Voice assistants can detect typing on nearby devices, which could potentially be used to work out what a person is writing on their phone from up to half a metre away. Ilia Shumailov at the University of Cambridge and his colleagues built a machine-learning system that could recognise the sound of tapping on a touchscreen and combined it with other artificial intelligence tools to try to determine what people were typing.

Way back in 1942 science fiction writer Isaac Asimov created the Three Laws of Robotics. They were written into a short story called "Runaround". Their influence on technological development has been significant and long lasting. Now, legal academic and AI expert Frank Pasquale has expanded that list. Building on Asimov's legacy, Professor Pasquale's four new laws of robotics are designed to ensure that the future development of artificial intelligence is done in the interest of humanity.

New drones equipped with a deep learning application that improves the images they collect during search and rescue missions can better distinguish people from their surroundings. Researchers from Austria's Johannes Kepler University have developed drones equipped with a deep learning application that improves the images they collect during search and rescue missions to better distinguish people from their surroundings. The team noted, "automated person detection under occlusion conditions can be notably improved by combining multi-perspective images before classification." The researchers achieved 96% precision and 93% recall rates with image integration using airborne optical sectioning, a synthetic aperture imaging technique that captures unstructured thermal light fields using camera drones, compared to 25% achieved by traditional thermal imaging. The researchers say the drones are ready for use.

In September 2019, the National Institute of Standards and Technology issued its first-ever warning for an attack on a commercial artificial intelligence algorithm. Security researchers had devised a way to attack a Proofpoint product that uses machine learning to identify spam emails. The system produced email headers that included a "score" of how likely a message was to be spam. But analyzing these scores, along with the contents of messages, made it possible to build a clone of the machine-learning model and craft spam messages that evaded detection. The vulnerability notice may be the first of many.

Humiliated by the killing of a top nuclear scientist, Iranian officials sought this week to rewrite the attack as an episode of science fiction: Israel had executed him entirely by remote control, spraying bullets from an automated machine gun propped up in a parked Nissan without a single assassin on the scene. Even hard-liners mocked the new spin. "Why don't you just say Tesla built the Nissan? It drove by itself, parked by itself, fired the shots and blew up by itself?" one hard-line social media account said. "Are you, like us, doubting this narrative?" Since the killing of the scientist on Friday, contradictory reports in the official news media about the escape or even existence of a hit team -- along with assertions of prior warnings from the Interior Ministry about the attack -- revealed tensions between competing Iranian intelligence agencies as each sought to dodge blame for an egregious security lapse.

Project Loon is using balloons such as this to set up an aerial wireless network for telecommunications.Credit: Loon The goal of an autonomous machine is to achieve an objective by making decisions while negotiating a dynamic environment. Given complete knowledge of a system's current state, artificial intelligence and machine learning can excel at this, and even outperform humans at certain tasks -- for example, when playing arcade and turn-based board games1. But beyond the idealized world of games, real-world deployment of automated machines is hampered by environments that can be noisy and chaotic, and which are not adequately observed. The difficulty of devising long-term strategies from incomplete data can also hinder the operation of independent AI agents in real-world challenges. Writing in Nature, Bellemare et al.2 describe a way forward by demonstrating that stratospheric balloons, guided by AI, can pursue a long-term strategy for positioning themselves about a location on the Equator, even when precise knowledge of buffeting winds is not known.

IBM, Microsoft and Amazon all recently announced they are either halting or pausing facial recognition technology initiatives. IBM even launched the Notre Dame-IBM Tech Ethics Lab, "a'convening sandbox' for affiliated scholars and industry leaders to explore and evaluate ethical frameworks and ideas." In my view, the governance that will yield ethical artificial intelligence (AI) -- specifically, unbiased decisioning based on AI -- won't spring from an academic sandbox. AI governance is a board-level issue. Boards of directors should care about AI governance because AI technology makes decisions that profoundly affect everyone.

Here are the most tweeted papers that were uploaded onto arXiv during November 2020. Results are powered by Arxiv Sanity Preserver. Abstract: Efficient gradient computation of the Jacobian determinant term is a core problem of the normalizing flow framework. Thus, most proposed flow models either restrict to a function class with easy evaluation of the Jacobian determinant, or an efficient estimator thereof. However, these restrictions limit the performance of such density models, frequently requiring significant depth to reach desired performance levels.

Alphabet-owned DeepMind has developed a piece of artificial intelligence software that can accurately predict the structure that proteins will fold into in a matter of days, solving a 50-year-old "grand challenge" that could pave the way for better understanding of diseases and drug discovery. Every living cell has thousands of different proteins inside that keep it alive and well. Predicting the shape that a protein will fold into is important because it determines their function and nearly all diseases, including cancer and dementia, are related to how proteins function. "Proteins are the most beautiful, gorgeous structures and the ability to predict exactly how they fold up is really very, very challenging and has occupied many people over many years," Professor Dame Janet Thornton from the European Bioinformatics Institute told journalists on a call. British research lab DeepMind's "AlphaFold" AI system was entered into a competition organized by a group called CASP (Critical Assessment for Structure Prediction). It's a community experiment organization with the mission of accelerating solutions to one problem: how to compute the 3D structure of protein molecules.

Startup Diagnoss has developed an artificial intelligence-based coding assistant to help automate the painstaking process of medical coding and billing. The Diagnoss AI medical coding engine acts as a "sidebar" to electronic health records (EHRs) and uses machine learning to improve a clinician's accuracy. The tool provides real-time feedback to medical practices during the administrative process and helps to reduce coding errors on claims. Abboud Chaballout, founder and CEO of Berkeley, California-based Diagnoss, compares the AI tool to an assistant whispering in a doctor's ear. The AI tool works similarly to the Grammarly AI grammar-checking tool.