11–20 of 76 posts
I wonder how long it will take for someone to integrate this with Google recaptcha...
Isn't recaptcha just a checkbox now?
It's exciting to see more of these services come to market. IBM Watson has a suite of vision APIs available that have some similar features. For example, the demo at http://vision.alchemy.ai/#demo has example images that demonstrate facial detection and identification, label extraction, object identification, and so on. Another demo at http://visual-insights-demo.mybluemix.net/ uses the Visual Insights [1] API to ide…
It's exciting to see more of these services come to market. IBM Watson has a suite of vision APIs available that have some similar features. For example, the demo at http://vision.alchemy.ai/#demo has example images that demonstrate facial detection and identification, label extraction, object identification, and so on. Another demo at http://visual-insights-demo.mybluemix.net/ uses the Visual Insights [1] API to ide…
Some feedback though:
- As an independent developer, pricing is important to me. It was very difficult to find pricing for the Watson APIs (apparently it was in Bluemix?) and if I wasn't a little more determined (thanks to the ability to train my own classifier), I wouldn't have persevered.
- If I already have a wealth of labelled data (I do), it seems difficult to train a new classifier for the Visual Recognition service. If I have 200 000 images each with an average of 20 labels (from a set of ~2000 labels) for example, the positive + negative sample per label is very time and bandwidth consuming, as I have to train 2000 classifiers using ~ 5000 images per classifier (for plenty of training data), for a total of ~10 000 000 uploads. It'd be far nicer to be able to upload a folder of JPEGs with a JSON blob per file containing labels (or a classifier name) and have Watson derive positive and negative samples from it.
I wonder what this means for computer vision startups like http://clarifai.com ? Its hard to compete with Google on a task like image classification when Google has immense computational resources, tons of data, and hoards of top researchers.
I wonder what this means for computer vision startups like http://clarifai.com ? Its hard to compete with Google on a task like image classification when Google has immense computational resources, tons of data, and hoards of top researchers.
I wonder what this means for computer vision startups like http://clarifai.com ? Its hard to compete with Google on a task like image classification when Google has immense computational resources, tons of data, and hoards of top researchers.
Earlier quoted context omitted.
They may already had the foresight to train what their Recaptcha looks like.
sort of how the old captcha helped digitize books, people entering recaptchas is free tagging to train vision's net.
It's exciting to see more of these services come to market. IBM Watson has a suite of vision APIs available that have some similar features. For example, the demo at http://vision.alchemy.ai/#demo has example images that demonstrate facial detection and identification, label extraction, object identification, and so on. Another demo at http://visual-insights-demo.mybluemix.net/ uses the Visual Insights [1] API to ide…
Wow, that looks fantastic! I was looking like something like this for ages. Some feedback though: - As an independent developer, pricing is important to me. It was very difficult to find pricing for the Watson APIs (apparently it was in Bluemix?) and if I wasn't a little more determined (thanks to the ability to train my own classifier), I wouldn't have persevered. - If I already have a wealth of labelled data (I do)…
Great! The recognition seems fairly accurate, based on the examples they provided (haven't used Google Photos myself much, though). I'm still wary, though; I really hope we won't see a repeat of the labeling black people as gorillas fiasco, which happened as recently as earlier this year: http://mashable.com/2015/07/01/google-photos-black-people-go... . The article mentions that Google was looking into how these mist…
But this is the nature of machine learning algorithms, including both the process of supervision and ability to view the impact feedbacks have on the algorithms, and also, the impact the quality of the training set given to the algorithms. At a lesser company, the problem could be as simple as very few black people represented in the training set, so that when the algorithm sees a dark-colored human-like shape, it is more "likely" that that shape is a gorilla (which is human like and pretty much always has dark fur) than it is a human, because the algorithm was trained mostly on light-colored humans. The Google Photos algorithm obviously takes in more kinds of input and factors besides visual composition so there was probably more to it than this.
Or maybe not...who knows? I'm not interested in reviving a discussion about importance of diversity in the engineering workforce, but this is one kind of problem that can slip by the most competent and well-intentioned of engineers simply because they're less aware of how disenfranchisement can propagate into technical problems, no matter how correct and powerful the math behind the algorithm.
Another example from a few years back was when HP released a auto-tracking webcam that became infamous after a black retail employee uploaded a YouTube video of how the camera ignored him but not his white co-worker:
http://www.cnn.com/2009/TECH/12/22/hp.webcams/
I'm in 100% agreement that this was likely not HP's intentional fault, and also that face detection of darker complexions is computationally more complex than it is for lighter complexions because of how contrast is used by the algorithm...but I most definitely know that if I were an HP engineer, and if the CEO and/or my direct boss were black and tried out a prototype that behaved as it does in the aforementioned YouTube video, there is almost no fucking way that the product would be released as-is, with my excuse being "Well, accurately detecting black faces requires a much more complicated training set -- that's just how math works!"