Artificial Intelligence vs Machine Learning: A comparative study

What is Artificial Intelligence?

Artificial Intelligence or AI, as coined by John McCarthy in 1956 is a technology that can make machines do tasks that are characteristics of human intelligence. While this can involve many basic tasks like planning, mapping and interpretation of a command, Artificial Intelligence can be used to perform much more complex tasks than that. The basic structure of AI is based on complex programming that enables a machine to perform a task in any given situation. In general we may call AI as the technology that makes machines fall into the ‘smart’ category. Depending on the tasks performed, AI can be split into two categories:

Applied AI – When this technology is applied to do a specific task, like a robot that works at a warehouse or a car that can drive you from one place to another without you having to sit behind the wheel. Applied AI comparatively requires less coding than a general AI. However, applied AI will only be able to perform a specific task, the one that it has been coded according to. An applied AI designed to work at a warehouse will be nearly useless for driving a car.

General AI – General AI is the more complex version of this technology and can do nearly anything and everything humanly possible. General AI is that technology that can be considered closest to the working of a human brain. Tasks ranging from physical labours to the ones requiring intelligence, like decision making and planning are all possible for general AI. The programming of it however is very tedious, requiring thousands of hypothetical scenarios and the consideration of the AI’s reaction in them. This is the reason that makes this technology obscenely expensive and less used.

Uses of AI

1. Finance – AI can be used to determine an algorithmic trading strategy that will lead to maximum gains for the seller

2. Technology – The top use of AI in technology is static image recognition. In most of the smart phones today, face recognition is used as a lock to protect the privacy of a phone

3. Healthcare – Machine Learning can be used for merely monitoring the vitals of a patients, but when the efficient processing of the data from the vitals of a patients is required, AI comes into play. This can also improve the accuracy of a doctor’s diagnosis and help in better treatment and healthcare facilities

4. Industry – AI can be used to for predictive equipment maintenance in the supply chain management

5. Social Media – Content distribution is one of the most tedious tasks on social media but it is the one that attracts the audience most effectively. Determining the kind of post that will appeal to the major audience on social media is a way through which people are making millions these days. AI has a huge application when it comes to content distribution on social media

What is Machine Learning?

Using AI solves a lot of problems for humans and indeed makes their lives easier. However, deploying it in every place is not feasible and often waste of its intelligence. To rectify this situation, machine learning was introduced to the users. It works on the principle of using a neural network that is trained in accordance to the situation in which it is intended to be used. The training of a neural network involves subjecting it to enormous amounts of data to help the network learn. For example, if a neural network is designed to identify a picture depending on whether it contains a cat or not. For this, it will be subjected to numerous pictures. Combining the human response with the data given as input, the system will eventually learn how to identify a picture that contains a cat. This is a very basic example of machine learning; however, it can be used to accomplish much more complex tasks based on the training given to it.

Uses of Machine Learning

1. Supply Chain – Predictive equipment maintenance is one of the places where ML has very frequently and effectively being used. Knowing the exact time when a machine will need a maintenance checkup to ensure its optimum functioning can increase the efficiency of a supply chain line up

2. Security – Detecting identity fraud has been made effective with the use of ML. This is one place where human error can cause a huge damage. Using ML in place of a human will decrease the cases of identity fraud to a bare minimum

3. Transportation – With the increase in number of vehicles on road in the recent years, traffic flow optimization has become very important. Doing it with the help of an ML in place is a very effective way of keeping the roads safe.

4. Retail – Understanding the need of a customer is primary when it comes to retail. ML can be used to determine buyer behavioral pattern to predict the demand of an item and stock it in advance

5. Healthcare – Even a very small instance of ignorance can lead to serious injury to a patient’s health when in a critical situation. ML can be used to monitor the health of a patient constantly and even train it to dispense a dose based on the vitals of a patient

Artificial Intelligence vs Machine Learning

December 05, 2017

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