Create a Text Generation Web App with 100% Python (NLP) - Harness GPT-Neo -- a natural language processing (NLP) text generation model. Demonstrate it with a 100% Python web app Created by Vennify Inc., Eric FillionPreview this Course - GET COUPON CODE GPT-3 is a state-of-the-art text generation natural language processing (NLP) model created by OpenAI. You can use it to generate text that resembles text generated by a human. This course will cover how to create a web app that uses an open-source version of GPT-3 called GPT-Neo with 100% Python. That's right, no HTML, Javascript, CSS or any other programming language is required.

We present CoTexT, a pre-trained, transformer-based encoder-decoder model that learns the representative context between natural language (NL) and programming language (PL). Using self-supervision, CoTexT is pre-trained on large programming language corpora to learn a general understanding of language and code. CoTexT supports downstream NL-PL tasks such as code summarizing/documentation, code generation, defect detection, and code debugging. We train CoTexT on different combinations of available PL corpus including both "bimodal" and "unimodal" data. Here, bimodal data is the combination of text and corresponding code snippets, whereas unimodal data is merely code snippets. We first evaluate CoTexT with multi-task learning: we perform Code Summarization on 6 different programming languages and Code Refinement on both small and medium size featured in the CodeXGLUE dataset. We further conduct extensive experiments to investigate CoTexT on other tasks within the CodeXGlue dataset, including Code Generation and Defect Detection. We consistently achieve SOTA results in these tasks, demonstrating the versatility of our models.

One of the growing problems today is misinformation: the proliferation of fake news and misleading content across social media platforms. While artificial intelligence (AI) helps in its spread, there has been growing proof of how it can be used to curb this problem. However, more than just the daily news article, misinformation has far-reaching - and often fearsome - implications in more critical fields such as cybersecurity, public safety, medicine, and even science. In fact, there have been published collaborative papers, one appearing in the April 2021 issue of PNAS, tackling misinformation as a result of common human biases and prevailing practices in the critique and release of scientific papers. This even includes respected, peer-reviewed journals.

A new artificial intelligence model developed by Chinese researchers is performing untold feats with image creation and natural language processing -- making rivals in Europe and the U.S. nervous about falling behind. The model, dubbed Wu Dao 2.0, is able to understand everything people say -- the grammar too -- but can also recognize images and generate realistic pictures based on descriptions. It can also write essays and poems in traditional Chinese, as well as predict the 3D structures of proteins, POLITICO'S AI: Decoded reported. Developed by the government-funded Beijing Academy of Artificial Intelligence and unveiled last week, Wu Dao 2.0 appears to be among the world's most sophisticated AI language models. Wu Dao 2.0's creators say it's 10 times more powerful than its closest rival GPT-3, developed by the U.S. firm OpenAI.

In their decades-long chase to create artificial intelligence, computer scientists have designed and developed all kinds of complicated mechanisms and technologies to replicate vision, language, reasoning, motor skills, and other abilities associated with intelligent life. While these efforts have resulted in AI systems that can efficiently solve specific problems in limited environments, they fall short of developing the kind of general intelligence seen in humans and animals. In a new paper submitted to the peer-reviewed Artificial Intelligence journal, scientists at U.K.-based AI lab DeepMind argue that intelligence and its associated abilities will emerge not from formulating and solving complicated problems but by sticking to a simple but powerful principle: reward maximization. Titled "Reward is Enough," the paper, which is still in pre-proof as of this writing, draws inspiration from studying the evolution of natural intelligence as well as drawing lessons from recent achievements in artificial intelligence. The authors suggest that reward maximization and trial-and-error experience are enough to develop behavior that exhibits the kind of abilities associated with intelligence.

In a study published today, OpenAI, the lab best known for its research on large language models, claims it's discovered a way to improve the "behavior" of language models with respect to ethical, moral, and societal values. The approach, OpenAI says, can give developers the tools to dictate the tone and personality of a model depending on the prompt that the model's given. Despite the potential of natural language models like GPT-3, many blockers exist. The models can't always answer math problems correctly or respond to questions without paraphrasing training data, and it's well-established that they amplify the biases in data on which they were trained. That's problematic in the language domain, because a portion of the data is often sourced from communities with pervasive gender, race, and religious prejudices.

I've been writing a few articles in the last months where I've tackled the subject of artificial intelligence (AI) and its incorporation into digital business processes and our daily life. As I was carrying out my search, I came across some resources about the usage of AI to produce art, like painting and music. By letting machines learn from the human artistic work, Artificial Intelligence Virtual Artists like AIVA can compose classical and symphonic music. Today, AIVA's YouTube channel has over 18K subscribers. In her post "Top 10 AI Music Composers in 2021," Lisa Brown has listed more examples of non-human music composers.

We've always thought of this podcast as a dialogue between us and you, so we're shooting for more interactivity! From now on, we're not recording in advance anymore, so that we can answer all the comments you leave us here, on twitter or at [notdailypodcast@gmail.com](mailto:notdailypodcast@gmail.com). This episode is mainly focused on addressing all the comments we've received from episode 1 to 6 so we can start this format from a clean slate! But before that, Yoann introduces us to the GPT2 machine learning algorithm that he trained on a corpus of his writings. You can find all the details in his blog post, and Vlad's reactions in this episode!

We introduce a new type of programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis, and release an open-source dataset of Python Programming Puzzles (P3). Each puzzle is defined by a short Python program $f$, and the goal is to find an input $x$ which makes $f$ output "True". The puzzles are objective in that each one is specified entirely by the source code of its verifier $f$, so evaluating $f(x)$ is all that is needed to test a candidate solution $x$. They do not require an answer key or input/output examples, nor do they depend on natural language understanding. The dataset is comprehensive in that it spans problems of a range of difficulties and domains, ranging from trivial string manipulation problems that are immediately obvious to human programmers (but not necessarily to AI), to classic programming puzzles (e.g., Towers of Hanoi), to interview/competitive-programming problems (e.g., dynamic programming), to longstanding open problems in algorithms and mathematics (e.g., factoring). The objective nature of P3 readily supports self-supervised bootstrapping. We develop baseline enumerative program synthesis and GPT-3 solvers that are capable of solving easy puzzles -- even without access to any reference solutions -- by learning from their own past solutions. Based on a small user study, we find puzzle difficulty to correlate between human programmers and the baseline AI solvers.

Their proposed Decision Transformer outputs optimal actions by leveraging a causally masked transformer and can generate future actions with desired returns. Moreover, despite Decision Transformer's relative simplicity, the proposed framework matches or outperforms the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks. Transformer architectures are able to efficiently model sequential data, and their self-attention mechanism allows the layer to assign "credit" by implicitly forming state-return associations via maximizing the dot product of the query and key vectors. Transformers can thus function effectively in the presence of sparse or distracting rewards. Previous studies have also shown that transformers can model a wide distribution of behaviours, enabling better generalization and transfer abilities.