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) …
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California based company Cognoa is using machine learning to detect cognitive disorders in children up to 13 months earlier than traditional diagnosis methods. The company's VP of data science, Halim Abbas, told Which-50 a machine learning approach is ideal for detecting developmental delays. "Machine learning algorithms can ingest very large numbers of historical patient records, and use them to capture incredibly subtle patterns that might indicate the presence of cognitive disorders." "Highly resilient to noise and subjectivity" the process ultimately produces models that can approximate the understanding of what constitutes autism from the many different doctors who contributed to the dataset, Abbas said. "This allows Machine Learning screeners to succeed when applied at complex assessments like autism spectrum disorder, which can present a wide and highly variant set of behavioural phenotypes."
The new Google venture is called the Launchpad Studio and it was unveiled in November 2017. The aim is to provide a new health-orientated artificial intelligence access path to Google experts for start-up companies to take advantage of. The service, PharmaPhorum reports, also aims to assist new ventures via product validation and also to give them feedback with their new projects and to help to nurture them into commercially viable healthcare solutions. As part of this process, Google will give eligible new ventures $50,000 in funding plus full access to business focused Google products, such as Google Cloud. Malika Cantor, a program manager with Launchpad, told TechCrunch: "It's our hypothesis that there's a lot of learning to be extracted by looking at an industry and all the ways machine learning can be applied across that industry.
When Amazon first came out with a smart recommendation algorithm for customers, millions of consumers receive their first tailored shopping experience personalized to their own interests. This changed the consumer world and introduced us to a whole new era of shopping. Amazon's algorithms, using a method called "item-to-item collaborative filtering", are able to provide targeted shopping recommendations by creating a personalized experience for each person. Even in a very basic form, this was the beginning of using machine learning in a very practical manner. But can such artificial intelligence and machine learning also act as an enabler for changes in medicine and healthcare, as much as Amazon's algorithm changed consumerism?
"Recognition of the effects of this disease on me has been painful, and I have been slow to grasp the gravity of it," he wrote. "For me, a Parkinson's diagnosis is not a stop sign but rather a signal that I must make lifestyle changes and dedicate myself to physical therapy in hopes of slowing the disease's progression."
Machine learning could improve our ability to determine whether a new drug works in the brain, potentially enabling researchers to detect drug effects that would be missed entirely by conventional statistical tests, finds a new UCL study published in Brain. "Current statistical models are too simple. They fail to capture complex biological variations across people, discarding them as mere noise. We suspected this could partly explain why so many drug trials work in simple animals but fail in the complex brains of humans. If so, machine learning capable of modelling the human brain in its full complexity may uncover treatment effects that would otherwise be missed," said the study's lead author, Dr Parashkev Nachev (UCL Institute of Neurology).
Since heart disease is a primary killer of human beings around the world, it's no surprise that effort and focus from many AI innovators is on heart disease diagnosis and prevention. The current process to determine an individual's risk factor for a heart attack is to look at the American College of Cardiology/American Heart Association's (ACC/AHA) list of risk factors that include age, blood pressure and more. However, this is really a simplistic approach and doesn't take into account medications someone might be on, the health of the patient's other biological systems and other factors that could increase odds of a heart ailment. Several research teams, including those at Carnegie Mellon University and a study from Stephen Weng and his associates at University of Nottingham in the United Kingdom, are working toward enhancing machine learning so algorithms will be able to predict (better than humans) who is at risk and when they might be at risk for a heart attack. Preliminary results of the AI algorithms were significantly better at predicting heart attacks than the ACC/AHA guidelines.
Making decisions is not always easy, especially when choosing between two options that have both positive and negative elements, such as deciding between a job with a high salary but long hours, and a lower-paying job that allows for more leisure time. MIT neuroscientists have now discovered that making decisions in this type of situation, known as a cost-benefit conflict, is dramatically affected by chronic stress. In a study of mice, they found that stressed animals were far likelier to choose high-risk, high-payoff options. The researchers also found that impairments of a specific brain circuit underlie this abnormal decision making, and they showed that they could restore normal behavior by manipulating this circuit. If a method for tuning this circuit in humans were developed, it could help patients with disorders such as depression, addiction, and anxiety, which often feature poor decision-making.
Scientists have developed a new test that can pick out women at high risk of relapsing from breast cancer within 10 years of diagnosis. Their study looked for immune cell'hotspots' in and around tumours, and found that women who had a high number of hotspots were more likely to relapse than those with lower numbers. The new test could help more accurately assess the risk of cancer returning.
In an ordinary hospital room in Los Angeles, a young woman named Lauren Dickerson waits for her chance to make history. She's 25 years old, a teacher's assistant in a middle school, with warm eyes and computer cables emerging like futuristic dreadlocks from the bandages wrapped around her head. Three days earlier, a neurosurgeon drilled 11 holes through her skull, slid 11 wires the size of spaghetti into her brain, and connected the wires to a bank of computers. Now she's caged in by bed rails, with plastic tubes snaking up her arm and medical monitors tracking her vital signs. She tries not to move.