How can we build computer systems that automatically improve themselves as a result of their experiences, and what are the basic rules that govern learning processes? According to Oliver G. Selfridge, these are the questions that underlie machine learning. Selfridge (1926–2008) was a pioneer in the field of artificial intelligence and was called the father of machine perception. Machine learning makes it possible, for example, to use energy demand forecasts to predict how much energy should be generated, thus making the energy market more efficient. Using neural networks, such learning programs can, for example, be fed with past weather forecasts and electricity production data. Although such a program doesn’t know at first what effects each parameter would have, it is trained by repeating the prediction process thousands of times to minimize the difference between its forecast and the actual values that occured in the real world.
Facts and Forecasts: Boom for Learning Systems
The global market for smart machines is growing by almost 20 percent annually.
Artificial neural networks operate according to the same principles as the human brain. This means that their basic units are neurons, which are connected to one another by synapses. The strength of a connection depends on the lessons that the system has learned. As a result, such systems can evaluate unknown data on the basis of examples that they have previously learned. Neural networks are also used for cell phone voice recognition systems in order to dramatically reduce error rates. Market research firm Research and Markets forecasts that the market for automatic voice recognition systems will grow by 16.3 percent worldwide.
Machine learning is a major trend today, due to the availability of powerful computing, growing data volumes, and the progress being made in the development of smart algorithms. As a result, the Gartner market research company forecasts that the era of smart machines will become one of the most disruptive phases in the history of IT. Disruptive technologies are innovations that completely supplant existing technologies.The market for machine learning is growing in line with this technology’s increasing importance. In a study released in May 2014, BCC Research , a market research company that specializes in technology markets, predicted that the global market for smart machines will grow to $15.3 billion by 2019, with an average annual growth rate of 19.7 percent. BCC Research divides the global market for smart machines into five segments: neurocomputers, expert systems, autonomous robots, smart embedded systems, and intelligent assistance systems.
Expert systems (e.g. medical decision support systems and smart grids) made up the largest share of the market in 2013, followed by autonomous robots. BCC Research expects the autonomous robot category to account for the largest share — 22.8 percent — of annual market growth until 2024 and thus to dominate the smart machine market. These advanced robots will have greater mobility, dexterity, flexibility, and adaptability than their predecessors, says BCC Research instrumentation and sensors analyst Andrew McWilliams. These technological advances, coupled with declining costs, are making possible new applications that range from space exploration and military operations to such simple tasks as mowing lawns.
According to a study conducted by Transparency Market Research, the market for predictive analytics software will amount to more than $6.5 billion worldwide in 2019. By contrast, it only totaled about $2 billion in 2012. As a result, the market is forecast to grow at an average annual rate of 17.8 percent. The predictive analytics market includes the following types of software solution:
· Customer intelligence
· Decision support systems
· Data mining and management
· Performance management
· Fraud and security intelligence
· Risk management and financial intelligence
· Operations and campaign management
Transparency Market Research expects the banking, financial services, and insurance sector to account for the largest share of the predictive analytics software market, although the retail and manufacturing sectors are expected to post the fastest growth. This is largely due to the immense growth of consumer-driven digital data and the subsequent need to extract strategically critical information from this data.
Technical assistants are also expected to have profound economic consequences. A study by Gartner focuses on the negative aspects of smart machines, which are expected to replace jobs in many industries ranging from manufacturing and warehousing to shipping. According to the study, millions of jobs will be eliminated in the course of the next decade. One example of that is provided by 3D printers, which create three-dimensional workpieces. Such printers were initially used to make prototypes, scale models, and small-batch workpieces. However, this technology is now increasingly being used for mass production as well. The advantage it offers is that the time-consuming production and switching of molds is no longer necessary, and associated material waste is avoided as well. In most cases, 3D printing also consumes less energy, because the material only has to be built up in the required size and mass once. But there are also drawbacks. It’s difficult to print finely structured, smooth surfaces, and the workpieces usually have to be reworked. The study also shows that most chief information officers are not aware of the associated risk of product piracy, and even fewer are prepared to deal with it.
Gartner predicts that at least 10 percent of potentially life-threatening activities will be performed by smart systems by 2024. Vehicle assistance systems are one example. They will enable the vehicles of the future to communicate with increasing effectiveness, scan their environments with ever greater precision, and process data faster and faster — and one day to drive completely on their own. According to Strategy Analytics, advanced driver assistance systems will have a global market volume of €16 billion in 2019, compared to €5 billion in 2012. Whether and when self-driving automobiles will actually hit the road depends not only on technological developments but also, and more importantly, on legal requirements.
Despite all of the skepticism, intelligent assistance systems, ranging from mobility apps to emergency warning systems, are already widely used today. They are becoming increasingly important for preventive medical examinations, diagnostics, treatment, and nursing. For example, systems for ambient assisted living (AAL), which enable elderly people to live at home longer, are a billion dollar market. A study titled Navigating the Digital Future by the tech consulting firm Booz & Company came to the conclusion that the industry that invested the most in digital enablers worldwide in 2013 was the healthcare sector ($13.8 billion), followed by the computing and electronics sector ($9.7 billion) and the software and Internet sector ($7.7 billion). Meanwhile, the Berg Insight market research company predicts that the number of home monitoring systems with integrated connectivity will grow at an average annual growth rate of 26.9 percent to reach 9.4 million connections worldwide in 2017.
“By 2020, all products costing more than $100 should have sensors embedded in them and should offer services on top of the products,” says Peter Sondergaard, Senior Vice President and head of research at Gartner. He predicts that by then every company will be an IT company and every company chairman will be an expert in digital applications.