If you follow the latest AI news, you probably came across several stunning applications of the latest Language Model (LM) released by OpenAI: GPT-3. The applications that this LM can fuel reach from question answering to generating Python code. The list of use cases is growing daily. Check out the following youtube videos: GPT-3 demo and explanation, 14 cool GPT-3 apps and 14 more GPT-3 apps. GPT-3 is currently in beta and only a restricted number of people have access, but will be released to everybody on October 1st.

That's a higher percentage growth than 2018 to 2019 when the volume of publications increased by 19.6 percent. China continues to be a growing force in AI R&D, overtaking the US for overall journal citations in artificial intelligence research last year. The country already publishes more AI papers than any other country, but the United States still has more cited papers at AI conferences -- one indicator of the novelty and significance of the underlying research. These figures come from the fourth annual AI Index, a collection of statistics, benchmarks, and milestones meant to gauge global progress in artificial intelligence. The report is collated with the help of Stanford University, and you can read all 222 pages here.

In June 2020, a new and powerful artificial intelligence (AI) began dazzling technologists in Silicon Valley. Called GPT-3 and created by the research firm OpenAI in San Francisco, California, it was the latest and most powerful in a series of'large language models': AIs that generate fluent streams of text after imbibing billions of words from books, articles and websites. GPT-3 had been trained on around 200 billion words, at an estimated cost of tens of millions of dollars. The developers who were invited to try out GPT-3 were astonished. "I have to say I'm blown away," wrote Arram Sabeti, founder of a technology start-up who is based in Silicon Valley. "It's far more coherent than any AI language system I've ever tried. All you have to do is write a prompt and it'll add text it thinks would plausibly follow. I've gotten it to write songs, stories, press releases, guitar tabs, interviews, essays, technical manuals. I feel like I've seen the future."

In the following sections, I will introduce various articles and papers not only on the above contents but also on the following five topics. Zero-Shot Text-to-Image Generation They proposed DALL-E, which generates images from text with zero-shot. First, as in VQVAE, they compress the image to 32x32 using an encoder, re-select a representation from the codebook that is close to each grid representation, and learn discrete VAE to generate images from it. Next, using the paired data of image and text, they train an autoregressive model to generate "image tokens" using the text as input and the 8192 expressions in the codebook as vocabulary.

We're used to medical chatbots giving dangerous advice, but one based on OpenAI's GPT-3 took it much further. If you've been living under a rock, GPT-3 is essentially a very clever text generator that's been making various headlines in recent months. Only Microsoft has permission to use it for commercial purposes after securing exclusive rights last month. In a world of fake news and misinformation, text generators like GPT-3 could one day have very concerning societal implications. Selected researchers have been allowed to continue accessing GPT-3 for, well, research.

Create, Schedule, Optimize and Publish All Your Content From One Dashboard. Create Content That Ranks, At Scale. I have been fascinated by computers in general since I was a kid. I can remember the computer lab when I was maybe 5 or 6 years old and we were being taught using a computer through the DOS prompt. I have seen computers and technology progress exponentially through time.

The most impressive thing about OpenAI's natural language processing (NLP) model, GPT-3, is its sheer size. With more than 175 billion weighted connections between words known as parameters, the transformer encoder-decoder model blows its 1.5 billion parameter predecessor, GPT-2, out of the water. This has allowed the model to generate text that is surprisingly human-like after only being fed a few examples of the task you want it to do. Its release in 2020 dominated headlines, and people were scrambling to get on the waitlist to access its API hosted on OpenAI's cloud service. Now, months later, as more users have gained access to the API (myself included), interesting applications and use cases have been popping up every day.

British YouTuber Tom Scott has such a distinctive style to his videos that they can -- and have -- lent themselves quite well to parody in the past, both from humans and AIs alike. But for his latest video, Scott decided to use this to his advantage. After getting access to OpenAI's GPT-3 text generator, Scott fed it a large number of his previous video titles to see if it could come up with some new and original story ideas for him. And after a few false starts with titles that were either too boring or nonsensical, it ended up nailing it. "GPT-3 has a setting called Temperature, which is basically how predictable it should be," explains Scott in the video above.

The machine learning company OpenAI is developing models that improve computer vision and can produce original images from a text prompt. Why it matters: The new models are the latest steps in ongoing efforts to create machine learning systems that exhibit elements of general intelligence, while performing tasks that are actually useful in the real world -- without breaking the bank on computing power. What's happening: OpenAI today is announcing two new systems that attempt to do for images what its landmark GPT-3 model did last year for text generation. What they're saying: "Last year, we were able to make substantial progress on text with GPT-3, but the thing is that the world isn't just built on text," says Sutskever. "This is a step towards the grander goal of building a neural network that can work in both images and text."

Here's an example of some listicles I created for the topic "quality blog content" using this tool: As you can see, if I wanted to write an article on this topic, I can use some of these suggestions as an outline for my post. With these, I can focus instead on researching the individual sub-topics. Here's another example of some website taglines that I created for Moz by entering the brand name and a brief description of "The Ultimate SEO tool you can trust" into the tool: If you were starting a new brand as an SEO, you can use NLG tools such as this, to discover awesome taglines to use for your brand. TF*IDF stands for "Term Frequency times Inverse Document Frequency". This measures how you use a term on a particular page and how it compares to a collection of pages for that specific keyword.