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) …
There is a consensus that feature engineering often has a bigger impact on the quality of a model than the model type or its parameters. Feature selection is a key part of feature engineering, not to mention Kernel functions and hidden layers are performing implicit feature space transformations. Therefore, is feature selection then still relevant in the age of support vector machines (SVMs) and Deep Learning? First, we can fool even the most complex model types. If we provide enough noise to overshadow the true patterns, it will be hard to find them.
An algorithm developed by researchers at Stanford University proved more effective than human radiologists in diagnosing cases of pneumonia. Much research has been shared on the potential of Artificial Intelligence applied to medicine, and in some cases, can reach a level of accuracy that exceeds the performance of professionals. Following this line, Stanford researchers published a document on CheXNet, the convolutional neuronal network, which they developed with the ability to detect pneumonia symptoms. To do this, he uses the traditional method, chest radiographs. It works with 112,120 images of chest X-rays referring to 14 types of diseases.
Earlier this year, a US-based company introduced a mobile app for buying and selling cars. Customers are asked to click an image of their vehicle's rear end. Within moments of uploading the image, the car's year, make and model, and the resale value are identified. That done, offering the car, or seeking refinancing and insurance is well, a smooth ride. The technology behind all this does seem like magic.
Recently there has been a great buzz around the words "neural network" in the field of computer science and it has attracted a great deal of attention from many people. But what is this all about, how do they work, and are these things really beneficial? Essentially, neural networks are composed of layers of computational units called neurons, with connections in different layers. These networks transform data until they can classify it as an output. Each neuron multiplies an initial value by some weight, sums results with other values coming into the same neuron, adjusts the resulting number by the neuron's bias, and then normalizes the output with an activation function.
If there's one thing I can't live without, it's not my phone or my laptop or my car -- it's music. I love music and getting lost in it. My inspiration for this project is finding out what it is about a song that I enjoy so much. After using Python and some data wrangling techniques, the data frame below is what I use to do some exploratory data analysis (EDA). Again, using Python, I was able to create this visualization of distributions between my Liked (blue) and Disliked (red) songs.
Mitsui O.S.K. Lines, Ltd. (MOL) today announced that it teamed up with Fujitsu Laboratories Ltd., and Tokyo University of Marine Science and Technology to verify the accuracy of technology to estimate vessel performance at sea by applying Fujitsu's artificial intelligence (AI) technology, "FUJITSU Human Centric AI Zinrai." This project is a part of MOL's initiative to assess the effectiveness of AI technology, and aims to reduce fuel consumption and vessels' environmental impact by verifying the accuracy of the technology, using Fujitsu's AI Technology to estimate vessel performance at sea. MOL provided actual voyage data collected from MOL fleet in operation to Fujitsu Laboratories, which, along with Tokyo University of Marine Science and Technology, verified the data by using their jointly developed machine learning method. Learned the correlation of each item of operation data using Fujitsu's unique AI technology and high-dimensional statistics analysis technology, and established the technology that estimates vessel performance. Estimated the ship speed from the data other than the speed and verified the comparison between that estimated value and actual operation data, in case to assess allowance of speed.
The world's most valuable company crammed a lot into the tablespoon-sized volume of an Apple Watch. There's GPS, a heart-rate sensor, cellular connectivity, and computing resources that not long ago would have filled a desk-dwelling beige box. The wonder gadget doesn't have a sphygmomanometer for measuring blood pressure or polysomnographic equipment found in a sleep lab--but thanks to machine learning, it might be able to help with their work. Research presented at the American Heart Association meeting in Anaheim Monday claims that when paired with the right machine-learning algorithms, the Apple Watch's heart-rate sensor and step counter can make a fair prediction of whether a person has high blood pressure, or sleep apnea, in which breathing stops and starts repeatedly through the night. Both are common--and commonly undiagnosed--conditions associated with life-threatening problems, including stroke and heart attack.
We trust in science because we can verify the accuracy of its claims. We test and verify that accuracy by repeating the scientist's original experiments. What happens when those tests fail, particularly in a field that has the potential to create billions of dollars of revenue? In 2016, Nature surveyed more than 1,500 scientists and found that more than 70% of them had tried and failed to reproduce experiments by other scientists published in scientific journals. More than half couldn't even reproduce their own work.
Summary: This is the first in a series about Chatbots. In this first installment we cover the basics including their brief technological history, uses, basic design choices, and where deep learning comes into play. In subsequent articles we'll describe in more detail about how they are actually programmed and best practice dos and don'ts. According to Chatbot.org there are currently 1,331 active chatbots in the world. That's a lot for a technology that didn't even exist two or three years ago.
Rhegmatogenous retinal detachment (RRD) is a highly curable condition if properly treated early1, 2; however, if it is left untreated and develops proliferative changes, it becomes an uncontrollable condition called proliferative vitreoretinopathy (PVR). PVR is a serious condition that can result in blindness regardless of repeated treatments3,4,5. It is important, therefore, for patients to be seen and treated at a vitreoretinal centre at the early RRD stage to preserve visual function. However, establishing such vitreoretinal centres that provide advanced ophthalmological procedures is not practical because of rising social security costs, a problem that is troubling many nations around the world6. On the other hand, medical equipment has made remarkable advances, and one such advancement is the ultra–wide-field scanning laser ophthalmoscope (Optos 200Tx; Optos PLC, Dunfermline, United Kingdom).