Cognitive AI in edge devices will enable the Internet of Things (IoT) to become more than a collection of sensors. AI on a Chip promises a revolution in IoT applications because it delivers actionable intelligence at the edge, where it previously was impossible.
We live in a digital world where everything can potentially be connected to everything and generate data. Data is a core resource and we need to capitalize on it. Instead of simply sensing our environment, we can transform it into something that is safer, more profitable, and insightful. The key is actionable intelligence, which is data and information that can be immediately acted upon without further processing by man or machine.
In its varied forms, from the mysterious brain of the octopus and the swarm intelligence of ants to Go-playing deep learning machines and driverless vehicles, intelligence is the most powerful and precious resource in existence. Despite recent advances in Artificial Intelligence (AI) that enable it to win games and drive cars, there are countless untapped opportunities for intelligence to have a significant impact on making the world a better place.
Cognitive AI is not about chatbots, talking virtual assistants, or playing chess against a machine. It’s about powering the next generation of commercial and industrial IoT edge devices, making it possible to apply them in scenarios that we can only dream about right now.
Businesses are eagerly embracing the Internet of Things and its potential to make new product offerings possible, provide actionable business insights, lower costs, and increase productivity and safety. BI Intelligence predicts that 34 billion devices will be connected to the internet by 2020, up from only 10 billion in 2015, with businesses being the top adopters of IoT solutions. A recent study by International Data Corporation (IDC) projected worldwide spending on the IoT to reach $772.5 billion in 2018, up from $674 billion in 2017, and surpassing $1 trillion in 2020.
Leading the pack in IoT spending this year will be the manufacturing ($189 billion), transportation ($85 billion), and utilities ($73 billion) industries. Healthcare is also a bright spot for the Internet of Things. The global market for wearable connected medical devices is projected to reach nearly $19.5 billion in 2021, up from $5.5 billion in 2016.
As the number of commercial and industrial IoT devices proliferate, connecting them and getting them to behave intelligently are among the biggest challenges to IoT realizing its full potential. An industrial facility might have 20-30,000 sensors monitoring status of thousands of machines and processes. But they often reside in silos that do not communicate. Some AI solutions are dependent on a cloud service architecture. Essentially a mainframe approach with centralized computing.
But in many industrial locations, sufficient bandwidth or even connectivity cannot be relied upon. Sensor data needs to be collected, correlated with historical performance data, and analyzed to provide actionable information and make decisions in real-time. There’s no time to reach out to the mother ship for answers. One important strategy for obtaining timely actionable intelligence is embedding intelligence at the source of the sensing. This development enables decisions to be made at the sensor rather than “phoning home” to headquarters or a cloud service for “what to do next”.
Since many IoT applications have operational control, making decisions quickly is essential. Unfortunately, the latency inherent in data processing and decision support far from the edge is too slow for many applications.
In some cases, IoT devices must be controlled within milliseconds by intelligence that resides within the control loop. A good example is military aircraft, where sensor data needs to be acted upon constantly, on the spot. If the thousands of sensors were wired to a central computer onboard the aircraft, the wiring and the computer could weigh more than the entire aircraft. This necessitates the use of edge computing architectures and equipping smart IoT devices with artificial intelligence. AI on a Chip Unlocks Intelligence to Operate Complex Systems and Makes it Tamper Proof
Today, 25% of organizations with established IoT strategies are also investing in AI. However, what commonly passes as “AI” these days – conventional software approaches designed to handle very large, complex data sets, or chatbots that possess rudimentary contextual awareness – are not sufficient.
Some chip companies are working on incorporating AI software on their chips. One software company, Beyond Limits, is doing the reverse: building advanced cognitive AI that can be embedded in off-the-shelf inexpensive chips. When edge devices are equipped with cognitive intelligence and are able to act without moving all the data to remote data centers for analysis, the number and type of new smart IoT applications is virtually limitless.
Consider These Real-World Industrial Applications:
Cognitive artificial intelligence – truly intelligent symbolic AI software with bio-inspired, human-like reasoning ability – will take IoT technologies to the next level and allow enterprises to make full use of their IoT investments. Using cognitive AI, IoT devices can work together to not only analyze time-sensitive data at the point of origin but also diagnose and solve problems in real-time, even when the devices cannot communicate with their operators.
More futuristic applications of cognitive AI in the IoT sphere include:
Sounds like science fiction? These scenarios are not so different from empowering semi-autonomous rovers on Mars that make decisions, from great distances and in the most extreme conditions imaginable – which has already been done by Caltech and NASA’s Jet Propulsion Laboratory. In 2012, AI technology was used to support the landing of the Curiosity Rover on Mars and operate it 150 million miles from Earth. Advanced AI technology has also been employed by NASA to monitor the Voyager 2 deep space probe and search for water on Mars.
The vision of the future for AI includes cognitive systems that can do what machine learning systems can’t: intelligently and fluently interact with human experts and provide articulate explanations and answers, even at the edge of the network. Across the board, you will see, and work with, systems endowed with rare and valuable intelligence.
About the Author:
AJ Abdallat is CEO of Beyond Limits, an artificial intelligence and cognitive computing company that is transforming proven space and defense technology from NASA and the U.S. Department of Defense into innovative solutions to address large and emerging markets.