From autonomous vehicles, predictive analytics applications, facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI are many. However, regardless of the application of AI, there is commonality to all these applications. Those who have implemented hundreds or even thousands of AI projects realize that despite all this diversity in application, AI use cases fall into one or more of seven common patterns. The seven patterns are: hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. Any customized approach to AI is going to require its own programming and pattern, but no matter what combination these trends are used in, they all follow their own pretty standard set of rules.

Technology developed by Kerry-based Dairymaster, which uses AI to optimise milking, was named winner of an Enterprise Ireland innovation award. Ahead of the National Ploughing Championships today (17 September), the 2019 Enterprise Ireland Innovation Arena Awards were announced. The pre-event is designed to highlight some of the latest agritech and agri-engineering projects underway in the country. The overall winner was the Mission Control system developed by Kerry-based Dairymaster, which has introduced artificial intelligence (AI) to rotary milking and includes an advanced cow recognition system called'CowNow'. The OptiCruise technology incorporated into the design adjusts the speed of milking so that milking time is optimised.

Artificial intelligence (AI) and emerging technologies (ET) are poised to transform modern society in profound ways. As with electricity in the last century, AI is an enabling technology that will animate everyday products and communications, endowing everything from cars to cameras with the ability to interact with the world around them, and with each other. These developments are just the beginning, and as AI/ET matures, it will have sweeping impacts on our work, security, politics, and very lives.1 These technologies are already impacting the world around us, as Darrell West and I wrote in our April 2018 piece "How artificial intelligence is transforming the world," and I highly recommend that anyone just discovering the topic of AI policy read it thoroughly. There, Darrell and I describe several important implications related to AI/ET, but chief among them is that these technology developments are on the cusp of ushering in a true revolution in human affairs at an increasingly fast pace. As AI continues to influence and shape existing industries and allows new ones to take root, its macro-level impact, particularly in the realm of economics, will become more and more apparent.

The Food and Drug Administration last year approved its first autonomous, artificially intelligent medical device. In a decision that seemed to take a page from science fiction, the FDA gave the OK to the IDx-DR, a device that uses artificial intelligence to analyze images of the back of a patient's eye to detect if they have diabetic retinopathy. It's the first FDA-approved device to provide a screening decision without requiring a clinician to interpret the results--which means providers who aren't eye specialists, such as primary-care physicians, can rely on it to screen for the eye disease. "Today's decision permits the marketing of a novel artificial intelligence technology that can be used in a primary-care doctor's office," Dr. Malvina Eydelman, director of the division of ophthalmic and ear, nose and throat devices at the FDA's Center for Devices and Radiological Health, said at the time. "The FDA will continue to facilitate the availability of safe and effective digital health devices that may improve patient access to needed healthcare," she added.

With turnover rates in technological fields remaining around 10%, there is a clear need for tech companies to hire and retain talented machine learning engineers. While it is advantageous to find eager workers from home, there is an alternative that offers many benefits: hiring F1-OPT machine learning engineers. You may be asking why you should do this, and more importantly, what it means to be a F1-OPT machine learning engineer. Fortunately, we have the answers to these questions. In order to qualify as a F1-OPT engineer, an individual must be an international student who recently received their advanced engineering degree(s) in the United States.

There are two key aspects of artificial intelligence (AI) that you should be aware of. First, it's being designed into an increasing percentage of embedded systems at the deep edge of the network, from industrial controls to automotive applications to consumer/mass market devices. So there's a good chance you'll be needing a primer on how to work with these AI-related components. The second aspect is that designing around AI is potentially a complex endeavor. And that's where we come in.

The fight over the future of facial recognition is heating up. But it is just the beginning, as even more intrusive methods of surveillance are being developed in research labs around the world. In the US, San Francisco, Somerville and Oakland recently banned the use of facial recognition by law enforcement and government agencies, while Portland is talking about forbidding the use of facial recognition entirely, including by private businesses. A coalition of 30 civil society organisations, representing over 15 million members combined, is calling for a federal ban on the use of facial recognition by US law enforcement. Meanwhile in the UK, revelations that London's Metropolitan Police secretly provided facial recognition data to the developers of the Kings Cross Estate for a covert facial recognition system have sparked outrage and calls for an inquiry.

Nvidia Corp. is upping its artificial intelligence game with the release of a new version of its TensorRT software platform for high-performance deep learning inference. TensorRT is a platform that combines a high-performance deep learning inference optimizer with a runtime that delivers low-latency, high-throughput inference for AI applications. Inference is an important aspect of AI. Whereas AI training relates to the development of an algorithm's ability to understand a data set, inference refers to its ability to act on that data to infer answers to specific queries. The latest version brings with it some dramatic improvements on the performance side.

Behavioral finance has shown that try as they might, human investors are not rational. Investors of all types, from retail to institutional investors, are susceptible to behavioral bias, says Michael Cicero, director of portfolio research and management at High Probability Advisors. You need look no further than the University of Chicago's sale of equity in 2008 for an example of loss aversion bias, or the emotional bias caused by investors feeling more pain from a loss than pleasure for a gain, by a sophisticated investment committee responsible for the endowment, he says. Irrational decisions such as those prompted by loss aversion "can be as detrimental to long-term expected return as poorly designed investment strategies," Cicero says. AI can help investors eliminate these biases, thus increasing "the odds of investment success."

Earth is enormous, and while humans have done a decent job of being able to map out the boundaries of countries and states, the roads in our cities and the location of geological sightseeing destinations, there remains a lot of the world that isn't precisely figured out. But a new project from Wenwen Li, associate professor in the School of Geographical Sciences and Urban Planning, aims to learn more about our world and its varying terrain by applying artificial intelligence. Artificial intelligence, or AI, has already made an indelible impact in daily life. From knowing our commutes or being able to suggest new shoes, what we divulge about ourselves and our habits has created a framework of information as it reveals hidden patterns in how we conduct our lives. The same can be true for our natural world as AI can help to reveal the patterns we haven't yet discovered.