Coexistence of Machine Learning & Artificial Intelligence

Introduction

Machine Learning (ML) & Artificial Intelligence (AI) are the most prominent and leading problem-solving practices adopted in many areas of industry and research. Let us first try and understand the difference between ML and AI.
ML is generally used alongside AI but they are not same. AI is a broad concept of machines being able to carry out tasks in a manner that would make us think of them as “smart”. ML is in fact a subset of AI. ML refers to systems that can learn by themselves. ML is an application of AI built around the concept that machines be given access to data so that they can learn by themselves. Today, most of the AI work involves ML, since intelligent behaviour needs substantial knowledge, and the easiest way to gain knowledge is by learning. Deep Learning (DL) is the next evolution of ML.

AI and ML

AI and ML are very much related. McCarthy one of the founders of the field has quoted “AI is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
Also, the behaviour of a machine is not only the result of the program, but also a result of the environment and its “body” that it is physically embedded in. In simple terms, if you can write an intelligent program that has, for example, human-like behaviour, it can be AI. But until and unless it automatically learns from data, it is not ML:
Examples of Coherent Existence of AI and ML
Day by day our world is becoming dependent on automating tasks for excellent results and high precision. Artificial Intelligence achieves this accuracy with the help of deep learning algorithms. To understand this further, we can consider the example of the interactions we have with Google search or Alexa. These machines are based on deep learning algorithms, and their results get more relevant and accurate with time, that means the more we interact with these devices. ML further improves the scenarios and makes products and services more efficient. With ML, any analytical model building can be automated, based on which results would be provided.
There are various real-life ML examples we come across in our daily lives. Let us list out a few which are most common to all:

• Google:

We know how well Google showcases its ML products with Google Assistant and Google Camera to the world. Google has also extended these features to Gmail and Google Photos. Gmail now has a smart reply feature which suggests small brief responses to e-mails received depending on the content in the e-mail.

• Netflix:

Netflix constantly tries to improvise the personalization and recommendations problems using ML. ML has also expanded into various other streams such as price modelling, content promotions, content delivery, and marketing as well. 80 percent of the Netflix platform runs through the recommendation engine. The neural network helps to track the user behaviour and program content.

• Uber:

ML is an integral part of this tech giant. From time assessment to determining how far a cab is from a given location, everything is achieved by ML. The platform uses algorithms to determine the results efficiently. Data is analysed from the previous trips and this data is put into the present scenario. The other branch of Uber, which is UberEATS follows the same pattern. Various factors such as food preparation time to estimating the delivery time are taken into consideration.

• Siri and Cortana:

These voice recognition systems are dependent on ML entirely. These famous voice recognition systems also comprise deep neural networks. These apps are trained in such a manner that they can replicate human interactions in exactly the same manner. As the interactions continue, these apps learn to understand the grammar of the language

• Spotify:

Spotify uses ML the same way as Netflix. With the weekly releases, it gives one a list of around 30 recommended songs. All of these songs are selected by ML algorithms which analyse one’s activities and matches songs based on what one has listened to in the past.

Conclusion

The growth of deep-learning models is expected to accelerate and create even more innovative applications in the next few year