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) …
Computer vision is the field of study surrounding how computers see and understand digital images and videos. Computer vision spans all tasks performed by biological vision systems, including "seeing" or sensing a visual stimulus, understanding what is being seen, and extracting complex information into a form that can be used in other processes. This interdisciplinary field simulates and automates these elements of human vision systems using sensors, computers, and machine learning algorithms. Computer vision is the theory underlying artificial intelligence systems' ability to see and understand their surrounding environment. There are many examples of computer vision applied because its theory spans any area where a computer will see its surroundings in some form.
The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images. It is an important extension to the GAN model and requires a conceptual shift away from a discriminator that predicts the probability of a generated image being "real" and toward the idea of a critic model that scores the "realness" of a given image. This conceptual shift is motivated mathematically using the earth mover distance, or Wasserstein distance, to train the GAN that measures the distance between the data distribution observed in the training dataset and the distribution observed in the generated examples. In this post, you will discover how to implement Wasserstein loss for Generative Adversarial Networks. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.
The performance of deep network learning strongly depends on the choice of the non-linear activation function associated with each neuron. However, deciding on the best activation is non-trivial and the choice depends on the architecture, hyper-parameters, and even on the dataset. Typically these activations are fixed by hand before training. Here, we demonstrate how to eliminate the reliance on first picking fixed activation functions by using flexible parametric rational functions instead. The resulting Padé Activation Units (PAUs) can both approximate common activation functions and also learn new ones while providing compact representations.
Sussing out water potability in the field is sometimes nigh impossible, depending on the remoteness of the region. The United Nations estimates that 2 million tons of sewage and industrial and agricultural waste are discharged into the world's water supply each day. And while a range of chemical kits can test for bacterial risks, not all are equally thorough. Even with recent technological advances, water pollution leads to roughly 9 million premature deaths a year and 16% of all deaths worldwide. This motivated a pair of researchers at the Thomas Jefferson High School for Science and Technology and the Department of Computer Science at the University of Maryland to investigate an AI-powered Android app capable of detecting water impurity, which they describe in a newly published paper on the preprint server Arxiv.org
Question Is a convolutional neural network able to extract prognostic information from chest radiographs? Findings In this prognostic study of data from 2 randomized clinical trials (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [n 10 464] and National Lung Screening Trial [n 5493]), a convolutional neural network identified persons at high risk of long-term mortality based on their chest radiographs, even with adjustment for the radiologists' diagnostic findings and standard risk factors. Meaning Individuals at high risk of mortality based on chest radiography may benefit from prevention, screening, and lifestyle interventions. Importance Chest radiography is the most common diagnostic imaging test in medicine and may also provide information about longevity and prognosis. Objective To develop and test a convolutional neural network (CNN) (named CXR-risk) to predict long-term mortality, including noncancer death, from chest radiographs. Design, Setting, and Participants In this prognostic study, CXR-risk CNN development (n 41 856) and testing (n 10 464) used data from the screening radiography arm of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) (n 52 320), a community cohort of asymptomatic nonsmokers and smokers (aged 55-74 years) enrolled at 10 US sites from November 8, 1993, through July 2, 2001. External testing used data from the screening radiography arm of the National Lung Screening Trial (NLST) (n 5493), a community cohort of heavy smokers (aged 55-74 years) enrolled at 21 US sites from August 2002, through April 2004. Data analysis was performed from January 1, 2018, to May 23, 2019. Exposure Deep learning CXR-risk score (very low, low, moderate, high, and very high) based on CNN analysis of the enrollment radiograph.
THE CHILD chess prodigy who created a computer that outplays human grandmasters--Demis Hassabis, founder of DeepMind--explains how games are a testing ground for algorithms and what real-world challenges he hopes to tackle with artificial intelligence. And, what can AlphaZero, the game-playing computer, teach human players? Kenneth Cukier also speaks to chess players Natasha Regan and Matthew Sadler, the authors of "Game Changer" on AlphaZero's chess strategy, as well as the chess historian Dominic Lawson about the future of machine intelligence and its interplay with human wisdom. Upgrade your inbox and get our Daily Dispatch and Editor's Picks.
A few years back, most of our data was structured or textual. Nowadays, with the Internet of Things (IoT) a large share of the data is made up of images and videos. There's nothing wrong with that, and it may seem like there's no problem here, but the thing is that many of the systems utilizing machine learning or deep learning are trained in a supervised way, so they require the data to be labeled. The fact that we produce vast amounts of data every day doesn't help either; we've reached a point where there aren't enough people to label all the data that's being created. There are databases that offer labeled data, including ImageNet which is a database with over 14 million images.
China has been often touted as the fastest emerging hub for AI development, even surpassing the superpowers such as the USA in the emerging tech. Chinese companies and government are taking the analytics and AI play quite seriously, bringing newer and favourable policies around its adoption. Numbers suggest that in 2018, 60 per cent of total global AI investments poured into China with investments from VCs, private equity and the Chinese government. Not just the companies but educational institutes are taking AI seriously as many schools are teaching AI courses to make its citizens AI-ready. There is no doubt that China has been serious about its AI strategy, but is its power and supremacy in artificial intelligence real or exaggerated?
Artificial intelligence has been touted by some in the security community as the silver bullet in malware detection. Its proponents say it's superior to traditional antivirus since it can catch new variants and never-before-seen malware--think zero-day exploits--that are the Achilles heel of antivirus. One of its biggest proponents is the security firm BlackBerry Cylance, which has staked its business model on the artificial intelligence engine in its endpoint PROTECT detection system, which the company says has the ability to detect new malicious files two years before their authors even create them. But researchers in Australia say they've found a way to subvert the machine-learning algorithm in PROTECT and cause it to falsely tag already known malware as "goodware." The method doesn't involve altering the malicious code, as hackers generally do to evade detection.