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) …
"There is a fundamental disconnect between what we roboticists say and what the public perceives," says Ian Reid, deputy director of the Australian Centre for Robotic Vision, in Brisbane. And that leads to the heart of the problem, and what researchers mean when they talk about "robotic vision": using cameras to guide robots to carry out tasks in increasingly uncontrolled environments. Is this another of Ian Reid's "disconnects" between the research world and the public's sci-fi driven expectations? "In rich countries like Japan where there are also demographic challenges, you will see a big increase in social robotics – in aged, robotic companions and robotic pets," Mahony predicts.
In this article, I've listed down the essential resources to master the basic and advanced version of data science using: Global Machine Learning Certifications – This list highlights the widely recognized & renowned certifications in machine learning which can add significant weight to your candidature, thereby increasing your chances to grab a data scientist job. This certification offers multiple courses such as algorithms for data science, probability and statistics, machine learning for data science, exploratory data analysis. It teaches aspiring data science candidates to learn data mining, machine learning, big data and data science projects and work with non-profits, federal agencies and local governments and make a social impact. It teaches real world, practical skills to become a data scientist / data engineer.
This technology will make your device more energy efficient, enable and improve virtual and augmented reality experiences, provide for smarter camera functionalities, improve device security, and of course, allow for better audio connections. Mobile processors like Qualcomm's Snapdragon 835 leverage machine learning in an effort to extend and expand the boundaries of mobile performance. Yes, artificial intelligence and machine learning can aid and improve all sorts of functions and processes in minutes and specific ways, but as it concerns you, the user, your phone will simply do everything that you need it to – but faster, better, and with greater efficiency. Many devices already feature some form of machine learning (those with the Snapdragon 835 mobile processor, for example, like ODG's R-8 and R-9 smart glasses).
"Through 3D printing, fast automation, artificial intelligence, advanced IT systems," Weber said. His lab recently trained a Baxter assembly robot to understand and respond to natural language commands. Researchers from MIT's Computer Science and AI Lab (CSAIL) recently revealed their similar efforts, which they've dubbed ComText -- as in "commands in context". The current problem is that robots generally see the world at a relatively low level -- in pixels and sensor readings -- but humans see it as related concepts, connected to form reasoning and higher order thinking, Paul explained.
Deep Learning or DL is a subfield of machine learning which makes use of artificial neural networks, a mathematical system inspired by the way neurons function in the human brain. AI is fast becoming an integral part of the drone industry by enhancing the intelligence of the drone systems with respect to flight operations, data management and most importantly traffic management. DL as already discussed earlier is an AI technique that gains knowledge through training a neural network, a computer system that's designed to process information like the human brain. It provides a complete end to end solution where it will design the optimum flight path for the drone in order to capture the most complete data for any use-case, fly the drone across this flight path and generate and analyze thorough 3D models based on the data captured.
Today, these advanced algorithms are transforming the way the manufacturing industry collects information, performs skilled labor, and predicts consumer behavior. Smart factories with integrated IT systems provide relevant data to both sides of the supply chain more easily, increasing production capacity by 20%. Robots and other automated technology are also integral in improving speed and efficiency, allowing manufacturing companies to "optimize production workflows, inventory, Work in Progress, and value chain decisions." With this new level of predictive accuracy comes an improvement in condition monitoring processes, providing manufacturers "with the scale to manage Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%."
In retail, supply chain efficiency is essential. Creating efficiencies in complex systems which involve multiple, often compartmentalized processes is an area where this technology excels. Monte Zweben – CEO of Splice Machine, which provides predictive systems for industry, talked me through three key areas where retailers are increasingly looking towards data-driven analytics in order to drive efficiencies in their supply chains. "So, now you can build a machine learning model," Zweben says, "and that model could make a prediction about any aspect of the operation based on the data it's got.
The global logistics industry is "unsophisticated" and due for a major shake-up, according to the boss of a cargo-handling giant. Mika Vehvilainen, chief executive of Finnish company Cargotec, expects autonomous vehicles and artificial intelligence to disrupt the market in coming years. "But it's also, from an operating cost point-of-view, clearly [beneficial] – about 40 per cent of port operating costs today in the Western world are labour costs. Meanwhile, the chief executive said his business had been negatively affected by the UK's Brexit vote last year.
The chief executive of Deutsche Bank has issued a stark warning about the impact of technology, saying a "big number" of his staff will lose their jobs as robots take over. In remarks reported by German publication Handelsblatt at a conference in Frankfurt, Cryan added: "The sad truth for the banking industry is, we won't need as many people as today." Cryan told the conference that Germany and Frankfurt had to decide how much they wanted to benefit from Brexit. While new finance jobs will be created in Dublin, Amsterdam and Paris – all vying for business leaving London – none of these have the infrastructure to take on the business.
These are two excellent books on machine learning (AKA, statistical learning; AKA, model building). If we're talking about entry level data scientists to intermediate level data scientists, I'd estimate that they spend less than 5% of their time actually doing mathematics. Even if you use "off the shelf" tools like R's caret and Python's scikit-learn – tools that do much of the hard math for you – you won't be able to make these tools work without a solid understanding of exploratory data analysis and data visualization. While this figure is about data science in general, it also applies to machine learning specifically: when you're building machine learning models, 80% of your time will be spent getting data, exploring it, cleaning it, and analyzing results (using data visualization).