The Alignment of Quality Analytics with Cognitive Technology
The discussion of Quality Analytics is steadily evolving to include a discussion about cognitive technology. In order to continue to evolve quality analytics, the best technology must be used and that right now is cognitive technology.
Cognitive technology is a broad term but a simple explanation of what it is is that it is any product in the field of artificial technology. Artificial intelligence continues to break bounds its capabilities continue to increase. Tasks that could be done only by humans can now be done by artificial intelligence software. Cognitive technology is broad and more and more are being made but the key cognitive technology products in the industry today are Natural Language Processing (NLP), speech recognition, robotics, Virtual reality and machine learning.
AI and Cognitive Technology
So as I said above, cognitive technology is any product, software that is produced in the field of artificial intelligence. Most cognitive technologies can simulate human thought and provide real-time analysis of their environment. These technologies are so advanced that they can understand context and intent and other otherwise human nuances. For example with speech pattern, voice recognition technologies can be trained to recognize stress in a person's voice and call 911 in a case of an emergency. A number of AI technologies are required for a computer system to build cognitive models that mimic human thought processes, including machine learning, deep learning, neural networks, NLP and sentiment analysis.
The ultimate goal of cognitive technology is to assist humans in both the decision-making process and in everyday life. It also means assisting workers and professionals in key industries such as the financial industry and the healthcare industry. In the healthcare industry, great leaps and bounds have been made in the integration of cognitive technology into everyday healthcare activities. From basic task such as recording patient information to complex tasks like performing non-invasive surgeries and assisting doctors in making a diagnosis. To put more clearly, where AI relies on algorithms to solve a problem or to identify patterns hidden in data, cognitive computing systems have the loftier goal of creating algorithms that mimic the human brain's reasoning process to solve an array of problems as the data and the problems change.
How Cognitive Computing Differs from Artificial Intelligence
Now even though cognitive technology falls under artificial intelligence and even though these terms are often used interchangeably they are quite different and they mean different things. Artificial intelligence is usually used as an umbrella term for all technology in the cognitive space but there are some nuances that need to be pointed out. AI technologies include -- but aren't limited to -- machine learning, neural networks, NLP and deep learning. With AI systems, data is fed into the algorithm over a long period of time so that the systems learn variables and can predict outcomes. Example of applications that are built with Artificial iNtelligence is Google Home, Amazon Alexa, and Apple Siri. Also, self-driving cars are also built using AI technology. Cognitive technology, on the other hand, includes technology like Natural Language Processing and Machine Learning.
From this perspective, it’s clear that while Cognitive Technologies are indeed a subset of Artificial Intelligence technologies, with the main difference being that AI can be applied both towards narrowly-focused AI applications. On the other hand, using the term Cognitive Technology instead of AI is an acceptance of the fact that the technology being applied borrows from AI capabilities but doesn’t have ambitions of being anything other than technology applied to a narrow, specific task.
Aspects of Cognitive Technology
The Ability to Perceive
This is the ability of technology to understand its surrounding environment and any external inputs that its sensors can pick up. For example, facial recognition technology falls under this. When a technology can practice object and image recognition. When you upload a group photo on facebook, facebook automatically recognizes the friends you have in the photo based on existing Facebook data. Same goes when google photos automatically put your photos in folders based on location, time take on people in it. Perception-focused capabilities are the area of AI research that got the biggest boost from the development of advanced neural network approaches, and Deep Learning in particular.
The Ability to Predict
This involves the machine or software being able to understand patterns and predict what will come or what will happen next. The machine also has the ability to draw from different systems that support it in order to improve its performance. This self-learning mechanism is a part of machine learning and it also makes use of neural networks. Prediction-focused cognitive technologies span the range from big data analytics to complex, human-like decision modes.
The Ability to Plan
Cognitive technology has the ability to use what it has learned and observed to plan on next steps and activities. This involves a lot of decision-making capabilities. The models are made to mimic how human decision making is done. This cognitive technology can mimic human emotions like emotional intelligence. This is not as advanced as the above two as human intelligence, intuition, common sense, emotional IQ are very abstract and qualitative capabilities and so they can be hard to mimic.