Cognitive technology is a field of computer science which mimics or copy functions of the human brain through various means, including natural language processing, speech recognition, computer vision, data mining, and pattern recognition. e.g.: The DARPA (Defense Advanced Research Projects Agency) is an agency of the United States Department of Defense. They came up with CT2WS (Cognitive Technology Threat Warning System) with the help of discoveries in flat-field, large pixel-count digital imagers, wide-angle optics, ultra-low power analog-digital hybrid signal processing electronics with cognitive visual processing algorithms, and neurally-based target detection signatures.

1.1. Four Major Categories of Cognitive Technologies.

 1.1.1. Robotic Process Automation (RPA): An advanced decision system. GUI automation and configuration. The pain points relived are Backlogs, Paperwork burden, and Resource constraints. It has led to Cost reduction, Increase in speed, Enhance reach and focusing more resources on a mission

1.1.2. Cognitive-Language Technologies: Natural language processing and generation, Speech recognition and synthesis and text analytics. The pain points relieved are Wait-time, Human resource, and Budget constraints. On the other hand, benefits are boosting citizen engagement, 24x7 support and Increase focus on mission-critical tasks.

1.1.3. Cognitive-Machine Learning: Supervised, Unsupervised and Deep Learning without the need for explicit programming. The pain points relieved are manual pattern detection and missing key patterns. Benefits of this are a more accurate prediction, Anomaly detection, Real-time tracking, Improve process allocation, Better-decision making, and Increased effectiveness.

1.1.4. Cognitive-Computer Vision: Image, Handwriting, Voice and Optical Character Recognition. Also, video analysis using computer software and hardware. Benefits are Multi-lingual, Responsive, reduce cost, etc. 

2. What drove the progress in cognitive technologies?

    The four key factors are

2.1. Moore’s Law: The exponential growth in computing power has facilitated advances in computer systems that wasn’t earlier.

2.2. Big Data: The rapid increase in the volume of data available and the AI techniques that use statistical models to train on large datasets to deliver reliable and up-to-date input.

2.3. The Internet and the Cloud: The rise of these computing technologies have enabled humans to collaborate with each other to train AI systems.

2.4. New Algorithms for machine learning: Increased sophisticated algorithms have improved the accuracy of data pattern identification and predictions.

3. Features of  Cognitive Technology.

The purpose of cognitive computing is the creation of computing frameworks that can solve complicated problems without constant human intervention like Siri, Google Assistant, Cortana Alexa, and Mitsuku etc. providing personal assistance by using natural language processing. Cognitive Computing consortium has recommended the following features:

3.1. Adaptive: Mimic the ability of human brain to learn and adapt from the surroundings. It should be dynamic in understanding goals, gathering data and requirements.

3.2. Interactive: Interact with all elements in the system-processor, devices, cloud services, and user. Should interact bi-directionally and understand human input.

3.3. Iterative and stateful: Remember previous interactions in a process and return suitable information. This needs a careful application of the data quality and validation methodologies to ensure that the system is always provided with enough information and the data sources on which it operates.

3.4. Contextual: Identify contextual elements such as meaning, syntax, time, location, user’s info etc. from multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs.

4. The scope of Cognitive Technology.

This technology can respond to complex situations characterized by ambiguity and have far-reaching impacts on our private lives, healthcare, business, etc. These 3 capabilities are how people think and demonstrate their cognitive abilities in everyday life:

4.1. Engagement: Availability to develop deep domain insights and provide expert assistance. The models built by these systems include the contextual relationships between various entities in a system’s world that enable it to form hypotheses and arguments

4.2. Decision: Autonomous decision making using reinforcement learning. Providing decision support capabilities and reducing paperwork allows people to spend time on various complex things.

4.3. Discovery: The system should “remember” previous interactions in a process and return information. Discovery involves finding insights and understanding the vast amount of information and developing skills. It can improve decision making, reduce cost and optimize outcomes. Developers can add intelligent features – such as emotion and sentiment detection, vision and speech recognition, knowledge, search and language understanding. E.g.: Specter.