Last spring, artificial intelligence research institute OpenAI said it had made software so good at generating text--including fake news articles--that it was too dangerous to release. That line in the sand was soon erased when two recent master's grads recreated the software and OpenAI released the original, saying awareness of the risks had grown and it hadn't seen evidence of misuse. Now the lab is back with a more powerful text generator and a new pitch: Pay us to put it to work in your business. Thursday, OpenAI launched a cloud service that a handful of companies are already using to improve search or provide feedback on answers to math problems. OpenAI was founded as a nonprofit in 2015 by Elon Musk and other Silicon Valley notables to ensure that future superhuman AI was a benign force.

A lot of ink has been spilled (or pixels illuminated) about the wonders of GPT-3, OpenAI's latest and greatest language model. A team of more than 30 OpenAI researchers have released a paper about GPT-3, a language model capable of achieving state-of-the-art results on a set of benchmark and unique natural language processing tasks that range from language translation to generating news articles to answering SAT questions. But like most examples spat out by language models, almost all of these were hand-selected by humans after many runs. Because not-so-good results just wouldn't make the news. Even bearing that in mind, I'm still blown away by what I've seen of GPT-3.

Did you know AI can now produce poetry and write fiction? That is not all it can also generate CODE ! Isn’t that amazing ! OpenAI’s newest AI language model – GPT-3, is trending all over the internet! Source: Unite.AI Background Elon Musk and Sam Altman started OpenAI in 2015 to advance the state of the art of AI and to ensure AI was used for the human good. OpenAI recently released the third version Generative Pre-training Transformer (GPT) – GPT-3. They first described GPT-3 through a research paper published in May but last week rolled out a beta version access to a select set of people and its capabilities are mind blowing! What is GPT-3 and why is everyone talking about it? Here are some quick pointers to help you understand what GPT-3 is, and why it is a step towards an AI revolution. Third version Generative Pre-training Transformer (GPT) A natural language generator (NLG) capable of producing human-like text on demand State-of-the-art language model made up of 175 billion parameters ~10X larger than Microsoft’s Turing NLG that has 17 billion parameters >100X larger than its own predecessor GPT-2 (released last year) trained on 1.5 billion parameters Does not require large custom, task specific datasets (which are usually difficult to get) Does not even require task specific model architectures What all can GPT-3 do for you? Here are a few things that GPT-3 can do for you and as more developers and experts experiment with it, more use cases will surface in the times to come. Answer questions with common sense (that doesn’t seem very difficult?) Write creative fiction – poetry, essays, stories Write news (Could that be a problem?) Solve arithmetic problems Generate Functioning Code (GPT-3 can CODE!) Design – developers demonstrated it with a Figma plugin (That’s truly creative!) This is just an illustrative list of what all GPT-3 can do! As more and more developers/experts experiment with the beta access, more innovations are bound to surface. Possible flaws and concerns pertaining to GPT-3? Although GPT-3 has proven brilliance in many ways in its current state but it also has certain flaws and concerns that it raises: Lacks an overarching, long-term sense of meaning and purpose As it generates its output word-by-word, based on the immediately surrounding text. It can struggle to maintain a coherent narrative or deliver a meaningful message over more than a few paragraphs is what experts say after initial experimentation. Possibility of being prone to certain biases Developers noticed that GPT-3 is prone to shoot out racist and sexist language, even when the prompt is something harmless. As GPT-3 is trained on internet scale data, these biases arise from biases in that training data reflecting possible societal views and opinions. Chances of misuse once released publicly Authors acknowledge that people can misuse it in several ways because of its ability to create text that is indistinguishable from that of written by humans. It can lead to creation of including generating misinformation and spam, phishing, and even fake academic essays. Possible impact on jobs As GPT-3 can perform human-like tasks across multiple domains, it could possibly have an impact on jobs that it can do like writers, coders, journalists, etc. These are just possible concerns and basis point 1 highlighted above many believe that instead of replacing humans, GPT-3 could become the perfect assistant for humans in these professions. Conclusion and what lies ahead? GPT-3 can execute plethora of NLP- based tasks, without fine-tuning for a specific task. Experts are even saying that this could be a step towards Artificial General Intelligence (AGI) – Read my article on Demystifying AI to know more! It is capable of performing machine translation, answering to questions, scripting poems and stories, elementary mathematics and can even generate code. OpenAI wants developers to help it explore what GPT-3 can do and with the beta release of its API has attracted plethora of experts and developers to experiment with the capabilities of GPT-3. It is only a matter of time before more exciting innovations surface in the developers’ community! Watch out for more interesting articles on AI! Feel free to share your thoughts. References [1] https://www.forbes.com/sites/robtoews/2020/07/19/gpt-3-is-amazingand-overhyped/#7c6166501b1c [2] https://analyticsindiamag.com/open-ai-gpt-3-code-generator-app-building/ [3] https://singularityhub.com/2020/06/18/openais-new-text-generator-writes-even-more-like-a-human/ [4] https://towardsdatascience.com/gpt-3-for-the-people-2cdd003d9a89 [5] https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-gpt-3-nlp/ [6] https://www.analyticssteps.com/blogs/what-openai-gpt-3 [7] https://www.cnbc.com/2020/07/23/openai-gpt3-explainer.html [8] https://analyticsindiamag.com/how-openais-gpt-3-can-be-alarming-for-the-society/ [9] https://www.forbes.com/sites/robtoews/2020/07/19/gpt-3-is-amazingand-overhyped/#7c6166501b1c [10] https://www.independent.co.uk/life-style/gadgets-and-tech/news/gpt3-ai-tool-designs-websites-medicine-a9627966.html [11] https://medium.com/fair-bytes/how-biased-is-gpt-3-5b2b91f1177 [12] https://towardsdatascience.com/gpt-3-demos-use-cases-implications-77f86e540dc1

The world has a new AI toy, and it's called GPT-3. The latest iteration of OpenAI's text generating model has left many starstruck by its abilities – although its hype may be too much. GPT-3 is a machine learning system that has been fed 45TB of text data, an unprecedented amount. All that training allows it to generate sorts of written content: stories, code, legal jargon, all based on just a few input words or sentences. And the beta test has already produced some jaw-dropping results.

OpenAI's paper describes many incredible tasks that GPT-3 can accomplish. For instance, given some text input, it can predict what should come next alarmingly well. In one study, test subjects were asked to differentiate between 500 word long articles written by humans or articles written by GTP-3. When GPT-3 is used with 175 billion parameters, they were only able to correctly identify text from other humans at a rate of 52%. That implies GPT-3 at maximum capacity can nearly replicate human written articles!

GPT-3 requires significant expertise and is too expensive for individuals and small businesses to run. This makes it hard for anyone except larger companies to benefit from the underlying technology. OpenAI gave this as one of 3 reasons why the model is only available via API and was not open-sourced. At 175 billion parameters, GPT-3 dwarfs GPT-2 which had "only" 1.5 billion parameters. Given the death of Moores Law, we won't see such models running on personal computers in the near future.

Advances in artificial intelligence are often framed as though they'll upend human society overnight, and for the last month technologists have had a new darling to obsess over. It's a piece of software called GPT-3, which can write essays, songs and computer code in response to requests that humans type into its interface in plain text. The program comes from OpenAI, a top artificial intelligence research organization. OpenAI recently began giving technologists access to it in a private testing period, and they are in love. GPT-3 can build a website layout based on a text-based description, or write a Harry Potter story in the style of a hard-boiled detective novel.

I typically think of the dog days of summer as a time when news slows down. It's typically when a lot of people take time off work, and the lull leads local news stations to cover inconsequential things like cat shows or a little baby squirrel on a little baby Jet Ski. But these are not typical times. Fallout surrounding issues of bias and discrimination continues at Facebook, as multiple news outlets reported that Instagram's content moderation algorithm was 50% more likely to flag and disable the accounts of Black users than White users. Facebook and Instagram are now creating teams to examine how algorithms impact the experiences of Black, Latinx, and other specific groups of users.

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions ($\geq 1024 \times 1024$). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are $2-3\times$ faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

With the recent tweet of Sharif Shameem of debuild, where he showed off a unique feature of OpenAI's GPT-3 which can generate codes, it has raised an interesting question in the programming scene. Whether this new language model released by OpenAI will kill coding or will create more productive programmers in the future. Being trained on billions of words from the internet has made GPT-3 capable of creating codes in CSS, JSX, Python etc. What's more, GPT-3 doesn't need to be trained at all for specific tasks, working on zero-shot learning, which makes it the largest model in the AI world with a strong performance on many NLP datasets allowing it to perform a range of tasks. Explaining the process, he said, "all I had to do is to re-write my two initial samples, and the GPT generated outputs in plain HTML/CSS." With GPT-3, I built a layout generator where you just describe any layout you want, and it generates the JSX code for you.