Recent developments in pre-trained neural language modeling have led to leaps in accuracy on commonsense question-answering benchmarks. However, there is increasing concern that models overfit to specific tasks, without learning to utilize external knowledge or perform general semantic reasoning. In contrast, zero-shot evaluations have shown promise as a more robust measure of a model's general reasoning abilities. In this paper, we propose a novel neuro-symbolic framework for zero-shot question answering across commonsense tasks. Guided by a set of hypotheses, the framework studies how to transform various pre-existing knowledge resources into a form that is most effective for pre-training models. We vary the set of language models, training regimes, knowledge sources, and data generation strategies, and measure their impact across tasks. Extending on prior work, we devise and compare four constrained distractor-sampling strategies. We provide empirical results across five commonsense question-answering tasks with data generated from five external knowledge resources. We show that, while an individual knowledge graph is better suited for specific tasks, a global knowledge graph brings consistent gains across different tasks. In addition, both preserving the structure of the task as well as generating fair and informative questions help language models learn more effectively.

Not quite the imitation of existing performers or interpretation of famous pieces -- but the discovery of hidden gems. Uncanny Valley is a passé. Indeed, the works are unique: every time a new never before existed music piece is generated -- and you can be sure (like in the case of GPT-3) that this sequence will never be repeated. My first experiment brought me goosebumps. Already the 2nd level was something special, not really in a way of music pieces.

For cutting-edge AI researchers looking for clean semantics models to represent the context-specific causal dependencies essential for causal induction, this DeepMind's algorithm encourages you to look at good old-fashioned probability trees. The probability tree diagram is used to represent a probability space. Tree diagrams illustrate a series of independent events or conditional probabilities. The Node on the probability tree diagram represents an event, and it's probability. The root node represents a particular event where probability equals one.

You've probably heard us say this countless times: GPT-3, the gargantuan AI that spews uncannily human-like language, is a marvel. You can tell with a simple trick: Ask it the color of sheep, and it will suggest "black" as often as "white"--reflecting the phrase "black sheep" in our vernacular. That's the problem with language models: because they're only trained on text, they lack common sense. Now researchers from the University of North Carolina, Chapel Hill, have designed a new technique to change that. They call it "vokenization," and it gives language models like GPT-3 the ability to "see."

While GPT-3 is the largest language model trained today, it is also exploring its application in almost every field. OpenAI now provides a developer API to interact with GPT-3 and build applications on top of it. Once set correctly, GPT-3 can perform mathematical calculations, generate answers in programming languages, etc. If you are looking for ideas for a new startup idea, then an application called'IdeasAI' can inspire you to make something cool. 'IdeasAI' is developed by Pieter Levels and powered by GPT-3.

You may have heard about GPT-3 this summer, the new cool kid on the AI block. GPT-3 came out of OpenAI, one of the top AI research labs in the world which was founded in late 2015 by Elon Musk, Sam Altman and others and later backed with a $1B investment from Microsoft. You've probably also heard about the ongoing AI revolution in healthcare, thanks to promising results in areas such as automated diagnosis, medical documentation and drug discovery, to name a few. Some have claimed that algorithms now outperform doctors on certain tasks and others have even announced that robots will soon receive medical degrees of their own! This can all sound far-fetched... but could this robot actually be GPT-3?

Microsoft and OpenAI's close relationship has taken another leap forward with the former gaining exclusive GPT-3 access. GPT-3 has been the talk of the AI town in recent months. OpenAI's innovation can help to create convincing articles and the company once deemed it too dangerous to release in a world where misinformation and fake news is already problematic. OpenAI never made GPT-3 publicly available but instead provided access to a limited number of trusted researchers. Microsoft announced today that it now has the exclusive rights to leverage GPT-3's "technical innovations to develop and deliver advanced AI solutions for our customers, as well as create new solutions that harness the amazing power of advanced natural language generation."

Are you a cutting-edge AI researcher looking for models with clean semantics that can represent the context-specific causal dependencies necessary for causal induction? If so, maybe you should take a look at good old-fashioned probability trees. Probability trees may have been around for decades, but they have received little attention from the AI and ML community. "Probability trees are one of the simplest models of causal generative processes," explains the new DeepMind paper Algorithms for Causal Reasoning in Probability Trees, which the authors say is the first to propose concrete algorithms for causal reasoning in discrete probability trees. Humans naturally learn to reason in large part through inducing causal relationships from our observations, and we do this remarkably well, cognitive scientists say. Even when the data we perceive is sparse and limited, humans can quickly learn causal structures such as interactions between physical objects, observations of the co-occurrence frequencies between causes and effects, etc. Causal induction is also a classic problem in statistics and machine learning.

This A.I was able to change his understanding of life after reading communist books. The Communist A.I was trained using GPT-2. It read books by Marx, Fanon, Gramsci, Lenin and other revolutionary authors. The project's aim is to see how deep GPT-2 can understand deep philosophical ideas and concepts. The results were quite entertaining and promising as we witnessed the A.I logically twisting whatever sentence we gave it into an excuse to bash capitalism and fight for the "workman".

Researchers in recent years have deployed reinforcement learning (RL) agents to solve increasingly challenging problems. As the trend continues, so has the development of new methods that enable the injection of "priors" (prior knowledge) into agents to help them better understand the structure of the world and come up with more effective solution strategies. In a new paper, researchers from Google, OpenAI, and DeepMind introduce "behaviour priors," a framework designed to capture common movement and interaction patterns that are shared across a set of related tasks or contexts. The researchers discuss how such behaviour patterns can be captured using probabilistic trajectory models and how they can be integrated effectively into RL schemes, such as for facilitating multi-task and transfer learning. Their method for learning behaviour priors can lead to significant speedups on complex tasks, the researchers say.