Traditional artificial intelligence (AI) has come on in leaps and bounds from its earliest beginnings, but it did hit a roadblock. As fast as mainframe computers could run computations, they were only as smart as the programmers who accessed the data and coded how to look at it. The computer program – the AI – lacked the ability to know or truly understand the data it was seeing. A new approach was needed to radically change how coding AI was approached. Only this way could computers get past their limitations.

Your company's ads target prospective customers, your CRM software delivers insights to sales for funnel optimization, and your chatbots converse with customers--these are all examples of machine learning at work. Machine learning is a type of artificial intelligence that enables computers to imitate human learning processes on their own based on data input. Computers learn from algorithms that programmers develop and the data set that programmers feed into it. In this type of machine learning, a developer feeds the computer a lot of data to train it to connect a particular feature to a target label. A feature could be images or text that the computer matches to an object to identify it.

The term Machine Learning might sound a bit intimidating. It is sometimes associated with scary accurate recommender systems or humanoid robots. However, the fundamentals of machine learning are actually not that complicated. If you, as a human, were given two (x, y) coordinates and had to draw a line through these points, you'd probably do this without much effort. However, if someone asked you to draw a line based on 1 million (x, y) coordinates, you'd probably politely tell this person to fuck off.

Python is my favorite computer language. There are plenty of packages, frameworks, and ready to use code that can be easily expanded. The language itself is very robust, with a huge number of active contributors. If you're new to Python, I recommend taking this amazing course on EdX. TensorFlow is a comprehensive open-source library for machine learning developed by Google.

Machine learning (ML) and artificial intelligence (AI) are turning out to be prevailing problem-tackling strategies in numerous areas of research and industry, not least due to the recent triumphs of deep learning (DL). However, the condition AI ML DL, as recently recommended in the news, web journals, and media, misses the mark. These fields share similar crucial speculations: calculation is a valuable method to demonstrate clever behavior in machines. Calculation neither rules out search, sensible, and probabilistic strategies, nor (deep) (un)supervised and reinforcement learning techniques, among others, as computational models do incorporate every one of them. They supplement one another, and the following breakthrough lies in pushing every one of them as well as in combining them.

Machine learning is a concept that is as old as computers. In 1950, Alan Turing created the Turning Test. It was a test for computers to see if a machine can convince a human it is a human and not a computer. Soon after that, in 1952, Arthur Samuel designed the first computer program where a computer can learn as it ran. This program was a checker game, where the computer learned the player's patterns during the match, and then use this knowledge to improve the computer's next moves.

Computers are slowly but surely learning to read our emotions. Will this mean a future without privacy, or perhaps a golden age of more compassionate and helpful machines? This edition of the Sleepwalkers podcast looks at AI's growing power to "read" us--and investigates the sinister and the positive uses of the technology. Poppy Crum, chief scientist at Dolby Labs and a professor at Stanford University, is using advanced sensors and AI to capture emotional signals. From thermal sensors that track blood flow to CO2 monitors that detect our breathing rates and cameras that track microscopic facial recognition, it's getting harder to maintain a poker face in front of machines.

Anyone who's even remotely familiar with the world of information technology might have come across words like machine learning and artificial intelligence. Artificial intelligence has long been a part of pop culture. If tech bigwigs are to be believed, artificial intelligence and machine learning are the future of our world and technology. But what is machine learning? And how are machine learning and artificial intelligence connected?

In a simple term, machines demonstrate intelligence by mimicking human behavior.

Note: This article is based on a transcript of The Dr. Data Show episode, "Five Ways Your Safety Depends on Machine Learning" (click to view). Your safety depends on machine learning. This technology protects you from harm every day by guiding the maintenance of bridges, buildings, and vehicles, and by guiding healthcare providers and law enforcement officers. This puts you in good hands. Hospitals, companies, and the government use machine learning to combat risk, actively protecting you from all sorts of dangers and hazards, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.