Since OpenAI first described its new AI language-generating system called GPT-3 in May, hundreds of media outlets (including MIT Technology Review) have written about the system and its capabilities. Twitter has been abuzz about its power and potential. The New York Times published an op-ed about it. Later this year, OpenAI will begin charging companies for access to GPT-3, hoping that its system can soon power a wide variety of AI products and services. Is GPT-3 an important step toward artificial general intelligence--the kind that would allow a machine to reason broadly in a manner similar to humans without having to train for every specific task it encounters?

The field of graph representation learning has grown at an incredible (and sometimes unwieldy) pace over the past seven years, transforming from a small subset of researchers working on a relatively niche topic to one of the fastest growing sub-areas of deep learning. This book is my attempt to provide a brief but comprehensive introduction to graph representation learning, including methods for embedding graph data, graph neural networks, and deep generative models of graphs. This book is a pre-publication draft of a book that will be published by Morgan & Claypool publishers in late 2020, and the publishers have generously agreed to allow the public hosting of the pre-publication draft. Feedback, typo corrections, and comments are welcome and should be sent to wlh@cs.mcgill.ca

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.

A bot that writes letters on behalf of nature. Those are just some of the recent stories written about GPT-3, the latest contraption of artificial intelligence research lab OpenAI. GPT-3 is the largest language model ever made, and it has triggered many discussions over how AI will soon transform many industries. But what has been less discussed is how GPT-3 has transformed OpenAI itself. In the process of creating the most successful natural language processing system ever created, OpenAI has gradually morphed from a nonprofit AI lab to a company that sells AI services. And hanging in the balance is the very mission for which OpenAI was founded.

The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge. Log-likelihood is an appealing metric due to its grounding in statistics and information theory, but it can be challenging to estimate for implicit generative models, and scalar-valued metrics give an incomplete picture of a model's quality. In this work, we propose to use rate distortion (RD) curves to evaluate and compare deep generative models. While estimating RD curves is seemingly even more computationally demanding than log-likelihood estimation, we show that we can approximate the entire RD curve using nearly the same computations as were previously used to achieve a single log-likelihood estimate. We evaluate lossy compression rates of VAEs, GANs, and adversarial autoencoders (AAEs) on the MNIST and CIFAR10 datasets. Measuring the entire RD curve gives a more complete picture than scalar-valued metrics, and we arrive at a number of insights not obtainable from log-likelihoods alone.

GPT-3 is OpenAI's latest and largest language AI model, which the San Francisco–based research lab began drip-feeding out in mid-July. In February of last year, OpenAI made headlines with GPT-2, an earlier version of the algorithm, which it announced it would withhold for fear it would be abused. The decision immediately sparked a backlash, as researchers accused the lab of pulling a stunt. By November, the lab had reversed position and released the model, saying it had detected "no strong evidence of misuse so far." The lab took a different approach with GPT-3; it neither withheld it nor granted public access.

Last month, OpenAI, the Elon Musk-founded artificial intelligence research lab, announced the arrival of the newest version of an AI system it had been working on that can mimic human language, a model called GPT-3. In the weeks that followed, people got the chance to play with the program. If you follow news about AI, you may have seen some headlines calling it a huge step forward, even a scary one. I've now spent the past few days looking at GPT-3 in greater depth and playing around with it. I'm here to tell you: The hype is real. It has its shortcomings, but make no mistake: GPT-3 represents a tremendous leap for AI. A year ago I sat down to play with GPT-3's precursor dubbed (you guessed it) GPT-2.

You have probably heard about an innovative language model called GPT3. The hype is so overwhelming that we decided to research its core and the consequences for the tech players. Let's explore whether the language deserves this much attention and what makes it so exceptional. GPT-3 is a text generating neural network that was released in June 2020 and tested for $14 million. Its creator is the AI research agency OpenAI headed by Sam Altman, Marc Benioff, Elon Musk, and Reid Hoffman. The language is based on 175 million parameters and is by far more accurate than its predecessors.

It seems like only last year that we were arguing about whether the slow-release rollout of the 1.5 billion parameter Generative Pretrained Transformer-2 (GPT-2) was reasonable. If the debate seems recent, that's because it is (writing from 2020): The notorious GPT-2 model was announced by OpenAI in February 2019, but it wasn't fully released until nearly 9 months later (although it was replicated before that). The release schedule was admittedly somewhat experimental, meant more to foster discussion of responsible open publishing, rather than a last-ditch effort to avert an AI apocalypse. All that is a bit moot by now because not only has OpenAI trained a much larger language model in GPT-3, but you can sign up to access it through their new API. Comparing GPT-3 to GPT-2 is like comparing apples to, well, raisins, because the model is about that much larger.

Bosses don't often play down their products. Sam Altman, the CEO of artificial intelligence company OpenAI, did just that when people went gaga over his company's latest software: the Generative Pretrained Transformer 3 (GPT-3). For some, GPT-3 represented a moment in which one scientific era ends and another is born. Mr Altman rightly lowered expectations. "The GPT-3 hype is way too much," he tweeted last month.