The difference is that JPEG and MP3 aren't trained codings: they are fixed codings. Also, one thing is the JPEG algorithm and its coefficients (e.g. Used that way, a neural network is not really distinct, it's just a bunch of coefficients that can be used to reconstruct the pixels/PCM values, etc. to reproduce media. But neural networks aren't used that way, unless you are talking about an autoencoder. If I train a classification network on a certain dataset, there isn't any way of recovering the dataset from the trained coefficients of the network... in fact, it might not even be possible to say which dataset(s) I used to train the network.

Scientists have developed a new artificial intelligence system that can monitor a person's sleep using ambient radio waves, without sensors attached to the body.

It's getting harder and harder to squeeze more performance out of your phone's camera hardware. That's why companies like Google are turning to computational photography: using algorithms and machine learning to improve your snaps. The latest research from the search giant, conducted with scientists from MIT, takes this work to a new level, producing algorithms that are capable of retouching your photos like a professional photographer in real time, before you take them. The researchers used machine learning to create their software, training neural networks on a dataset of 5,000 images created by Adobe and MIT. Each image in this collection has been retouched by five different photographers, and Google and MIT's algorithms used this data to learn what sort of improvements to make to different photos.

In 1977, the Eagles released "Hotel California", a song about drugs and the effects an addiction has on people. Putting "We are all just prisoners here, of our own device" in the context of our today's digital lifestyle we find a lot of truth. There is a reason why Google provides most of its services for free or why Amazon wants us to have an Echo in every home or why Facebook has become our directory of "friends". What looks pretty convenient is a threat. It is a threat to the end consumers but also a threat to the established economy. And even if we have the choice to check out any time – we will never leave.

An artificial intelligence has beaten one of the world's top Dota 2 players in single combat today.

A lot of Andrew's recent stuff has been about evangelizing deep learning to new users or to semi-technical people (like project managers), so I would assume this new course is aimed at a similar audience. I'd be interested to hear from someone who has looked at it more closely though.

In spite of the rapid development in data acquisition technology resulting in the explosive collection of acquired datasets, techniques such as data organization and classification, manipulation, and analysis of very large, diverse, heterogeneous datasets have only evolved modestly. This has led to hindrances in effective utility and better understanding of the acquired, large-scale data for knowledge discovery. In an industrial setting, an interesting visual from McKinsey illustrates that despite collecting data from tens of thousands of sensors, less than 1% is actually utilized. Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Typically, homogeneous data objects, i.e. data objects having the same data type, are grouped together using some of the well-known clustering algorithms.

Much of what you're about to read applies to the news in general, but it especially applies to news about technology. On this topic the question to ask yourself is not is this important or is it just hype? Hype about things that aren't important only matters if you mire yourself in triviality. As for things that are important, but don't get hyped, they will happen anyway – and someone, somewhere is almost certainly writing about them (if you look hard enough). However, when things are both hyped and important, the potential for serious misdirection is decidedly non-trivial.

Ok, I know a bit about dota (been playing it for 8 years now). I will try my best to put this into perspective. What: It beat players that many considered to be the absolute best at dota. The environment: 2 players move along a lane with the goal of destroying the other's defensive structure or killing the player 2 times for victory. Every 30 seconds weak npc minions enter the lane attack each other and players.

Tom Simonite talked about the rise in artificial intelligence use at companies such as Google and Facebook and what this means for the future of workplaces across the U.S. He spoke via video link from San Francisco.