Generative Pre-trained Transformer 3 is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation of language prediction model in the GPT-n series created by OpenAI. GPT-3 is an extension and scaled-up version of GPT-2 model architecture -- It includes the modified initialization, pre-normalization, and reversible tokenization and shows strong performance on many NLP tasks in the zero-shot, one-shot, and few-shot settings. In the above graph, it is clearly visible how GPT-3 dominates all the small models and gets substantial gains on almost all the NLP tasks. It is based on the approach of pretraining on a large dataset followed by fine-tuning or priming for a specific task.

French start-up Iktos has announced a collaboration with Pfizer on the use of its artificial intelligence technology for drug design. This partnership comes in response to the considerable progress in the development of AI algorithms and computing power that has enabled the development of innovative approaches to small molecule drug design. Founded in 2016, Iktos develops generative AI technology in numerous collaborations with pharmaceutical and biotech companies. A fundamental aspect of the technology lies in the exploration of chemical space performed by generating compounds in silico under the constraints of the program's final objectives, rather than by screening compound libraries. As part of the collaboration, Pfizer has deployed Iktos' generative AI technology and is applying it to several small molecule research programs.

In the following sections, I will introduce various articles and papers not only on the above contents but also on the following five topics. Zero-Shot Text-to-Image Generation They proposed DALL-E, which generates images from text with zero-shot. First, as in VQVAE, they compress the image to 32x32 using an encoder, re-select a representation from the codebook that is close to each grid representation, and learn discrete VAE to generate images from it. Next, using the paired data of image and text, they train an autoregressive model to generate "image tokens" using the text as input and the 8192 expressions in the codebook as vocabulary.

Here is my python source code for training an agent to play contra nes. By using Proximal Policy Optimization (PPO) algorithm introduced in the paper Proximal Policy Optimization Algorithms paper. For your information, PPO is the algorithm proposed by OpenAI and used for training OpenAI Five, which is the first AI to beat the world champions in an esports game. Specifically, The OpenAI Five dispatched a team of casters and ex-pros with MMR rankings in the 99.95th percentile of Dota 2 players in August 2018. It has been a while since I have released my A3C implementation (A3C code) and PPO implementation (PPO code) for training an agent to play super mario bros.

Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through the features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions.

Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTS's individually, and do not leverage the dynamic distributions underlying the MTS's, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting time series. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.

We're used to medical chatbots giving dangerous advice, but one based on OpenAI's GPT-3 took it much further. If you've been living under a rock, GPT-3 is essentially a very clever text generator that's been making various headlines in recent months. Only Microsoft has permission to use it for commercial purposes after securing exclusive rights last month. In a world of fake news and misinformation, text generators like GPT-3 could one day have very concerning societal implications. Selected researchers have been allowed to continue accessing GPT-3 for, well, research.

The most impressive thing about OpenAI's natural language processing (NLP) model, GPT-3, is its sheer size. With more than 175 billion weighted connections between words known as parameters, the transformer encoder-decoder model blows its 1.5 billion parameter predecessor, GPT-2, out of the water. This has allowed the model to generate text that is surprisingly human-like after only being fed a few examples of the task you want it to do. Its release in 2020 dominated headlines, and people were scrambling to get on the waitlist to access its API hosted on OpenAI's cloud service. Now, months later, as more users have gained access to the API (myself included), interesting applications and use cases have been popping up every day.

The machine learning company OpenAI is developing models that improve computer vision and can produce original images from a text prompt. Why it matters: The new models are the latest steps in ongoing efforts to create machine learning systems that exhibit elements of general intelligence, while performing tasks that are actually useful in the real world -- without breaking the bank on computing power. What's happening: OpenAI today is announcing two new systems that attempt to do for images what its landmark GPT-3 model did last year for text generation. What they're saying: "Last year, we were able to make substantial progress on text with GPT-3, but the thing is that the world isn't just built on text," says Sutskever. "This is a step towards the grander goal of building a neural network that can work in both images and text."

DALL-E from Open AI can generate images from text captions. It is very similar to GPT-3 and Image GPT and produces amazing results.