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) …
Robot bridge inspector uses sensors and machine learning to hunt for defects Researchers at the University of Nevada have developed an autonomous robot, designed to inspect bridges and detect any structural damage before it can cause potential injury. The four-wheeled robot bridge inspector, called Seekur, uses a variety of tools to carry out its important task. Researchers at the University of Nevada have developed an autonomous robot, designed to inspect bridges and detect any structural damage before it can cause potential injury. The four-wheeled robot bridge inspector, called Seekur, uses a variety of tools to carry out its important task.
Summary: Reinforcement Learning (RL) is likely to be the next big push in artificial intelligence. It's the core technique for robotics, smart IoT, game play, and many other emerging areas. But the concept of modeling in RL is very different from our statistical techniques and deep learning. In this two part series we'll take a look at the basics of RL models, how they're built and used. In the next part, we'll address some of the complexities that make development a challenge.
To beat Big Data, according to German electronics company Robert Bosch, we need to tier the solution by making every level smart -- from edge sensors to concentration hubs to analytics in the cloud. Luckily, we have the smart sensors of the brain -- eye, ears, nose, taste-buds and touch sensitivity -- as the smartest model in the universe (as we know it) after which to fashion our electronic Big Data solutions to the Internet of Things (IoT), said Marcellino Gemelli, head of business development at Bosch Sensortec. "We need to feed our Big Data problems into a model generator based on the human brain, then use this model to generate a prediction of what the optimal solution will look like," Gemelli told the attendees at the recent SEMI MEMS & Sensor Executive Congress (MSEC). "These machine learning solutions will work on multiple levels, because of the versatility of the neuron." Neurons are the microprocessors of the brain -- accepting thousands of Big Data inputs, but outputting a single voltage spike down their axon after receiving the right kind of input from thousands of dendrites mediated by memory synapses.
The new Google venture is called the Launchpad Studio and it was unveiled in November 2017. The aim is to provide a new health-orientated artificial intelligence access path to Google experts for start-up companies to take advantage of. The service, PharmaPhorum reports, also aims to assist new ventures via product validation and also to give them feedback with their new projects and to help to nurture them into commercially viable healthcare solutions. As part of this process, Google will give eligible new ventures $50,000 in funding plus full access to business focused Google products, such as Google Cloud. Malika Cantor, a program manager with Launchpad, told TechCrunch: "It's our hypothesis that there's a lot of learning to be extracted by looking at an industry and all the ways machine learning can be applied across that industry.
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Cloud computing and the Internet of Things (IoT) have spent the last several years in a sort of maximum-acceleration race where they've lapped the other players several times over and have only one another to measure against. Neither is slowing down, particularly the IoT. According to analysis firm Gartner, the number of IoT devices will hit 20.8 billion by 2020. The world population is expected to reach 8 billion in 2020, meaning there will be 2.5 IoT devices per person on the entire planet. In 2016, the IoT was growing at the rate of 5.5 million new things getting connected every day.
Can artificial intelligence save our food system? From precision farming to personalized nutrition, there are many potential technological applications in farming, food production, and food consumption. However, technological performances, user acceptance, and practical applications of the technology continue to pose challenges. In this three-part series, Chiara Cecchini investigates the main challenges and opportunities of this niche, exploring how we might use artificial brains leverage to ensure healthy lives and promote well-being. According to The One Hundred Year Study on Artificial Intelligence, lead by Stanford University, artificial neural networks can now be trained with huge data sets and large-scale computing (deep learning), boosting data-driven solutions for improving decisionmaking.
When you think of agricultural pests, elephants are probably near the bottom of the list. But they do an enormous amount of damage to nut and banana plantations precisely because they are too big, tough and smart to scare off once they start eating. Now, Australian researchers have developed an AI scarecrow that can do the job. It has been so successful that they are looking to adapt it to other smart pests – the long term goal is a scarecrow that understands the type of pest approaching and can tailor its scaring strategy. Scarecrow technology has a long history of ignominious failure, and not just for elephants – animals quickly learn to tune out a deterrent if it becomes apparent that there is no threat.
Manufacturing bosses are holding back on 4IR investment over uncertainties about ROI. AI, machine learning, sensors and automation technology will boost Britain's competitive edge in global commerce, yet a lack of skills stands in the way of immediate investment, new research has found. A study from Barclays Corporate Banking has shown that 83% of manufacturers are confident about Britain's ability to compete in the international marketplace over the next five years, with two-fifths attributing their optimism to fourth industrial revolution (4IR) technologies including AI. Just over half (51%) of manufacturers report smart technology has improved productivity, yet 43% have not invested in 4IR at all. One in five decision makers said they were not clear on what the return on investment would be, with one in three citing a dearth of information on tangible benefits of AI and machine learning as a main drawback.
Since 1996, most cars made for sale in the US have had what's known as an on-board diagnostics, or OBD-II, port. Located under the dashboard, this opening allows mechanics and manufacturers to access data about the vehicle's mileage and current state of health. By plugging a specialized sensor into this port and downloading an app to interpret its findings, you can bypass the pros and tap into this on-board information yourself. You can't go wrong with Dash, which provides both the free app (available for iOS and Android) and the hardware you'll need. In fact, it offers a variety of sensors, ranging in price from $10 to $99.