Since a few years, the terms artificial intelligence and machine learning has got very much popularity. You might have seen news of robots taking on human beings on technology news and websites. Nowadays Machine Learning and Artificial Intelligence are being integrated into almost all fields. And it has shown great results to us. That is why these technologies are rapidly being implemented in various sectors.
Generally, two things seem clear that – firstly, the term AI(Artificial Intelligence) is a bit older than Machine Learning(ML). Secondly, most people believe Machine Learning to be a subset of Artificial Intelligence.
Though these two words are often used as synonyms, most experts believe that they have subtle differences between them.
Artificial Intelligence vs. Machine Learning
The term Al was first coined by John McCarthy in 1956. AI can be defined as the field of computer science related to solving cognitive problems commonly associated with human intelligence, such as learning, problem-solving, and pattern recognition, in essence, it is the idea that machines can possess intelligence as humans. The main part of an Artificial Intelligence-based system is its model. A model is kind of a program that improves its knowledge base through a learning process by making observations about its environment. Such type of learning based model is often grouped under supervised Learning. However, there are also other models which come under the category of unsupervised learning.
AI can be divided into two categories – general and narrow. General AI would have almost all of the of human characteristics including recognizing objects, understanding languages and sounds & problem-solving. While Narrow AI would have only a few of the human characteristics. For instance, having a face recognition feature but nothing else would be a kind of Narrow AI.
Since Machine Learning is considered as a subset of Artificial Intelligence. It can simply be defined as “the simple way of attaining AI”. The term was first coined by Arthur Samuel in 1959 when the concept of data mining was taking off around the1990s. Data mining is a concept of looking for patterns in a given set of data using some kind of algorithms. AI is somewhat hard to achieve without ML. So that instead of hard-coding the software routines for handling specific tasks, we can actually teach the algorithm to accomplish that task with efficiency and accuracy.
An application of ML has gained popularity these days is image recognition(the ability of a machine to recognize an object in an image). Before its application the AI must be trained first in other words, humans have provided a bunch of pictures and tell the system what is in the picture. Then, the algorithm tries to build a model and learn to recognize the object in a picture accurately. After many repetitions, the AI(or algorithm) actually learns about the pixels patterns associated with different objects like cats, dogs, flowers, faces,etc., and able to recognize the content of images.
Many companies powers their services with AI. Some good examples will be, Facebook to decide what to display in your newsfeeds, Ad Services, Amazon Product Recommendations, Amazon Movies Suggestions, etc.
Other Related terms: Cognitive Computing, Deep Learning & Neural Networks
Of course, there are few other related terms associated with ML & AI. Basically, the terms Deep learning, Cognitive Computing & Neural Networks are often used togetherly. IBM frequently uses the term “cognitive computing,” with their IBM Watson(IBM’s cognitive computer system), which is quite synonymous with AI.
What is Cognitive Computing?
Cognitive computing is similar to AI in terms many of the underlying technologies. Cognitive Computing uses computerized models to mimic the human cognition process in order to solve complex problems. It uses self-learning technologies to find solutions, which are generally done by humans. They are often used for data mining, pattern recognition and natural language processing (NLP), etc. things which require human intelligence.
What is Deep Learning?
Deep learning is a subset of machine learning concerned with algorithms structured and function like the human brain due to neural networks. Deep Learning is a part of Machine Learning methods. Deep learning models generally relate to the information processing and communication patterns found in case of biological nervous systems. Deep learning is applied in various fields including, computer vision, speech recognition, natural language processing, etc. Deep learning is also known as deep structured learning or hierarchical learning because data is represented in multiple levels to system/algorithm to actually be able to learn it.
What is Neural Networks?
A Neural Network(NN) or Artificial Neural Network (ANN) is a kind of network which processes information as like human brains. It consists of highly interconnected processing elements(neurons) which togetherly works to solve complex problems. NN or ANN possess the ability to solve really complicated problems and can extract patterns which are too complex to be noticed by either humans or other computing techniques. Neural Networks helps AI to attain its intelligence. Neural Networks are generally trained by providing some set of data and possible outcomes so that the NN can actually learn it before they can be applied on.
Since all the terms are related, people generally get confused about them. However, it is important to notice that many kinds of research are being carried on in this field. Nowadays AI is in trending. They are being integrated into Smartphones, IoT devices, and other gadgets. Also, its worth noting that many new inventions may be seen in this field in near future. What do you think? Tell us in the comments below. We would love to hear.
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