Pocket-size ultrasound devices that cost 50 times less than the machines in hospitals (and connect to your phone). These are just some of the innovations now transforming medicine at a remarkable pace. No one can predict the future, but it can at least be glimpsed in the dozen inventions and concepts below. Like the people behind them, they stand at the vanguard of health care. Neither exhaustive nor exclusive, the list is, rather, representative of the recasting of public health and medical science likely to come in the 2020s.

HONG KONG – The co-founder of China's SenseTime Group Ltd. was visiting New York to encourage more collaboration with the U.S. on artificial intelligence when he heard the news: The Trump administration had blacklisted his company. Xu Bing, the 29-year-old co-founder, knew SenseTime was at risk given rising tensions between China and the U.S., but the timing took him by surprise. He was spending a few days showing off his latest products and meeting other AI researchers earlier this month when the Commerce Department put his company and seven others on its "Entity List," prohibiting American companies from providing crucial supplies like semiconductors. His phone flooded with calls and emails from worried employees and investors. SenseTime is emblematic of the clash between the world's two biggest economies.

Machines' ability to learn by processing data gleaned from sensors underlies automated vehicles, medical devices and a host of other emerging technologies. But that learning ability leaves systems vulnerable to hackers in unexpected ways, researchers at Princeton University have found. In a series of recent papers, a research team has explored how adversarial tactics applied to artificial intelligence (AI) could, for instance, trick a traffic-efficiency system into causing gridlock or manipulate a health-related AI application to reveal patients' private medical history. As an example of one such attack, the team altered a driving robot's perception of a road sign from a speed limit to a "Stop" sign, which could cause the vehicle to dangerously slam the brakes at highway speeds; in other examples, they altered Stop signs to be perceived as a variety of other traffic instructions. "If machine learning is the software of the future, we're at a very basic starting point for securing it," said Prateek Mittal, the lead researcher and an associate professor in the Department of Electrical Engineering at Princeton. "For machine learning technologies to achieve their full potential, we have to understand how machine learning works in the presence of adversaries.

The area which grew at the greatest rate was research level AI which increased by 13% in the previous 12 months. Robot process automation (RPA) and general automation both also increased by 10%, while voice recognition technology grew by 9%. The study, which was undertaken by YouGov, sought the responses of 1,000 business leaders and 4,000 UK healthcare industry employees. The sample was comprised of 84 healthcare leaders and 140 healthcare employees including NHS staff. Darren Atkins, Chief Technology Officer at East Suffolk and North Essex NHS Foundation Trust, commented: "AI in healthcare is an extremely exciting prospect. It is not about replacing staff, but allowing them to maximise their skills, be more efficient, spend more time with patients and, ultimately, get better outcomes."

While it is difficult for people to agree on a vision of utopia, it is relatively easy to agree on what a "better world" might look like. The United Nations "Sustainable Development Goals," for example, are an important set of agreed-upon global priorities in the near-term: These objectives (alleviation of poverty, food for all, etc.) are important to keep society from crumbling and to keep large swaths of humanity in misery, and they serve as common reference points for combined governmental or nonprofit initiatives. However, they don't help inform humanity as to which future scenarios we want to move closer or farther to as the human condition is radically altered by technology. As artificial intelligence and neurotechnologies become more and more a part of our lives in the coming two decades, humanity will need a shared set of goals about what kinds of intelligence we develop and unleash in the world, and I suspect that failure to do so will lead to massive conflict. Given these hypotheses, I've argued that there are only two major questions that humanity must ultimately be concerned with: In the rest of this article, I'll argue that current united human efforts at prioritization are important, but incomplete in preventing conflict and maximizing the likelihood of a beneficial long-term (40 year) outcome for humanity.

One of the most frequently-used phrases at business events these days is "the future of work." It's increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they're already present in many organizations for many different jobs. The situation brings to mind the William Gibson comment, "The future is already here--it's just not evenly distributed." The job and incumbent described below is an example of this phenomenon.

Digital agencies today are brimming with ideas that can help brands integrate with voice-enabled technology. For Niraj Ruparel, National Head- Mobile, Mindshare India, conversational commerce using voice skill technology in India means serious business. In conversation with exchange4media, he delves into the nuances of voice technology, how brands can ensure that voice interaction for users is a seamless experience and what is working in the favour of Voice as the next big digital trend. The agency's tryst with voice began in January this year, explains Ruparel after global giants Google and Amazon recognised the potential for voice in India. "Voice is pretty big in Tier 2 markets, which is in terms of the penetration or how we reach out to audiences in the rural market. If you talk about the ecosystem per se, we're talking about close to 100 crore active sim cards in India and almost 450 million sim cards are resting on feature phones where the only mode of communication is Voice, so that plays a dominant role there. But now we see those 550 million SIMs which are sitting on 400 million smartphones, 30 per cent of those people have now started querying on Voice Assistants."

Notebooks are the data scientist best friend and can also be a nightmare to work with. For someone accustomed to work with modern integrated develop environments(IDEs), working with notebooks feels like going back decades. Furthermore, modern notebook environments is mostly constrained to Python programs and lack first-class support for other programming languages. Polynote was born out of the necessity to accelerate data science experimentation at Netflix. Over the years, Netflix has built a world-class machine learning platform mostly based on JVM languages like Scala.

On October 22, 2019, the Centers for Medicare and Medicaid Services ("CMS") issued a Request for Information ("RFI") to obtain input on how CMS can utilize Artificial Intelligence ("AI") and other new technologies to improve its operations. CMS' objectives to leverage AI chiefly include identifying and preventing fraud, waste, and abuse. The RFI specifically states CMS' aim "to ensure proper claims payment, reduce provider burden, and overall, conduct program integrity activities in a more efficient manner." The RFI follows last month's White House Summit on Artificial Intelligence in Government, where over 175 government leaders and industry experts gathered to discuss how the Federal government can adopt AI "to achieve its mission and improve services to the American people." Advances in AI technologies have made the possibility of automated fraud detection at exponentially greater speed and scale a reality. A 2018 study by consulting firm McKinsey & Company estimated that machine learning could help US health insurance companies reduce fraud, waste, and abuse by $20-30 billion.

Machine learning algorithms identified patients with cancer who were at risk for short-term mortality and could benefit from immediate discussions about end-of-life preferences, according to results of a prospective study presented at Supportive Care in Oncology Symposium and published simultaneously in JAMA Oncology. "On any given day, it's actually pretty difficult to identify which patients in my clinic would benefit most from a proactive advanced care planning conversation," Ravi B. Parikh, MD, MPP, instructor of medical ethics and health policy at University of Pennsylvania and staff physician at Corporal Michael J. Crescenz VA Medical Center, said in a press release. "Patients oftentimes don't bring up their wishes and goals unless they are prompted, and doctors may not have the time to do so in a busy clinic. Having an algorithm like this may make doctors in clinic stop and [ask themselves], 'Is this is the right time to talk about this patient's preferences?'" Previous studies have shown that machine learning algorithms, using electronic health record data, can accurately predict short-term mortality among patients in general medicine settings and, with oncology-specific algorithms, among those starting chemotherapy.