Aurora, led by veterans of driverless car efforts at Google and Uber, says it has previously received "significant investment" from Amazon, which is known to be exploring the possibility of driverless delivery vehicles. South Korean carmaker Hyundai has also backed Aurora,which has offices in four US cities.

Estimates of koala populations have historically varied wildly. In 2016, scientists estimated there were over 300,000 koalas in Australia. In mid-2019, the Australian Koala Foundation estimated that fewer than 80,000 remained in the country, and said the number could be as low as 43,000. Concern and confusion over the koalas' numbers intensified during Australia's devastating bushfires last year, leading to news articles that the animals were "functionally extinct." But scientists challenged the accuracy of that narrative.

Has one of the most important questions of science now been answered? From the headlines you might think so. For some biology's "holy grail" had been found. For others planetary extinction could be averted. On the surface the news seemed rather mundane.

Hundreds of Google employees and more than 1,000 academic researchers are speaking out in protest after a prominent Black scientist studying the ethics of artificial intelligence said she was fired by Google after the company attempted to suppress her research and she criticized its diversity efforts. Timnit Gebru, who was the technical co-lead of Google's Ethical AI team, wrote on Twitter on Wednesday that she had been fired after sending an email to an internal group for women and allies working in the company's AI unit. The email, which was first published by the tech newsletter Platformer, referenced a dispute over a research paper, but more broadly expressed frustration at Google's diversity programs. In it, Gebru argued that "there is zero accountability" or real incentive for Google leadership to change. "Your life gets worse when you start advocating for underrepresented people, you start making the other leaders upset," Gebru wrote.

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.

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.