Image Processing vs Computer Vision 7. Image Processing Example 1) Rescaling: zoom in, zoom out, cropping 2) Correcting illumination: brightness and contrast 3) Color manipulations: black and white, gray-scale, HSV, RGB, BGR 4) Filter: mean, median, low pass, high pass, gaussian, laplacian 5) Edge detection Original Sobel Laplacian Canny 8. Image Processing Example 6) Morphology: - Erode (local minimum) - Dilate (local maximum) - Opening (erosion then dilation) - Closing (dilation then erosion) - Morphology Gradient (MG) (difference between dilation and erosion) - Top Hat (TH) (difference between image and opening) - Black Hat (difference between image and closing) 9. Computer Vision Example Scene modeling Object detection Object recognition Object tracking Pose estimation Motion estimation Image restoration 10. Artificial Intelligence (AI) AI is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals.

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A non-profit AI research company, OpenAI, basically, is now, to its list is releasing a new batch of seven unsolved problems which have come up in the course of their research at OpenAI. Very similar to their original Requests for Research which resulted in the upbringing of several papers, the company expects these problems for new people to enter the field to be a fun and a meaningful way to do the same, as well as to hone the skills for practitioners. Not to forget that is also is a great way to get a job at OpenAI that aims at enacting and discovering the path to safe general artificial intelligence. Also, If one is not sure where to begin, they also have some solved starter problems. Environment: Start with two snakes, and scale from there and then with multiple snakes have a reasonably large field; snakes grow when eating randomly-appearing fruit; a snake dies when colliding with another snake, itself, or the wall; and the game ends when all snakes die.

If you're not sure where to begin, here are some solved starter problems. Train an LSTM to solve the XOR problem: that is, given a sequence of bits, determine its parity. The LSTM should consume the sequence, one bit at a time, and then output the correct answer at the sequence's end.