Is his Machine Learning course still relatively up to date? I was considering devoting some time to it, or a course like it soon.
Opening a new chapter of my work in AI
21–30 of 82 posts
Re: Opening a new chapter of my work in AI
#22Just as electricity transformed many industries roughly 100 years ago, AI will also now change nearly every major industry This is an underrated point, and something I don't think most people outside of the high level Machine Learning/AI world take seriously. It's also one of the biggest challenges for the industry going forward because of natural monopolies. I say that because if AI is electricity then data is the c…
Data is important now, but when we have solved vision, speech, text and robotics to a decent degree, data won't matter as much. The great thing about AI is that we can cheaply copy already trained models or already labeled datasets. There aren't so many datasets needed to solve the most interesting and financially profitable few problems. Of course, there will always be fringe projects where more data is needed, but the main applications will be in the commons. You can copy an AI model if you can talk to it (use it to produce sample outputs). Any model could be copied in a dataset and transferred into another model. The great thing about machine learning is that it learns directly from data, so it's cheap to copy by tracing the inputs and outputs of other public AIs, just as current AIs are taught by tracing the inputs and outputs of people (supervision).
Re: Opening a new chapter of my work in AI
#23Just as electricity transformed many industries roughly 100 years ago, AI will also now change nearly every major industry This is an underrated point, and something I don't think most people outside of the high level Machine Learning/AI world take seriously. It's also one of the biggest challenges for the industry going forward because of natural monopolies. I say that because if AI is electricity then data is the c…
> AI is electricity then data is the coal/oil that drives it. The big technology players have a massive advantage in their ability to build and deploy tools that collect the data, and then bring it back to be turned into "electricity." Data is important now, but when we have solved vision, speech, text and robotics to a decent degree, data won't matter as much. The great thing about AI is that we can cheaply copy alr…
In the former case we are taking data, labeling it, then using it to build our nets and models. You are correct to an extent that it's a usable model once trained and that data is less important.
However, equally if not more important is the data that is being put into the net to come out as a result/action. Arguably this data comes through the same pipe as training data - and the pipes are similarly limited. So its ALWAYS important because you can't take an action or classify or otherwise without it.
When you add in the reinforcement mechanism, or later unsupervised techniques then those data mechanisms blur between training and action data so the point is moot. It's not a one run process in the long run, it's iterative and always evolving based on the user.
Re: Opening a new chapter of my work in AI
#24I didn't find a single reference other than this as to what he is going to do post resigning. >I will also explore new ways to support all of you in the global AI community, so that we can all work together to bring this AI-powered society to fruition. It is true that AI is the new electricity which will change practically everything in our lives and it is good to see that alliances like OpenAI are forming to democra…
Re: Opening a new chapter of my work in AI
#25Sadly, the post has no meaningful information beyond that he's resigning.
Re: Opening a new chapter of my work in AI
#26I didn't find a single reference other than this as to what he is going to do post resigning. >I will also explore new ways to support all of you in the global AI community, so that we can all work together to bring this AI-powered society to fruition. It is true that AI is the new electricity which will change practically everything in our lives and it is good to see that alliances like OpenAI are forming to democra…
>it is good to see that alliances like OpenAI are forming to democratize the knowledge... Very true! I hope he joins OpenAI and helps achieve their mission [1]. It would be a huge boost to their efforts. [1] https://openai.com/blog/introducing-openai/
Re: Opening a new chapter of my work in AI
#27Earlier quoted context omitted.
>it is good to see that alliances like OpenAI are forming to democratize the knowledge... Very true! I hope he joins OpenAI and helps achieve their mission [1]. It would be a huge boost to their efforts. [1] https://openai.com/blog/introducing-openai/
I actually hope he doesn't.
Re: Opening a new chapter of my work in AI
#28Big blow for Baidu. Wonder if he'll join Google again. They are the foremost in AI.
Re: Opening a new chapter of my work in AI
#29Ng's wife's company, Drive.ai, has made more progress in less time and with fewer resources than anybody in the autonomous driving space. So it's probably a billion dollar company. I wonder if that has anything to do with it. There's a lot of money in autonomous driving startups that can deliver results.
Re: Opening a new chapter of my work in AI
#30Is his Machine Learning course still relatively up to date? I was considering devoting some time to it, or a course like it soon.
1. Do Practical Deep Learning For Coders:-
http://course.fast.ai/ To take a plunge directly into deep learning AI, this course has rave reviews. This course will allow you to do practical industry level stuff first, then learn theory behind it, rather than other way.
Course description:
>This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF. Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.
2. Read http://www.deeplearningbook.org/ to gain the relevant math behind it. If you don't know some of the math like calculus or linear algebra presented in the book, learn as you read it from sources like Khan academy.
Now we are up to date on practical side of things, especially deep learning part. We can move on to gain a more generalized and rigorous outlook on various machine learning techniques.
3. Do https://see.stanford.edu/Course/CS229/ - CS229 By Andrew NG , its more rigorous, and complete compared to coursera course. And coursera course is not
I think within a year (max) just this coursework plan would give a strong foundations on practical, theoretical side of things in AI.
I get distracted trying to learn so many stuff at once, (clojure , sicp, haskell, advanced algo) etc etc. So I made this lesson plan to follow as I am interested in AI the most.