We are now living longer, and the number of people worldwide aged 65 and over is expected to grow from 703 million in 2019 to 2.2 billion in 2080, according to the World Population Prospects Report published by the United Nations last year. The proportion of the global population that is elderly is also on the rise, almost doubling from 5.5% in 1974 to 10.3% last year, and it is projected to grow to 20.7% by 2074. A consequence of aging is that we are more likely to have medical problems. At the same time, the healthcare system in many countries is already stretched due to a lack of workers. "There are just not enough doctors and nurses to deal with a growing elderly population," said Massimiliano Zecca, a professor of healthcare technology at Loughborough University in the U.K. In the U.S, for example, a severe shortage of doctors is expected by 2034, with between 37,800 and 124,000 physicians lacking, partly fueled by the growing number of seniors, according to a recent report by the Association of American Medical Colleges (AAMC).

Last week, WIRED published a series of in-depth, data-driven stories about a problematic algorithm the Dutch city of Rotterdam deployed with the aim of rooting out benefits fraud. In partnership with Lighthouse Reports, a European organization that specializes in investigative journalism, WIRED gained access to the inner workings of the algorithm under freedom-of-information laws and explored how it evaluates who is most likely to commit fraud. We found that the algorithm discriminates based on ethnicity and gender--unfairly giving women and minorities higher risk scores, which can lead to investigations that cause significant damage to claimants' personal lives. An interactive article digs into the guts of the algorithm, taking you through two hypothetical examples to show that while race and gender are not among the factors fed into the algorithm, other data, such as a person's Dutch language proficiency, can act as a proxy that enables discrimination. The project shows how algorithms designed to make governments more efficient--and which are often heralded as fairer and more data-driven--can covertly amplify societal biases.

The ever-increasing adoption of shared transportation modalities across the United States has the potential to fundamentally change the preferences and usage of different mobilities. It also raises several challenges with respect to the design and development of automated mobilities that can enable a large population to take advantage of this emergent technology. One such challenge is the lack of understanding of how trust in one automated mobility may impact trust in another. Without this understanding, it is difficult for researchers to determine whether future mobility solutions will have acceptance within different population groups. This study focuses on identifying the differences in trust across different mobility and how trust evolves across their use for participants who preferred an aggressive driving style. A dual mobility simulator study was designed in which 48 participants experienced two different automated mobilities (car and sidewalk). The results found that participants showed increasing levels of trust when they transitioned from the car to the sidewalk mobility. In comparison, participants showed decreasing levels of trust when they transitioned from the sidewalk to the car mobility. The findings from the study help inform and identify how people can develop trust in future mobility platforms and could inform the design of interventions that may help improve the trust and acceptance of future mobility.

Blinken warns China that assisting Russia with Ukraine would be a'serious problem' Supreme Court hears defense of President Joe Biden's student loan forgiveness plan Ukraine forces claim to have'repelled' Russia's attacks on Bakhmut region It's almost hard to remember a time before people could turn to "Dr. Some of the information was wrong. Much of it was terrifying. But it helped empower patients who could, for the first time, research their own symptoms and learn more about their conditions. Now, ChatGPT and similar language processing tools promise to upend medical care again, providing patients with more data than a simple online search and explaining conditions and treatments in language nonexperts can understand. For clinicians, these chatbots might provide a brainstorming tool, guard against mistakes and relieve some of the burden of filling out paperwork, which could alleviate burnout and allow more facetime with patients. Get all the news you need in your inbox each morning. But – and it's a big "but" – the information these digital assistants provide might be more inaccurate and misleading than basic internet searches. "I see no potential for it in medicine," said Emily Bender, a linguistics professor at the University of Washington. By their very design, these large-language technologies are inappropriate sources of medical information, she said. Others argue that large language models could supplement, though not replace, primary care. "A human in the loop is still very much needed," said Katie Link, a machine learning engineer at Hugging Face, a company that develops collaborative machine learning tools. Link, who specializes in health care and biomedicine, thinks chatbots will be useful in medicine someday, but it isn't yet ready. And whether this technology should be available to patients, as well as doctors and researchers, and how much it should be regulated remain open questions. Regardless of the debate, there's little doubt such technologies are coming – and fast. ChatGPT launched its research preview on a Monday in December. By that Wednesday, it reportedly already had 1 million users. Earlier this month, both Microsoft and Google announced plans to include AI programs similar to ChatGPT in their search engines. "The idea that we would tell patients they shouldn't use these tools seems implausible.

The retirement wars are heating up. As millions of baby boomers leave their jobs in the coming years and transition into retirement, there is a huge competition for who will manage their savings. On one hand are traditional wealth managers, firms like Edward Jones, who either employ full-time human financial advisors or empower independent contractors to help clients plan through their finances. On the other side has been the rise of "roboadvisors" like Wealthfront that use algorithms and simple financial products like ETFs to advise people at lower cost. VCs have been bullish on roboadvisors -- startups like Wealthfront and Personal Capital have each raised more than $200 million according to Crunchbase -- but there has been less investment activity trying to help the financial advisors themselves.

Given all the relentless hype about its Artificial Intelligence and its transformative potential for healthcare, it would be understandable if some health systems might be casting about in search of AI or machine learning projects they could try. But that sort of rushed, ad hoc approach is precisely the wrong one to take, says Tushar Mehrotra, senior vice president of analytics at Optum. "The only way you are going to get value out of AI is to link the clinical or business problem to the organization's overall strategy and make sure you have a rich enough data set to train the model so it generates actionable insights," said Mehrotra. "Making sure you are building and designing your AI effort the right way means putting in the work up front to create a clear understanding of what you are trying to solve so it can be embedded in the decision-making workflow," he said. "Too often, AI projects start with a quest for academic insight." At HIMSS20, Mehrotra and his colleague, Optum SVP of Artificial Intelligence and Analytics Sanji Fernando will offer their perspectives on how AI can be applied to promote growth and speed strategies for digital transformation.

Given all the relentless hype about its artificial intelligence and its transformative potential for healthcare, it would be understandable if some health systems might be casting about in search of AI or machine learning projects they could try. But that sort of rushed, ad hoc approach is precisely the wrong one to take, says Tushar Mehrotra, senior vice president of analytics at Optum. "The only way you are going to get value out of AI is to link the clinical or business problem to the organization's overall strategy and make sure you have a rich enough data set to train the model so it generates actionable insights," said Mehrotra. "Making sure you are building and designing your AI effort the right way means putting in the work up front to create a clear understanding of what you are trying to solve so it can be embedded in the decision-making workflow," he said. "Too often, AI projects start with a quest for academic insight."

The founders of Vise AI met when they were 13, a couple of teenagers more interested in applied artificial intelligence than English class. Fast-forward several years and the pair has relocated from the Midwest to San Francisco to raise money for a financial technology business they've been self-funding since 2016. As teenagers with an inordinate amount of AI knowledge, Samir Vasavada and Runik Mehrotra proved to be quite useful to large businesses, investment bankers and other financiers. Leveraging their AI know-how, they were paid $700 per hour by a consulting firm to teach financial "experts" about AI. Mehrotra, according to Vasavada, is a mathematical prodigy: "And that translates extremely well to AI, right, because what underlies AI is math," Vasavada, co-founder and chief executive officer of Vise AI, tells TechCrunch.

The founders of Vise AI met when they were 13, a couple of teenagers more interested in applied artificial intelligence than English class. Fast-forward several years and the pair has relocated from the Midwest to San Francisco to raise money for a financial technology business they've been self-funding since 2016. As teenagers with an inordinate amount of AI knowledge, Samir Vasavada and Runik Mehrotra proved to be quite useful to large businesses, investment bankers and other financiers. Leveraging their AI know-how, they were paid $700 per hour by a consulting firm to teach financial "experts" about AI. Mehrotra, according to Vasavada, is a mathematical prodigy: "And that translates extremely well to AI, right, because what underlies AI is math," Vasavada, co-founder and chief executive officer of Vise AI, tells TechCrunch.

You may think the bathroom is one of few places you can expect to be left alone. But toilets may one day be giving health advice by analysing your urine and faeces, a technology boss has claimed. The chief of a company making computer chips says artificial intelligence will one day analyse people's waste in real time. This could save the need for trips to the doctor and pick up on illnesses earlier than people do, said Sanjay Mehrotra, chief executive of Micron Technology. AI could one day be used in toilets to scan people's urine and faeces to try and pick up on any diseases or health problems earlier than someone might notice them and go to the doctor And his claims aren't so far-fetched – artificial intelligence (AI) is already capable of detecting diabetes or Alzheimer's disease from scans.