A Cognitive computer or system learn at scale, reasons with purpose and interacts with humans naturally. Rather than being explicitly programmed, these systems learn and reason from their interactions with human beings and their experiences with their environment. Cognitive computing overlaps with Artificial Intelligence and involves similar technologies to power cognitive applications.
What is Cognitive Computing?
Cognitive Computing or Cognitive AI refers to individual technologies that perform specific tasks to facilitate human intelligence. Basically, these are smart decision support systems that we have been working with since the beginning of the internet boom. With recent breakthroughs in technology, these support systems simply use better data, better algorithms in order to get a better analysis of a huge amount of information.
Also, you can refer to Cognitive Computing as:
- Understanding and simulating reasoning
- Understanding and simulating human behavior
Using cognitive computing systems helps in making better human decisions at work. Some of the applications of cognitive computing include speech recognition, sentiment analysis, face detection, risk assessment, and fraud detection.
Now that you know what is cognitive computing, let’s move on and see how cognitive AI works.
How Cognitive Computing Works?
Cognitive computing systems synthesize data from various information sources while weighing context and conflicting evidence to suggest suitable answers. To achieve this, cognitive systems include self-learning technologies using data mining, pattern recognition, and natural language processing (NLP) to understand the way the human brain works.
Using computer systems to solve problems that are supposed to be done by humans require huge structured and unstructured data. With time, cognitive systems learn to refine the way they identify patterns and the way they process data to become capable of anticipating new problems and model possible solutions.
To achieve these capabilities, cognitive computing systems must have some key attributes.
- Adaptive: Cognitive systems must be flexible enough to understand the changes in the information. Also, the systems must be able to digest dynamic data in real-time and make adjustments as the data and environment change.
- Interactive: Human-computer interaction (HCI) is a critical component in cognitive systems. Users must be able to interact with cognitive machines and define their needs as those needs change. The technologies must also be able to interact with other processors, devices and cloud platforms.
- Iterative and stateful: Also, these systems must be able to identify problems by asking questions or pulling in additional data if the problem is incomplete. The systems do this by maintaining information about similar situations that have previously occurred.
- Contextual: Cognitive systems must understand, identify and mine contextual data, such as syntax, time, location, domain, requirements, a specific user’s profile, tasks or goals. They may draw on multiple sources of information, including structured and unstructured data and visual, auditory or sensor data.
Cognitive computing is a subset of Artificial Intelligence. There are various similarities and differences between the two. So let’s move on with our Cognitive AI article and understand the difference between the two.
Cognitive Computing vs AI
The technologies behind Cognitive Computing are similar to the technologies behind AI. These include machine learning, deep learning, NLP, neural networks, etc. But they have various differences as well.
|Cognitive Computing||Artificial Intelligence|
|Cognitive Computing focuses on mimicking human behavior and reasoning to solve complex problems.||AI augments human thinking to solve complex problems. It focuses on providing accurate results.|
|It simulates human thought processes to find solutions to complex problems.||AI finds patterns to learn or reveal hidden information and find solutions.|
|They simply supplement information for humans to make decisions.||AI is responsible for making decisions on their own minimizing the role of humans.|
|It is mostly used in sectors like customer service, health care, industries, etc.||It is mostly used in finance, security, healthcare, retail, manufacturing, etc.|
So these were some of the differences between the two. Now let’s move ahead and understand the concept of Cognitive AI with example.
Cognitive AI: Use Case
Cognitive computing and AI are technologies that rely on data to make decisions. But there are nuances between the two terms, which can be found within their purposes and applications.
Let us imagine a scenario where a person is deciding on a career change. An AI assistant will automatically assess the job seeker’s skills, find a relevant job where his skills match the position, negotiate pay and benefits. And at the closing stage, it will inform the person that a decision has been made on his behalf.
Whereas, a cognitive assistant suggests potential career paths to the job seeker, besides furnishing the person with important details like additional education requirements, salary comparison data, and open job positions. However, in this case, the final decision must be still taken by the job seeker.
Thus, we can say, cognitive computing helps us make smarter decisions on our own leveraging machines. Whereas, AI is rooted in the idea that machines can make better decisions on our behalf.
Applications of Cognitive AI
- Smart IoT: This includes connecting and optimizing devices, data and the IoT. But assuming we get more sensors and devices, the real key is what’s going to connect them.
- AI-Enabled Cybersecurity: We can fight the cyber-attacks with the use of data security encryption and enhanced situational awareness powered by AI. This will provide a document, data, and network locking using smart distributed data secured by an AI key.
- Content AI: A solution powered by cognitive intelligence continuously learns and reasons and can simultaneously integrate location, time of day, user habits, semantic intensity, intent, sentiment, social media, contextual awareness, and other personal attributes
- Cognitive Analytics in Healthcare: The technology implements human-like reasoning software functions that perform deductive, inductive and abductive analysis for life sciences applications.
- Intent-Based NLP: Cognitive intelligence can help a business become more analytical in their approach to management and decision making. This will work as the next step from machine learning and the future applications of AI will incline towards using this for performing logical reasoning and analysis.