Thank goodness for this thread. I came to /r/machinelearning with a question in my back pocket, but I feared it's a little too abstract to warrant its own post. My boss has two waveforms. He feels that there is a neural interpretation of the black box function that transforms waveform A into B. That converting from A to B should be possible for a machine learning algorithm to do, provided sufficient amounts of simulation data.

There is hardly any field left that is not being touched by Artificial intelligence. AI has revolutionized the way of almost all industries.

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I am trying to figure out how to deal with overfitting in my data. Basically no matter what hyper-parameters I tweak, I see a big discrepancy between my validation-set R2 and my in-fit R2 (r2 0.8 for in fit, 0.25 for validation). Typically this is a seen as a "red-flag" for overfitting, but there is no way for me to close the gap between in- and out-of-fit R2 without lowering my overall validation R2. Below is the plot of validation R2 vs in-fit R2 for 15,000 different hyper-parameter combinations. The only way I can have maximum out-of-fit accuracy is by having a big discrepancy between in and out of fit R2.

What I mean is training one layer at a time by freezing all but one and repeating this until the weights converge and give the best loss. I've seen narrow work on this in 2019 but it's surprising it hasn't been done before or at least explored. If there have been studies on this, what's the basic conclusion?

Despite the buzz around GPT-3, it is, in and of itself, not AGI. However, language modelling has one crucial difference from Chess or Go or image classification. By harnessing the world model embedded in the language model, it may be possible to build a proto- AGI.

High Score, Netflix's new documentary series about the early history of video games, is filled with fascinating characters, flickering arcade cabinets, gorgeous pixel animations, flashy graphics, loosely woven yarns, and an aw-shucks sense that video games are just the best. Like another recent hit docuseries, ESPN's The Last Dance, it delivers hit after hit of 1980s and '90s nostalgia, a powerful and, lately, especially welcome drug. But also like that documentary, it's unlikely to transform the viewer's perception of its central subject. It's a show obsessed with loving games, not understanding them. Each of High Score's six episodes (which all premiere on Netflix on Wednesday) is a rough account of a specific period or genre in early gaming culture, told through a series of interlocking character profiles.

It's nice to see a concise implementation of GPT, in pytorch, as it is true Hugging Face's Transformer's is excellent, but it is quite difficult to trace. They are trying to build it out constantly with loads of features, so you get lost. His wiki states he works for OpenAI and Tesla is at least affiliated with Openai. Also it's very far from computer vision domain, so why spend the time on an open source implementation and make some guesses on GPT-2/GPT-3. His implementation is easy to follow, which is nice, most reimplementations I see have bugs or are unecessary complex.

Text classification is one of the important applications of NLP. Applications such as Sentiment Analysis and Identifying spam, bots, and offensive comments come under Text Classification. Until now, the approaches used for solving these problems included building Machine Learning or Deep Learning models from scratch, training them on your text data, and fine-tuning it with hyperparameters. Even though such models give decent results for applications like classifying whether a movie review is positive or negative, they may perform terribly if things become more ambiguous because most of the time there's just not enough amount of labeled data to learn from. Isn't the Imagenet using the same approach to classify the images?

With the emergence of incredibly powerful machine learning technologies, such as Deepfakes and Generative Neural Networks, it is much easier now to spread false information. In this article, we will briefly introduce deepfakes and generative neural networks, as well as a few ways to spot AI-generated content and protect yourself against misinformation. I have many elderly relatives and some middle-aged relatives that just aren't well-versed with technology. Some of these people believe nearly everything they read, or at least believe it enough to share it on social media. While that doesn't sound so bad, it depends on what you are sharing.