A team of Chinese researchers funded by China's People's Liberation Army (PLA) recently claimed to have used artificial intelligence (AI) to design an electrical layout of a warship with 100 percent accuracy and at an unprecedented speed. A team of researchers from the China Ship Design and Research Center, headed by Luo Wei, a senior engineer with the ship design center, published a paper in the Chinese-language journal Computer Integrated Manufacturing Systems on February 27. The researchers claimed in the paper that their AI designer took only a day to complete work that humans would need nearly a year to achieve with the most advanced computer tools. Considering the scale and complexity of modern warships, mistakes are sure to happen during the design process, and it can take several hours to discover and rectify them. However, when the researchers put the AI designer to the test, with more than 400 challenging tasks, they found that the AI could accomplish 100 percent accuracy.

"I've spent the majority of my adult life working dead-end jobs for minimum wage and I have little to no relationship with any educational institution," he says. "But this is such a powerful tool. I've managed to create the blueprint for the most hyped pair of sneakers in the world, and I think that's really saying something." Skjellerup's referring to a series of Nike shoes that he showcased on Instagram last week, which look almost exactly like the kind of thing Simone Rocha would design, surfaced in laser-cut mesh, rubberised petals, and ribboned laces. Far from the haunted DALL-E renderings that have been popularised online – all scorched edges and Francis Bacon wails – Skjellerup's creations manage to look real.

Recent developments in artificial intelligence (AI) and machine learning, such as deep learning, has the potential to make medical decision-making more efficient and accurate. Deep learning technologies can improve how medical doctors gather and analyze patient data as a part of diagnostic procedures, prognoses and predictions, treatments, and prevention of disease (Becker, 2019; Ienca & Ignatiadis, 2020; Topol, 2019a, 2019b). However, applied artificial intelligence raises numerous ethical problems, such as the severe risk of error and bias (Ienca & Ignatiadis, 2020, p. 82; Marcus & Davis, 2019), lack of transparency (Müller, 2020), and disruption of accountability (De Laat, 2018). Describing the ethical challenges and concerns has so far been the main focus of the increasing research literature in general AI ethics (Müller, 2020) and ethics of medical AI (e.g., Char et al., 2018, 2020; Grote & Berens, 2019; McDougall, 2019; Vayena et al., 2018). Furthermore, if clinicians' decisions are to be substantially assisted, or even replaced by AI and machine learning, shared decision-making--a central ethical ideal in medicine that protects patient autonomy by letting patients make informed choices about their healthcare in line with their values--is challenged.

As technology has advanced and become ubiquitous in our lives, a common philosophical question is whether technology itself is neutral. There are many good arguments to be made that it is -- and that it is how technology is used and deployed that creates good or bad outcomes for individuals, companies, and society. This question is important for the digital transformation shaping businesses today. With data acting as the fuel for artificial intelligence, the issues surrounding customer privacy and data tracking are increasing. Organizations and governments are recognizing this, as evidenced by the European Union's General Data Protection Regulation, which went into effect in 2018 to protect the privacy of European citizens.

"Like all new technology, artificial intelligence…can also be misused and cause harm", warmed Tedros Adhanom Ghebreyesus, Director-General of the World health Organization (WHO). To regulate and govern AI, WHO published new guidance that provides six principles to limit the risks and maximize the opportunities intrinsic to AI for health. Artificial Intelligence (#AI) holds enormous potential for improving the health of millions of people around, but only if ethics & human rights are put at the heart of its design, deployment, & use. WHO's Ethics and governance of artificial intelligence for health report points out that AI can be and, in some wealthy countries is already being, used to improve the speed and accuracy of diagnosis and screening for diseases; assist with clinical care; strengthen health research and drug development; and support diverse public health interventions, including outbreak response and health systems management. AI could also empower patients to take greater control of their own health care and enable resource-poor countries to bridge health service access gaps.

"Like all new technology, artificial intelligence…can also be misused and cause harm", warmed Tedros Adhanom Ghebreyesus, Director-General of the World health Organization (WHO). To regulate and govern AI, WHO published new guidance that provides six principles to limit the risks and maximize the opportunities intrinsic to AI for health. Artificial Intelligence (#AI) holds enormous potential for improving the health of millions of people around, but only if ethics & human rights are put at the heart of its design, deployment, & use. WHO's Ethics and governance of artificial intelligence for health report points out that AI can be and, in some wealthy countries is already being, used to improve the speed and accuracy of diagnosis and screening for diseases; assist with clinical care; strengthen health research and drug development; and support diverse public health interventions, including outbreak response and health systems management. AI could also empower patients to take greater control of their own health care and enable resource-poor countries to bridge health service access gaps.

People who are interested in AI often ask me what an AI designer is, and I've attempted to answer that question in this article. I wanted to go a step further, by helping designers and product teams understand how designing AI-based product experiences is different from traditional product design. Here is what I've learned over the last two years of managing AI design & innovation teams, about how AI-first product thinking is evolving the traditional product design process. When the transition from web to mobile occurred (more than a decade ago) I remember feeling confused as a designer. Exactly what did mobile-first design really mean? How was designing for mobile going to be different from designing websites?

In recent years, we have seen many products being created with increasing participation of Artificial Intelligence (AI), Machine Learning, and other wild cards that compose the last decade's frenzy: Data Science. Good examples start with an AI Designer (or artificial intelligence designer), another design buddy from startups immersed in Data Science. In charge of transforming machine learning, data relationship, and manipulation techniques into excellent experiences for users and the business, AI Designers "teach the machine to take into account human experience", working as part of a tech squad to shape new technologies. AI Designers work with engineers to create tools for collecting and annotating data to design platforms that optimize the efficiency of these processes and make AI intuitive in identifying and collecting good quality data. Frequently asked questions from designers at Apple have brought data scientists and developers closer to the product design process.

In recent years, we have seen many products being created with increasing participation of Artificial Intelligence (AI), Machine Learning, and other wild cards that compose the last decade's frenzy: Data Science. In this field, the acronym MLUX circulates around the world and refers to Machine Learning User Experience, a path of no return. Products that can guess when you are leaving home, what you would like to eat, what is the best route to get to work and et cetera; instigate practical, ethical, and methodological questions. It doesn't take a truckload of information to understand this, just good examples of content are enough to illustrate how Research (with a capital "R" and everything!) in technology, especially UX, has exchanged insights and data with machines that learn while we use them. Good examples start with an AI Designer (or artificial intelligence designer), another design buddy from startups immersed in Data Science.

If an advanced AI system were instructed to make paper clips, or to fetch coffee, we would not want it to carry out this task at any cost. For example, we would rather the AI not kill anyone in the process or use valuable resources that ought to be used for other purposes. Rather, we want the AI to know how to achieve this goal in a way that's consistent with human values (i.e. Figuring out how to design AI systems so that they do not inadvertently act in ways that would be contrary to human values is known as the Value Alignment Problem. It's no revelation to point out misalignment between ANI and humans today, nor that AI designers need to better understand their users' values.