31–40 of 76 posts
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I would be interested in seeing a comparison - how the different vision APIs react to the same 10-15 images.
It would be very interesting if the two looped together, each using the other as a reference to improve their own parameters ;).
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.
They won't be the only ones with this tech forever. In so much as a solution depends on "cleverness" rather than raw computing power, it'll have room for competition. They put a lot of effort figuring out what works and what doesn't but now that they have a product, the lesson is out and will be recreated by others. Google is leading, but they have also shown the direction to go and provided something to benchmark ag…
Unfortunately, it's looking more and more like it's going to be a competition on training data and raw computational power, and it's hard to compete with Google's corpora from the web, gmail, captchas, maps, etc. -- not to mention Google's tremendous number crunching resources.
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…
If they have an African-American engineer working on the product, they would have detected this during development.
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.
Forget new services, even a small change in their existing products by Google can destroy lot of companies. Everyone remembers how they ruined email marketing by caching gmail images.
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They likely didn't purposefully "do" anything wrong -- at least in the sense of some racist engineer tampering with the system to have it come out with those results. 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 c…
I have no doubt that neither Google nor HP made those error maliciously. I was just curious as to whether or not it's possible to incorporate some sort of... tact?... into these recognition algorithms to avoid labeling people (or other things) offensively. Is it just a matter of a larger training set? It would be hard to cover all sorts of people in all sorts of poses, with all sorts of lighting conditions, etc.
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…
I would be interested in seeing a comparison - how the different vision APIs react to the same 10-15 images.
http://imagga.com/blog/imagga-and-6-alternative-image-recogn...
and here: http://imagga.com/blog/some-more-image-recognition-tests-som...
Disclaimer: I'm CEO & CTO of Imagga.
Wondering how this can be used for specific content detection! Say I want to take pictures of a crop using a drone and analyse the pictures for pests and disesases etc ...
Is anyone else having the same issue/know where I can find this ID?