In 2008, Daniel Hulme started Satalia, a company that uses data science, machine learning, and optimization (making the best use of resources) to build customized platforms that solve tough logistics problems involving products, services, and people. Lately, Hulme has spent a good portion of his time explaining the ins and outs of artificial intelligence to other CEOs. He sees a big information gap at the top of most companies -- yet this is where technology investment decisions are made. Misunderstanding AI, Hulme believes, can mean both overestimating its value and underestimating its impact. Satalia's work is a leading example of what AI is currently good at. Not coincidentally, it is also the commercialization of Hulme's research at University College London (UCL), where he is the director of the business analytics master's degree program. Satalia's clients are household names in the U.K.; they include Tesco, DFS, and the British Broadcasting Corporation. PwC's Global CEO Survey: Providing unique insight into the thinking of corporate leaders around the world, PwC's annual Global CEO Survey covers issues such as the prospects for economic growth, the challenges of building a workforce, the threats facing companies today, and the impact of AI. www.ceosurvey.pwc The increasingly competitive market for AI expertise is both a blessing and a curse for Satalia.
By age 7 or 8, most kids feel comfortable using devices like smartphones -- recent research puts the percentage of American kids between the ages of 10 and 12 who have their own service plans/smartphones at around 45%. Another survey found that 42% of U.S. children aged 8 or younger have their own tablet devices. The age you'll want to introduce kids to devices like these understandably varies from family to family. Developing a "family media use plan" is one way parents can set appropriate limits for screen time -- for every member of the crew (yup, that includes you, Mom and Dad). Tech devices that are custom-tailored to kids, such as the Amazon Echo Dot Kids Edition or an Amazon Fire Kids Edition Tablet, are a perfect introduction to digital devices, and with features like Amazon FreeTime Unlimited, a subscription service that provides access to hundreds of hours of fun and educational content, families can enjoy a breadth of exploration together.
This article looks at the ways in which firms across the various sectors of the economy adopt Artificial Intelligence (AI) techniques. However, before we review the sectors affected it is important to note the underlying drivers that are fuelling the growth in the influence and reach of Machine Learning across the sectors of the economy will only grow as we move forwards. This is because Big Data is only getting larger, velocity of data faster, plus the availability of cheaper data storage plus the arrival of powerful Graphical Processing Units (GPUs) to enable Deep Learning algorithms to be deployed. Furthermore, new research in areas of Deep Learning and other Machine Learning areas will continue to emerge into real world production over the next few years leading to new opportunities and applications. The DLS team strongly believe that the advent of 5G around 2021 will be a transformative and revolutionary moment in human history.
Artificial intelligence (AI) – the science of making computers mimic humans using logic, decision trees, deep learning, and machine learning – is fast approaching the market opportunity around preventive and predictive maintenance. According to a recent GlobalData survey, the top two business challenges in Australia are in improving operational efficiency and reducing costs. Many businesses, such as manufacturers, producers of natural resources, through to the agriculture and health sectors, need ongoing reliability of machines and their constituent parts to keep the lights on in the business. Unplanned outages, for example, can cost an oil and gas company, on average $50 million dollars annually. In the case of a windfarm, in the event of one single fail, an entire turbine needs to come down, a technical crew with a crane needs to be on site costing $100,000 or more for each time a part fails.
In "Why Use K-Means for Time Series Data? (Part One)," I give an overview of how to use different statistical functions and K-Means Clustering for anomaly detection for time series data. I recommend checking that out if you're unfamiliar with either. I am borrowing the code and dataset for this portion from Amid Fish's tutorial. Please take a look at it, it's pretty awesome. In this example, I will show you how you can detect anomalies in EKG data via contextual anomaly detection with K-Means Clustering.
There is an important distinction related to data mining. First the difference between mining the data to find patterns and build models, and second using the results of data mining. Data Mining results inform the data mining process itself. Cross-industry standard process for data mining, known as CRISP-DM, is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model and breaks the process of data mining into six major process.
In the mid-20th century, food production from agriculture sharply increased worldwide; however, this was achieved through heavy use of agrochemicals. Extensive collateral damage from excessive use of pesticides, herbicides, and fertilizers has occurred to the wider environment. This has led to biodiversity loss, pesticide resistance and the emergence of new pests, pollution and decline of freshwater supplies, and soil degradation and erosion, as well as direct harm to health. In a Review, Pretty examines the alternative approaches that can achieve sustainable intensification of farming systems by integrating pest management with agroecological systems to minimize costs, maximize yields, restore ecosystem services, and ensure environmental enhancement. The mid-20th century brought agricultural transformation and the "Green Revolution." New crop varieties and livestock breeds--combined with increased use of inorganic fertilizers, manufactured pesticides, and machinery--led to sharp increases in food production from agriculture worldwide. Yet this period of agricultural intensification was accompanied by considerable harm to the environment. This imposed costs on economies and made agricultural systems less efficient by degrading ecosystem goods and services. The desire for agriculture to produce more food without environmental harm, and even to make positive contributions to natural and social capital, has been reflected in many calls for more sustainable agriculture. Sustainable intensification (SI) comprises agricultural processes or systems in which production is maintained or increased while progressing toward substantial enhancement of environmental outcomes.
Artificial intelligence (AI) is quickly becoming a critical component in how government, business and citizens defend themselves against cyber attacks. Starting with technology designed to automate specific manual tasks, and advancing to machine learning using increasingly complex systems to parse data, breakthroughs in deep learning capabilities will become an integral part of the security agenda. Much attention is paid to how these capabilities are helping to build a defence posture. But how enemies might harness AI to drive a new generation of attack vectors, and how the community might respond, is often overlooked. Ultimately, the real danger of AI lies in how it will enable attackers.
Developed by computing pioneer Alan Turing, the Turing test is considered to be one of the key measures of artificial intelligence. Turing suggested that a way of testing a machine's'intelligence' would be its ability to fool a human into thinking it was human. In some areas, this has arguably been achieved, with chatbots that can convincingly converse with humans or write realistic looking online reviews. But some critics point out that the Turing Test doesn't measure true intelligence – only the ability to mimic it. Early forms of machine learning used data such as images that had been painstakingly labelled to help algorithms identify the objects they contained.