There's a certain feeling that happens when a new technology adjusts your thinking about computing. OpenAI is doing it with ChatGPT.

OpenAI has released Point-E, which is similar to DALL-E but capable of 3D modelling. Point-E draws attention especially by being twice as fast as competing systems. The next breakthrough that will take the world of artificial intelligence by storm could be tools that produce 3D models. Open-source Point-E, developed and made available by OpenAI, will appear in this area. In short, Point-E is a system similar to DALL-E, but differently, it transforms the desired into a 3D model.

OpenAI, the company specializing in artificial intelligence and which counts Elon Musk among its founders, has just announced yet another new AI. After Dall-E, an AI capable of generating images from a line of text, and ChatGPT, a new generation chatbot that handles natural language, the latest project is tackling 3D. Dot-E works a lot like Dall-E, in that you just tell it what you want to achieve. However, this time it is not necessary to depict a scene but an object. The AI will then model the object in three dimensions in the form of a cloud of 4,000 points.

"I've spent the majority of my adult life working dead-end jobs for minimum wage and I have little to no relationship with any educational institution," he says. "But this is such a powerful tool. I've managed to create the blueprint for the most hyped pair of sneakers in the world, and I think that's really saying something." Skjellerup's referring to a series of Nike shoes that he showcased on Instagram last week, which look almost exactly like the kind of thing Simone Rocha would design, surfaced in laser-cut mesh, rubberised petals, and ribboned laces. Far from the haunted DALL-E renderings that have been popularised online – all scorched edges and Francis Bacon wails – Skjellerup's creations manage to look real.

Over the last week, millions of people have tried the new AIchat release from OpenAI, built on an upgrade to GPT3 (Generative Pre-trained Transformer). The tool uses a neural network to generate responses from data sources from the internet. OpenAI, supported by Microsoft, also built and released the currently free DALL-E – AI-generated art. By creating an easy user interface, the ChatGPT likely has many educators wondering about the future of learning. This platform, based on GPT3 models, will be rapidly improved when next-generation GPT4 models emerge in the next 1-2 years – meaning, it's only going to get better AI already does and will continue to impact education – along with every other sector.

So about six months ago, we started incorporating the OpenAI GPT3 API into our technology, which is basically the same thing ChatGPT uses. And about three months ago we really started to get it working properly,

While everybody was focused on crypto and web3 during the last two years, behind the scenes something that might have an impact of the same magnitude on the web and perhaps even more was rooting: Generative AI. But it's during the past months that everything seemed to accelerate. It's like every hope we had for AI in the last 20 years has come 10x closer to reality in a matter of weeks. This article is the first of a series of two medium posts regarding Generative AI. Today I'll focus on explaining what it is, how it works, how it emerged and what could be the underlying use cases.

OpenAI's ChatGPT is the new cool AI in town and has taken the world by storm. We've all seen countless Twitter threads, medium articles, etc., that highlight the different ways ChatGPT can be used. Some developers have already started to build applications, plugins, services, etc., that leverage ChatGPT. While the exact workings of ChatGPT aren't yet known since OpenAI hasn't released a paper or open-sourced their code yet. We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup.

While recent advancements in artificial intelligence (AI) language models demonstrate cutting-edge performance when working with English texts, equivalent models do not exist in other languages or do not reach the same performance level. This undesired effect of AI advancements increases the gap between access to new technology from different populations across the world. This unsought bias mainly discriminates against individuals whose English skills are less developed, e.g., non-English speakers children. Following significant advancements in AI research in recent years, OpenAI has recently presented DALL-E: a powerful tool for creating images based on English text prompts. While DALL-E is a promising tool for many applications, its decreased performance when given input in a different language, limits its audience and deepens the gap between populations. An additional limitation of the current DALL-E model is that it only allows for the creation of a few images in response to a given input prompt, rather than a series of consecutive coherent frames that tell a story or describe a process that changes over time. Here, we present an easy-to-use automatic DALL-E storytelling framework that leverages the existing DALL-E model to enable fast and coherent visualizations of non-English songs and stories, pushing the limit of the one-step-at-a-time option DALL-E currently offers. We show that our framework is able to effectively visualize stories from non-English texts and portray the changes in the plot over time. It is also able to create a narrative and maintain interpretable changes in the description across frames. Additionally, our framework offers users the ability to specify constraints on the story elements, such as a specific location or context, and to maintain a consistent style throughout the visualization.

In 2022, large language models (LLMs) finally got good. Specifically, Google and OpenAI have led the way in creating foundation models that respond to instructions more usefully. For OpenAI, this came in the form of Instruct-GPT (OpenAI blogpost), while for Google this was reflected in their FLAN training method (Wei et al. 2022, arxiv). Flan's which beat the Hypermind forecast for MMLU performance two years early: But the best is yet to come. The really exciting applications will be action-driven, where the model acts like an agent choosing actions.