ROBOTICS CLUSTER

AI and machine learning technologies
and techniques are being used
to make robotic systems more
intelligent
been used in support of decision making, object identification, vision processing, speech translation, navigation,
motor control, sensor integration, and other functions, as well as facial and emotion recognition.
6.8.3.2. Deep Learning
Deep learning, a class of machine learning techniques, has received much coverage from the technology and
business media as of late, and for good reason. The technology, which employs multi-level (“deep”) neural
networks to create inferencing systems for pattern and feature detection in large datasets, has proven critical
for commercial applications ranging from speech and music recognition, to industrial process control and
product recommendation. Deep learning techniques continue to improve, as do results.
6.8.3.3. Research and Investment
Deep learning research is robust and ongoing, and investment in the technology is strong. Some of the
world’s leading technology firms are now invested in the technology. Google’s purchase of U.K. AI startup
DeepMind Technologies in 2014 for an estimated US$400 million exemplifies this. So, too, does Facebook’s
launch of its own AI laboratory in 2014, and IBM’s launch of its Watson Group (also in 2014). Both research
groups are focused on deep learning methods (among other research topics). IBM also purchased AlchemyAPI,
a provider of AI-based text analysis and computer vision cloud services, in March 2015, and has other
cognitive computing and deep learning initiatives underway. Microsoft’s Project Adam, the goal of which is to
enable software to visually recognize any object, also exploits deep learning techniques.
6.8.3.4. Cloud and Local
Deep learning methods are computationally intensive, requiring a great deal of computing resources. Software
typically executes on powerful processors running on banks of highly optimized servers. As a result,
devices running applications that use deep learning methods must typically access services residing in the
cloud. Many efforts are currently underway to provide deep learning capabilities without the necessity of
off-device processing in the cloud.
6.8.3.5. Deep Learning and Robotics
AI and machine learning
technologies and techniques are
being used to make robotic systems
more intelligent.
Many of the capabilities enabled by deep learning methods are critical for robotics systems. Examples include
computer vision, facial recognition, and natural language processing. Object recognition is especially
important. A number of techniques have been employed to assist robots in recognizing objects, with deep
learning methods central to many of them. Commercial companies are now coming to market with deep
learning solutions designed specifically for robotics systems.
6.8.4. Robot Operating System (ROS) and Open Source Solutions
Developing and deploying robotics systems are difficult, time-consuming, and error-prone, and as a result,
robotics innovation and commercialization efforts have been slowed or stalled. To address the problem,
ROS, open source system software for robotics, was developed (Quigley et al., 2009). ROS includes software
libraries, tools, and a run-time environment specifically designed to ease the burden of creating advanced
robotics applications.
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THE MASSACHUSETTS ROBOTICS CLUSTER
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