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Andrew Ng is raising a $150M AI Fund

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Re: Andrew Ng is raising a $150M AI Fund

#41

> Many of these funds are putting time and resources into securing data sets, technical mentors and advanced simulation tools to support the unique needs of AI startups What are "advanced simulation tools" ? something like https://github.com/marcotcr/lime ?

I would guess "environment simulators"? Environments are to RL as datasets are to supervised/unsupervised learning.

Re: Andrew Ng is raising a $150M AI Fund

#43
post #35

"Many of these funds are putting time and resources into securing data sets" - this is key.

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…

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.

Re: Andrew Ng is raising a $150M AI Fund

#45
post #35

"Many of these funds are putting time and resources into securing data sets" - this is key.

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 don't think you're up to speed with the latest in ML approaches. You have to be narrow before you can be broad, drawing cross correlations between great narrow inference towards more generalized problem sets.

Re: Andrew Ng is raising a $150M AI Fund

#46

> Many of these funds are putting time and resources into securing data sets, technical mentors and advanced simulation tools to support the unique needs of AI startups What are "advanced simulation tools" ? something like https://github.com/marcotcr/lime ?

Simulated worlds / games like the OpenAI gym and DeepMind lab come to mind:

https://gym.openai.com/

https://github.com/deepmind/lab

Re: Andrew Ng is raising a $150M AI Fund

#47
post #2

Is it likely that I (and other Andrew Ng 'fans') will be able to buy stock in his company?

there is usually a minimum investment amount. for a fund of this size, im guessing it would be around $5m

I find this really frustrating about tech investing. Much of the early investment opportunities are only available to the richest. It's an insider's game.

Re: Andrew Ng is raising a $150M AI Fund

#48
post #35

Earlier 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…

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.

I'm personally most interested in approaches to AI not based as heavily on dataset-collecting. There's a now almost standard method of: 1) first curate a nice, large dataset (often explicitly labeled by humans), then 2) carefully engineer a model architecture that has good performance on the problem represented by that dataset. Example: dataset is ImageNet, problem is tagging images. Obviously there's a lot of practical value in that, and it's a perfectly reasonable thing to research and use, but that's only one specific kind of inference.

Re: Andrew Ng is raising a $150M AI Fund

#49
post #35

Earlier 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…

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 narrow to be considered intelligent in any way.

If you can feed a series of timestamped photos to a system, basically an unlabeled arbitrary video stream, and you can demonstrate that it formed some notion of what a cat is, that would be very interesting indeed.

Re: Andrew Ng is raising a $150M AI Fund

#50
post #6

Earlier quoted context omitted.

Looks like Data sets are going to be the moat of AI companies.

It was the reason Blekko had value to IBM's Watson effort, the crawler was state of the art. With that and the 'web' you can create data sets that others can't. It is the not-so-secret advantage that both Google and Microsoft leverage (their search engine crawlers).

Could you describe at a high level(or whatever level you'd like,) what goes into, or more specifically, what are the components of a crawler that makes it superior for these purposes?
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