Rafaela Vasquez liked to work nights, alone, buffered from a world she had her reasons to distrust. One Sunday night in March 2018, Uber assigned her the Scottsdale loop. She drove a gray Volvo SUV, rigged up with cameras and lidar sensors, through the company's garage, past the rows of identical cars, past a poster depicting a driver staring down at a cell phone that warned, "It Can Wait." The clock ticked past 9:15, and Vasquez reached the route's entry point. She flipped the Volvo into autonomous mode, and the car navigated itself through a blur of suburban Arizona, past auto dealers and Zorba's Adult Shop and the check-cashing place and McDonald's.

Americans welcomed voice assistants into their homes on claims that Siri, Alexa and Google Assistant would be like quasi-human helpers, seamlessly managing our appointments, grocery lists and music libraries. From 2019 to 2021, the use of voice assistants among online adults in the United States rose to 30 percent from 21 percent, according to data from market research firm Forrester. Of the options, Siri is the most popular -- 34 percent of us have interacted with Apple's voice assistant in the last year. Amazon's Alexa is next with 32 percent; 25 percent have used Google Assistant; and Microsoft's Cortana and Samsung's Bixby trail behind with five percent each.

In April 2018, Bronx public defender Kaitlin Jackson was assigned to represent a man accused of stealing a pair of socks from a TJ Maxx store. The man said he couldn't have stolen the socks because at the time the theft occurred, he was at a hospital about three-quarters of a mile away, where his son was born about an hour later. Jackson couldn't understand how police had identified and arrested her client months after the theft. She called the Bronx District Attorney's Office, and a prosecutor told her police had identified her client from a security camera photo using facial recognition. A security guard at the store, the only witness to the theft, later told an investigator from her office that police had sent him a mugshot of her client and asked in a text message "Is this the guy?" Jackson calls that tactic "as suggestive as you can get."

Predicting molecular properties quickly and accurately is important to advancing scientific discovery and application in areas ranging from materials science to pharmaceuticals. Because experiments and simulations to explore potential options are time-consuming and costly, scientists have investigated using machine learning (ML) methods to aid in computational chemistry research. But, most ML models can only make use of known, or labeled, data. This makes it nearly impossible to predict with accuracy the properties of novel compounds. In an industry like drug discovery, there are millions of molecules from which to select for use in a potential drug candidate.

But it falls short of the definition of artificial general intelligence (AGI), which would be a machine capable of understanding the world as well as any human. In the 1950s, researchers including AI pioneer Herbert A. Simon were convinced that AGI would exist within the next few decades. Since then, AGI has proven to be a daunting, perhaps even impossible-to-achieve milestone. Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and AGI is as wide as the gulf between current space flight and faster-than-light travel. Still, others insist that AGI is drawing close within reach.

Hearing loss is a rapidly growing area of scientific research as the number of baby boomers dealing with hearing loss continues to increase as they age. To understand how hearing loss impacts people, researchers study people's ability to recognize speech. It is more difficult for people to recognize human speech if there is reverberation, some hearing impairment, or significant background noise, such as traffic noise or multiple speakers. As a result, hearing aid algorithms are often used to improve human speech recognition. To evaluate such algorithms, researchers perform experiments that aim to determine the signal-to-noise ratio at which a specific number of words (commonly 50%) are recognized.

Original article by Azfar Adib, who is currently pursuing his PhD in Electrical and Computer Engineering in Concordia University in Montreal. He is a Senior Member in the Institute of Electrical and Electronic Engineers (IEEE). A recent announcement by Meta about terminating the face recognition system in Facebook sparked worldwide attention. It comes as a sort of new reality for many Facebook users, who have been habituated for years to the automatic people recognition feature in Facebook photos and videos. Since the arrival of mankind on earth, facial outlook has remained as the most common identifier for humans.

If you're interested in obscure things, there are two reasons why your searches for items and products are likely to be less related to your interests than those of your'mainstream' peers; either you're a monetization'edge case' whose interests will only be catered to if you're also in the upper categories of economic purchasing power (for example, products and services related to'wealth management'); or the search algorithms that you're using are leveraging collaborative filtering (CF), which favors the interests of the majority. Since collaborative filtering is cheaper and more established than other potentially more capable algorithms and frameworks, it's possible that both these cases apply. CF-based search results will prioritize items that are perceived to be popular among'people like you', as best the host framework can understand what kind of a consumer you are. If you're wary of providing data profiling information to the host system – for instance, not inclined to press the'Like' buttons in Netflix and other video content services – you're likely to be classified quite generically in your earliest interactions with the system, and the recommendations you receive will reflect the most popular trends. On a streaming platform, that could mean being recommended whatever shows and movies are currently'hot', such as reality TV and forensic murder documentaries, irrespective of your interest in these.

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, Ph.D., Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, U.S.. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases." Promising applications of AI, DL, and ML presented in this issue include identifying early-stage cancers, inferring the site of the specific cancer, aiding in the assignment of appropriate therapeutic options for each patient, characterizing the tumor microenvironment, and predicting the response to immunotherapy.

Hospice is a compassionate approach focusing on quality of life for terminally ill patients and their caregivers, with approximately 1.55 million Medicare beneficiaries enrolled in hospice care for at least one day during 2018 – 17% more than in 2014. However, at least 14% of Medicare beneficiaries enrolled in hospice stayed for more than 180 days, and hospice stays beyond six months can result in substantial excess costs to healthcare organizations under value-based care arrangements. David Klebonis, COO of Palm Beach Accountable Care Organization, has developed highly interpretable machine learning models that, because of the sensitivity of the clinical decision involved, cannot only accurately predict hospice overstays to drive appropriate hospice referrals, but also surface decision criteria that satisfy clinician scrutiny and promote adoption. "Artificial intelligence and machine learning have the potential to use data to predict patients with a high probability of expiring within the next six months, so that physicians can enter into conversations with these patients and their families about the possibility of referral to hospice," he said. Klebonis, who will address the topic this month at HIMSS22, said in Florida about 58% of Medicare decedents were in hospice at the time of death.