It's easy to mix up artificial intelligence (AI) and machine learning (ML). Despite the fact that machine learning is an element of artificial intelligence, these two phrases refer to two separate concepts that can be difficult to distinguish between. Put simply, machine learning is a subset of artificial intelligence, which is a large field of study. Machine learning is an application of AI that allows machines to learn from data without being explicitly programmed. AI is a larger idea that aims to produce intelligent machines that can replicate human thinking capabilities and behavior.

Machine learning is capable of doing all sorts of things as long as you have the data to teach it how. That's not always easy, and researchers are always looking for a way to add a bit of "common sense" to AI so you don't have to show it 500 pictures of a cat before it gets it. Facebook's newest research takes a big step towards reducing the data bottleneck. The company's formidable AI research division has been working on how to advance and scale things like advanced computer vision algorithms for years now, and has made steady progress, generally shared with the rest of the research community. One interesting development Facebook has pursued in particular is what's called "semi-supervised learning."

Of course, I'm kidding, how can anybody get sick of dog pictures on Facebook? Nvidia's research teams have been doing some pretty crazy stuff with AI the last few years. This latest one is pretty funny from an amusement level standpoint but quite groundbreaking from a technical one. It's a challenging task for computers, although it has been done in the past. Previously, though, it required many images in order to make it work.

Welcome to the third chapter of ML lessons from Ng's experience! Yes, this one is the continuation of the series entirely based on a recent course by Andrew Ng on Coursera. Although this post can be an independent learning, reading the previous two articles will only help understand this one better. Here are the links to the first and second articles in the series. When trying to solve a new machine learning problem (one which does not have too many online resources available already), Andrew Ng advises to build you first system real quick and then iterate on it.

Computer vision startup Clarifai has launched a mobile software development kit (SDK) in limited preview today to process and carry out artificial intelligence on iOS devices. Versions for Android and Internet of Things devices are also in the works. The news was announced onstage today by Clarifai founder Matt Zeiler at MB 2017, a gathering of AI and bot industry innovators being held at Fort Mason in San Francisco, California. The news is significant because it allows mobile users to carry out AI computations -- even on their iPhone without a connection to the cloud, which is typically how machine learning is handled on mobile devices today. Computer vision that requires no internet connection will be useful for mobile app developers and users in parts of the world where internet speed or connections aren't reliable.

If you paid any attention to Google's big developer conference earlier this year then you'll know artificial intelligence is about to get big - really big. It's already powering most of Google's apps, one way or another, and the other giants in tech are scrambling to keep up. Here we're going to dig deeper into some of the AI announcements Google shared at I/O 2017, and explain how they're going to change the way you interact with your gadgets - from your smartphone to your music speakers. In broad terms artificial intelligence (usually) refers to a piece of software or a machine that simulates smart, human-like intelligence - even if it's just a hollow robot being operated by a person behind a curtain, pretending to respond to your commands, that's still a kind of AI. Within that you've got all kinds of branches, categories and approaches.