You've probably seen a handful of amazing black hole images on the internet, like the one below. Or perhaps you've seen this illustration, which shows the immense mass of a black hole warping the space around it into a gravitational lens: And here's a view of a black hole belching particles near the speed of light -- also called relativistic jet or "blazar": No matter how awe-inspiring these images are, though, they're fantasy. We have no idea how the event horizon (or boundary) of a black hole might actually look to the human eye, since the gravity of black holes is so strong that not even light can escape. Black holes are also incredibly far away, and usually very compact. And we don't really know what happens as an object approaches an event horizon, or what, if anything, might be on the other side.

Evans Data's Global Development Survey has revealed that the Internet of Things (IoT) was the foremost priority for developers working on artificial intelligence across a range of technologies including machine learning, neural networks, deep learning, and pattern recognition. Though targets for these technologies remain fragmented, IoT was the foremost target for all of them and in most instances the only target that gained a double digit response. Non-computer related professional, scientific and technical services came second as a target for the mentioned disciplines while it was first in the category of Natural Language Processing. Prescriptive analytics, fact extraction and reasoning, machine translation, and image recognition were among the most commonly cited areas of work in machine learning applications, with over 30% of developers working with machine learning mentioning those areas for development. Janel Garvin, CEO of Evans Data, said: "All the related disciplines that are commonly lumped together as artificial intelligence are being stimulated by the burgeoning growth of Internet of Things. These technologies are being incorporated very rapidly into the design and development process across a host of industries, and types of applications, but it's IoT that is the strongest driver."

While hardware vendors view it as another vehicle for selling more chips and servers that would allow enterprises to move processing power closer to data, the torrent of sensor and other information the Internet of Things (IoT) is expected to generate also is driving adoption of artificial intelligence technology, concludes a new survey of software developers. Evans Data Corp., the market intelligence specialist based on Santa Cruz, Calif., released a survey of developers this week that placed IoT at the top of a list of technologies propelling AI adoption. The findings are somewhat surprising given the amount of development related to big data technologies. The developer survey nevertheless found that IoT topped the list of AI drivers that included machine learning, neural networks, deep learning and pattern recognition technologies. "All the related disciplines that are commonly lumped together as artificial intelligence are being stimulated by the burgeoning growth of Internet of Things."

Driving styles have a great influence on vehicle fuel economy, active safety, and drivability. To recognize driving styles of path-tracking behaviors for different divers, a statistical pattern-recognition method is developed to deal with the uncertainty of driving styles or characteristics based on probability density estimation. First, to describe driver path-tracking styles, vehicle speed and throttle opening are selected as the discriminative parameters, and a conditional kernel density function of vehicle speed and throttle opening is built, respectively, to describe the uncertainty and probability of two representative driving styles, e.g., aggressive and normal. Meanwhile, a posterior probability of each element in feature vector is obtained using full Bayesian theory. Second, a Euclidean distance method is involved to decide to which class the driver should be subject instead of calculating the complex covariance between every two elements of feature vectors. By comparing the Euclidean distance between every elements in feature vector, driving styles are classified into seven levels ranging from low normal to high aggressive. Subsequently, to show benefits of the proposed pattern-recognition method, a cross-validated method is used, compared with a fuzzy logic-based pattern-recognition method. The experiment results show that the proposed statistical pattern-recognition method for driving styles based on kernel density estimation is more efficient and stable than the fuzzy logic-based method.

Innate immune cells act as a surveillance system, detecting and responding to pathogens and endogenous danger signals. The complex patterns of signals they receive are detected by a variety of pattern recognition receptors (PRRs). On page 1232 of this issue, Zanoni et al. (1) find that innate immune responses to microbial products do not occur in a vacuum; rather, there is a complex array of danger signals in surrounding damaged tissue that can determine an immune cell type–specific response to pathogens. They describe a host-derived lipid that binds to a PRR to induce a hyperactive innate immune response that enhances long-lived protective immunity against invading microbes.

June 1, 2016, The Internet of Things topped the target list for developers working with artificial intelligence across a wide spectrum of technologies including machine learning, neural networks, deep learning, and pattern recognition, according to Evans Data's just released Global Development Survey. While targets for these technologies remain fragmented, IoT was the top target for all of them and in most cases the only target with a double digit response. Non-computer related professional, scientific and technical services was cited second as a target for the above disciplines, and was first in the category of Natural Language Processing. "All the related disciplines that are commonly lumped together as artificial intelligence are being stimulated by the burgeoning growth of Internet of Things," said Janel Garvin, CEO of Evans Data. "These technologies are being incorporated very rapidly into the design and development process across a host of industries, and types of applications, but it's IoT that is the strongest driver."

Stock photo giant Shutterstock is boosting its artificial intelligence (AI) credentials today with the launch of a new reverse image search feature within its iOS app. The New York-based company offers more than 80 million images for bloggers and media outlets, but keyword searches aren't always the most effective way to find images relevant to a story. If you want to search for photos that are similar to ones you already have in your possession, or if you want to find alternative photos based on the shapes, mood, color scheme, and general mise en scène around you, reverse image search comes into play. You can search Shutterstock by using the camera on your iPhone or the photos on your camera roll to find similar images. The launch comes three months after Shutterstock first introduced the feature through its desktop version, though extending it to smartphones does feel like a natural move, given that smartphones are cameras in their own right. "When we unveiled Reverse Image Search this past spring, we knew that it was a perfect fit for our mobile application -- it's arguably one of the best use cases for computer vision technology, in general," said Shutterstock CEO and founder Jon Oringer.

Amazon is looking to launch a new service for businesses which will enable them to run artificial intelligence (AI) software on the cloud. Currently, the company is testing the new service, which is aimed at stepping up competition with rivals such as Google, Microsoft and IBM, sources familiar with the matter told Bloomberg. The service aims to create more powerful applications that can perform tasks like pattern recognition and speech transcription, by allowing businesses to run a range of AI software. Some AWS clients are already testing the new services, sources told the publication, requesting anonymity, as the announcement regarding the roll out hasn't been made. An Amazon spokeswoman said that the company is currently working on other machine learning capabilities for cloud customers.

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code.

In the picture above, you see some dude (it me) taking a picture of himself in his dingy bathroom mirror. A classic mirror selfie, if you will. When even the most advanced artificially intelligent image recognition software sees that image, however, it sees something very different. It might see a man, a phone, a white checkered shirt--this would be a cutting-edge analysis that's only newly possible, by the way--but it wouldn't identify the image as a mirror selfie, or a selfie at all. Solving the mirror selfie problem is one of the most important questions left when it comes to machine vision: Image recognition software can now fairly reliably describe what it's seeing, but it lacks the cultural knowledge or reasoning skills to put it into context.