After a long time we are back to the new technology track but this one is available in the society for long time with different the name sorry we are the one who can confuse this with another one.
Hey!!! Did you start it again starts to confuses us?
Sorry but it's my way of writing to make you confuse and also clear the confusion so wait for the better content.
Before move on to today's content first we have to know what is AI (Artifical Intelligence)?
Yes, we all know that AI is the technology that make the system to think in a smarter way simply said that makes the system yo work without human interaction. Right. Bit we all have one misunderstanding here, i.e we all think that AI is the way to make a computer to think like a human brain but it is wrong. Because the technology AI using many other technology to analysis the patterns for more efficient work, this means it will work not independently but it will work interdependently based on the predicted patterns.
Then it means there is no technology for replica of human. No that is also wrong there is the technology that will replica the human but the name of the technology is not AI but it is named as Cognitive Computing.
So for this name you will give this long definition right.
Yes, you're right. And this long definition will help you to understand the following content in a better way that's why I tell you the above. OK no more discussion let's go on to the topic. First the definition part.
The goal of cognitive computing is to simulate human thought processes in a computerized model. Using self-learning algorithms that use data mining, pattern recognition and natural language processing, the computer can mimic the way the human brain works.
Now you all know that what is cognitive computing but we have to know the working process of cognitive computing.
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
- Interactive
- Iterative and stateful
- Contextual
Overall, 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.
Some of the real world examples for cognitive computing are listed ad follows:
- IBM Watson
- Google DeepMind
- Microsoft Cognitive Services
- SparkCognition
We all know that "Nothing is perfect" through this one it has some limitations also they are as follows.
The limitations of cognitive computing necessitate caution when incorporating this technology into its various and widespread applications. These can include security risks, as these systems handle large amounts of data and may be vulnerable to breaches. Another challenge is the lengthy development time that a highly complex technology needs to get off the ground. This may make it harder for companies with smaller development teams to develop applications with integrated cognitive computing processes.
Now you all understand the concept right.
Our article is concluded here. If you have any doubt let me know through the comments section thank you for reading!!!!!