Introduction
Artificial Intelligence (AI) has been changing the world and the way it operates. There has been a lot of discussion around cognitive computing and traditional computing in recent years. Cognitive computing is a new form of computing that mimics human thought processes, while traditional computing focuses on task-oriented computing. This blog post will explore the difference between cognitive computing and traditional computing and help you understand which one is better for your business.
Cognitive Computing
Cognitive computing involves using a set of algorithms that attempt to mimic human cognitive functions such as perception, reasoning, prediction, and natural language processing. Cognitive computing enables the system to understand unstructured data such as images, videos, and texts, thus improving the accuracy of decision-making. It can enable data analysis at a faster rate compared to traditional computing. One of the key features of cognitive computing is machine learning (ML). Machine learning is the process of training a computer system to learn from data and make predictions or decisions by using algorithms.
Traditional Computing
Traditional computing is based on a set of rules or instructions executed by a computer. This type of computing is mostly used for task-oriented activities such as data entry, transaction processing, and simple calculations. Unlike cognitive computing, traditional computing cannot replicate the cognitive functions of humans, and it cannot analyze unstructured data. It often requires that data be formatted in a structured manner before processing, making traditional computing much slower for unstructured data.
Comparison between Cognitive and Traditional Computing
When comparing cognitive computing and traditional computing, the following are the key differences:
Data Analysis
Cognitive computing is faster in data analysis, as it can analyze unstructured data such as images, videos, and text. Traditional computing, on the other hand, requires data to be formatted in a structured manner, making it slower to process.
Self-Adaptation
Cognitive computing has self-adaptation capabilities, which allow the system to learn and make better predictions over time. Traditional computing lacks the ability to improve, and it has to be reprogrammed to learn.
Decision Making
Cognitive computing can analyze different types of data and make decisions based on the data analysis, making it more reliable. Traditional computing can only analyze structured data, which makes it less reliable for decision-making.
Accuracy
Because of its ability to analyze unstructured data, cognitive computing is more accurate in making predictions, while traditional computing is less accurate for complex predictions.
Conclusion
Both cognitive computing and traditional computing have their advantages and disadvantages. If you are dealing with unstructured data, need faster decision-making, and require self-adaptation, cognitive computing is the better option. However, if you are dealing with structured data and do not need a self-adaptive system, traditional computing will work just fine. Ultimately, your choice will depend on your business needs and resources.
References
- D. Kiron, R. LaValle, E. M�ller, P. Shockley, and N. Staton, "Beyond the Hype: Artificial Intelligence in Business - An Overview." MIT Sloan Management Review. January 2018, available: https://sloanreview.mit.edu/article/beyond-the-hype-artificial-intelligence-in-business/
- N. Duan, W. Dong, Z. Zheng, R. Zhang, H. Bu, and X. Chen, "Artificial intelligence and its application in sphericity detection: A review." Powder Technology. May 2019. doi: https://doi.org/10.1016/j.powtec.2019.03.050