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) …
Buying new smartphones gets worse for the wallet every year. The tenth generation started at $1,549. Today, the top-shelf iPhone 12 Pro Max with more than 500gb of storage is an uncomfortable $2,369 – about a third more than the cheapest Apple laptop. On top of that, e-waste is the world's fastest-growing solid-waste stream, increasing at a rate three times faster than general waste in Australia. One or both of these factors have sparked increased demand for cheaper refurbished phones.
Researchers at China's Zhejiang University published a study last year that showed many of the most popular smart speakers and smartphones, equipped with digital assistants, could be easily tricked into being controlled by hackers. They used a technique called DolphinAttack, which translates voice commands into ultrasonic frequencies that are too high for the human ear to recognize. While the commands may go unheard by humans, the low-frequency audio commands can be picked up, recovered and then interpreted by speech recognition systems. The team were able to launch attacks, which are higher than 20kHz, by using less than £2.20 ($3) of equipment which was attached to a Galaxy S6 Edge. They used an external battery, an amplifier, and an ultrasonic transducer.
In the last three decades, we have seen a significant increase in trading goods and services through online auctions. However, this business created an attractive environment for malicious moneymakers who can commit different types of fraud activities, such as Shill Bidding (SB). The latter is predominant across many auctions but this type of fraud is difficult to detect due to its similarity to normal bidding behaviour. The unavailability of SB datasets makes the development of SB detection and classification models burdensome. Furthermore, to implement efficient SB detection models, we should produce SB data from actual auctions of commercial sites. In this study, we first scraped a large number of eBay auctions of a popular product. After preprocessing the raw auction data, we build a high-quality SB dataset based on the most reliable SB strategies. The aim of our research is to share the preprocessed auction dataset as well as the SB training (unlabelled) dataset, thereby researchers can apply various machine learning techniques by using authentic data of auctions and fraud.
The future of audio isn't wired--and Apple knows it. The iPhone's headphone jack, a beloved former hardware accessory, was eliminated with the iPhone 7. "It's clear to me that Apple has forceful, but considered opinions about how the next generation of phones should fit into our lives," the Verge's Nilay Patel wrote in his review of the phone in 2016. "But it's also clear that the iPhone 7 is a transitional step to that vision of the future, not a complete expression of it." We learned more about what that future would encompass when Apple also introduced AirPods, its wireless, Siri-imbued earbuds. However, since the launch of the iPhone 7, Apple has babied those of us not ready commit to the wireless audio way of life by including Lightning adapter–based white earbuds and a Lightning-to-3.5 mm jack dongle in the box with new iPhones.
Video: Big Green Apple: Tech giant embraces clean energy (almost) everywhere. Two years after unveiling Liam, the robotic iPhone disassembler, Apple has developed a smaller, smarter and more flexible successor called Daisy. Daisy can disassemble 200 iPhones per hour or around one every 18 seconds, which is six seconds slower than Liam's teardown time.
SHANGHAI – U.S. President Donald Trump often tweets from his iPhone about pressuring China to address its $375 billion trade surplus with the United States. But a closer look at the Apple smartphone reveals how the headline figure is distorted. The big trade imbalance -- at the heart of a potential trade war, with Trump expected to impose tariffs on Chinese imports this week -- exists in large part because of electrical goods and tech, the biggest U.S. import item from China. Apple Inc.'s iPhone, however, illustrates how a big portion of that imbalance is due to imports of American-branded products -- many of which use global suppliers for parts but are put together in China and shipped around the world. Take a look at the iPhone X. IHS Markit estimates its components cost a total of $370.25.
US President Donald Trump often tweets from his iPhone about pressuring China to address its $375bn trade surplus with the United States. But a closer look at the Apple smartphone reveals how the headline figure is distorted. The big trade imbalance - at the heart of a potential trade war, with Trump expected to impose tariffs on Chinese imports this week - exists in large part because of electrical goods and tech, the biggest US import item from China. Apple Inc's iPhone, however, illustrates how a big portion of that imbalance is due to imports of American-branded products - many of which use global suppliers for parts, but are put together in China and shipped around the world. Take a look at the iPhone X. IHS Markit estimates its components cost a total of $370.25.
Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error when accelerations are double integrated to obtain displacement. Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds. We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows. The challenge becomes estimating the latent states of each window, such as velocity and orientation, as these are not directly observable from sensor data. We demonstrate how to formulate this as an optimization problem, and show how deep recurrent neural networks can yield highly accurate trajectories, outperforming state-of-the-art shallow techniques, on a wide range of tests and attachments. In particular, we demonstrate that IONet can generalize to estimate odometry for non-periodic motion, such as a shopping trolley or baby-stroller, an extremely challenging task for existing techniques.