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In our previous tutorial, you learned how to improve the accuracy of Tesseract OCR by supplying the appropriate page segmentation mode (PSM). The PSM allows you to select a segmentation method dependent on your particular image and the environment in which it was captured. However, there are times when changing the PSM is not sufficient, and you instead need to use a bit of computer vision and image processing to clean up the image before you pass it through the Tesseract OCR engine. To learn how to improve OCR results using basic image processing, just keep reading. Exactly which image processing algorithms or techniques you utilize is heavily dependent on your exact situation, project requirements, and input images; however, with that said, it's still important to gain experience applying image processing to clean up images before OCR'ing them.

As artificial intelligence (AI) technologies become increasingly powerful and prominent in society, their misuse is a growing concern. In educational settings, AI technologies could be used by students to cheat on assignments and exams. In this paper we explore whether transformers can be used to solve introductory level programming assignments while bypassing commonly used AI tools to detect plagiarism. We find that a student using GPT-J [Wang and Komatsuzaki, 2021] can complete introductory level programming assignments without triggering suspicion from MOSS [Aiken, 2000], a widely used plagiarism detection tool. This holds despite the fact that GPT-J was not trained on the problems in question and is not provided with any examples to work from. We further find that the code written by GPT-J is diverse in structure, lacking any particular tells that future plagiarism detection techniques may use to try to identify algorithmically generated code. We conclude with a discussion of the ethical and educational implications of large language models and directions for future research.

I give a short introduction to data ethics. My focal audience is mathematicians, but I hope that my discussion will also be useful to others. I am not an expert about data ethics, and my article is only a starting point. I encourage readers to examine the resources that I discuss and to continue to reflect carefully on data ethics and on the societal implications of data and data analysis throughout their lives.

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I can try explaining basic AI concepts, but your brain will probably just get more and more confused: That's a very normal thing in the AI world, which is why… If there is one thing you can take away from this article, it's that AI is hands-on, and trying to learn it by theory will not get you anywhere. This means that instead of reading giant books and watching hours long videos explaining fundamental concepts, get into it right away and start learning by building things! WAIT WHAT⁉ I said anyone can learn AI, and now I am saying there are prerequisites to start? Because AI is all about math and programming (obviously)! NOTE: You will be able to build out projects without understanding the math behind it if you are using libraries such as TensorFlow or PyTorch, but it is always an asset to have the math concepts down as they would help you get a deeper understanding of what you're doing .

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