when ML gave us models that "can X better than humans" and the enthusiasm will give way to a feeling of "I overpaid for this". It happened before with databases way back when, then with specialist systems, then ... The only difference is now the layperson hears about it with an astounding frequency. I don't know, maybe that will make things different but I can't see it having any other effect than making that 'disappointment crash' harder
- mark_l_watson 9y agoNow I now why Andrew is two months late delivering the final 5th class for his deep learning Coursera specialization. He has been busy! This is really a different kind of venture, and it will be interesting in a few years to see how they do.
- yeukhon 9y agoFinal as in this will be the last time Ng will teach the course?
- andyjohnson0 9y ago> Final as in this will be the last time Ng will teach the course? Courses on Coursera aren't really taught that way. The course instructor(s) prepare the course materials (videos, presentations, quizzes, assignments, etc.) once only. Courses start on a regular schedule and the platform, rather than the instructors, takes you through the material in sequence. There are forums with moderators for peer-support, but you don't typically interact with the instructors as they're not really there. I assume the OP is referring to Ng building the fifth of the five courses that comprise the specialisation.
- yeukhon 9y agoI see, although I have seen some refresh contents in other courses on Coursera. Good to know he has a series of courses.
- person77 9y agoIs it worth learning deep learning with coursera,?I was considering doing the MIT deep learning path on YouTube. I'm not really focused on AI I just want an overview.
- ccozan 9y agoAs other fellow commenters remarked, and as an AI enthusiast/researcher myself, I fail to see AI as a business. What I would like is a fund, or similar that is funding research, because fundamentally we need so much research in this area. AI as we have it now is just a bunch of ML algos and *NNs that perform well in different niches and, well, after all cat pictures were categorized, what to do next? So, in case Andrew is reading this post, I would like to have a chat with him about my research ideas and if they could be funded. Because if this happens, many more would follow and then we can see a real progres towards real/hard AI.
- CabSauce 9y agoThere are tons and tons of meaningful uses for the current state of ML. Just about every industry is full of opportunities to apply the most basic algorithms and drive tremendous value. It goes without saying that research is still valuable, but what I haven't seen yet is large, widespread use of applied ML.
- ccozan 9y agoOf course ML has his uses, even now I am doing fully industrial ML. But, in the end, is just an algorithm/NN that does what your suppose to programm it. And is a heck of manual work involved. AI is on a different scale andmay have less to do with the actual implementations.
- urlwolf 9y agoAndrew Ng's VC fund will use their own teams instead of listening to pitches. What can we read from this? (1) the market is saturated with 'wannabe startups' that talk about ML but don't have a clue (2) VCs get many of these pitches 'X but with AI' with noone knowing ML in the team. Andrew has the reputation to do this and not be hated. But you know who did exactly this? Rocket internet, the clone-war masters.
- nora4 9y ago"In his view, AI businesses are also different from regular startups because you generally get a closed feedback loop that allows you to quickly see what works (and what doesn’t)." What is he talking about here? First of all, this is just the basic lean startup methodology. Any startup (especially consumer ones) try to do that, i.e. A/B testing new features. Second, he may be referring to the case that once you have a big comprehensive dataset already gathered, then you can just iterate improving the model. My (cynical) answer: good luck with that. The real issue with AI especially in mission-critical situations is data in edge cases. Even if you can improve on your train/test data, that barely translates to customer satisfaction unless the edge cases are treated well. And edge cases will be revealed very slowly by encountering data in the field. Just ask anybody working on voice-assistant agents (Alexa, Siri, Google one) or self-driving. It's not a fast loop by any means. Of course, Ng probably knows all this which makes me feel even more bothered.
- otoburb 9y ago>>Of course, Ng probably knows all this which makes me feel even more bothered. Ng's implied claim is that he has a head start on how to tackle the edge cases, along with other hard problems encountered in the field, based on his experience at Baidu. This is what got him the "easy" capital raise. If he really has an edge (no pun intended), then it makes sense for him to apply it while it's still relevant.
- nora4 9y agoMaybe. Part of me thinks it's somewhat of a small money grab. (hope it's not.) What I will bet money on though is that no massive success will come out of this. The key to AI is data & hardware (and not algorithm or methodology -- where everyone is essentially doing the same) and that's the domain of already giant tech companies. There can be a lot of good use cases in teaching far-off industries on how to use AI (manufacturing, etc.) but that's essentially a consulting business which will have a limited window. So lots of little successes is completely achievable; massive successes I hardly doubt.
- danjoc 9y agoThe amount of money in this field puzzles me. The difference between relative novice and competition winner on kaggle.com is often in the single digit percentages or less. When 26 year old George Hotz can build his own self driving car in his garage, it seems like a very narrow moat.
- otoburb 9y agoYou make it sound like George Hotz is any run-of-the-mill 26 year old, except he's anything but[1]. [1] https://en.wikipedia.org/wiki/George_Hotz https://en.wikipedia.org/wiki/George_Hotz
- danjoc 9y agoBut the point is, for $3M I can fund George Hotz and have an entry in the self driving car arena. For nothing, I can go grab open source code and be a small percentage away from the absolute state of the art. How do companies justify such a tiny edge as being worth so much? I wish Andrew Ng the best. I do. He's a class act. I enjoyed his course years ago, but came away a little disappointed. At the end, I realized there's no intelligence in the algorithms. It's all just applied statistics and a lot of sweat preparing training data. Maybe I should just shut up and vie for some of the easy money sloshing around. I think I would just feel weird asking for a ton of cash when the end product is just a bit of recursive math.
- CodeCube 9y agoI wonder if anyone who has received an https://aigrant.org https://aigrant.org will make their way over to aifund