With Thanksgiving and Christmas just around the corner, Airports are turning to AI companies like Zensors to help relieve passenger's travel stress Air travel, whether for business or pleasure has become a regular part of millions of Americans' lives and so has the hassle and stress that flying through crowded US airports poses to travelers. From the mad dash for parking at the airport to the long lines at security checkpoints to navigating long crowded airport concourses air travel is not for the faint of heart. Despite nearly a trillion dollars expected to be spent on airport construction and expansion projects over the next 10 years, things are going to get worse before they get better as the numbers of flights and passengers continue to increase, further straining airports' capacity. More than 2.7 million passengers fly every day and airports are likely to see up to a 25% increase in passenger volume from late November through early January. Some airports are taking steps to help manage stressful holiday travel.

Waymo Chief Executive John Krafcik said the self-driving vehicle company is now offering limited "rider-only" trips in Phoenix, Arizona, as it looks beyond the robo-taxis business to generate future revenue. Waymo, a unit of Alphabet, has begun offering fully automated rides, without attendants in the vehicle, to a few hundred early users of its robo-taxi service in Phoenix, Krafcik confirmed on Sunday at a dinner with journalists ahead of a conference in Detroit. He did not say when or how quickly Waymo would expand "rider-only" services. Riders signed up for the fully automated service have signed non-disclosure agreements, he said. Waymo continues to look for new ways to sell its technology, beyond robo-taxi services, Krafcik said.

Using Artificial Intelligence (AI) with thyroid ultrasound offers a quick and non-invasive approach to thyroid cancer screening, says a new study. The study, published in the journal PLOS Pathogens, suggests that automated machine learning shows promise as an additional diagnostic tool that could improve the efficiency of thyroid cancer diagnosis. "Machine learning is a low-cost and efficient tool that could help physicians arrive at a quicker decision as to how to approach an indeterminate nodule," said the study's lead author John Eisenbrey from Thomas Jefferson University in the US. According to the researchers, at present ultrasounds can tell if a nodule looks suspicious, and then the decision is made whether to do a needle biopsy, but fine-needle biopsies only act as a peephole, they don't reveal the whole picture. As a result, some biopsies return inconclusive results as to whether the nodule is malignant, or cancerous in other words.

Like any new technology, artificial intelligence is capable of immensely good or bad outcomes. The public seems increasingly focused on the bad, especially when it comes to the potential for bias in AI. This concern is both well-founded and well-documented. It is the simulation of human processes by machines. This fear of biased AI ignores a critical fact: The deepest-rooted source of bias in AI is the human behavior it is simulating. It is the biased data set used to train the algorithm.

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Using large healthcare encounter datasets, a machine learning algorithm is able to identify patients with a common genetic disorder that carries a high risk for early heart attacks and strokes. While individuals with familial hypercholesterolaemia (FH) have 20 times the risk of developing cardiovascular disease than the general population, fewer than 10 percent of the 1.3 million Americans born with the genetic disease are diagnosed. "People born with familial hypercholesterolemia develop cardiovascular damage by puberty, often culminating in early heart attacks or the need for surgery as young or middle-aged adults," says Katherine Wilemon, founder and CEO of the FH Foundation, a non-profit research and advocacy organization. "Since diagnosis of this deadly but treatable condition has stalled in the American medical system, the FH Foundation harnessed artificial intelligence and big data to accelerate identification of those most likely to have FH." In a new study, a machine learning model created by the FH Foundation successfully leveraged healthcare encounter databases to identify individuals with the genetic disorder.

Continuing Our series of posts on how to interpret Machine Learning algorithms and predictions. Part 0 (optional) -- What is Data Science and the Data Scientist Part 1 -- Introduction to Interpretability Part 1.5 (optional) -- A Brief History of Statistics (May be useful to understand this post) Part 2 -- (this post) Interpreting models of high bias and low variance. Part 4 -- Is it possible to resolve the trade-off between bias and variance? Using Shapley to finally open the black box! In this post we will focus on the interpretation of high bias and low variance models, as we explained in the previous post, these algorithms are the easiest to interpret so assume several prerequisites in the data.

Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A–3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers' unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to ...

Our recent research indicates that patients are reluctant to use health care provided by medical artificial intelligence even when it outperforms human doctors. Because patients believe that their medical needs are unique and cannot be adequately addressed by algorithms. To realize the many advantages and cost savings that medical AI promises, care providers must find ways to overcome these misgivings. Medical artificial intelligence (AI) can perform with expert-level accuracy and deliver cost-effective care at scale. IBM's Watson diagnoses heart disease better than cardiologists do.