A new language model, OpenAI's GPT-3, is making waves for its ability to mimic writing, but it falls short on common sense. Some experts think an emerging technique called neuro-symbolic AI is the answer.

We're introducing OpenAI Microscope, a collection of visualizations of every significant layer and neuron of eight vision "model organisms" which are often studied in interpretability. Microscope makes it easier to analyze the features that form inside these neural networks, and we hope it will help the research community as we move towards understanding these complicated systems. The abilities of modern neural networks are the result of the interactions of thousands of neurons (sometimes tens of thousands or more!). In order to understand their behavior, we'd like to be able to quickly and easily investigate these neurons interactions in detail, and share those observations. This is especially true in collaborative environments.

First, you must know that the sun is actually a cat. Also, you must know that the sun is actually not a cat. Over the past couple of weeks, the ML community had their handsful discussing and displaying the wide range of utilities of GPT-3. Many developers, both professionals and amateurs, have expressed their surprise saying how most of the demos generated using GPT-3 in a few minutes would usually require significant engineering effort and machine learning expertise. In the next section, we list 15 exciting ways in which GPT-3 has been leveraged.

GPT-3 is a language model that is automated by a neural system, launched by OpenAI in July 2020. It's a text generator that can compose articles, poetry, sentiment essays, and working code--which is the reason it has the entire world humming, some with excitement, some with skepticism. The previous GPT model had 1.5 billion parameters and was the biggest model in those days, which was before long overshadowed by NVIDIA's Megatron, with 8 billion parameters followed by Microsoft's Turing NLG that had 17 billion parameters. Presently, OpenAI changes the situation by deploying a model that is 10 times bigger than Turing NLG. Current NLP frameworks still to a great extent struggle to learn from a couple of models.

In June, OpenAI unveiled the largest language model in the world, a text-generating tool called GPT-3 that can write creative fiction, translate legalese into plain English, and answer obscure trivia questions. It's the latest feat of intelligence achieved by deep learning, a machine learning method patterned after the way neurons in the brain process and store information. But it came at a hefty price: at least $4.6 million and 355 years in computing time, assuming the model was trained on a standard neural network chip, or […]

In a paper published on the preprint server Arxiv.org, This alone isn't surprising, but the problem is that data from these communities are often used to train large language models like OpenAI's GPT-3. That in turn is important because, as OpenAI itself notes, this sort of bias leads to placing words like "naughty" or "sucked" near female pronouns and "Islam" near words like "terrorism." The scientists' approach uses representations of words called embeddings to discover and categorize language biases, which could enable data scientists to trace the severity of bias in different communities and take steps to counteract this bias. To spotlight examples of potentially offensive content on Reddit subcommunities, given a language model and two sets of words representing concepts to compare and discover biases from, the method identifies the most biased words toward the concepts in a given community.

OpenAI's latest language generation model, GPT-3, has made quite the splash within AI circles, astounding reporters to the point where even Sam Altman, OpenAI's leader, mentioned on Twitter that it may be overhyped. Still, there is no doubt that GPT-3 is powerful. Those with early-stage access to OpenAI's GPT-3 API have shown how to translate natural language into code for websites, solve complex medical question-and-answer problems, create basic tabular financial reports, and even write code to train machine learning models -- all with just a few well-crafted examples as input (i.e., via "few-shot learning"). Soon, anyone will be able to purchase GPT-3's generative power to make use of the language model, opening doors to build tools that will quietly (but significantly) shape our world. Enterprises aiming to take advantage of GPT-3, and the increasingly powerful iterations that will surely follow, must take great care to ensure that they install extensive guardrails when using the model, because of the many ways that it can expose a company to legal and reputational risk.

Deep Q-Network (DQN) based multi-agent systems (MAS) for reinforcement learning (RL) use various schemes where in the agents have to learn and communicate. The learning is however specific to each agent and communication may be satisfactorily designed for the agents. As more complex Deep Q-Networks come to the fore, the overall complexity of the multi-agent system increases leading to issues like difficulty in training, need for higher resources and more training time, difficulty in fine-tuning, etc. To address these issues we propose a simple but efficient DQN based MAS for RL which uses shared state and rewards, but agent-specific actions, for updation of the experience replay pool of the DQNs, where each agent is a DQN. The benefits of the approach are overall simplicity, faster convergence and better performance as compared to conventional DQN based approaches. It should be noted that the method can be extended to any DQN. As such we use simple DQN and DDQN (Double Q-learning) respectively on three separate tasks i.e. Cartpole-v1 (OpenAI Gym environment), LunarLander-v2 (OpenAI Gym environment) and Maze Traversal (customized environment). The proposed approach outperforms the baseline on these tasks by decent margins respectively.

OpenAI's GPT-3 is the talk of the town, and the media is giving it all the attention. Many analysts are even comparing it to AGI because of its practical applicability. Initially disclosed in a research paper in May, GPT-3 is the next version of GPT-2 and is 100x larger than it. It is far more competent than its forerunner due to the number of parameters it is trained on, which is 175 billion for GPT-3 versus 1.5 billion for GPT-2. After the successful launch of GPT-3, other AI companies seem to have been overshadowed.

When OpenAI first published a paper on their new language generation AI, GPT-3, the hype was slow to build. The paper indicated GPT-3, the biggest natural language AI model yet, was advanced, but it only had a few written examples of its output. Then OpenAI gave select access to a beta version of GPT-3 to see what developers would do with it, and minds were blown. Developers playing with GPT-3 have taken to Twitter with examples of its capabilities: short stories, press releases, articles about itself, a search engine. Perhaps most surprising was the discovery GPT-3 can write simple computer code. When web developer, Sharif Shameem, modified it to spit out HTML instead of natural language, the program generated code for webpage layouts from prompts like "a button that looks like a watermelon."