61–70 of 196 posts
15.8% accuracy != singularity is near. No more supplements-eating Kurzweil, walking Terminators and Skynet-like BS please.
I wonder what accuracy would human get if you trained him/her only with 10 million static 200x200 px images in complete silence.
I'm seriously considering quitting my job and studying ML for a few months in a desperate attempt to get work in projects like this. I feel like I'm missing out but too dumb for traditional grad school.
Could anyone with expertise say if this would be enough to build a foundation? How much math background do you need?
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I wonder what accuracy would human get if you trained him/her only with 10 million static 200x200 px images in complete silence.
I don't think this was "complete silence" in this case. They actually trained the computer (meaning they provided data as input).
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Wait until they become conscious and demand human rights. Then we can no longer exploit them and we're back to square one.
Seriously, a Phd thesis not far from now may have the title: "The limits of AI: how far can we exploit the machines before we are limited by machine rights"
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I wonder what accuracy would human get if you trained him/her only with 10 million static 200x200 px images in complete silence.
Babies can outperform any CV model with less data than that.
Earlier quoted context omitted.
I wonder what accuracy would human get if you trained him/her only with 10 million static 200x200 px images in complete silence.
I don't think this was "complete silence" in this case. They actually trained the computer (meaning they provided data as input).
Earlier quoted context omitted.
I don't think this was "complete silence" in this case. They actually trained the computer (meaning they provided data as input).
scotty79 means to showing a human only the 10 million 200x200 images. We probably see a lot more than that, in 3D from different perspectives with continuity in motion, and in a lot more detail and possibilities for filtering.
Then, what kind of thing are you measuring? Recognizing patterns ? We know humans are very good at that. We can see thousands of different people everyday but we can recognize in the blink of an eye a familiar face. A computer or computer network is very, very, very far from being able to do that yet.
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It automatically creates a set features that you can then use a final layer of machine learning to get what you want. In machine learning, normally you have to create a set of features (call feature engineering - basically think algorithms to better represent your data). The amazing thing about deep learning is that the computer does this for you! You just need a few 10s/100s face/nonface images - same for 20,000 oth…
This paper is actually more interesting: it automatically learns some "neuron" which its firing represents a detected face, without any supervise technique. It shows the possibility to extract complex information solely from data.
Wouldn't that make training it much quicker and make it much more accurate?
Or are we trying to avoid any human interaction at all with the earning loop?
See, for example this company (one of many) that trains bees to smell certain odours.