While artificial intelligence (AI) has the potential to revolutionize a number of industries, the technology isn't without its controversies. Over the past few years, researchers and developers have raised concerns around the potential impacts of widespread AI adoption--and how a lack of existing ethical frameworks may put consumers at risk. These concerns may be especially relevant to medical device manufacturers, which are increasingly using AI in new medical devices like smart monitors and health wearables. New standards and regulations on ethical AI may provide essential guidance for medical device manufacturers interested in leveraging AI. The widespread use of AI could pose a number of ethical challenges.

Concerns about ethics of AI are older than AI itself. The phrase "artificial intelligence" was first used by McCarthy and colleagues in 1955 (McCarthy et al. 1955). However, in 1920 Capek already had published his science fiction play in which robots suffering abuse rebelled against human tyranny (Capek 1920), and by 1942, Asimov had proposed his famous three "laws of robotics" about robots not harming humans, not harming other robots, and not harming themselves (Asimov 1942). During much of the last century, when AI was mostly confined to research laboratories, concerns about ethics of AI were mostly limited to futurist writers of fiction and fantasy. In this century, as AI has begun to penetrate almost all aspects of life, worries about AI ethics have started permeating mainstream media.

An EPFL Master's student has shown that artificial intelligence (AI) can be used to further automate the process of land-use classification in Switzerland, especially for rare and complicated land categories that until now have been classified manually. A stretch of land in Valais Canton served as the sample for her research. Switzerland regularly maps land use in the country in order to better track urbanization, monitor soil permeability and combat urban sprawl. Surveyors take aerial photos of the land every three years, but the survey itself is published only every six years because classifying the images into over 70 different categories is still done mostly by hand. To help speed up the process, the Swiss Federal Statistical Office (FSO) is evaluating the potential of AI with the Arealstatistik Deep Learning (ADELE) project.

The Australian Federal Court recently handed down its first-instance judgement in Thaler v Commissioner of Patents [2021] FCA 879 where the central issue considered was whether an artificial intelligence (AI) system could be an'inventor' for the purposes of the Australian Patents Act 1990 (Act) and its corresponding regulations. The Court found that an AI system can be an inventor – where'inventor' may be construed broadly to include a'person or thing that invents'1. This decision puts Australia in the spotlight as a favourable country to patent AI-created inventions – for now. Given the subject-matter and controversy generated by this decision, an appeal to the Full Federal Court is almost certain. This Federal Court decision is an appeal from a Patent Office hearing where the Office rejected Australian patent application no. Interestingly, the objection to inventorship was initially raised in a formalities objection issued within a few weeks after the application was filed, and not during examination which would be years later under normal circumstances.

The US military's AI experiments are growing particularly ambitious. The Drive reports that US Northern Command recently completed a string of tests for Global Information Dominance Experiments (GIDE), a combination of AI, cloud computing and sensors that could give the Pentagon the ability to predict events "days in advance," according to Command leader General Glen VanHerck. It's not as mystical as it sounds, but it could lead to a major change in military and government operations. The machine learning-based system observes changes in raw, real-time data that hint at possible trouble. If satellite imagery shows signs that a rival nation's submarine is preparing to leave port, for instance, the AI could flag that mobilization knowing the vessel will likely leave soon. Military analysts can take hours or even days to comb through this information -- GIDE technology could send an alert within "seconds," VanHerck said.

The structure of human interleukin-12 protein bound to its receptor, as predicted by machine-learning software.Credit: Ian Haydon, UW Medicine Institute for Protein Design Software that accurately determines the 3D shape of proteins is set to become widely available to scientists. On 15 July, the London-based company DeepMind released an open-source version of its deep-learning neural network AlphaFold 2 and described its approach in a paper in Nature1. The network dominated a protein-structure prediction competition last year. Meanwhile, an academic team has developed its own protein-prediction tool inspired by AlphaFold 2, which is already gaining popularity with scientists. That system, called RoseTTaFold, performs nearly as well as AlphaFold 2, and is described in a Science paper also published on 15 July2.

Astronomers have designed and trained a computer program that can classify tens of thousands of galaxies in just a few seconds, a task that usually takes months to accomplish. In research published today, astrophysicists from Australia have used machine learning to speed up a process that is often done manually by astronomers and citizen scientists around the world. "Galaxies come in different shapes and sizes," said lead author Mitchell Cavanagh, a Ph.D. candidate based at the University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR). "Classifying the shapes of galaxies is an important step in understanding their formation and evolution, and can even shed light on the nature of the Universe itself." Cavanagh said that with larger surveys of the sky happening all the time, astronomers are collecting too many galaxies to look at and classify on their own.

The work is a first step towards relaying real time information from space to disaster response teams. The Oxford team has developed a machine learning / artificial intelligence model called'Worldfloods' designed specifically for deployment in specialized hardware in space on low-cost satellites in Low Earth Orbit. The model is a flood segmentation model that has the purpose of detecting flood events and significantly improving disaster response operations. It has major implications in bringing down the cost of such technologies and making it accessible for low income countries. Atilim Güneş Baydin, based at the Departments of Engineering Science and Computer Science, Oxford, said: 'This will be the first time a machine learning model for this type of task will be actually deployed in space.

Artificial intelligence (AI) is at an inflection point in health care. A 50-year span of algorithm and software development has produced some powerful approaches to extracting patterns from big data. For example, deep-learning neural networks have been shown to be effective for image analysis, resulting in the first FDA-approved AI-aided diagnosis of an eye disease called diabetic retinopathy, using only photos of a patient's eye. However, the application of AI in the health care domain has also revealed many of its weaknesses, outlined in a recent guidance document from the World Health Organization (WHO). The document covers a lengthy list of topics, each of which are just as important as the last: responsible, accountable, inclusive, equitable, ethical, unbiased, responsive, sustainable, transparent, trustworthy and explainable AI.

A writer and military historian responds to Justina Ireland's "Collateral Damage." The histories of the military and technology often go hand in hand. Soldiers and military thinkers throughout the past have continually come up with new ways to fill the people over there full of holes as a means to encourage them to stop trying to do the same to their opponents. After the introduction of a new weapon or the improvement of an existing one, strategists spend their time trying to come up with the best way to deploy their forces to take advantage of the tools and/or to blunt their effectiveness by devising countermeasures. The development of the Greek phalanx helped protect soldiers from cavalry, the deployment of English longbows helped stymie large formations of enemy soldiers, new construction methods changed the shape of fortifications, line infantry helped European formations take advantage of firearms, and anti-aircraft cannons helped protect against incoming enemy aircraft.