Andrew Ng is raising a $150M AI Fund

What kind of useful curated datasets do you imagine could be created by a game? Like driving data from a GTA-like game?

Maybe not if you are someone lucky enough to have gone to Stanford, and are a CS major.

When I took the ML Class (I also took the AI Class at the same time, but had to drop out due to personal reasons - but I stayed in on the ML Class and finished it), I hadn't really touched linear algebra since high school.

I graduated high school in 1991; Ng's course was 20 years later.

I also didn't have any stats or probability experience under my belt. Nor anything about derivatives or integrals.

I basically had to pick all of this up on-the-fly (fortunately there are internet resources), and even to this day, I barely understand them (I understand matrix operations mostly, but I struggle with probabilities, and I have little-to-no idea on derivatives or integrals).

After high school I went on to get a 1 year, virtually worthless today associates degree from a now-defunct voc-tech school here in Phoenix. Since then, I've been steadily employed as a software engineer here in the valley, and well compensated (I believe) for it. I own my own house, and I have zero debt except for a mortgage.

Given all of that, one should be able to see how such a course would be a challenge. There were a ton of people who signed up, but from what I understand, the majority dropped out after the first couple of weeks. This actually seems "par for the course" though for MOOCs.

I know it was a simplified intro to ML, but for me, it and what I took of the AI Class taught me more than what I ever was able to figure out on my own, especially on neural networks. The light really clicked on for me there. But I was really disappointed to have to drop out of the AI Class.

Later, in the Spring 2012, after Udacity had been established, they weren't able to offer the AI Class as one of their courses. So Thrun came up with another course, which was originally titled "CS373 - How to Build Your Own Self-Driving Vehicle" - and I jumped on that one, and completed it as well. I found it fairly challenging too (but not as challenging as the AI Class was). This course has since been renamed to "AI for Robotics" - which is more apt, I think.

It took a while - but eventually the AI Class was made into a course (I think there was some kind of licensing issue, but I don't know for sure, that was preventing it from being part of Udacity's offerings). I have yet to retake it, but it is on my list (plus a ton of others).

Today, I'm in the home stretch of the 3rd term of Udacity's Self-Driving Car Engineer nanodegree. I'm struggling mightily to get my path planner project to work properly, but I almost have it done (it can make it around the track, but for some reason my behavior planner isn't costing things properly). Got an elective, and the integration project to do, all by mid-October or so.

I don't know if any of this will lead anywhere for me career-wise. I'm happy with my current employer, so I expect to stick around here for a while. I have hopes, dreams, ambitions to perhaps get a degree of some sort in CompSci. I want to really learn more mathematics. I've always been a lifelong learner, but this kind of stuff is really fascinating to me, even if it is (what seems to me at least) complex and not always intuitive. But if it were easy, it probably wouldn't be as fun (but I will say Keras and Tensorflow really make things much easier than when we had to implement a neural net in Octave and Python).

Don't have anything relevant to say except to make a fun note that we can change your second sentence, keeping it correct, while increasing its ambiguity. If we restate it as:

Are they talking about basically getting the most out of the current ML type systems?

Since Deep learning is a subset of machine learning, the sentence retains correctness but there are now two equally valid interpretations.

Okay, I do have something relevant to say. I don't see what advantage there is in raw sensory data streams, models can be trained off-line to operate on sensor streams just fine. What we actually want are systems that learn adaptively and on-line. To do that well, they'd need to also be data efficient.

Some of the more interesting AI research is being done in the area of developing very accurate models of the real world. This way the many varied iterations needed to develop the AI model can be done without the same physical limitations of aggregating human data. Games are definitely in this same realm, although it's not always practical to make a human playable simulation-game and an AI playable simulation game given development constraints.

What I find fascinating is what AR might do for training AI models. To execute on AR we'll need to digitize a model of our physical surroundings so the software can interact. At that point we'll have a compelling pipeline of actionable data in regards to machine learning - especially for robotics.

I watched several of the course videos and was struck by how long it's been since college and I did anything with matrices. I kept pausing to go look up what things meant and meanwhile some of his students were catching errors in real time in class