Last spring, an artificial intelligence lab called OpenAI unveiled technology that lets you create digital images simply by describing what you want to see. Called DALL-E, it sparked a wave of similar tools with names like Midjourney and Stable Diffusion. Promising to speed the work of digital artists, this new breed of AI captured the imagination of both the public and the pundits -- and threatened to generate new levels of online disinformation. Social media is now teeming with the surprisingly conceptual, in which shockingly detailed, often photorealistic images are generated by DALL-E and other tools. "Photo of a teddy bear riding a skateboard in Times Square." "Cute corgi in a house made out of sushi."

"One of the most powerful things about this technology is that, like DALL-E, it does what you tell it to do," said Nate Bennett, one of the researchers working in the University of Washington lab. "From a single prompt, it can generate an endless number of designs." To generate images, DALL-E relies on what artificial intelligence researchers call a neural network, a mathematical system loosely modeled on the network of neurons in the brain. This is the same technology that recognizes the commands you bark into your smartphone, enables self-driving cars to identify (and avoid) pedestrians and translates languages on services like Skype. A neural network learns skills by analyzing vast amounts of digital data.

Researchers at the Massachusetts Institute of Technology have developed a deep learning model that can rapidly predict the likely three-dimensional shape of a molecule, given a two-dimensional graph of its structure. The deep learning GeoMol model developed by Massachusetts Institute of Technology (MIT) researchers can rapidly predict the three-dimensional shapes of drug-like molecules, which could expedite drug discovery. GeoMol's predictions are based solely on two-dimensional molecular graphs, and it can process molecules in seconds while outperforming other machine learning models, according to the researchers. The system utilizes a message passing neural network to forecast the lengths of chemical bonds between atoms and those bonds' angles; GeoMol then predicts the structure of each atom's local neighborhood and constructs neighboring pairs of rotatable bonds by computing and aligning torsion angles. MIT's Octavian-Eugen Ganea said GeoMol could help drugmakers indentify new drugs faster by reducing the number of molecules on which they must experiment.

DeepMind's AlphaFold represents the first time a significant scientific problem has been solved by ... [ ] AI. It can be difficult to distinguish between substance and hype in the field of artificial intelligence. In order to stay grounded, it is important to step back from time to time and ask a simple question: what has AI actually accomplished or enabled that makes a difference in the real world? This summer, DeepMind delivered the strongest answer yet to that question in the decades-long history of AI research: AlphaFold, a software platform that will revolutionize our understanding of biology. In 1972, in his acceptance speech for the Nobel Prize in Chemistry, Christian Anfinsen made a historic prediction: it should in principle be possible to determine a protein's three-dimensional shape based solely on the one-dimensional string of molecules that comprise it. Finding a solution to this puzzle, known as the "protein folding problem," has stood as a grand challenge in the field of biology for half a century.

State-of-the-art machine learning algorithms can extract two-dimensional objects from photographs and render them faithfully in three dimensions. It's a technique that's applicable to augmented reality apps and robotics as well as navigation, which is why it's an acute area of research for Facebook. In a blog post today ahead of the International Conference on Computer Vision (ICCV) in Seoul, Facebook highlighted its latest advancements with respect to intelligent content-understanding. It says that together, its systems can be used to detect even complex foreground and background objects, like the legs of a chair or overlapping furniture. "[Our] research builds on recent advances in using deep learning to predict and localize objects in an image, as well as new tools and architectures for 3D shape understanding, like voxels, point clouds, and meshes," wrote Facebook researchers Georgia Gkioxari, Shubham Tulsiani, and David Novotny in a blog post.

Most recent advances in artificial intelligence -- such as mobile apps that convert speech to text -- are the result of machine learning, in which computers are turned loose on huge data sets to look for patterns. To make machine-learning applications easier to build, computer scientists have begun developing so-called probabilistic programming languages, which let researchers mix and match machine-learning techniques that have worked well in other contexts. In 2013, the U.S. Defense Advanced Research Projects Agency, an incubator of cutting-edge technology, launched a four-year program to fund probabilistic-programming research. At the Computer Vision and Pattern Recognition conference in June, MIT researchers will demonstrate that on some standard computer-vision tasks, short programs -- less than 50 lines long -- written in a probabilistic programming language are competitive with conventional systems with thousands of lines of code. "This is the first time that we're introducing probabilistic programming in the vision area," says Tejas Kulkarni, an MIT graduate student in brain and cognitive sciences and first author on the new paper.