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) …
Utah-based HireVue uses video interviews to examine candidates' word choice, voice inflection, and micro gestures for subtle clues, such as whether their facial expressions contradict their words. Yale School of Management professor Jason Dana, who has studied hiring for years, recently made waves with a high-profile article in the New York Times that excoriated job interviews as useless. But when Google examined its internal evidence, it found that grades, test scores, and a school's pedigree weren't a good predictor of job success. Google created a program called qDroid, which drafts questions for interviewers based on how qDroid parses the data the applicant provided on the qualities Google emphasizes.
As data scientists, we are aware that bias exists in the world. We read up on stories about how cognitive biases can affect decision-making. We know that, for instance, a resume with a white-sounding name will receive a different response than the same resume with a black-sounding name, and that writers of performance reviews use different language to describe contributions by women and men in the workplace. We read stories in the news about ageism in healthcare and racism in mortgage lending. Data scientists are problem solvers at heart, and we love our data and our algorithms that sometimes seem to work like magic, so we may be inclined to try to solve these problems stemming from human bias by turning the decisions over to machines.
Along with an entire generation of comic book fans, Chao Wang grew up following the exploits of Wolverine, a.k.a Now an assistant professor of chemistry at University of Califormia, Riverside, Wang recently paid tribute to his childhood hero, in a chemical engineering sort of way. Wang and a group of collaborators have developed a transparent and stretchable material that could give future robots the ability to heal rapidly, similar to Wolverine's handy superpower. According to the research team, the space-age material could power artificial muscles that mend themselves after injury or normal wear-and-tear. Researchers say that the artificial skin represents the first time scientists have created an ionic conductor that's stretchable, transparent and able to heal itself.
Simply put, B uses machine learning algorithms and AI which then allows it to better use and process the data provided by the user. With our interest piqued, it was not long before we learnt that B is the star running the show. As the impressive AI, B is a health assistant designed to help you stay active, eat healthy and sleep better. Designed to analyze your body movements in real time, we love how B is able to not only give you voice cues to run faster and perform better, but the technology has been created so it is able to give you more personalized coaching. Basically B learns to get to know you.
Objective Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media.
Using machine learning to predict (& prevent!) injuries in endurance athletes: Part 1 Alan Couzens, M.Sc. I've talked about different ways that we can assess the individual'dose- response' relationship or, more specifically, how we can work out just "what it takes" for a given athlete to reach a given performance level. I have suggested that, given recent advances in machine learning, the current models are largely out-dated and that we can find more accuracy in models that look at the independent impact of volume & intensity rather than wrapping these variables into one'training stress' metric. But there is another addition to the current performance models that is far more important and has the potential to be even more powerful in its application than load- fitness modeling: Turning the focus of our models to those things that prevent us from ultimately doing more load! This is the flipside of the'more is better' dose- response model.
I am really excited to announce that the general availability of the Azure N-Series will be December 1st, 2016. Azure N-Series virtual machines are powered by NVIDIA GPUs and provide customers and developers access to industry-leading accelerated computing and visualization experiences. I am also excited to announce global access to the sizes, with N-series available in South Central US, East US, West Europe and South East Asia, all available on December 1st. We've had thousands of customers participate in the N-Series preview since we launched it back in August. We've heard positive feedback on the enhanced performance and the work we have down with NVIDIA to make this a completely turnkey experience for you.
Google researchers got an eye-scanning algorithm to figure out on its own how to detect a common form of blindness, showing the potential for artificial intelligence to transform medicine remarkably soon. The algorithm can look at retinal images and detect diabetic retinopathy--which affects almost a third of diabetes patients--as well as a highly trained ophthalmologist can. It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual deterioration of vision. If caught early it can be treated, but a sufferer may experience no symptoms early on, making screening vital.
Question How does the performance of an automated deep learning algorithm compare with manual grading by ophthalmologists for identifying diabetic retinopathy in retinal fundus photographs? Finding In 2 validation sets of 9963 images and 1748 images, at the operating point selected for high specificity, the algorithm had 90.3% and 87.0% sensitivity and 98.1% and 98.5% specificity for detecting referable diabetic retinopathy, defined as moderate or worse diabetic retinopathy or referable macular edema by the majority decision of a panel of at least 7 US board-certified ophthalmologists. At the operating point selected for high sensitivity, the algorithm had 97.5% and 96.1% sensitivity and 93.4% and 93.9% specificity in the 2 validation sets. Meaning Deep learning algorithms had high sensitivity and specificity for detecting diabetic retinopathy and macular edema in retinal fundus photographs. Importance Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly.
Artificial intelligence has become a frequent topic in the news cycle, with reports of breakthroughs in speech recognition, computer vision, and textual understanding that have made their way into a bevy of products and services that are used every day. In contrast, clinical care has yet to reach the much lower bar of automating health care information transactions in the form of electronic health records. Medical leaders in the 1960s and 1970s were already speculating about the opportunities to bring automated inference methods to patient care,1 but the methods and data had not yet reached the critical mass needed to achieve those goals. The intellectual roots of "deep learning," which power the commodity and consumer implementations of present-day artificial intelligence, were planted even earlier in the 1940s and 1950s with the development of "artificial neural network" algorithms.2,3 These algorithms, as their name suggests, are very loosely based on the way in which the brain's web of neurons adaptively becomes rewired in response to external stimuli to perform learning and pattern recognition.