"You can see it as a sort of super coding agent," says Pushmeet Kohli, a vice president at Google DeepMind who leads its AI for Science teams. "It doesn't just propose a piece of code or an edit, it actually produces a result that maybe nobody was aware of." In particular, AlphaEvolve came up with a way to improve the software Google uses to allocate jobs to its many millions of servers around the world. Google DeepMind claims the company has been using this new software across all of its data centers for more than a year, freeing up 0.7% of Google's total computing resources. That might not sound like much, but at Google's scale it's huge.

Researchers at the University of Waterloo have developed an AI model that enables computers to process a wider variety of human languages. This is an important step forward in the field given how many languages are often left behind in the programming process. African languages often don't get focused on by computer scientists, which has led to natural language processing (NLP) capabilities being limited on the continent.

In 2018, artificial intelligence took on new tasks, with these smarty-pants algorithms acing everything from disease diagnosis to crater counting. In April, the U.S. Food and Drug Administration permitted marketing of the first artificial intelligence that diagnoses health problems at primary care clinics without specialist supervision (SN: 3/31/18, p. 15). The program, which inspects eye images for signs of diabetes-related vision loss, could be a boon for people in remote or low-resource areas where ophthalmologists are scarce. Other eye-inspecting AI programs are learning to recognize everything from age-related vision loss to heart problems. One artificial intelligence is a celestial cartographer after Galileo's own heart.

Lifelogging devices are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. Given an egocentric video/images sequence acquired by the camera, our algorithm uses both the appearance extracted by means of a convolutional neural network and an object refill methodology that allows to discover objects even in case of small amount of object appearance in the collection of images. An SVM filtering strategy is applied to deal with the great part of the False Positive object candidates found by most of the state of the art object detectors. We validate our method on a new egocentric dataset of 4912 daily images acquired by 4 persons as well as on both PASCAL 2012 and MSRC datasets. We obtain for all of them results that largely outperform the state of the art approach. We make public both the EDUB dataset and the algorithm code.