Earlier quoted context omitted.
It's far from challenging. The Stanford one (CS229) is though.
Difficulty is subjective
Not if you compare one course to another.
Andrew Ng is raising a $150M AI Fund
91–100 of 155 posts
Re: Andrew Ng is raising a $150M AI Fund
#92Earlier quoted context omitted.
What is the difference between a data set and a raw sensory data stream? More specifically, isn't a raw sensory data stream just a data set? I think you are getting hung up on semantics. Or is it just the time correlation that interests you? Because some of these data sets are very likely to indeed be time correlated. Like a video/audio data set for example.
Yes in the most general sense, a "data set" could be anything, but I'm talking about the highly curated and labeled data sets that are used to train contemporary ML systems. You can feed a million images that are labeled "cat" or "no cat" to one of these systems and it can achieve a human-level proficiency at identifying cats in images. But, it won't be able to do anything other than identify cats, it's far too narro…
Have you ever seen a child learn how to speak? He also needs loads of "labelled" data to initially learn about concepts.
Re: Andrew Ng is raising a $150M AI Fund
#93I look at announcements like this, and past ones about Ng, and I always marvel at how things have gone since I took and completed his 2011 ML Class... That was one helluva course, challenging and interesting, and fun all at the same time (and so much "concretely" - lol). From what I understand, that course is still available thru Coursera (which Ng booted up after the ML Class experiment; Udacity was Thrun's contribu…
The final project in that course should be using ML to detect every time Ng says "concretely" and compile those clips into a video montage.
Re: Andrew Ng is raising a $150M AI Fund
#94Earlier quoted context omitted.
Yes in the most general sense, a "data set" could be anything, but I'm talking about the highly curated and labeled data sets that are used to train contemporary ML systems. You can feed a million images that are labeled "cat" or "no cat" to one of these systems and it can achieve a human-level proficiency at identifying cats in images. But, it won't be able to do anything other than identify cats, it's far too narro…
Have you ever seen a child learn how to speak? He also needs loads of "labelled" data to initially learn about concepts.
The data is not labelled in the same way. The child has still acquired their knowledge solely from sensory experience. How do they know to apply the spoken label "mom" to the recurring pattern in their visual data stream?
With modern data sets and labelling, so much of the problem domain is deeply hard-coded into the system. It doesn't have to learn what letters and words mean and how to identify them in a totally arbitrary* visual or audio stream. It just gets a relatively minute amount of structured data that it has an embedded understanding of what to do with.
Sure there are things like OCR and speech-to-text, but I don't think you could just run your streams through those, there's just so much subtle information loss. In order to make AI that really has a chance of reaching what humans would call intelligence, I firmly believe it has to make meaning out of some kind of raw sensory experience analogous to ours.
*Ok, not totally arbitrary, a human would not learn language from a video of a forest, and that's where the "labelling" comes in, from observing other people using language, but a child can still learn any language from just that, and the labels are often vague, inaccurate, contradictory, complex, abstract, etc. There's no master training set with the right answers, you have to decide for yourself. And humans were also capable of bootstrapping language from nothing. I just don't see modern supervised learning systems ever doing things like that.
Re: Andrew Ng is raising a $150M AI Fund
#95Aside: how do you pronounce his surname?
He spent a couple of years in Singapore, so I'm going by the pronunciation there. (May be different in different regions, and I'm not sure about his preferred pronunciation now). It would do something like this: Start with the word "urn". Now don't drag it out, make it short. Make the "n" and "ng" sound at the end (urng). Now take out the "r" sound (uhng). It seems like Americans do tend to pronounce it "ehng" instea…
So it should rhyme with Hung and Sung?
Re: Andrew Ng is raising a $150M AI Fund
#96Earlier quoted context omitted.
Yes in the most general sense, a "data set" could be anything, but I'm talking about the highly curated and labeled data sets that are used to train contemporary ML systems. You can feed a million images that are labeled "cat" or "no cat" to one of these systems and it can achieve a human-level proficiency at identifying cats in images. But, it won't be able to do anything other than identify cats, it's far too narro…
Have you ever seen a child learn how to speak? He also needs loads of "labelled" data to initially learn about concepts.
I would disagree on this point, humans unlike current AI systems can learn from one or two data points, especially at easier tasks like identifying cats. Current AI algorithms need huge labeled data sets for solving narrow problems so one needs to build more generalization ability to our current AI systems.
Re: Andrew Ng is raising a $150M AI Fund
#97I look at announcements like this, and past ones about Ng, and I always marvel at how things have gone since I took and completed his 2011 ML Class... That was one helluva course, challenging and interesting, and fun all at the same time (and so much "concretely" - lol). From what I understand, that course is still available thru Coursera (which Ng booted up after the ML Class experiment; Udacity was Thrun's contribu…
It's far from challenging. The Stanford one (CS229) is though.
The Stanford cs classes on ML and deep learning were honestly surprisingly easy for a Stanford DL class. Or maybe that was because they were good teachers, who knows :)
Re: Andrew Ng is raising a $150M AI Fund
#98I look at announcements like this, and past ones about Ng, and I always marvel at how things have gone since I took and completed his 2011 ML Class... That was one helluva course, challenging and interesting, and fun all at the same time (and so much "concretely" - lol). From what I understand, that course is still available thru Coursera (which Ng booted up after the ML Class experiment; Udacity was Thrun's contribu…
I took CS221 from Andrew in 2006 (or was it 2007?) Even more has changed since then ;-) It was my second ML course, after taking Daphne Koller's punishing CS229. Right then though I knew ML will sweep the world pretty soon.
Ng famously taught CS229 too, which also looks punishing. Those 2008 videos are available on YouTube: https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599
Re: Andrew Ng is raising a $150M AI Fund
#99I look at announcements like this, and past ones about Ng, and I always marvel at how things have gone since I took and completed his 2011 ML Class... That was one helluva course, challenging and interesting, and fun all at the same time (and so much "concretely" - lol). From what I understand, that course is still available thru Coursera (which Ng booted up after the ML Class experiment; Udacity was Thrun's contribu…
The final project in that course should be using ML to detect every time Ng says "concretely" and compile those clips into a video montage.
Its already done https://www.youtube.com/watch?v=5ZNJPSe1nZs
Re: Andrew Ng is raising a $150M AI Fund
#100Earlier quoted context omitted.
Whenever I see announcements like this, it's very unclear to me what is meant by "AI." Are they talking about basically getting the most out of the current ML/deep learning type systems? If so then I guess building data sets makes sense but it seems more like an uninteresting business strategy than what I think of as pushing AI forward. If, on the other hand, they are talking about making progress on the more traditi…
I am of the opinion that `true AI` is the science/engineering of understanding and replicating human intelligence. Why are we able to come up with abstract concepts from the surrounding physical environments? Why do we look at the stars and wonder what they are (and why)? How are we able to communicate with one another through pictures, words, writings, snapchat. Is that something special about our brains, our collec…
I agree. I think the fields of "computational cognitive science" and developmental psychology are the ones to look into to make progress towards the "hard fundamental problems". Some of the leading labs working on this are MIT CBMM (https://cbmm.mit.edu/, they have a nice youtube channel) and Berkeley Cocosci (https://cocosci.berkeley.edu/index.php).
Google Brain/DeepMind are also pushing some of those ideas. They must be, since they aggressively poach all the top researchers from those labs...
Ng approach is different: he wants a world powered by Deep Learning, so his goal is to make applied deep learning thrive. His strategy to do that: give those data-hungry models even more data, which is completely reasonable.
Those two approaches - fundamental research and applied deep learning - are often referred to as AI, causing much confusion.