Researchers from machine learning lab OpenAI have discovered that their state-of-the-art computer vision system can be deceived by tools no more sophisticated than a pen and a pad. As illustrated in the image above, simply writing down the name of an object and sticking it on another can be enough to trick the software into misidentifying what it sees. "We refer to these attacks as typographic attacks," write OpenAI's researchers in a blog post. "By exploiting the model's ability to read text robustly, we find that even photographs of hand-written text can often fool the model." They note that such attacks are similar to "adversarial images" that can fool commercial machine vision systems, but far simpler to produce.

Dall-E is a model based on GPT-3 that generates images using a short text caption. The name "DALL-E" is a portmanteau of the artist Salvador Dal-- and Pixar--s WALL--E . For example, if you asked for an armchair like an avocado, what might it look like?

Elon Musk-founded startup OpenAI has discovered multimodal neurons in an Artificial Intelligence system that works just like some neurons work in the human brain. The revelation comes 15 years after the discovery that the human brain possesses multimodal neurons that respond to clusters of abstract concepts centered around a common high-level theme, rather than any specific visual feature. The most famous of these was the "Halle Berry" neuron that responds to photographs, sketches, and the text "Halle Berry" -- but not other names. Two months ago, OpenAI announced a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized. It is trained to recognize and people and objects within abstract contexts -- sketches, cartoons, and even statues of the objects.

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.