How far is the idea of human-like AI from becoming a reality? A pedestal where AI can talk like a person, give advice, share thoughts, and more. This fiction could well turn into a reality with the coming of OpenAI’s Generative Pre-trained Transformer 3 (GPT-3). It is a new language model that can generate high-quality texts that are very difficult for humans to differentiate human-to-human conversation from AI-to-human. In an article on “The Future is Here! Have you checked OpenAI’s GPT-3 Yet?” ARTiBA discusses the secrets of this titan technology. We take a few excerpts from that and try to understand what is GPT-3, how it works, a few examples of its usage, and its significance for AI professionals. For the complete article, check ARTiBA’s Blogs. Just like humans OpenAI GPT-3 codes, programs, tweets, emails, writes news, answers questions, translates, and even composes poetry. It can efficiently complete any English language task. It has been dubbed as one of the most significant advancements in the field of AI. GPT-3 has raised the bar for AI and its potential. For AI professionals, it is a big opportunity to create applications and software operating like human intelligence. GPT-3: A Few Examples An example of a short conversation about Python programming language with GPT-3 AI. Human: Hi, how are you? AI: I am great! What would you like to talk about? Human: Have you done any coding with Python? AI: Yes! I’ve done plenty of Python coding since it’s my favorite. Human: Nice, mine too! What all have you done with it? AI: Oh, a lot! I have done everything – from creating Python modules to creating micro-websites. Human: What web framework do you use? AI: I used Django, but it was difficult for me. I found Flask a better option as it’s lighter and simpler. Human: Wow. I agree 100%. Have you used WebSocket? AI: Yes. I have written a chatbot using it. Interesting! Isn’t it? The text and conversation generated by GPT-3 is next-level. It is virtually indistinguishable! Here is another example. These paragraphs were written by a GPT-3 engine after training by showing just the first paragraph about GPT-3 on Wikipedia. This is the unedited version of what it produced: GPT-3 is a deep neural network that uses the attention mechanism to predict the next word in a sentence. It is trained on a corpus of over 1 billion words and can generate text at character level accuracy. GPT-3's architecture consists of two main components: an encoder and a decoder. The encoder takes as input the previous word in the sentence and produces a vector representation of it, which is then passed through an attention mechanism to produce the next word prediction. The decoder takes as input both the previous word and its vector representation and outputs a probability distribution over all possible words given those inputs. GPT-3's performance is on par with the best language models for text generation, which is significantly better than previous GPT models. Microsoft's Turing NLG model can generate text at character level accuracy on a test set of Wikipedia articles but requires an enormous amount of training data to do so. OpenAI claims that GPT-3 can achieve this level of performance without any additional training data after its initial pre-training period. In addition, GPT-3 is capable of generating longer sentences and paragraphs than earlier models such as Google's BERT and Stanford NLP's Transformer. Know how GPT-3 works in detail, and its benefits for AI professionals, on ARTiBA’s blog. We reproduce a few bits from the blog piece below. Working of GPT-3 It can be used to build a high-end chatbot. It can be used as a writing assistant. It can be used for compositions by writers, marketers, among others. Other than its uses, what’s even more interesting to know is how it does all of that. GPT-3 generates text using already trained algorithms. The data that drives this titan comes from over 570GB of information from OpenAI and CommonCrawl data. The latter is a publicly available dataset. What’s more? It works on over 175 billion parameters. The good news for engineers is this technology is prepped with all the data it can need to complete tasks efficiently. To understand, how the publicly available information is interpreted by GPT-3, visit ARTiBA’s blogs. OpenAI GPT-3 is a 3rd generation prediction model for language that uses deep learning. What’s in it for AI Engineers? OpenAI released GPT-3 API for developers to give them access to advanced technologies for building AI applications. Unlike other AI systems that are developed for the single use-case, OpenAI GPT-3 provides a general-purpose text in and text out interface that enables users to try it on any English language task. It’s a kind advancement in the discipline of AI. For AI engineers, there can be no better time to create brilliant applications powerful enough to sway the world of work and people’s everyday lives. AI is getting stronger. Are you ready to jump on the bandwagon? The Artificial Intelligence field is hard-pressed for talent (LinkedIn). Be the solution that industry and the world needs. Get the latest knowledge on AI, and learn how to apply it with professional certification as offered by ARTiBA – Artificial Intelligence Engineer – to empower yourself to innovate business with AI.
The Race for Intelligent AI. GPT3 like architectures and their limitations
The types of content are many and good content has driven industries like printing, publishing, broadcast, film, search engines, AI etc.
I wrote about the economy of GPT-3, OpenAI’s massive language model. I discussed what it would take for the company to make the AI algorithm profitable and secure future funding for its artificial general intelligence research.
GPT-3 was trained on a simple task: given the previous words in a text, predict the next word. This required the model to consume very large datasets of Internet text, such as Common Crawl and Wikipedia, totalling 499 billion tokens (i.e. words and numbers).
Will Human Writing and AI Writing Become Indistinguishable? The essay wasn’t created by a robot per se, but by a new piece of software called GPT-3, a text generation AI engine created by San Francisco based Open AI.
GPT-3 is not a super-intelligence or a human-like AI that transhumanists blindly claim. But OpenAI has created a breakthrough. The advance is significant enough to open up real questions.
Three months since OpenAI has released their GPT-3 API, it’s now a shared notion — getting to STOA results is mostly a function of effective prompts programming, rather than an NLP task.
The Guardian claims that entire article has been written by OpenAI GPT-3 and was aimed towards convincing humans that “robots come in peace.” Artificial Intelligence will not destroy humans.
The Guardian’s GPT-3-written article misleads readers about AI. Here’s why. This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
China’s Global Initiatives, AI Fighter Pilots And More In This Week’s Top News. China has finally decided that it is not going to bow down and have announced global initiative.
Now, as part of GPT3 exploration, here we demonstrate a low-code, no ML approach (yes, no model training and deploying) to build a movie recommender to help user find the related or similar movies given a specific movie or tv show title as the input.
f you have been following the recent developments in the NLP space, then it would be almost impossible to avoid the GPT-3 hype in the last few months. It all started with researchers in OpenAl researchers publishing their paper “Language Models are few Shot Learners” which introduced the GPT-3 family of models. GPT-3’s size and language capabilities are breathtaking, it can create fiction, develop program code, compose thoughtful business memos, summarize text and much more.
GPT-3 is a good start for human-like natural language performance. Perhaps a better analogy might be the “Homo habilis”  of Artificial Intelligence Natural Language.
Do you know how much GPT3 API will it cost? A rough calculation tells me it can go a maximum of 790 requests/$. GPT3 is pretty huge(175B parameters = 700GB) and you know how costly GPU inferences can be. Even if we find a use case for it, we still need to justify the ROI. There are many blogs on the potential applications but I haven’t found anything on its pricing.
The mechanism that makes language models focus on the important things. Hi and welcome to the second step of the Language Modeling Path; a series of articles from Machine Learning Reply aimed to cover the most important milestones that brought to life the huge language models able to imitate and (let’s say) understand the human language like BERT and GPT-3.
I’m a product marketing manager for a tech company. Before that, I was a content marketing manager. I’ve also borne the titles of content marketing manager, content marketing writer, marketing writer, freelance content marketing writer strategist thought leader, and other such labels. All that to say, for the last several years, I’ve worked as a “corporate writer” in one form or another. The chief skill for which I’ve been compensated is writing.
TL;DR; GPT-3 will not take your programming job (Unless you are a terrible programmer, in which case you would have lost your job anyway) Once again the hype of artificial intelligence has broken in the news. This time under the name of GPT-3, the successor of GPT-2 (of course), a model that is so large and so powerful that is making people think we finally made AGI, artificial general intelligence, possible
Google has published a new way of pre-training a language model which is augmented using a knowledge retrieval mechanism, that looks up existing knowledge from an external Wikipedia corpus. This makes the outputs the trained language model generates more fact-based and vast. It uses masked language modeling transformers for training and learns to retrieve and attend from millions of Wiki documents.
Making AI accessible to everyone. AI is particularly important for businesses as it enables personalized services on a massive scale, and customers are increasingly demanding it.