If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Although early and requiring further research before implementation, the findings could aid doctors investigating unexplained strokes or heart failure, enabling appropriate treatment. Researchers have trained an artificial intelligence model to detect the signature of atrial fibrillation in 10-second electrocardiograms (ECG) taken from patients in normal rhythm. The study, involving almost 181,000 patients and published in The Lancet, is the first to use deep learning to identify patients with potentially undetected atrial fibrillation and had an overall accuracy of 83%. Atrial fibrillation is estimated to affect 2.7–6.1 million people in the United States and is associated with increased risk of stroke, heart failure and mortality. It is difficult to detect on a single ECG because patients' hearts can go in and out of this abnormal rhythm, so atrial fibrillation often goes undiagnosed.
Rather than relying on exit interviews and their comparisons to occasional employee surveys to determine engagement, organizations can turn instead to big data and advanced analytics to identify those workers at greatest risk of quitting. A new Harvard Business Review article outlines how applying machine learning algorithms to turnover data and employee information can provide a much more accurate picture of workplace satisfaction. This measure of "turnover propensity" comprised two main indicators: turnover shocks, which are organizational and personal events that cause workers to reconsider their jobs, and job embeddedness, which describes an employee's social ties in their workplace and interest in the work they do. Though achieving this kind of "proactive anticipation" will require a sizable investment of time and effort to develop the necessary data and algorithms, the payoff will likely be worth it: "Leaders can proactively engage valued employees at risk of leaving through interviews, to better understand how the firm can increase the odds that they stay," per HBR. More articles on leadership and management: Can your anesthesia department handle NORA?
The government's announcement of a £250 million investment into artificial intelligence (AI) is very exciting and will solve some of the healthcare systems most difficult challenges. Although the UK is making leeway in the battle against cancer, the breakthroughs are only significant if early disease detection is made sooner rather than later, helping the treatments work more efficiently. Early detection of various diseases is crucial, and in cases like ovarian cancer, a woman has no symptoms in the early stages. AI and genomics can possibly help detect this cancer early, which means treatments can start sooner and more lives can be saved. AI is already making practical improvements in the healthcare system.
DeepMind's Demis Hassabis once pointed to the human brain as a paramount inspiration for building AI with human-like intelligence. The meteoric success of deep learning showcases how insights from neuroscience--memory, learning, decision-making, vision--can be distilled into algorithms that bestow silicon minds with a shadow of our cognitive prowess. This month, the prestigious journal Nature published an entire series highlighting the symbiotic growth between neuroscience and AI. It's been a long time coming. At their core, both disciplines are solving the same central problem--intelligence--but coming from different angles, and at different levels of abstraction.
A paramedic gurney flies through the trauma bay carrying an unconscious elderly gentleman. He is already intubated and has a hive of doctors and nurses running alongside, placing intravenous lines and injecting medicine into his blood stream. He's suffered a serious head injury in a car accident. It was a cold winter afternoon in 2017, and the patient had been taken to a major regional hospital. When he arrived, the neurosurgeon on call had minutes to counsel the family on the man's prognosis, and together they needed to decide whether to operate; surgery could save the patient's life, but it could also commit him to a life dependent on a ventilator and a feeding tube, trapped in a coma or with limited brain function.
WASHINGTON – Once confined to comic books, exosuits that enhance a wearer's physical abilities took a step forward Thursday as researchers unveiled a pair of robotic shorts that assist in walking and running. The entire get-up, which includes a battery that straps around the waist and a motor on the lower back that connects to pull-cables, weighs just 5 kilograms (11 pounds) and detects its wearer's gait to appropriately adjust its output. Walking and running are very different activities from a biomechanical viewpoint, and previous devices had focused on boosting one or the other, but not both, co-author Conor Walsh from Harvard's Wyss Institute for Biologically Inspired Engineering said. "So I think it's a step towards these devices not only helping with a single activity, but devices that eventually can help people in their everyday lives, in many different ways across many different activities," he said. The breakthrough required developing a control algorithm that used three sensors to detect with 99 percent accuracy what the wearer was doing and respond accordingly.
What do Joe DiMaggio and birth control pills have to do with AI? And what does a lost submarine have to do with the future of robotics? AIQ is a book written by two statistics professors who attempt to use major moments in the history of war, sports, and data science to demonstrate how AI shapes the world today. AIQ uses plain English to explain mathematical concepts that underlie the major artificial intelligence trends today, including pattern recognition, prediction, and simultaneous localization and mapping (SLAM). The original hardcover was released in 2018, and the paperback is out today.
Breast cancer is the leading cause of cancer-related death among women, and it is difficult to diagnose. Nearly 1 in 10 cancers is misdiagnosed as not cancerous; on the other hand, the more mammograms a woman has, the greater the chance she will see a false positive result and face an unnecessary invasive procedure--most likely a biopsy. More accurate diagnostic techniques are emerging. But what if instead we relied on the guidance of an algorithm? Assad Oberai, Hughes Professor in the Aerospace and Mechanical Engineering Department at the USC Viterbi School of Engineering, asked this exact question in a recent paper published in ScienceDirect.
Researchers at the University of Utah Health have used machine learning to start making links between seemingly instinctive, random behaviors and the genetic factors that shape such behaviors. Using machine learning to study mice with differences in their genetics and age, the team found that these differences influenced the behavioral sequences the animals expressed while they foraged for food. The researchers believe the methodology could one day be applied to help understand the genomic elements that may shape complex behaviors in humans, including those that lead to disease or psychiatric disorders. Patterns of complex behavior, like searching for food, are composed of sequences that feel random, spontaneous and free. Using machine learning, we are finding discrete sequences that are reproduced more frequently than you would expect by chance and these sequences are rooted in biology." Gregg and colleagues are venturing into what has previously been considered a controversial new territory called behavioral sequencing. The aim is to understand the architecture of complex behavior and how genetics shape these patterns. The concerns surrounding behavioral genetics research are based on fears that it could lead to eugenic policies. Literally meaning "well-born," eugenics refers to the improvement of humanity using scientific methods such as selective breeding. As outlined by the Nuffield Council on Bioethics, the use of "negative eugenics" has led to some of the worst atrocities in recent history such as the segregation and sterilization of hundreds of thousands of people in the United States and Europe. However, members of the council point out that contemporary research into the area is not necessarily pursuing eugenics-based goals and that the devastating events that have occurred in the past could be learned from to prevent such abuse in the future. The council acknowledges that there are certain concerns that need to be addressed if research into the field is going to be encouraged. Defining and measuring behaviors can be challenging and there is a risk of misinterpreting or misapplying statistical estimates of heritability. Other concerns include the lack of replicated findings and difficulties in predicting how behavior develops, given how complex the interaction between genes and the environment is. However, the council concludes that despite these concerns, identifying and investigating the genes that influence behavior is still practicable and worthwhile. "There are currently no practical applications of research in the genetics of behavior within the normal range.
Imagine a couple of caffeine-addled biochemistry majors late at night in their dorm kitchen cooking up a new medicine that proves remarkably effective at soothing colds but inadvertently causes permanent behavioral changes. Those who ingest it become radically politicized and shout uncontrollably in casual conversation. Still, the concoction sells to billions of people. This sounds preposterous, because the FDA would never let such a drug reach the market. Olaf J. Groth is founding CEO of Cambrian Labs and a professor at Hult Business School.