Earlier today, Google presented a new vision for its flagship search engine, one that is uniquely tailored to the generative-AI moment. With advanced technology at its disposal, "Google will do the Googling for you," Liz Reid, the company's head of search, declared onstage at the company's annual software conference. Googling something rarely yields an immediate, definitive answer. You enter a query, confront a wall of blue links, open a zillion tabs, and wade through them to find the most relevant information. If that doesn't work, you refine the search and start again.

End-to-end self-supervised models have been proposed for estimating the success of future candidate grasps and video predictive models for generating future observations. However, none have yet studied these two strategies side-by-side for addressing the aforementioned grasping problem. We investigate and compare a model-free approach, to estimate the success of a candidate grasp, against a model-based alternative that exploits a self-supervised learnt predictive model that generates a future observation of the gripper about to grasp an object. Our experiments demonstrate that despite the end-to-end model-free model obtaining a best accuracy of 72%, the proposed model-based pipeline yields a significantly higher accuracy of 82%.

The spectre of artificial intelligence is worrying lots of workers, but one office is welcoming it with open arms and an apple pie in the oven. "There are many people who, at 2 a.m., are on their phones, looking at what's on the market," Fredrik Eklund, of the real-estate agency the Eklund Gomes Team, said the other day. He sat in the reception area of his Flatiron office wearing a pale-pink blazer, jeans, and thick black-framed eyeglasses. "Now they can talk to Maya. Her shop is open 24/7, and she is always there."

The software-driven successes of deep learning have been profound, but the real world is made of materials. Researchers are turning to artificial intelligence (AI) to help find new materials to provide better electronics and transportation, and the energy to run them. Despite its undeniable power, however, "Machine learning, especially the deep learning revolution, relies heavily on large amounts of data," said Carla Gomes, a computer scientist at Cornell University. "This is not how science works. "Machine learning as we know it is not enough for scientific discovery," she said. "We still have a long way to go." Nevertheless, researchers are off to a promising start in addressing materials science. One of the challenges in materials discovery is the astronomical number of compositions that might have interesting properties. "High-entropy alloys" (HEA), for example, combine four or more metals. "If you consider all the elements in the periodic table and you will find that you have ...

MILWAUKEE -- Agriculture is among the last major industries to become digitized. It's doesn't come as a major surprise, seeing as how off-road, rural environments are more challenging than roadway systems or manufacturing floors. However, as the connectivity gap continues to close, there is tremendous opportunity to capture data that can ultimately lead to transformative technologies like artificial intelligence (AI). "To put it as simply as possible, AI allows computer systems to complete tasks that are normally performed by humans," said Mark Kuehn, OEM sales manager for North America at Trimble. Given that definition, AI could mean everything from cognitive tasks like data analytics and forecasting to physical tasks like spraying weeds and picking produce.

Game developers strive to have engaging believable characters in their work. One of the elements that has been pointed out as contributing to believability is social behavior. A category of social behavior is interpersonal conflict. In our current research we compare two AI approaches to model NPC conflict resolution strategies: one using the reactive planning language ABL and another using the AI framework FAtiMA. We identify the following metrics to evaluate social behavior modeling: mapping theory, emotion, model checking, variability, policy change. In our analysis we found it was easier to map conflict concepts in ABL and the model checking process was faster. FAtiMA had better support for emotion and other emergent attributes.

A group of scientists at the U.S. Department of Energy's Ames Laboratory has developed computational quantum algorithms that are capable of efficient and highly accurate simulations of static and dynamic properties of quantum systems. The algorithms are valuable tools to gain greater insight into the physics and chemistry of complex materials, and they are specifically designed to work on existing and near-future quantum computers. Scientist Yong-Xin Yao and his research partners at Ames Lab use the power of advanced computers to speed discovery in condensed matter physics, modeling incredibly complex quantum mechanics and how they change over ultra-fast timescales. Current high performance computers can model the properties of very simple, small quantum systems, but larger or more complex systems rapidly expand the number of calculations a computer must perform to arrive at an accurate model, slowing the pace not only of computation, but also discovery. "This is a real challenge given the current early-stage of existing quantum computing capabilities," said Yao, "but it is also a very promising opportunity, since these calculations overwhelm classical computer systems, or take far too long to provide timely answers."

Say you're driving with a friend in a familiar neighborhood, and the friend asks you to turn at the next intersection. The friend doesn't say which way to turn, but since you both know it's a one-way street, it's understood. That type of reasoning is at the heart of a new artificial-intelligence framework – tested successfully on overlapping Sudoku puzzles – that could speed discovery in materials science, renewable energy technology and other areas. An interdisciplinary research team led by Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science in the Cornell Ann S. Bowers College of Computing and Information Science, has developed Deep Reasoning Networks (DRNets), which combine deep learning – even with a relatively small amount of data – with an understanding of the subject's boundaries and rules, known as "constraint reasoning." Di Chen, a computer science doctoral student in Gomes' group, is first author of "Automating Crystal-Structure Phase Mapping by Combining Deep Learning with Constraint Reasoning," published Sept. 16 in Nature Machine Intelligence.

Alán Aspuru-Guzik's research group at the University of Toronto has launched an open-access tool that promises to accelerate the discovery of new chemical reactions that underpin the development of everything from smartphones to life-saving drugs. The free tool, called Kraken, is a library of virtual, machine-learning calculated organic compounds – roughly 300,000 of them, with 190 descriptors each. It was created through a collaboration between Aspuru-Guzik's Matter Lab, the Sigman Research Group at the University of Utah, Technische Universität Berlin, Karlsruhe Institute of Technology, Vector Institute for Artificial Intelligence, the Center for Computer Assisted Synthesis at the University of Notre Dame, IBM Research and AstraZeneca "The world has no time for science as usual," says Aspuru-Guzik, a professor in U of T's departments of chemistry and computer science in the Faculty of Arts & Science. "Neither for science done in a silo. "This is a collaborative effort to accelerate catalysis science that involves a very exciting team from academia and industry." When developing a transition-metal catalyzed chemical reaction, a chemist must find a suitable combination of metal and ligand. Despite the innovations in computer-optimized ligand design led by the Sigman group, ligands would typically be identified by trial and error in the lab. With Kraken, however, chemists will eventually have a vast data-rich collection at their fingertips, reducing the number of trials necessary to achieve optimal results. "It takes a long time, a lot of money, and a whole lot of human resources to discover, develop and understand new catalysts and chemical reactions." "These are some of the tools that allow molecular scientists to precisely develop materials and drugs, from the plastics in your smartphone to the probes that allowed for humanity to achieve the COVID-19 vaccines at an unforeseen pace.

Lisbon musician Moullinex has shared an exclusive short music video showing an endlessly changing landscape of brutalist buildings drawn up by a generative design algorithm with Dezeen. Moullinex, whose real name is Luís Clara Gomes, created two videos that use artificial intelligence (AI) to imagine a series of brutalist buildings. The first video, which the artist shared on his Facebook page, is based on 200 photographs of modernist, concrete buildings. These images acted as the dataset, which was used to train a generative network via the machine learning tool StyleGAN2, to create a string of entirely non-existent buildings with similar characteristics. "It's akin to showing thousands of pictures of a cat to a child and then asking them to draw a brand new cat based on what they now know are cat-like characteristics," Gomes told Dezeen.