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Andrew Ng: Unbiggen AI
21–30 of 90 posts
Re: Andrew Ng: Unbiggen AI
#22Yeah 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.
I would say that Andrew Ng has some credibility in putting practice to his preaching.
Re: Andrew Ng: Unbiggen AI
#23That 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.
when you learn to drive you need [approximately 16 years training your visual system hardware that took 6 million years to evolve and] to see approximately one example and you're able to identify them all.
FTFY.
Yet Tesla have been working on both the hardware and software for 10 years? Amazing progress right?
Re: Andrew Ng: Unbiggen AI
#24Earlier quoted context omitted.
OR and ML have their own space in manufacturing. OR is perfect when you can describe explicitly what the decision space is and what the restrictions are. ML is great fit when you want to identify and use patterns. Quality control with machine vision is a good application for ML. NLP for PDF documents is a huge field for manufacturing as well. Companies have so much data in email attachments that they do not currently…
> OR is perfect when you can describe explicitly what the decision space is and what the restrictions are. As opposed to having to figure it out later from the outputs of a black box? > Quality control with machine vision is a good application for ML. I can't imagine CV could be an actual replacement for actual SPC in many industries. There's a reason we need to take samples and stress test, analyze composition, etc.…
> As opposed to having to figure it out later from the outputs of a black box?
Not all problems can be formulated as a set of explicit equalities, constraints and variables (e.g. machine vision). If explicit modeling is an option, of course you should do it. I am seeing efforts to try reinforcement learning on systems that we know how to describe with equations, and of course the results are laughable compared to the traditional methods.
> I can't imagine CV could be an actual replacement for actual SPC in many industries. There's a reason we need to take samples and stress test, analyze composition, etc.
In one big manufacturing company they were using Machine vision and a cheap web camera to control flaring. Could they do it with fancy sensors instead? Of course, but it would be more expensive, and they never did in the past.
Another manufacturing company is using machine vision to raise an alarm if the door of a cargo car of a train is not closed after loading. Could they install sensors in all of the doors of the train instead? Sure, but it would be cost prohibitive.
>NPL could be big everywhere... if it provides actual value, which is not a given. ML has a lot of tangential applications (you could also say, better forecasting), but how will directly improve manufacturing processes?
In manufacturing we have multiple people opening pdfs from emails to copy contract numbers to excel spreadsheets. Others are getting orders in emails and then type them in SAP manually. I think that these tasks can be automated specially with the recent versions of NLP networks.
>I apologize for being abrasive, but I'm so tired of cs people descending upon all industries, plugging shit data into pytorch and doing shitty ML like it will automatically add value. Even more so in industrial engineering, which in my experience is full of people way better at math than computer scientists and requires a deep understanding of the product and the manufacturing process.
All is good :) There has been a lot of unsubstantiated hype in ML, made even worse by big consulting companies and cloud providers who just sell the hype.
Re: Andrew Ng: Unbiggen AI
#25Pretty interesting. Mr. Ng claims that for some applications having a small set of quality data can be as good as using huge set of noisy data. I wonder if, assuming the data is of highest quality, with minimal noise, having more data will matter for training or not. And if it matters, on what degree?
This is at the heart of the ML training problem.
In general you want to add more variants of data but not so much that the network doesn't get trained by them. Typical practice is to find images whose inclusion causes high variation in final accuracy (under k-fold validation, aka removing/adding the image causes a big difference) and prefer more of those.
Now, why not simply add everything? Well in general it takes too long to train.
Re: Andrew Ng: Unbiggen AI
#26That 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…
Do you have a link for that Tesla talk?
Re: Andrew Ng: Unbiggen AI
#27Earlier quoted context omitted.
> OR is perfect when you can describe explicitly what the decision space is and what the restrictions are. As opposed to having to figure it out later from the outputs of a black box? > Quality control with machine vision is a good application for ML. I can't imagine CV could be an actual replacement for actual SPC in many industries. There's a reason we need to take samples and stress test, analyze composition, etc.…
> As opposed to having to figure it out later from the outputs of a black box? Not all problems can be formulated as a set of explicit equalities, constraints and variables (e.g. machine vision). If explicit modeling is an option, of course you should do it. I am seeing efforts to try reinforcement learning on systems that we know how to describe with equations, and of course the results are laughable compared to the…
There is a significant amount of research from the field of computer vision before ML even existed. It was quite robust as well within certain constraints. Those techniques simply did not generalize anywhere even close to as well as deep learning.
However, that said, in a tightly controlled environment such as a manufacturing line trying to spot defects I would imagine they would have a good chance at performing a lot better than deep learning.
A lot of the advancements in deep learning have also come out of ideas from that research. While they didn't use the techniques directly, there is a lot of knowledge that we'd be lost without.
This is one thing that scares me about ML. We are losing research into the fundamental physics/science to deeply understand these things and instead just throwing models at them.
Re: Andrew Ng: Unbiggen AI
#28data quality is important. every ai project i've worked on has started with visualizing the data and thinking about it.
it's easy to get complacent and focus on building big datasets. in practice, looking at the data often reveals issues sometimes in data quality and sometimes scope of what's in there (if you're missing key examples, it's simply not going to work).
most ml is actually data engineering.
Re: Andrew Ng: Unbiggen AI
#29Glad to see the term ML being used more often than AI in the comments as it looks like most "AI" models are trained for image classification.
Having said that, the idea of "doing more with less" sounds interesting and I wonder what it means exactly. Does it mean taking a dataset of 50 images and to create 1000s of synthetic images from it?
Re: Andrew Ng: Unbiggen AI
#30Earlier quoted context omitted.
Interestingly, when you learn how to drive you need to see approximately one example and you're able to identify them all.
That is called transfer learning. You might only need to see one photo of a sign to identify it in real life (although arguably learner drivers take a while to notice signs) but that is only because you have been training on identifying generic objects since you left the womb. You brain already knows how to select the most important features of a sign. The shape, the size and the color. You have also learned how to u…
I'm not sure "training" is the right way to think about it. Children don't train to identify objects, they quickly develop the ability to recognize objects and are able to correlate them with prior information that was retained and learned. Case in point: if you take a child born blind, give them the ability to see, they are immediately able to recognize and correlate objects around them.