The key difference between cognitive computing and machine learning is that cognitive computing is a technology whereas machine learning refers to algorithms to solve problems. Cognitive computing uses machine learning algorithms.
Cognitive Computing gives the ability to a computer to simulate and complement human’s cognitive abilities to make decisions. Machine learning allows developing self-learning algorithms to analyse data, learn from them, recognise patterns and make decisions accordingly. However, it is difficult to draw a boundary and divide the cognitive computing based and machine learning based applications.
1. Overview and Key Difference
2. What is Cognitive Computing
3. What is Machine Learning
4. Relationship Between Cognitive Computing and Machine Learning
5. Side by Side Comparison – Cognitive Computing vs Machine Learning in Tabular Form
What is Cognitive Computing?
Cognitive Computing technology allows making accurate models on how the human brain senses, reasons and responses to tasks. It uses self-learning systems that use machine learning, data mining, natural language processing, and pattern recognition, etc. It helps to develop automated systems that can solve problems without human involvement.
In the modern world, a large quantity of data produces daily. They contain complex patterns to interpret. To make smart decisions, it is vital to recognize the patterns in them. Cognitive computing allows to take business decisions using correct data. Therefore, it helps to come to conclusions with confidence. The cognitive computing systems can take better decisions using feedbacks, past experiences, and new data. Virtual reality and robotics are few examples that use cognitive computing.
What is Machine Learning?
Machine Learning refers to algorithms that can learn from data without relying on standard programming practices such as object oriented programming. Machine learning algorithms analyze data, learn from them and make decisions. It uses input data and uses statistical analysis to predict outputs. The most common languages to develop machine learning applications are R and Python. Other than that, C++, Java, and MATLAB also help to develop machine learning applications.
Machine learning divides into two types. They are called supervised learning and unsupervised learning. In supervised learning, we train a model, so it predicts the future instances accordingly. A labeled dataset helps to train this model. The labeled dataset consists of inputs and corresponding outputs. Based on them, the system can predict the output for new input. Further, the two types of supervised learning are regression and classification. Regression predicts the future outcomes based on the previously labeled data whereas classification categorizes the labeled data.
In unsupervised learning, we do not train a model. Instead, the algorithm itself discovers the information on its own. Therefore, unsupervised learning algorithms use unlabeled to data to come to the conclusions. It helps to find groups or clusters from unlabeled data. Usually, unsupervised learning algorithms are difficult than supervised learning algorithms. Overall, machine learning algorithms help to develop self-learning systems.
What is the Relationship Between Cognitive Computing and Machine Learning?
- Cognitive computing systems use machine learning algorithms.
What is the Difference Between Cognitive Computing and Machine Learning?
Cognitive Computing is the technology that refers to new hardware and/or software that mimics the functioning of the human brain to improve decision-making. Machining learning refers to algorithms that use statistical techniques to give computers to learn from data and to progressively improve performance on a specific task. Cognitive Computing is a technology but, Machine Learning refers to algorithms. This is the main difference between cognitive computing and machine learning.
Further, Cognitive Computing gives the ability for a computer to simulate and complement human’s cognitive abilities to make decisions while Machine learning allows developing self-learning algorithms to analyze data, learn from them, recognize patterns and make decisions accordingly.
Summary – Cognitive Computing vs Machine Learning
The difference between cognitive computing and machine learning is that cognitive computing is a technology whereas machine learning refers to algorithms to solve problems. They are used in wide variety of applications such as robotics, computer vision, business predictions and many more.
1.SciTechUK. Cognitive Computing | What Can It Be Used for?, Science and Technology Facilities Council, 10 May 2016. Available here
2.TheBigDataUniversity. Machine Learning – Supervised VS Unsupervised Learning, Cognitive Class, 13 Mar. 2017. Available here
1.’2729781′ by GDJ (CC0) via pixabay