Imagine if Siri could write you a college essay, or Alexa could spit out a movie review in the style of Shakespeare. OpenAI last week opened up access to ChatGPT, an AI-powered chatbot that interacts with users in an eerily convincing and conversational way. Its ability to provide lengthy, thoughtful and thorough responses to questions and prompts – even if inaccurate – has stunned users, including academics and some in the tech industry. The tool quickly went viral. On Monday, Open AI's co-founder Sam Altman, a prominent Silicon Valley investor, said on Twitter that ChatGPT crossed one million users.

OpenAI's newly unveiled ChatGPT bot is making waves when it comes to all the amazing things it can do--from writing music to coding to generating vulnerability exploits, and what not. As the erudite machinery turns into a viral sensation, humans have started to discover some of the AI's biases, like the desire to wipe out humanity. Yesterday, BleepingComputer ran a piece listing 10 coolest things you can do with ChatGPT. And, that doesn't even begin to cover all use cases like having the AI compose music for you [1, 2]. Within six days of its launch, ChatGPT surpassed a million users to the extent its servers couldn't keep up.

Runway's cofounders (from left) Anastasis Germanidis, Alejandro Matamala-Ortiz and Cristóbal Valenzuela are immigrants who met while studying as art students at New York University. Runway ML, one of the two startups behind the popular AI text-to-image model Stable Diffusion, has raised new funding at a $500 million valuation, Forbes has learned. Felicis is leading the new funding, the sources said, which comes on the heels of a boom in generative AI that has captured the public's attention in recent months thanks to releases that also include OpenAI's Dall-E and ChatGPT. Runway quickly emerged as one of the buzziest startups in the mix with its video editing software, for which the company has been releasing a bevy of generative AI features. For example, from a photo of a forest, a user can type a short text phrase into Runway's software and instantly conjure a lake or a castle among the trees.

Many observers are marveling at ChatGPT's prospects as a replacement for traditional search engines, and human jobs like tutoring and economics punditry. But while this chatbot can in some cases be more helpful in tackling a coding problem than your Intro to Python 101 classmates, it probably isn't coming for your 9-to-5 any time soon. It even says so itself.

There has been a surge of interest in what generative AI can do. But what does this technology really mean for the legal sector? To find out we must navigate a path between'Death of the Lawyer 2.0' hysteria and those who dismiss the whole thing as a gimmick. Artificial Lawyer looks at what this tech can really do. Generative AI (gen AI), working via Large Language Models such as OpenAI's GPT-3, can do some amazing things.

The launch of ChatGPT and GPT 3.5 (Generative Progressive Transformer-3.5) -- which many claim will herald a new era in dialogue-based conversational AI -- has ended the year on a high for conversational AI. People are using ChatGPT for tasks ranging from correcting code errors to rewriting the Bohemian Rhapsody and the number of ChatGPT users surpassed the million mark in less than a week last month. While 2022 was about newer and more advanced tools and models, commercial use cases, regulation, and standardisation of AI are expected to define 2023 for this domain. Here's what to expect from the AI industry in 2023. Generative AI, which is artificial intelligence that can create text, images, videos etc. without supervision, set the tone for this year and the trend will spill on to 2023 as well.

We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response. We are excited to introduce ChatGPT to get users' feedback and learn about its strengths and weaknesses. During the research preview, usage of ChatGPT is free.

Making actors look older or younger has been a eternal challenge for movie studios. It used to be achieved through fairly cumbersome and not always convincing prosthetics and makeup effects. That was then largely replaced by time-consuming digital VFX techniques, but it looks like Disney's come up with a game changer. While publicly accessible AI image generators make an impact on the creative fields, Disney has been working on a studio-quality AI model that can age (and de-age) actors in a way that looks so realistic, it's scary (for more on the use of AI in other creative fields, see how to use DALL-E 2). Making actors look older or younger isn't new. Makeup artist have done some incredible jobs on the likes of David Bowie in The Hunger (1983) and Brad Pitt in the Curious Case of Benjamin Button (2008) – the latter had 56 people working in hair and makeup.

These are the craziest, most futuristic cars created by artificial intelligence tool, DALL.E. DALL.E is a new AI system that can artificially draw some of the most realistic images based on whatever keywords you feed it. We plugged in some crazy search terms, like'armored Ferrari', 'off-road Bugatti', and'futuristic flying car', and this is what it came up with. While some are super realistic, some are just out-of-this-world bizarre. First, we asked DALL.E to show us what it believes the future of flying cars will look like.

Artificial Intelligence and particularly machine learning and deep learning have taken the world by storm. Nowadays it is used in many fields, ranging from manufacturing with vision-based defect inspection platforms (e.g., LandingLens by LandingAI), to voice assistants (e.g., Apple's Siri, Amazon's Alexa …), passing by medical diagnosis (e.g., brain tumor detection). Nevertheless, one important aspect to consider when using machine learning is data: how much (quality) data do you have? That is one of the issues faced particularly in medical imaging. Because annotated data is scarce, it makes it difficult to build good machine learning models.