Report Highlights
The global market for smart machines reached $6.6 billion in 2015. The market should reach $7.4 billion in 2016 and nearly $15.0 billion in 2021, at a compound annual growth rate (CAGR) of 15.0% from 2016 to 2021.
Report Includes
- 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 2015 estimates for 2016, as well as five and ten year projections of CAGRs through 2021 and 2026.
- 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.
- A relative patent analysis.
- Profiles of major players in the industry.
Report Scope
This report identifies and quantifies 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 2026 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 2026.
- Detailed market estimates and projections for each technology and application during the period 2015 to 2021.
- General assessment of expected market trends in the longer term (2021 to 2026).
- Companies, academic and government laboratories that are developing smart machines.
- Patent analysis.
Analyst Credentials
Andrew McWilliams spent more than 25 years as a consultant with Ernst & Young, McKinsey & Company and A.T. Kearny focused on manufacturing before segueing into research analysis. He has been covering myriad technology categories for BCC Research for more than 15 years. McWilliams has a BA from Princeton University and an MA from Harvard University. He has worked in more than 40 countries and he resides in the greater Boston area.
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Report Highlights
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.
Report Includes
- 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.
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