Microsoft Corp. said Monday it is making a multiyear, multibillion-dollar investment in OpenAI, substantially bolstering its relationship with the startup behind the viral ChatGPT chatbot as the software giant looks to expand the use of artificial intelligence in its products. Microsoft said the latest partnership builds upon the company's 2019 and 2021 investments in OpenAI.

Today, we are announcing the third phase of our long-term partnership with OpenAI through a multiyear, multibillion dollar investment to accelerate AI breakthroughs to ensure these benefits are broadly shared with the world. This agreement follows our previous investments in 2019 and 2021. It extends our ongoing collaboration across AI supercomputing and research and enables each of us to independently commercialize the resulting advanced AI technologies. "We formed our partnership with OpenAI around a shared ambition to responsibly advance cutting-edge AI research and democratize AI as a new technology platform," said Satya Nadella, Chairman and CEO, Microsoft. "In this next phase of our partnership, developers and organizations across industries will have access to the best AI infrastructure, models, and toolchain with Azure to build and run their applications."

DALL·E 2 is the newest AI model by OpenAI. If you've seen some of its creations and think they're amazing, keep reading to understand why you're totally right -- but also wrong. OpenAI published a blog post and a paper entitled "Hierarchical Text-Conditional Image Generation with CLIP Latents" on DALL·E 2. The post is fine if you want to get a glimpse at the results and the paper is great for understanding the technical details, but neither explains DALL·E 2's amazingness -- and the not-so-amazing -- in depth. That's what this article is for. If this in-depth educational content is useful for you, subscribe to our AI mailing list to be alerted when we release new material. DALL·E 2 is the new version of DALL·E, a generative language model that takes sentences and creates corresponding original images. At 3.5B parameters, DALL·E 2 is a large model but not nearly as large as GPT-3 and, interestingly, smaller than its predecessor (12B). Despite its size, DALL·E 2 generates 4x better resolution images than DALL·E and it's preferred by human judges 70% of the time both in caption matching and photorealism. As they did with DALL·E, OpenAI didn't release DALL·E 2 (you can always join the never-ending waitlist). However, they open-sourced CLIP which, although only indirectly related to DALL·E, forms the basis of DALL·E 2. (CLIP is also the basis of the apps and notebooks people who can't access DALL·E 2 are using.)

Now that AI programs speak with us in natural language, turn our thoughts into illustrations, and embody our voices, a major conflict over their ethics is en route. And if you thought the content moderation fight was intense, just wait for this one. At stake is how chatbots address political issues, how AI illustrators portray the world, and whether some applications like voice emulators should even exist. Given the scale and power of this blossoming technology, the activists won't be subtle. They've had their practice fighting over human speech online, and they'll bring that experience to this war.

We're starting to see the very early stages of a tech stack emerge in generative artificial intelligence (AI). Hundreds of new startups are rushing into the market to develop foundation models, build AI-native apps, and stand up infrastructure/tooling. Many hot technology trends get over-hyped far before the market catches up. But the generative AI boom has been accompanied by real gains in real markets, and real traction from real companies. Models like Stable Diffusion and ChatGPT are setting historical records for user growth, and several applications have reached $100 million of annualized revenue less than a year after launch. Side-by-side comparisons show AI models outperforming humans in some tasks by multiple orders of magnitude.

A team of researchers at San Francisco-based OpenAI, has announced the development of a machine-learning system that can create 3D images from text much more quickly than other systems. The group has published a paper describing their new system, called Point-E, on the arXiv preprint server. Over the past year, several groups have announced products or systems that can generate a 3D-modeled image based on a text prompt, e.g., "a blue chair on a red floor," or "a young boy wearing a green hat and riding a purple bicycle." Such systems generally have two parts. The first reads the text and tries to make sense of it.

Artificial intelligence (AI) technologies are widely deployed in smartphone photography; and prompt-based image synthesis models have rapidly become commonplace. In this paper, we describe a Research-through-Design (RtD) project which explores this shift in the means and modes of image production via the creation and use of the Entoptic Field Camera. Entoptic phenomena usually refer to perceptions of floaters or bright blue dots stemming from the physiological interplay of the eye and brain. We use the term entoptic as a metaphor to investigate how the material interplay of data and models in AI technologies shapes human experiences of reality. Through our case study using first-person design and a field study, we offer implications for critical, reflective, more-than-human and ludic design to engage AI technologies; the conceptualisation of an RtD research space which contributes to AI literacy discourses; and outline a research trajectory concerning materiality and design affordances of AI technologies.

Getty is bringing its intellectual property rights infringement complaint to London's High Court of Justice. Stability AI already faces a separate major legal battle begun this week when a group of artists filed a class action lawsuit in California against it, along with Stable Diffusion platforms Midjourney and DeviantArt. Some of the billions of pictures in the LAION-5B dataset employed to train Stable Diffusion may have been scraped from the web, including Getty's servers, without their creators' awareness. Notably, Stability AI has suggested there will be an opt-out option for any artist whose work might be used to train new iterations of Stable Diffusion. Getty hasn't mentioned any financial compensation or desire to shut down Stable Diffusion in its case.

The arrival of generative artificial intelligence technologies heralds a once-a-decade platform shift as big as the advent of the smartphone or the Web, many tech observers believe. Yes, but: The wave of investment and hype recalls similar recent surges around Web3 and the metaverse. Those projects are now faltering, and the jury is out on whether the new AI boom will pay off. What they're saying: " It seems clear to me that this is a new epoch in technology," Stratechery's Ben Thompson wrote Monday. Be smart: Tech platform shifts only take off when a big leap in the capacity of tools is matched by irresistible real-world applications and user excitement.

The artificial-intelligence (AI) chatbot ChatGPT that has taken the world by storm has made its formal debut in the scientific literature -- racking up at least four authorship credits on published papers and preprints. Journal editors, researchers and publishers are now debating the place of such AI tools in the published literature, and whether it's appropriate to cite the bot as an author. Publishers are racing to create policies for the chatbot, which was released as a free-to-use tool in November by tech company OpenAI in San Francisco, California. AI bot ChatGPT writes smart essays -- should professors worry? ChatGPT is a large language model (LLM), which generates convincing sentences by mimicking the statistical patterns of language in a huge database of text collated from the Internet. The bot is already disrupting sectors including academia: in particular, it is raising questions about the future of university essays and research production.