A drone is flown during a property inspection following Hurricane Harvey in Houston. The mass destruction brought on by Harvey has been a seminal moment for drone operators, proving that they can effectively map flooding, locate people in need of rescue and verify damage to speed insurance claims. A drone is flown during a property inspection following Hurricane Harvey in Houston. The mass destruction brought on by Harvey has been a seminal moment for drone operators, proving that they can effectively map flooding, locate people in need of rescue and verify damage to speed insurance claims. Tropical Storm Harvey disrupted at least 17 emergency call centers and 320 cellular sites, and it caused outages for more than 148,000 Internet, TV, and phone customers, according to the Federal Communications Commission.

The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. In multi-label classification, we want to predict multiple output variables for each input instance. This different from the'standard' case (binary, or multi-class classification) which involves only a single target variable. MEKA is based on the WEKA Machine Learning Toolkit; it includes dozens of multi-label methods from the scientific literature, as well as a wrapper to the related MULAN framework. NEW RELEASE April 12, 2017: Meka 1.9.1 is released.

A new $240 million center at MIT may help advance the field of artificial intelligence by developing novel devices and materials to power the latest machine-learning algorithms. It could, perhaps, also help IBM reclaim its reputation for doing cutting-edge AI. The project, announced by IBM and MIT today, will research new approaches in deep learning, a technique in AI that has led to big advances in areas such as machine vision and voice recognition. But it will also explore completely new computing devices, materials, and physical phenomena, including efforts to harness quantum computers--exotic but potentially very powerful new machines--to make AI even more capable. "A lot of innovation is happening using standard silicon and architectures, but what about the devices and the material science?" says Dario Gil, vice president of AI at IBM Research.

You might not have, but Alexa did. Voice assistants have been successfully hijacked using sounds above the range of human hearing. Once in, hackers were able to make phone calls, post on social media and disconnect wireless services, among other things. Assistants falling for the ploy included Amazon Alexa, Apple's Siri, Google Now, Samsung S Voice, Microsoft Cortana and Huawei HiVoice, as well as some voice control systems used in cars. The hack was created by Guoming Zhang, Chen Yan and their team at Zhejiang University in China.

The House just passed a bipartisan bill to encourage autonomous vehicles testing. On Wednesday, the House of Representatives did something that's woefully uncommon these days: It passed a bill with bipartisan support. The bill, called the SELF DRIVE Act, lays out a basic federal framework for autonomous vehicle regulation, signaling that federal lawmakers are finally ready to think seriously about self-driving cars and what they mean for the future of the country. "With this legislation, innovation can flourish without the heavy hand of government," said Representative Bob Latta, the Ohio Republican who heads up the Digital Commerce and Consumer Protection Subcommittee, in a floor speech just before the SELF DRIVE Act passed by a two-thirds majority. The Senate will need to pass its own bill before the legislative framework can become law.

Also, neural networks are very difficult for humans to interpret. You may be in a situation where it is important that you know exactly what factors in your model lead you to your results. That can be extremely difficult with a neural network considering the massive amount of factors that go into a neural network's output. Simpler ML models can show more precisely what factors lead to your results.

Science fiction has an uncanny ability to predict the future of technology, from Star Trek's Padd, essentially an iPad, to the Jetsons' robot vacuum, basically a Roomba. Now that the voice assistant is here, that's another checklist off the sci-fi predictor, but while our Alexas, Siris, Cortanas and Google Assistants are pretty basic right now, if sci-fi continues its great prelude to the future, what will the computers of the future really be like? According to Amazon's head of devices, Dave Limp, the next phase in computing is less about the physical thing and more about how and where you access it. He says: "We think of it as ambient computing, which is computer access that's less dedicated personally to you but more ubiquitous. "Our vision is to create that Star Trek computer and work backwards from that.

For drone users, Hurricane Harvey is likely to be the event that propelled unmanned aircraft to become an integral part of government and corporate disaster-recovery efforts. In the first six days after the storm hit, the Federal Aviation Administration issued more than 40 separate authorizations for emergency drone activities above flood-ravaged Houston and surrounding areas. They ranged from inspecting roadways to checking railroad tracks to assessing the condition of water plants, oil refineries and power lines. That total climbed above 70 last Friday and topped 100 by Sunday, including some flights prohibited under routine circumstances, according to people familiar with the details. Industry officials said all of the operations--except for a handful flown by media outlets--were conducted in conjunction with, or on behalf of, local, state or federal agencies.

In essence, people who interact with advanced technology want to be able to trust it appropriately, and then act on that trust. In interpersonal relationships, and otherwise, humans act largely based on trust. For example, a supervisor asks a subordinate to accomplish a task based on several factors that indicate they can trust them to accomplish that task. When consumers make purchases, they do so with trust that the product will perform as promised. Likewise, when using something like an autonomous vehicle, the user must be able to trust it appropriately in order to use it properly. With the rapid advancement of the capabilities of intelligent computing technology to do tasks that were previously assumed to be too complicated for computers, there has been much recent discussion regarding how humans can trust this technology - although the connection to trust is not always made explicit, per se.

Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection, has attracted a lot of interest in computer vision. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics in salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance and suggest future research directions.