How do humans recognise face from any given angle? That's a puzzle that scientists at MIT have been trying to solve and as a step towards that ultimate goal have developed a new machine learning system that effectively shows what could be going on inside our brains when facial recognition is being carried out. The work is being dubbed as a study of social intelligence which is an important part of human intelligence by Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the Center for Brains, Minds, and Machines (CBMM). The paper published in Current Biology described a theory that explains how our visual system learns to compute invariant descriptions of an object and applies to the case of faces where scientists have been able to make the machine recognise a face that it has seen only once from a certain point and scale it to other points. For example the machine would have been trained to see the face from 90 degrees, but not from 45 degrees; however, through its intelligence it learns to recognise the face when it is shown at an angle of 45 degree rotation.

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines machine learning as "the science of getting computers to act without being explicitly programmed". You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.

At 2016's Code Conference, Bill Gates, when asked about AIs, said that "it is the most exciting thing going on right now". He's not the only one who feels that way. Sundar Pichai, CEO of Google, had the same sentiments. Pichai pointed out, that Google had already been using algorithms to improve its search functions long ago. What is noteworthy, is the fact the 1 out of 5 searches that happen now, is done by voice.

Microsoft today said that starting in early 2017, its Word and PowerPoint applications will be able to automatically come up with descriptions of photos that users can add into documents. Office 365 subscribers will see this first in Word and PowerPoint for PCs. Ordinarily, if you drop a photo into PowerPoint, you can type out an "Alt Text" title and description for the photo. But not everyone does that when they're making slide decks. Then, when a blind person opens the slide deck, they aren't able to understand what's going on in the picture, which could make the slide or the entire deck more difficult to fully grasp.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or make business forecasting related decisions. Read data files and perform regression machine learning operations by installing related packages and running script code on RStudio IDE. Approximate ensemble methods such as random forest regression and gradient boosting machine regression to enhance decision tree regression prediction accuracy. Analyze multi-layer perceptron methods such as optimal number of hidden nodes artificial neural network. Read data files and perform regression machine learning operations by installing related packages and running script code on RStudio IDE.

OpenAI, the billion-dollar San Francisco artificial intelligence lab backed by Tesla CEO Elon Musk, just unveiled a new virtual world. It's called Universe, and it's a virtual world like no other. It's a place where AI can learn to do just about anything. Other AI labs have built similar worlds where AI agents can learn on their own. Researchers at the University of Alberta offer the Atari Learning Environment, where agents can learn to play old Atari games like Breakout and Space Invaders. Microsoft offers Malmo, based on the game Minecraft.

"AI," "big data," and "machine learning" are all trending buzzwords, and you might be curious about how they apply to your domain. You might even have startups beating down your door, pitching you their new "AI-powered" product. So how can you know which problems in your business are amenable to machine learning? To decide, you need to think about the problem to be solved and the available data, and ask questions about feasibility, intuition, and expectations. Machine learning can help automate your processes, but not all automation problems require learning. Automation without learning is appropriate when the problem is relatively straightforward.

I spent the last three months learning about every artificial intelligence, machine learning, or data related startup I could find -- my current list has 2,529 of them to be exact. A few years ago, investors and startups were chasing "big data" (I helped put together a landscape on that industry). Now we're seeing a similar explosion of companies calling themselves artificial intelligence, machine learning, or somesuch -- collectively I call these "machine intelligence" (I'll get into the definitions in a second). Our fund, Bloomberg Beta, which is focused on the future of work, has been investing in these approaches. I created this landscape to start to put startups into context.

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