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) …
British scientists have developed a lightweight and highly sensitive brain imaging device that can be worn as a helmet, allowing the patient to move about naturally. Results from tests of the scanner showed that patients were able to stretch, nod and even drink tea or play table tennis while their brain activity was being recorded, millisecond by millisecond, by the magnetoencephalography (MEG) system. Researchers who developed the device and published their results in the journal Nature said they hoped the new scanner would improve research and treatment for patients who can't use traditional fixed MEG scanners. This could include children with epilepsy, babies, or patients with disorders like Parkinson's disease. British scientists have developed a lightweight and highly sensitive brain imaging device that can be worn as a helmet, allowing the patient to move about naturally.
We already use AI in medicine to examine medical scans and spot signs of diabetes, among other applications. In China, though, artificial intelligence can do more than just assist medical professionals: it can help alleviate the country's doctor shortage. A hospital in Beijing, for instance, will start running all its lung scans through an algorithm that can expedite the screening process starting next month. The software was developed by a Beijing-based startup called PereDoc, and it can quickly spot nodules and other early signs of lung diseases. According to MIT's Technology Review, China has been beefing up its health care facilities with AI tools as part of its nationwide AI push, especially since there are only 1.5 doctors for every 1,000 people in the country, compared with 2.5 for every thousand in the US.
Sure, artificial intelligence might end up being the downfall of humanity as we know it -- that is, if Elon Musk's fears come to fruition -- but for the time being it's actually quite useful. A new research effort by an international team of scientists reveals that machine-learning algorithms can be a powerful tool for medicine. The group, which published its work in the journal Nature, managed to create and train an AI to successfully identify different types of brain tumors with impressive accuracy. In order to identify between different types of brain cancer, the team needed some criteria the computer could use to differentiate between them. With over 100 types of brain tumors already in the medical record, the process of identification can be tricky even for human doctors.
The 4th Industrial Revolution continues to demonstrate what I call exponential "A Triple C": Perhaps nowhere is this "A Triple C" dynamic more on display than in the realm of artificial intelligence (AI), which is expected to underpin many of the key emerging technologies and power business growth across industries. It's clear that AI is becoming the new electricity, and its rapid proliferation has happened in a very short span of three years – with a large leap in the past 12 months. But what's really exciting is AI's potential to improve lives at a pace and scale not seen before. Aiding this potential is a significant business and investment shift toward a greater focus on social good. Taken together, these dynamics are now resulting in a rising number of use cases for the application of AI to accelerate progress on the United Nations' Sustainable Development Goals (SDGs).
Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Stephen Hawking, renowned physicist and best-selling author, died on March 14 at the age of 76. Hawking is well-known as one of the greatest scientists of our time, whose discoveries transformed our knowledge of black holes and whose popular science texts inspired millions. He is also well known for having amyotrophic lateral sclerosis, a disease that left him almost completely paralyzed, and for losing his ability to speak after a bout of pneumonia that required a tracheotomy. Fortunately for us all, Hawking refused to let his disabilities prevent him from sharing his brilliant ideas and insights with all of us.
A new Journal of Internal Medicine article proposes that artificial intelligence tools, such as machine learning algorithms, have the potential for building predictive models for the diagnosis and treatment of diseases linked to imbalances in gut microbial communities, or microbiota. The article focuses mainly on patients with cancer, who often undergo treatments that can cause profound alterations in the gut microbiota and potentially contribute to the development of complications. Because research on the human microbiome is an emerging science and the application of artificial intelligence in medicine is in its infancy, it is important to consider ethical, legal, and social issues simultaneously with technical refinements required for applying these technologies to the clinic. "Artificial intelligence algorithms have the potential to change the everyday medical practices and offer the prospect of identifying new associations not yet detected by humans, which will be very useful for better understanding the complexity of the human microbiota," said author Dr. J. Luis Espinoza, of the Kindai University Faculty of Medicine, in Japan.
A radiologist's ability to make accurate diagnoses from high-quality diagnostic imaging studies directly impacts patient outcome. However, acquiring sufficient data to generate the best quality imaging comes at a cost - increased radiation dose for computed tomography (CT) and positron emission tomography (PET) or uncomfortably long scan times for magnetic resonance imaging (MRI). Now researchers with the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH) have addressed this challenge with a new technique based on artificial intelligence and machine learning, enabling clinicians to acquire higher quality images without having to collect additional data. They describe the technique - dubbed AUTOMAP (automated transform by manifold approximation) - in a paper published today in the journal Nature. "An essential part of the clinical imaging pipeline is image reconstruction, which transforms the raw data coming off the scanner into images for radiologists to evaluate," says Bo Zhu, PhD, a research fellow in the MGH Martinos Center and first author of the Nature paper.
"An essential part of the clinical imaging pipeline is image reconstruction, which transforms the raw data coming off the scanner into images for radiologists to evaluate," says Bo Zhu, PhD, a research fellow in the MGH Martinos Center and first author of the Nature paper. "The conventional approach to image reconstruction uses a chain of handcrafted signal processing modules that require expert manual parameter tuning and often are unable to handle imperfections of the raw data, such as noise. We introduce a new paradigm in which the correct image reconstruction algorithm is automatically determined by deep learning artificial intelligence. "With AUTOMAP, we've taught imaging systems to'see' the way humans learn to see after birth, not through directly programming the brain but by promoting neural connections to adapt organically through repeated training on real-world examples," Zhu explains. "This approach allows our imaging systems to automatically find the best computational strategies to produce clear, accurate images in a wide variety of imaging scenarios."
It's just before 9 a.m. on a sunny morning in late July 2015, and a dozen police officers are gathered near the Healthy Fit chiropractic and pain management clinic in north Toronto. They have a search warrant, and they're about to seize evidence in what will eventually become one of the largest health insurance benefits fraud cases in the country. Together, they take the elevator to the fifth floor of a nondescript six-storey office building just off Highway 401 and enter through the glass door of unit 502. Detective Constable Kevin Williams is among the first to go in, followed by the rest of the officers, some in full uniform, others in plain clothes. He approaches the desk and explains their plans to the receptionist, who is quiet, if unhelpful. Williams is looking for something specific. He searches the clinic's office and an examining room, but doesn't find much. Then he decides to check the closet. Opening the door, Williams traces his eyes over shelves, boxes and bags full of orthotics, orthopedic shoes and braces for treating sore backs, knees and arms. "I think there was also one or two of those tension machines, like the Dr-Ho's kind of machine," Williams recalls, referring to the electrical muscle and pain therapy device made famous by infomercials.
Artificial intelligence and robotics are disrupting every aspect of work and redefining productivity. The old ways of not just working, but also assessing capabilities, hiring and compensation, are undergoing a massive change. In a conversation with Knowledge@Wharton, Srikanth Karra, chief human resource officer at Indian IT services firm Mphasis, discusses what this means for individuals, organizations and countries. Karra said managerial jobs and tasks that are repetitive in nature will be displaced and the ability to learn new skills will be critical for individuals who want to stay relevant. Companies will need to devise new ways of training and assessing the skills of employees while countries must develop a learning ecosystem. "Work will be more contractual in nature and deep technical skills, creativity and learnability will be at a premium," he noted.