The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that define flexible generative models and to automatically invert them to interpret real-world images. Generative probabilistic graphics programs (GPGP) consist of a stochastic scene generator, a renderer based on graphics software, a stochastic likelihood model linking the renderer's output and the data, and latent variables that adjust the fidelity of the renderer and the tolerance of the likelihood. Representations and algorithms from computer graphics are used as the deterministic backbone for highly approximate and stochastic generative models. This formulation combines probabilistic programming, computer graphics, and approximate Bayesian computation, and depends only on generalpurpose, automatic inference techniques. We describe two applications: reading sequences of degraded and adversarially obscured characters, and inferring 3D road models from vehicle-mounted camera images. Each of the probabilistic graphics programs we present relies on under 20 lines of probabilistic code, and yields accurate, approximately Bayesian inferences about real-world images.

ACM A.M. Turing Award recipient Bob Metcalfe--engineer, entrepreneur, and Professor Emeritus at the University of Texas at Austin--is embarking on his sixth career, as a Computational Engineer at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL). He is always willing to tell the story of his first career, as a researcher at the Xerox Palo Alto Research Center (PARC) where, in 1973, Metcalfe and then-graduate student David Boggs invented Ethernet, a standard for connecting computers over short distances. In the ensuing years, thanks in no small part to Metcalfe's entrepreneurship and advocacy, Ethernet has become the industry standard for local area networks. Leah Hoffmann spoke to Metcalfe about the development of Ethernet and what it has meant for the future of connectivity. You published your first paper about Ethernet in Communications in July 1976 (https://bit.ly/403Sxmm).

With the release of platforms like DALL-E 2 and Midjourney, diffusion generative models have achieved mainstream popularity, owing to their ability to generate a series of absurd, breathtaking, and often meme-worthy images from text prompts like "teddy bears working on new AI research on the moon in the 1980s." But a team of researchers at MIT's Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) thinks there could be more to diffusion generative models than just creating surreal images -- they could accelerate the development of new drugs and reduce the likelihood of adverse side effects. A paper introducing this new molecular docking model, called DiffDock, will be presented at the 11th International Conference on Learning Representations. The model's unique approach to computational drug design is a paradigm shift from current state-of-the-art tools that most pharmaceutical companies use, presenting a major opportunity for an overhaul of the traditional drug development pipeline. Drugs typically function by interacting with the proteins that make up our bodies, or proteins of bacteria and viruses.

"Much harder are tasks that require situational awareness, involving almost instantaneous adaptations to changing circumstances in the environment," explains Theodoros Stouraitis, a visiting scientist in the Interactive Robotics Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "Things become even more complicated when a robot has to interact with a human and work together to safely and successfully complete a task," adds Shen Li, a Ph.D. candidate in the MIT Department of Aeronautics and Astronautics. In a new work, described in a paper that appears in an April 2022 issue of IEEE Robotics and Automation, Li, Stouraitis, Gienger, Vijayakumar, and Shah explain the headway they've made on a more demanding problem--robot-assisted dressing with sleeved clothes. While other researchers have made state estimation predictions of this sort, what distinguishes this new work is that the MIT investigators and their partners can set a clear upper limit on the uncertainty and guarantee that the elbow will be somewhere within a prescribed box. Such an algorithm could, for instance, guide a robot to recognize the intentions of its human partner as it works collaboratively to move blocks around in an orderly manner or set a dinner table.

Antibodies are small proteins formed by the immune system with the capability of attaching to specific parts of a virus to offset it. As experts continue to fight SARS-CoV-2, the virus that triggered COVID-19, one possible defense route is a synthetic antibody that binds with the spike proteins of the virus to stop the virus from penetrating a human cell. To build an effective synthetic antibody, scientists have to understand precisely how that binding will take place. Proteins, with lumpy 3D structures comprising many folds, can adhere together in millions of combinations, so discovering the right protein complex among virtually countless contenders is very laborious. To simplify the process, MIT scientists developed a machine-learning model that can directly predict the complex that will develop when two proteins stick together.

Equidock, the machine learning system the researchers developed, can directly predict a protein complex like this in a matter of seconds. This deep-learning model can learn these types of interactions from data," says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper. The model the researchers developed, called Equidock, focuses on rigid body docking--which occurs when two proteins attach by rotating or translating in 3D space, but their shapes don't squeeze or bend. In addition to using this method with traditional models, the team wants to incorporate specific atomic interactions into Equidock so it can make more accurate predictions. These molecules bind with protein surfaces in specific ways, so rapidly determining how that attachment occurs could shorten the drug development timeline. Equidock, the machine learning system the researchers developed, can directly predict a protein complex like this in a matter of seconds. This deep-learning model can learn these types of interactions from data," says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.

Have you ever spent hours searching for a suitable dataset for your data science project? It can get pretty daunting, right? Whether you are a student or a professional looking for high-quality datasets for machine learning or data analysis projects--we've got you covered! In today's article, we will share with you a comprehensive list of 65 open machine learning datasets that you can access for free. We will regularly update this list, so feel free to suggest datasets you are using and we will make sure to add them. "Where can I get free datasets for machine learning?" Here's the list of the best open dataset finders that you can use to browse through a wide variety of niche-specific datasets for your data science projects.

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Model developed at MIT's Computer Science and Artificial Intelligence Laboratory could reduce false positives and unnecessary surgeries. Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they're still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries. One common cause of false positives are so-called "high-risk" lesions that appear suspicious on mammograms and have abnormal cells when tested by needle biopsy. In this case, the patient typically undergoes surgery to have the lesion removed; however, the lesions turn out to be benign at surgery 90 percent of the time.

It is the first graduate level, research-based AI university in the world. MBZUAI will enable graduate students, businesses, and governments to advance artificial intelligence. The university is named after His Highness Sheikh Mohamed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces, who has long advocated for the UAE's development of human capital through knowledge and scientific thinking to take the nation into the future. MBZUAI will introduce a new model of academia and research to the field of AI, providing students and faculty access to some of the world's most advanced AI systems to unleash its potential for economic and societal development. The announcement was made at a press conference at the University campus in Masdar City and was immediately followed by the first meeting of the MBZUAI Board of Trustees. Dr Sultan Ahmed Al Jaber, UAE Minister of State, who has been appointed Chair of the MBZUAI board of trustees and is spearheading the establishment of the University, said: "Mohamed bin Zayed University of Artificial Intelligence aligns with the vision of the UAE leadership that is based on sustainable development, progress and the overall well-being of humanity and underpinned by capacity-building and active participation in finding practical solutions based on innovation and state-of-the-art technology.