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Cloud Video Intelligence API

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Re: Cloud Video Intelligence API

#81

Post by asher_ »

cs2818 wrote: post #79
tyre wrote: post #50

I think their model should take a second pass on the words and probabilities, independent of the video. Look at their example: Animal: 97.76% Tiger: 90.11% Terrestrial animal: 68.17% So we are 90% sure it is a tiger but only 68% sure it is a land animal? I don't think that makes sense. It could be that this is a weakness of seeding AI data with human inputs. I can believe that 90% of people who saw the video would ag…

It seems like an intersting next step after producing these output labels might be to use something like ConceptNet [0] to evaluate the relationship between the labels and somehow incorporate this as feedback. [0] http://conceptnet.io

This is spot on. What makes the above probabilities look incorrect is that people are assuming that the algorithm understands the relationship between tiger and animal the same way that humans do. Clearly they are evaluating each independently.

Re: Cloud Video Intelligence API

#82

Post by thomasdullien »

njohnson41 wrote: post #68
crack-the-code wrote: post #63

Earlier quoted context omitted.

It's probably more likely that they want each output to be independent of the other. Certain features may be predominantly associated with a tiger, but not necessarily indicative of a terrestrial animal. If the 9.89% chance that they could have been wrong would have been the case, then that should not influence whether or not it was a terrestrial animal. In my opinion, the consumer of the output values should be able…

Still, in any consistent way of assigning probabilities to events, if A implies B, then P(A) Neural network outputs are not probabilities. I think that's the main lesson here.

Given that the last layer of a NN is a logistic regression, they are in fact well-calibrated probabilities under the assumption disjoint classes.

The issue at hand is training them on overlapping classes :-)

I will shut up now, sorry for nitpicking.

Re: Cloud Video Intelligence API

#83

Post by ragebol »

cs2818 wrote: post #79
tyre wrote: post #50

I think their model should take a second pass on the words and probabilities, independent of the video. Look at their example: Animal: 97.76% Tiger: 90.11% Terrestrial animal: 68.17% So we are 90% sure it is a tiger but only 68% sure it is a land animal? I don't think that makes sense. It could be that this is a weakness of seeding AI data with human inputs. I can believe that 90% of people who saw the video would ag…

It seems like an intersting next step after producing these output labels might be to use something like ConceptNet [0] to evaluate the relationship between the labels and somehow incorporate this as feedback. [0] http://conceptnet.io

Indeed. I wonder if the networks could learn a more and more general description of the concepts in the hierarchy when going up that hierarchy. E.g. there's a bunch of tiger species [0] each with a specific underlying model, but some traits are common to all tiger species. And some traits are common to, say, carnivores. Could you share parts of the model/network via that hierarchy, e.g. that a tiger inherits some parts of the model for carnivores etc.

For example, can the 'stripe model' or 'fang model' be shared among tigers and carnivores etc.

I tried something similar for my thesis, but this was before the advent of DNNs etc.

[0] http://conceptnet.io/c/en/tiger?rel=/r/IsA&limit=1000

Re: Cloud Video Intelligence API

#84

Post by e12e »

microcolonel wrote: post #72
crack-the-code wrote: post #63

Earlier quoted context omitted.

It's probably more likely that they want each output to be independent of the other. Certain features may be predominantly associated with a tiger, but not necessarily indicative of a terrestrial animal. If the 9.89% chance that they could have been wrong would have been the case, then that should not influence whether or not it was a terrestrial animal. In my opinion, the consumer of the output values should be able…

It's really tough to take the outputs seriously if they are not even on the same scale. That is, tiger , cat , and bengal tiger all imply terrestrial animal . That means that they should all be scaled around that. That is to say that terrestrial animal would need to be at least max( tiger , cat , bengal tiger ).

Maybe. Or maybe this is a human-centric view. Imagine that the classifier worked on sound. A low growl could be a cat, a tiger or a submarine engine. Then the probabilities might be flipped - if it's a land animal, it might be 40/60 that it's a tiger or a cat.

A visual classifier that identify "4 moving things" might indicate some kind of land animal, or slow motion video of a Dragonfly in flight.

Sample/"evidence"-based reasoning will always have these kind of odd inconsistencies - I'm not sure if mapping such output to a logic model is an improvement. It might be - to take output from a classifier like this, and plug it into an expert system like a Prolog/datalog database or something. Or it might just end up being just as limited as those systems already are.

But when one says "tiger implies terrestrial mammal (or animal)", one is really talking about ontologies -- perhaps training the classifier to come up with things like "90% sure four legs, 60% sure fur" and plugging that into a logic based system would yield good hybrid systems?

I do think one would then loose the "magic" effectiveness of these pure(ish) learning systems though? Perhaps someone more familiar with the domains might shed some light?

Re: Cloud Video Intelligence API

#85

Post by tambourine_man »

cobookman wrote: post #69
tambourine_man wrote: post #49

It amazes me how smart these guys at google are, and yet, they can't design a mobile site if their lives depended on it: http://imgur.com/bXGuNfL

If you could share with me what mobile phone / web browser you used that produced the styling issue, I'll be sure to pass it on to the relavent people within google so that it gets resolved. Also if you send me an email at bookman@google.com I'll be sure to update you as to when the styling errors are resolved. (Disclaimer, I work for google cloud)

iPhone SE, iOS 10.2.1

Also, see my previous comment on a similar issue with Google Cloud Calculator:

https://news.ycombinator.com/item?id=13729466

Re: Cloud Video Intelligence API

#86

Post by mrfusion »

cs2818 wrote: post #79
tyre wrote: post #50

I think their model should take a second pass on the words and probabilities, independent of the video. Look at their example: Animal: 97.76% Tiger: 90.11% Terrestrial animal: 68.17% So we are 90% sure it is a tiger but only 68% sure it is a land animal? I don't think that makes sense. It could be that this is a weakness of seeding AI data with human inputs. I can believe that 90% of people who saw the video would ag…

It seems like an intersting next step after producing these output labels might be to use something like ConceptNet [0] to evaluate the relationship between the labels and somehow incorporate this as feedback. [0] http://conceptnet.io

Or even something like a tree of life diagram entered in. (I forget what those are called)

Re: Cloud Video Intelligence API

#87

Post by nojvek »

sharun wrote: post #73
nostrademons wrote: post #28

Earlier quoted context omitted.

Or we need eyes that can drive a physical response faster & more accurately than a human can react. This is the whole robotics/drone/hardware market. Right now, most of these efforts work at the same speed humans work (probably because we still need to monitor them to make sure they're doing the right thing). But imagine self-driving cars with no traffic lights, because the cars can react & communicate fast enough to…

For human level vision and speech (and even search) long term we might not need computational clouds and data centers. We don't see them in nature. The models/indexes will likely just come prepackaged on device.

This is only a matter of time. The imagenet model can already run on my iPhone on the gpu with BNNS. Not real-time but going there very soon.

Once you have real time local inferencre on your mobile phone. Boom!

Re: Cloud Video Intelligence API

#88

Post by ComodoHacker »

visarga wrote: post #77
wyc wrote: post #12

I think the most commercially successful application of computer vision has been quality-control devices (citation needed). Agriculture is very interested in CV for a return-optimization technique known as precision farming. Manufacturers pay for inspection of production throughout the pipeline. To predict where a mass-market CV could be successful, I think we should look for industries with similar problems but cann…

CV will be the technology that kills what jobs we still have left: in agriculture (picking fruit, weeding), in logistics (picking items from shelves), in custodial work (cleaning bots), security, driving, reading MRIs and x-rays - practically all the jobs that depended on vision and could only be done by people in the last 60 years are going to be automated. When CV is fully deployed, the world will be totally differ…

> When CV is fully deployed, the world will be totally different.

It's really scary to think about the long-term consequences of this. You know what evolution does to features not required for survival anymore.

Re: Cloud Video Intelligence API

#89

Post by njohnson41 »

thomasdullien wrote: post #82
njohnson41 wrote: post #68

Earlier quoted context omitted.

Still, in any consistent way of assigning probabilities to events, if A implies B, then P(A) Neural network outputs are not probabilities. I think that's the main lesson here.

Given that the last layer of a NN is a logistic regression, they are in fact well-calibrated probabilities under the assumption disjoint classes. The issue at hand is training them on overlapping classes :-) I will shut up now, sorry for nitpicking.

You're right (and nitpicking nitpicks seems appropriate to me :P).

But, I'm pretty sure the assumptions of logistic regression are even stronger than just that. The inputs are assumed to be independent given the output class, and the log odds of the output vary as a linear function of each input. The first one is essentially the naive Bayes assumption, and the second one is completely unreasonable for almost any problem ever (roughly equivalent to assuming every dataset has a multivariate normal distribution). If they are both correct, though, you get a perfectly good Bayesian posterior probability of each output class.

I think the lesson is that gradient descent will build a decent function approximation out of pretty much anything powerful enough, which is why neural networks still work even when probability theory has been thrown completely out the window.

Re: Cloud Video Intelligence API

#90

Post by cobookman »

tambourine_man wrote: post #85
cobookman wrote: post #69

Earlier quoted context omitted.

If you could share with me what mobile phone / web browser you used that produced the styling issue, I'll be sure to pass it on to the relavent people within google so that it gets resolved. Also if you send me an email at bookman@google.com I'll be sure to update you as to when the styling errors are resolved. (Disclaimer, I work for google cloud)

iPhone SE, iOS 10.2.1 Also, see my previous comment on a similar issue with Google Cloud Calculator: https://news.ycombinator.com/item?id=13729466

thanks. Submitted your issue. Along with your google cloud calculator issue. Feel free to email me if you'd like to be cc'd on the status.
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