51–60 of 155 posts
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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.
"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…
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…
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.
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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 practi…
I'm curious to the opinions of people here on companies collecting data to build data sets vs. privacy.
Regardless of our individual opinions, probably far more than enough of the general public does not care about or know about securing their online privacy than is needed for an invasive AI startup to get what they need to make a valuable product.
I'm curious to the opinions of people here on companies collecting data to build data sets vs. privacy.
Privacy activists have long ignored any benefits of data collection and as we continue to extract more and more value from data this should become more evident and we will be forced to start discussing concrete harms rather than people's general discomfort.
"Many of these funds are putting time and resources into securing data sets" - this is key.
Looks like Data sets are going to be the moat of AI companies.
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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 practi…
this all seems quite naive of the literature in the field. what other "kind of inference" do you wish he would pursue?
The problem with the dataset-first approach is that humans are still providing a lage part of the intelligence: defining the domain, defining what good performance on it looks like, carefully designing model architectures, collecting and labeling large datasets, etc. This is fine for narrow task-specific problems, but is not really the end-all of AI, and does not even seem to work well on all well-defined tasks. As an example of another kind of inference, how about mathematical reasoning? I purposely pick one here that is seemingly very formal; should be possible for a computer to do it. Mathematicians are somehow able to invent conjectures and prove theorems without first being exposed to terabytes of labeled mathematical facts. The scientific method is kind of an even messier version of this. Or to take something laypeople do, people can usually learn games to at least a passable level from just a handful of playthroughs, not AlphaGo-style millions of plays (imagine if you had to play even 1,000 games of MT:G before you got the basic hang of it...).
All this kind of stuff is quite well-represented in the literature though, if you mean the scientific literature. Pop-press AI writing tends to cover a pretty specific subset of what's going on at AI conferences.
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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.
I don't think it is not unique to tech investing, but it is true for all small companies.
I believe most regulations that block this kind of investment are done in the name of protecting the little guy.