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72 bookmarks by garrettc
"DALL·E 2 is a system for text-to-image generation developed by my coauthors and me at OpenAI. When prompted with a caption, the system will attempt to generate a novel image from scratch that matches it."
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"…can machine learning be used to identify a type of cheese?"
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Tag it in a Tweet containing an image, and it will reply with a version of the same image turned into a little animation which sings the Maiahi song.
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Two artificial neural networks generated posters while thinking of famous movies. Can you guess which movie it is?
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"In the last two years, more than 200 papers have been written on how Machine Learning (ML) can fail because of adversarial attacks on the algorithms and data; this number balloons if we were to incorporate non-adversarial failure modes. The spate of papers has made it difficult for ML practitioners, let alone engineers, lawyers and policymakers, to keep up with the attacks against and defenses of ML systems. However, as these systems become more pervasive, the need to understand how they fail, whether by the hand of an adversary or due to the inherent design of a system, will only become more pressing."
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The Swiss Army knife of machine learning
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Use AI to explore thousands of hours of humpback whale songs and make your own discoveries
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"Conventional approaches of extracting keywords involve manual assignment of keywords based on the article content and the authors’ judgment. This involves a lot of time & effort and also may not be accurate in terms of selecting the appropriate keywords. With the emergence of Natural Language Processing (NLP), keyword extraction has evolved into being effective as well as efficient. And in this article, we will combine the two — we’ll be applying NLP on a collection of articles (more on this below) to extract keywords."
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"Systems that use artificial intelligence are quietly becoming present in more and more parts of our lives. But what does this technology really mean for people, both right now and in the future?"
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A practical approach to learning machine learning with Python and notebooks.
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"…an implementation of an arbitrary image stylization algorithm running purely in the browser using TensorFlow.js. As with all neural style transfer algorithms, a neural network attempts to "draw" one picture, the Content (usually a photograph), in the style of another, the Style (usually a painting)."
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Analyze text using ML to extract relevant entities, understand the overall sentiment, identify parts of speech and create dependency parse trees.
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How does artificial intelligence actually work, and what are its limitations?
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"Machine learning algorithms are not like other computer programs. In the usual sort of programming, a human programmer tells the computer exactly what to do. In machine learning, the human programmer merely gives the algorithm the problem to be solved, and through trial-and-error the algorithm has to figure out how to solve it. […] But it doesn’t always work well. Sometimes the programmer will think the algorithm is doing really well, only to look closer and discover it’s solved an entirely different problem from the one the programmer intended."
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"Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies)."
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An educational tool for teaching kids about machine learning, by letting them train a computer to recognise text, pictures or numbers, and make things with it in Scratch.
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"We've co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats."
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"The deepfakes app is a deep learning algorithm that learns how to reconstruct faces. Give it a bunch of pictures, let it run a few hours, and it spits out fuzzy copies of those images. Do note, it doesn’t create a copy. It learns what a face looks like, in different expressions, and is able to output that face solely based on that."
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Software that inspects quality and tracks progress so you can react in minutes, not months.
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"Stanford Question Answering Dataset (SQuAD) is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets."
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"In this post, we’ll teach a neural network how to code a basic a HTML and CSS website based on a picture of a design mockup. Here's a quick overview of the process."
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"Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research."
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Collaboratively build, visualize, and design neural nets in browser.
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"For this guide, I spent a dozen hours trying to identify every online machine learning course offered as of May 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My end goal was to identify the three best courses available and present them to you, below."
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Free online image upscale and JPEG artifact removal using netural networks. Image superresolution and image enhancement online.
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Get prerequisites for and download of Intel's machine learning courses.
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We've developed an approach to generate 3D adversarial objects that reliably fool neural networks in the real world, no matter how the objects are looked at.
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Artificial intelligence? It’s complicated. It’s the here and now of hyper-efficient algorithms, but it’s also the heady possibility of sentient systems.
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A showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more.
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