Smart Machines: Technologies and Global Markets 2014
LONDON, Oct. 14, 2014 /PRNewswire/ -- The global market for smart machines was valued at $5.3 billion in 2013. This market is expected to increase to over $6.2 billion in 2014 and nearly $15.3 billion in 2019, a compound annual growth rate (CAGR) of 19.7% for the five-year period 2014 to 2019.
This report provides:
- An overview of the global market for smart machines.
- The details which define these machines including the ability to adapt to and learn from changing conditions in real time, the capacity to operate autonomously (without direct human supervision), and having the capability to communicate with other machines.
- Analyses of global market trends, with data from 2013, estimates for 2014 and 2019, and projections of CAGRs through 2024.
- Potential markets for various types of smart machines such as virtual reality assistants, intelligent agents, expert systems, embedded software, autonomous robots, and purpose-built smart machines.
- Key enabling technologies for the next generation of smart machines, including voice recognition technology, micro- and nanosensors, radio frequency technologies, and neurocomputing.
STUDY BACKGROUND
The possibilities and challenges created by "smart" machines replacing humans have been a staple of science fiction for nearly 150 years, beginning with Samuel Butler's novel Erewhon. Since then, the theme of smart machines has been developed in numerous books and films, most of which portray smart machines either as mankind's helpers (e.g., Isaac Asimov's Robot books), its equal partners (Star Trek: The Next Generation's Lt. Commander Data) or its implacable enemies (The Terminator's Skynet).
As a result, it is difficult not to feel a sense of déjà vu at the recent Gartner Research report, "Surviving the Rise of 'Smart Machines', the Loss of 'Dream Jobs' and '90% Unemployment' ", published in late 2013. The report's authors warn that by 2020, labor reductions caused by smart machines could cause social unrest in some advanced economies (i.e., smart machines as the enemy). Smart machines may also help to retrain workers and help them to acquire the skills needed for new jobs (smart machines as helpers), but in the end, smart machines should be recognized as part of the workforce (smart machines as partners).
Some critics have downplayed the Gartner report's conclusions as overly pessimistic or at least premature, but there is no doubt the number of smart machines and their role in the modern economy and society are expanding rapidly. Not only that, the rate of progress in smart machines is set to increase dramatically thanks to a combination of Moore's law and the melding of various enabling technologies, e.g., machine learning, voice recognition and nanotechnology.[1]
STUDY GOALS AND OBJECTIVES
Amid the debate about the benefits and risks of smart machines, or the effects they might have on society or the economy, the goal of this report is more concrete. Its goal is both to provide the reader with an understanding of the technologies that will influence the future size and structure of the market for smart machines and related technologies. Specific objectives include:
Identifying smart machine products and related technologies with the greatest commercial potential over the next ten years (2013 to 2024).
Analyzing the key drivers and constraints that will shape the market for these products and technologies over the next ten years.
Estimating the current and future demand for smart machine products and enabling technologies.
Ascertaining which companies are best positioned to meet this demand, because of proprietary technologies, strategic alliances or other advantages.
INTENDED AUDIENCE
This report is intended especially for executives, entrepreneurs, investors, venture capitalists, and other readers with a need to know where the market for smart machines and related technologies is headed and where the greatest potential for growth exists in the next ten years.
Although the report is organized around specific technologies, it is largely nontechnical in nature and coverage. That is, it is concerned less with theory and jargon than with what works; how much of the latter the market is likely to purchase, and at what price.
The report has not been written specifically for scientists and technologists. However, the report's findings concerning the market for their work, including the availability of government and corporate research funding for different technologies and applications, should interest them as well.
SCOPE AND FORMAT
The proposed report will identify and quantify the potential market for various types of smart machines, including:
Virtual reality assistants (e.g., Siri).
Intelligent agents (e.g., automated online assistants).
Expert systems (e.g., medical decision support systems, smart grid).
Embedded software (machine monitoring and control systems).
Autonomous robots (including self-driving vehicles).
Purpose-built smart machines (such as neural computers).
These technologies are generally at an early stage of development, so it will be necessary to consider the market at least as far out as 2024 to obtain meaningful results. A separate section of the report discusses the development and future sales of key enabling technologies for the next generation of smart machines, such as:
Voice recognition technology.
Micro- and nanosensors.
Radio frequency technologies.
Neurocomputing.
The study format includes the following major elements:
Executive summary.
Definitions.
Historical milestones.
Technologies and applications that have the greatest commercial potential through 2024.
Detailed market estimates and projections for each technology and application during the period 2013 to 2019.
General assessment of expected market trends in the longer term (2019 to 2024).
Companies, academic and government laboratories that are developing smart machines.
Patent analysis.
INFORMATION SOURCES AND METHODOLOGY
Projecting the market for emerging technologies such as smart machines, whose commercial potential has not yet been proven, is a challenging task. This may help to explain why many analysts focus on supply-side technology assessments.
However, BCC Research's objective in this report is to provide not just a technology assessment but also an initial commercial assessment of the potential commercial market for smart machines. To accomplish this objective, BCC Research used a multiphase approach to identify the technologies and applications with the greatest commercial potential and quantify the related markets.
In the first phase of the analysis, BCC Research identified a long list of smart machines and related technologies and applications, including those that are still under development. In the second phase, BCC Research eliminated those technologies and applications that appear to have little likelihood of making it into commercial use in the next five to 10 years, which was accomplished through a literature review and interviews with industry sources. The result of phase two was a short list of technologies and applications with the greatest commercial potential over the time period covered by this report.
The third phase focused on quantifying the potential market for each short-listed technology, by application, and identifying the main prerequisites for commercial success. Phase three actually had two phases: 1) development of near- to midterm (2013 to 2019) projections, and 2) development of longer-term (2019 to 2024) projections.
The development of such long-term projections is a departure from the usual BCC Research report format, necessitated by the long time frame for commercialization of many of the technologies analyzed in this report. Obviously, the projections for the out-years beyond 2019 are more tentative than the projections for 2014 to 2019.
The specific assumptions and approach BCC Research used to develop the projections (both near/midterm and long-term) for each technology and application are documented in detail under the various segments addressed. This way, readers can see how the market estimates were developed and, if they so desire, test the impact on the final numbers of changing the underlying assumptions.
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