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Phil Libin is the ex-CEO of Evernote.
Maybe there's room for multiple players in this space, but I imagine the name recognition and bona fides of Andrew Ng is going to suck the air out of All Turtles.
True. And the key to keep (increasing) velocity is momentum. I believe the fundamentals to that is infrastructure. Once you get to the "flywheel stage" of an AI company (http://nicodjimenez.github.io/2018/01/25/stages.html), experimentation becomes so easy that new models can be rolled out nightly.
Sounds incredibly similar to Phil Libin's project: https://all-turtles.com . Phil Libin is the ex-CEO of Evernote. Maybe there's room for multiple players in this space, but I imagine the name recognition and bona fides of Andrew Ng is going to suck the air out of All Turtles.
I can imagine that Andrew focuses more on companies that have or have the potential to have significant AI themselves. I doubt that Andrew would consider a bot that uses the Google Vision API and Dialogflow an "AI" company. If that makes sense.
PS: I do think that the latter should be considered an AI company. Just like a company using AWS to host is called web company. In fact, probably a lot of companies will be using some sort of AI api pretty soon.
Earlier quoted context omitted.
I'm at the end of a PhD. Once I've this baby wrapped up I'm jumping on that course quicker than you can say "rise of the robots". I've heard only good things about it :)
What is the domain of your PhD? If it's computer science or mathematics then you might find that ML class on Coursera too easy, way below your level of competence.
Earlier quoted context omitted.
It turns out...
If this seems confusing to you, don't worry about it...
Sounds interesting! Does anyone have examples of AI startups that solve real world problems? Just trying to get a picture of what companies would fit their fund.
It is battle tested by me and I can say it is surprisingly accurate.
I hope this fund wont be limited by only ML, but also will work on formal logic approaches.
We are on the very top of that hype curve, a few of years from now we'll forget how absolutely stupefied we were when ML gave us models that "can X better than humans" and the enthusiasm will give way to a feeling of "I overpaid for this". It happened before with databases way back when, then with specialist systems, then ...
The only difference is now the layperson hears about it with an astounding frequency. I don't know, maybe that will make things different but I can't see it having any other effect than making that 'disappointment crash' harder
Sounds interesting! Does anyone have examples of AI startups that solve real world problems? Just trying to get a picture of what companies would fit their fund.
We would not fit his definition of a vertical specific startup. (We have also been around a while though) The bulk of what we do is time series.
Applications we do for real paying customers include:
Detecting theft of power on the raw grid
Online payments fraud
Detecting people stealing from the telco network
Detecting faults in assembly line machines
Detecting computers about to fail
Detecting root cause of dropped calls
Kind of researchy, but we've also done robotics with RL to teach a robot to learn an obstacle course.
“One of my philosophies of building companies is the importance of velocity,” True. And the key to keep (increasing) velocity is momentum. I believe the fundamentals to that is infrastructure . Once you get to the "flywheel stage" of an AI company ( http://nicodjimenez.github.io/2018/01/25/stages.html ), experimentation becomes so easy that new models can be rolled out nightly.
If that makes any sense at all, that is.