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) …
Google on Thursday unveiled several new security cameras that are part of its Nest smart home lineup. There's a new video doorbell, a floodlight camera to help you monitor your driveway or a dark side of your home, and two new Nest Cams -- one that's battery-powered and designed for use anywhere -- and another that's designed to monitor inside your home. In addition to new hardware, Google is also making some of the features that used to be behind a Nest Aware monthly subscription free. You can still sign up for Google's Nest Aware service to add 30-day event history, the ability to create and share clips, and turns your smart displays and speakers into listening devices that act as a makeshift home security system. The new cameras discard the Nest app and instead will be managed entirely from the Google Home app. This is a transition that Google has been making with several of its products, including Nest Wi-Fi, for the last few years.
Google is refreshing its Nest lineup with three new products and a refresh for the wired indoor Nest Cam. Among the newcomers are Google's first battery-powered Nest Cam and Doorbell, as a recent leak indicated. You'll be able to install them just about anywhere around your home, and connect them to a wired power source, if you prefer. The battery life depends on how many recorded events the devices detect and factors like the temperature and settings. Google says the Doorbell's battery will run for up to six months on a single charge, while the Nest Cam can run for up to seven months before you need to juice it up.
To tackle climate change, scientists and advocates have called for a bevy of actions that include reducing fossil fuel use, electrifying transportation, reforming agriculture, and mopping up excess carbon dioxide from the atmosphere. But many of these challenges will be insurmountable without behind-the-scenes breakthroughs in materials science. Today's materials lack key properties needed for scalable climate-friendly technologies. Batteries, for example, require improved materials that can yield higher energy densities and longer discharge times. Without such improvements, commercial batteries won't be able to power mass-market electric vehicles and support a renewable-powered grid.
With the rise of deep learning models in the field of computer vision, new possibilities for their application in industrial processes proves to return great benefits. Nevertheless, the actual fit of machine learning for highly standardised industrial processes is still under debate. This paper addresses the challenges on the industrial realization of the AI tools, considering the use case of Laser Beam Welding quality control as an example. We use object detection algorithms from the TensorFlow object detection API and adapt them to our use case using transfer learning. The baseline models we develop are used as benchmarks and evaluated and compared to models that undergo dataset scaling and hyperparameter tuning. We find that moderate scaling of the dataset via image augmentation leads to improvements in intersection over union (IoU) and recall, whereas high levels of augmentation and scaling may lead to deterioration of results. Finally, we put our results into perspective of the underlying use case and evaluate their fit.
The world is about to be deluged by artificial intelligence software that could be inside of a sticker stuck to a lamppost. What's called TinyML, a broad movement to write machine learning forms of AI that can run on very-low-powered devices, is now getting its own suite of benchmark tests of performance and power consumption. The test, MLPerf, is the creation of the MLCommons, an industry consortium that already issues annual benchmark evaluations of computers for the two parts of machine learning, so-called training, where a neural network is built by having its settings refined in multiple experiments; and so-called inference, where the finished neural network makes predictions as it receives new data. Those benchmark tests, however, were focused on conventional computing devices ranging from laptops to supercomputers. MLPerf Tiny Inference, as the new exam is called, focuses on the new frontier of things running on smartphones down to things that could be thin as a postage stamp, with no battery at all.
Researchers have figured out a way to rapidly recharge ultra dense batteries capable of powering flying cars, theoretically making them suitable for everyday use. The breakthrough with electric vertical take-off and landing (eVTOL) vehicles could enable the commercialisation of next-generation transport systems in the near future, according to the researchers from Penn State university who made the discovery. "I hope that the work we have done in this paper will give people a solid idea that we don't need another 20 years to finally get these vehicles," said Chao-Yang Wang, director of the Electrochemical Engine Center, Penn State. "I believe we have demonstrated that the eVTOL is commercially viable." The research was published today, 7 June, in the scientific journal Joule.
There is a growing interest in using electric vehicles (EVs) and drones for many applications. However, battery-oriented issues, including range anxiety and battery degradation, impede adoption. Battery swap stations are one alternative to reduce these concerns that allow the swap of depleted for full batteries in minutes. We consider the problem of deriving actions at a battery swap station when explicitly considering the uncertain arrival of swap demand, battery degradation, and replacement. We model the operations at a battery swap station using a finite horizon Markov Decision Process model for the stochastic scheduling, allocation, and inventory replenishment problem (SAIRP), which determines when and how many batteries are charged, discharged, and replaced over time. We present theoretical proofs for the monotonicity of the value function and monotone structure of an optimal policy for special SAIRP cases. Due to the curses of dimensionality, we develop a new monotone approximate dynamic programming (ADP) method, which intelligently initializes a value function approximation using regression. In computational tests, we demonstrate the superior performance of the new regression-based monotone ADP method as compared to exact methods and other monotone ADP methods. Further, with the tests, we deduce policy insights for drone swap stations.
TinyML reduces the complexity of adding AI to the edge, enabling new applications where streaming data back to the cloud is prohibitive. One common factor for all these applications is the low cost and power usage of the hardware they run on. Sure, we can detect audio and visual wake words or analyze sensor data for predictive maintenance on a desktop computer. But, for a lot of these applications to be viable, the hardware needs to be inexpensive and power efficient (so it can run on batteries for an extended time). Fortunately, the hardware is now getting to the point where running real-time analytics is possible.
The quest for new and better actuation technologies and'soft' robotics is often based on principles of biomimetics, in which machine components are designed to mimic the movement of human muscles -- and ideally, to outperform them. Despite the performance of actuators like electric motors and hydraulic pistons, their rigid form limits how they can be deployed. As robots transition to more biological forms and as people ask for more biomimetic prostheses, actuators need to evolve. Associate professor (and alum) Michael Shafer and professor Heidi Feigenbaum of Northern Arizona University's Department of Mechanical Engineering, along with graduate student researcher Diego Higueras-Ruiz, published a paper in Science Robotics presenting a new, high-performance artificial muscle technology they developed in NAU's Dynamic Active Systems Laboratory. The paper, titled "Cavatappi artificial muscles from drawing, twisting, and coiling polymer tubes," details how the new technology enables more human-like motion due to its flexibility and adaptability, but outperforms human skeletal muscle in several metrics.