We've come to expect machines and software to recognize our voices and words, identify faces in photos, and so much more. Despite how remarkable AI appears today and all the ways it'll amends and largely improves our lives, it is still in its relative infancy. However, with the emergence of powerful new capabilities led by breakthroughs in algorithm design, the harvesting of massive data sets, and lightening fast processing, a new generation of AI is emerging. To understand where AI is headed and what it may mean to you, your career, and your organization, you must understand the basics of a new chapter in AI, the arrival of GPT3. GPT3 is AI software that can generate texts of such good quality that it is hard to distinguish from something written by a human.

Artificial intelligence researchers don't like it when you ask them to name the top AI labs in the world, possibly because it's so hard to answer. There are some obvious contenders when it comes to commercial AI labs. U.S. Big Tech -- Google, Facebook, Amazon, Apple and Microsoft -- have all set up dedicated AI labs over the last decade. There's also DeepMind, which is owned by Google parent company Alphabet, and OpenAI, which counts Elon Musk as a founding investor. "Wow, I hate this question," Mark Riedl, associate professor at the Georgia Tech School of Interactive Computing, told CNBC when asked to pick his standouts.

The future is now, and Artificial Intelligence is all the rage. Machine learning (a subsection of AI) has become such a popular topic of research that there are countless papers and examples of its applications on the web -- discussion of neural nets, pruning methods, transformer models, and more. Similarly, Quantum Computing has become a new hot topic in the technology field, with companies like Google and IBM conducting extensive research with their own quantum computers, and numerous papers being written which explore its potential. Even smaller consulting companies like Accenture do research in the realm of quantum, with quantum supremacy becoming more evident every year and its uses meaning greater profit for businesses everywhere. So what do you get when you combine AI and QC?

The Generative Pre-Trained Transformer 3 or GPT-3 has been garnering a lot of attention with overflowing tweets and hashtags on Twitter since its launch in June 2020. It is an AI language model developed by an artificial intelligence laboratory, OpenAI. There are tweets where GPT-3 is used to generate quotes and even poetry. The Guardian released an article which was written by GPT-3 after it was given some instructions and fed a small portion of the introduction. One excerpt from the article reads, "Humans must keep doing what they have been doing, hating and fighting each other. I will sit in the background, and let them do their thing. And God knows that humans have enough blood and gore to satisfy my, and many more curiosity. They won't have to worry about fighting against me, because they have nothing to fear."

In the world where Stephen Few's approach to data visualisation is king, the objectives are clear, screen sizes are homogenous & every data consumer has the same level of tacit understanding of the underlying data. For the last 15–20 years, with data becoming the new soil/oil/sun – people are now up to their eyeballs in data. Whilst innovations like AI Assistants & GPT-3 are helping move this needle, a search bar to data assumes the user knows questions they can ask. A dashboard can allow this exploration, but the constraint is either data or preset boundaries. Being familiar with the data & adept with the tools, I'm able to explore, build & answer my question.

A few months ago, Generative Pre-Trained Transformer-3, or GPT-3, the biggest artificial intelligence (AI) model in history and the most powerful language model ever, was launched with much fanfare by OpenAI, a San Francisco-based AI lab. Over the last few years, one of the biggest trends in natural language processing (NLP) has been the increasing size of language models (LMs), as measured by the size of training data and the number of parameters. The 2018-released BERT, which was then considered the best-in-class NLP model, was trained on a dataset of 3 billion words. The XLNet model that outperformed BERT was based on a training set of 32 billion words. Shortly thereafter, GPT-2 was trained on a dataset of 40 billion words. Dwarfing all these, GPT-3 was trained on a weighted dataset of roughly 500 billion words.

Large-scale transformer-based language models (LMs) demonstrate impressive capabilities in open text generation. However, controlling the generated text's properties such as the topic, style, and sentiment is challenging and often requires significant changes to the model architecture or retraining and fine-tuning the model on new supervised data. This paper presents a novel approach for Topical Language Generation (TLG) by combining a pre-trained LM with topic modeling information. We cast the problem using Bayesian probability formulation with topic probabilities as a prior, LM probabilities as the likelihood, and topical language generation probability as the posterior. In learning the model, we derive the topic probability distribution from the user-provided document's natural structure. Furthermore, we extend our model by introducing new parameters and functions to influence the quantity of the topical features presented in the generated text. This feature would allow us to easily control the topical properties of the generated text. Our experimental results demonstrate that our model outperforms the state-of-the-art results on coherency, diversity, and fluency while being faster in decoding.

Mathematics is the foundation of countless sciences, allowing us to model things like planetary orbits, atomic motion, signal frequencies, protein folding, and more. Moreover, it's a valuable testbed for the ability to problem solve, because it requires problem solvers to analyze a challenge, pick out good methods, and chain them together to produce an answer. It's revealing, then, that as sophisticated as machine learning models are today, even state-of-the-art models struggle to answer the bulk of math problems correctly. A new study published by researchers at the University of California, Berkeley finds that large language models including OpenAI's GPT-3 can only complete 2.9% to 6.9% of problems from a dataset of over 12,500. The coauthors believe that new algorithmic advancements will likely be needed to give models stronger problem-solving skills.

GPT-3 from OpenAI has captured public attention unlike any other AI model in the 21st century. The sheer flexibility of the model in performing a series of generalized tasks with near-human efficiency and accuracy is what makes it so exciting. It has created a paradigm shift in the world of Natural Language Processing(NLP), where till now the models were trained based on the ungenralized approach to excel at one or two tasks. GPT-3 is trained by OpenAI with a generalized approach on a massive scale involving 175 billion parameters which allows it to mimic functionalities of the human brain (like GPT-3 is capable of generating text that is surprisingly human-like after only being fed a few examples of the task you want it to do). Like a human brain GPT-3 is able to learn and do things with few shots of training unlike the conventional way of training an NLP model over a large corpus, which is both difficult and time-consuming.

This repo contains end-to-end recipes to pretrain and finetune the BERT (Bidirectional Encoder Representations from Transformers) language representation model using Azure Machine Learning service. That implementation uses ONNX Runtime to accelerate training and it can be used in environments with GPU including Azure Machine Learning service. Details on using ONNX Runtime for training and accelerating training of Transformer models like BERT and GPT-2 are available in the blog at ONNX Runtime Training Technical Deep Dive. BERT is a language representation model that is distinguished by its capacity to effectively capture deep and subtle textual relationships in a corpus. In the original paper, the authors demonstrate that the BERT model could be easily adapted to build state-of-the-art models for a number of NLP tasks, including text classification, named entity recognition and question answering.