Artificial Intelligence versus Machine Learning
So, basically Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn the things. It is also a field of study which tries to make computers “smart” than ever before. They work on their own without being encoded with commands. They also learn new and different things by themselves. So this article is all about artificial intelligence versus machine learning.
Who discovered name and how?
John McCarthy came up with the name “artificial intelligence” in 1955. The main characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.
The term artificial intelligence was coined in 1955, but A.I has become more popular in some few previous years. Thanks to increased data volumes, advanced algorithms, technology, and improvements in computing power and storage.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from its experience without being explicitly programmed. It mainly focuses on the development of computer programs that can access the data and use it for learning for themselves because that is the only thing that it needs.
Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the specified needed tasks.
It involves computers discovering how they can perform tasks without being explicitly programmed to do so. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer’s part, no learning is needed.
The best programming language for machine learning is Python. Python is the most popular, general purpose programming language suitable for a variety of tasks in machine learning. And overall, python is easy to learn and the most productive.
WHAT IS THE REALTION BETWEEN AI AND ML?
The key relation between AI and ML are :-
The goal is to learn from data on certain task to maximize the performance of machine on this task. And also, AI is decision making.
ML allows system to learn new things from data. It leads to develop a system to mimic human to respond behave in a circumstances. They do whatever you want them to do.
AI and ML took a great boost within past few years. It’s demand will be even more in upcoming years. This is because of the increase in technical knowledge and demand of people.
For eg. almost every person nowadays has a smartphone. So, it’s demand is also increasing nowadays. That’s the reason that it will have a high demand in future because of it’s high use.
Differentiating Artificial Intelligence and Machine Learning
|MACHINE LEARNING||ARTIFICIAL INTELLIGENCE|
|1. AI stands for Artificial intelligence, where intelligence is defined acquisition of knowledge intelligence is defined as a ability to acquire and apply knowledge.||1. ML stands for Machine Learning which is defined as the acquisition of knowledge or skill.|
|2. The aim is to increase chance of success and not accuracy.||2. The aim is to increase accuracy, but it does not care about success|
|3. It work as a computer program that does smart work.||3. It is a simple concept machine takes data and learn from data.|
|4. The goal is to simulate natural intelligence to solve complex problem.||4. The goal is to learn from data on certain task to maximize the performance of machine on this task.|
|5. AI is decision making.||5. ML allows system to learn new things from data.|
|6. It leads to develop a system to mimic human to respond behave in a circumstances.||6. It involves in creating self learning algorithms.|
|7. AI will go for finding the optimal solution.||7. ML will go for only solution for that whether it is optimal or not.|
The history of AI and machine learning
So where did AI come from? Well, it didn’t leap from single-player chess games straight into self-driving cars or any kind of robotics. The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science.
Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning than ever before.
Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods.
In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.
For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Google,
Amazon or Microsoft tackled similar projects. This work paved the way for the automation and formal reasoning that we see in computers today.
Everyday examples of Artificial Intelligence and Machine Learning
1. Smart Email Categorization
Gmail is an application of Google that uses a similar and almost same approach to categorize your emails into primary, social, and promotion inboxes. As well as labelling emails as important. In a research paper which is titled as, “The Learning behind Gmail Priority Inbox”,
Google outlines its machine learning approach and notes that is in its algorithm “a huge variation between user preferences for volume of important mail. Thus, we need some manual intervention from users to tune their threshold.
When a user marks messages in a consistent direction, they perform a real-time increment to their threshold. Every time you mark an email as important, Gmail learns.
The researchers tested the effectiveness of Priority Inbox on Google employees and found that those with priority inbox “spent 6% less time reading email overall, and 13% less time reading unimportant emails.”
2. Google’s AI-Powered Predictions
Using anonymous location data from smartphones, Google Maps (Maps). Google Maps can analyze the speed and location and movement of traffic at any given time. And, with its acquisition of crowd sourced traffic app Waze in 2013 and now too.
Maps can more easily incorporate user – reported traffic incidents like construction and accidents. Access to vast amount of data being fed to its algorithms and codes means maps can reduce commutes by suggesting the fastest routes to and from work.
3. Commercial Flights Use an AI Autopilot
AI autopilots in commercial airlines is surprisingly an early use of AI technology that dates as far back as 1914. Depending on how loosely you define autopilot and also how easily they do it.
The New York Times reports that the average flight of a Boeing plane. It involves only seven minutes of human – steered flight, and also which is typically reserved only for take-off and landing.