Andrew Ng: Unbiggen AI
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Andrew Ng: Unbiggen AI
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Re: Andrew Ng: Unbiggen AI
#2Yeah that'd be great.
I also want cars that run on salt water.
I'm not saying that small data ai is equally impossible, but simply saying "we should make this better thing" isn't enough.
Re: Andrew Ng: Unbiggen AI
#3Yeah that'd be great. I also want cars that run on salt water. I'm not saying that small data ai is equally impossible, but simply saying "we should make this better thing" isn't enough.
Atleast someone's working on it.
Re: Andrew Ng: Unbiggen AI
#4Yeah that'd be great. I also want cars that run on salt water. I'm not saying that small data ai is equally impossible, but simply saying "we should make this better thing" isn't enough.
> simply saying "we should make this better thing" isn't enough.
Besides the references to his company which has customers and a product that already works on these principles the literature currently shows that this is very much possible if you dig into the correct niches. Besides the SOTA in few-shot and meta-learning it is possible to smartly choose the correct few samples for the network that yield the same results.
It has also been my primary focus for the past 5 years and the core of the company I founded.
Re: Andrew Ng: Unbiggen AI
#5My understanding is that they are trying to automate the data preparation steps that seasoned ML practitioners are doing anyway today.
The fact that he tries this in manufacturing makes the case stronger. In most manufacturing companies you do not have access to top ML talent.
You have Greg who knows python and recently visualized some production metrics.
If we could empower Greg with automated ML libraries that guide him in the data preparation steps in combination with precooked networks like autogluon, then manufacturing could become a huge beneficiary of the ML revolution.
Re: Andrew Ng: Unbiggen AI
#6That is the problem with generalization and cop outs like these. It's no good to people in the field doing actual work where the devil is in the detail.
Big data is fairly important to a lot of things, for example I was listening to Tesla's use of Deep net models where they mentioned that there were literally so many variations of Stop Signs that they needed to learn what was really in the "tail" of the distribution of Stop Sign types to construct reliable AI
Re: Andrew Ng: Unbiggen AI
#7That is the problem with generalization and cop outs like these. It's no good to people in the field doing actual work where the devil is in the detail. Big data is fairly important to a lot of things, for example I was listening to Tesla's use of Deep net models where they mentioned that there were literally so many variations of Stop Signs that they needed to learn what was really in the "tail" of the distribution…
Interestingly, when you learn how to drive you need to see approximately one example and you're able to identify them all.
Re: Andrew Ng: Unbiggen AI
#8That is the problem with generalization and cop outs like these. It's no good to people in the field doing actual work where the devil is in the detail. Big data is fairly important to a lot of things, for example I was listening to Tesla's use of Deep net models where they mentioned that there were literally so many variations of Stop Signs that they needed to learn what was really in the "tail" of the distribution…
Sounds like they missed the forest and instead "deep learned" all the variations of trees.
Re: Andrew Ng: Unbiggen AI
#9Yeah that'd be great. I also want cars that run on salt water. I'm not saying that small data ai is equally impossible, but simply saying "we should make this better thing" isn't enough.
It's more of "this direction seems higher ROI than that direction", in particular quality vs quantity of data.
Already in 2018 SenseTime reported that for face recognition, clean dataset surpasses accuracy of 4x larger raw dataset.
Re: Andrew Ng: Unbiggen AI
#10That is the problem with generalization and cop outs like these. It's no good to people in the field doing actual work where the devil is in the detail. Big data is fairly important to a lot of things, for example I was listening to Tesla's use of Deep net models where they mentioned that there were literally so many variations of Stop Signs that they needed to learn what was really in the "tail" of the distribution…
Interestingly, when you learn how to drive you need to see approximately one example and you're able to identify them all.
Is there some underlying point to this statement? It comes off as a passive dismissal of something but I'm not sure what. It might be helpful to directly state what you're trying to say so that other people can engage with it.