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) …
AUTOMAKERS, while focused on building self-driving cars, are also keen on using technology to improve existing processes and functions. Porsche is one such company. Although a global brand, the company employs just 30,000 employees -- a fraction of the workforce in comparison to many of its competitors. However, its size is also an advantage. It allows the company to innovate more intimately.
A new smart bracelet allows you to use your index finger as a phone using technology that conducts sound via vibrations through your wrist bone. The Get bracelet, which costs £200 ($250)connects to your smartphone and translates the sound from your device into vibrations, conducted into the fingers. Users just have to stick a finger in their ear to speak on the phone, and make outgoing calls by using the bracelet's voice recognition technology. Because the device uses vibration only, instead of sound, conversations can't be overheard by people nearby. Get has no buttons, and no screen, but uses your voice and gestures to control its features, according to its Italian inventors.
Researchers at the Georgia Institute of Technology have created a new type of tiny 3D-printed robot that moves by harnessing the vibration from piezoelectric actuators, ultrasound sources or even tiny speakers. The size of the world's smallest ant, these "micro-bristle-bots" could sense changes in the environment and swarm together to move materials--or perhaps one day repair injuries inside the human body. Prototypes of the robot respond to different vibration frequencies depending on their configurations, which allows researchers to control individual bots by adjusting the vibration. Though they are only 2 millimeters long, the bots can cover four times their own length in a second. The micro-bristle-bots consist of a piezoelectric actuator glued onto a polymer body that is 3D printed using two-photon polymerization lithography (TPP).
Slender marine structures such as deep-water marine risers are subjected to currents and will normally experience Vortex Induced Vibrations (VIV), which can cause fast accumulation of fatigue damage. The ocean current is often three-dimensional (3D), i.e., the direction and magnitude of the current vary throughout the water column. Today, semi-empirical tools are used by the industry to predict VIV induced fatigue on risers. The load model and hydrodynamic parameters in present VIV prediction tools are developed based on two-dimensional (2D) flow conditions, as it is challenging to consider the effect of 3D flow along the risers. Accordingly, the current profiles must be purposely made 2D during the design process, which leads to significant uncertainty in the prediction results. Further, due to the limitations in the laboratory, VIV model tests are mostly carried out under 2D flow conditions and thus little experimental data exist to document VIV response of riser subjected to varying directions of the current. However, a few experiments have been conducted with 3D current. We have used results from one of these experiments to investigate how well 1) traditional and 2) an alternative method based on a data driven prediction can describe VIV in 3D currents. Data driven modelling is particularly suited for complicated problems with many parameters and non-linear relationships. We have applied a data clustering algorithm to the experimental 3D flow data in order to identify measurable parameters that can influence responses. The riser responses are grouped based on their statistical characteristics, which relate to the direction of the flow. Furthermore we fit a random forest regression model to the measured VIV response and compare its performance with the predictions of existing VIV prediction tools (VIVANA-FD).
Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility of an early detection and diagnosis of the faulty condition can save both time and money. Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for expert knowledge and human work. The emergence of deep learning architectures in condition-based maintenance promises to ensure high performance fault diagnosis while lowering necessity for expert knowledge and human work. This paper presents developed technique for deep learning-based data-driven fault diagnosis of rotary machinery. The proposed technique input raw three axis accelerometer signal as high-definition image into deep learning layers which automatically extract signal features, enabling high classification accuracy.
You shouldn't let anyone see you enter your phone's login password -- but there could also be a danger from hackers hearing it over your smartphone's microphone. Experts from England and Sweden have shown how hacked microphones can be used to decode the sound of typing on a smartphone screen into the keys pressed. In a test, their algorithm could correctly guess 31 out of 50 four-digit login pins in just 10 attempts based on recordings made of the participants as they typed. These potential attacks would likely begin with the accidental download of malicious software -- so users should keep themselves safe by only using trusted apps. Limiting microphone access to only those apps that need it will also help to make your smartphone more secure.
Drones and self-driving cars may soon come with'spidey' senses. That's according to engineers in America, who believe the unmanned machines would benefit from sensory detectors similar to those often seen in arachinds. Specifically, they're referring the hairs on a spider's legs, which are linked to special neurons called mechanoreceptors, which flag-up danger through vibrations. If machines had similar characteristics, they'd be able to navigate more effectively in dangerous environments. Until now, sensor technology hasn't always been able to process data fast enough, or as smoothly, as nature.
In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their analysis can then require a large amount of computing power. The Big Data issue can be solved by aggregation, generating smart and meaningful data. This paper presents a new knowledge-based multi-level aggregation strategy to support decision making. Manufacturing knowledge is used at each level to design the monitoring criteria or aggregation operators. The proposed approach has been implemented as a demonstrator and successfully applied to a real machining database from the aeronautic industry. Decision Making; Machining; Knowledge based system
In this paper, we introduce the concept of intermittent learning, which enables energy harvested computing platforms to execute certain classes of machine learning tasks. We identify unique challenges to intermittent learning relating to the data and application semantics of machine learning tasks. To address these challenges, we devise an algorithm that determines a sequence of actions to achieve the desired learning objective under tight energy constraints. We further increase the energy efficiency of the system by proposing three heuristics that help an intermittent learner decide whether to learn or discard training examples at run-time. In order to provide a probabilistic bound on the completion of a learning task, we perform an energy event-based analysis that helps us analyze intermittent learning systems where the uncertainty lies in both energy and training example generation. We implement and evaluate three intermittent learning applications that learn the air quality, human presence, and vibration using solar, RF, and kinetic energy harvesters, respectively. We demonstrate that the proposed framework improves the energy efficiency of a learner by up to 100% and cuts down the number of learning examples by up to 50% when compared to state-of-the-art intermittent computing systems without our framework.
For all the advances in medical diagnostics made over the last two centuries of modern medicine, from the ability to peer deep inside the body with the help of superconducting magnets to harnessing the power of molecular biology, it seems strange that the enduring symbol of the medical profession is something as simple as the stethoscope. Hardly a medical examination goes by without the frigid kiss of a stethoscope against one's chest, while we search the practitioner's face for a telltale frown revealing something wrong from deep inside us. The stethoscope has changed little since its invention and yet remains a valuable if problematic diagnostic tool. Efforts have been made to solve these problems over the years, but only with relatively recent advances in digital signal processing (DSP), microelectromechanical systems (MEMS), and artificial intelligence has any real progress been made. This leaves so-called smart stethoscopes poised to make a real difference in diagnostics, especially in the developing world and under austere or emergency situations.