The Composite Nature of Embedded Cognition

As cognitive approaches become embedded in business processes and devices they will become indispensable. Processes modeled upon thought will simply become part of the landscape of helper applications expected in the digital environment. We have already seen discrete intelligent processes becoming commonplace and even expected. Examples include spell checking and grammar checking in documents; machine translation in webpages and search results; packet filtering in communications; and personal photo enhancement. These technologies offer useful services. The underlying mechanism is often complex but, as with an automobile or any other advanced machine, the actual mechanics need not be known to the user.

Artificial intelligence even in its most profound forms is likely to follow this same path. We can expect smart processes to be brought together to make operations more efficient, friendlier, or easier to use. Embedded artificial intelligence is likely to become increasingly important.

Local intelligence, or the ability to make choices automatically at the individual operation level will become normal. When intelligence is embedded at the process level, many functions that might have required expert intervention, or paused for user selection, will be routinely and automatically performed. Leading the way will be further development of the user interface. This is the most visible element of IBM’s Watson Analytics program; the ability to understand and respond to natural language requests, framing those requests in a way that they can be submitted to databases and analytics to produce a guided result. The most advanced intelligence need not be in the assembly, analysis, or predictive capabilities of big data; but, rather, in the understanding of the request itself and ability to formulate an adequate response.

Embedded cognition provides a new range of challenges and opportunities. Its most visible impact is in robotics and in Industry 4.0. It will be inherently important to all autonomous systems. Embedded intelligence will not be online all the time, nor will it be capable of being adapted or updated on a real-time basis. This means it must be secure. Autonomous intelligence must also cooperate and communicate with other systems. Standardization will make the routines and operations in this category into commodity parts to be assembled in creating a greater whole. Any machine which has complex parts today demonstrates how this will work. Automobiles have transmissions whose technology may be swapped between vehicles. Principles of operations remain the same. Multiple complex parts may be assembled to create a machine of the next level of complexity. People will interact with this environment through personal devices as well as through monitoring of their own behavior.

In looking at embedded cognition it is already apparent that there are vast differences among the various smart processes being embedded in processes and devices. Some are for visual recognition and perception; some are for speech recognition; some are for decision-making under uncertainty; and some are for translation between languages. While similar principles are involved their specific requirements are not the same. Each defines a particular utility which may be brought together with other smart components to create a more complex machine. Embedded natural language processing can be linked to big data analysis to provide answers to questions; machine learning and pattern recognition can be applied separately to issues such as fraud. Pattern recognition capabilities can be embedded in equipment used to search for cellular components, or symptoms of a disease. They may become a part of an appliance which includes artificial intelligence in the same way that it includes the use of electricity. The AI capability is simply applied at the level where it is required.

While artificial intelligence does not presume to replace the human brain, it does provide a next level of flexibility and machine control, making it possible to respond to an evolving matrix of real time data. This can provide a finer and more efficient guidance of individual processes than might be available with manual or semi automated controls.

The applications of embedded cognition in industry and robotics are patently obvious. But it is significant that this technology will be available piecemeal and often taken for granted. Already we see elements of this appearing around the home with intelligent control mechanisms and voice-actuated modules capable of understanding and responding to limited commands. These commands might go out to a mechanism such as a multimedia device which itself contains intelligence for responding to the mood of the user. Or, it might query the refrigerator for information on user behavior that can be used to produce shopping lists or provide guidance in the purchase of food.

All of this is in a nascent state of development. The key issue is that artificial intelligence will develop for specific tasks and these tasks can then be combined in a modular fashion to create a broader and more effective process or mechanism based upon cognition in all components. Assembling such a composite appliance or process becomes a matter of orchestration—which may itself be handled by an intelligent machine.

The ability to create innumerable separate “smart” processes will act like algorithms in programming. Once established, they will become part of the language of a cognitive device universe. Combining these components will lead to the ability to create unique mechanisms with specific intelligence that will bring machine capability to a higher level of complexity and effectiveness. The beginnings of this are visible in autonomous social robots.

Companies need to monitor development of the building blocks of cognitive processes, and understand the growing capabilities. There will be no artificial brain, but there will be many tiny interactive minds that could create unforeseeable consequences as different capacities are drawn together. This will have implications for Security,a s well as for the future development of the IoT.


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