Over the past year or two, I've heard these buzz words being tossed around a lot, and it's something that has definitely seized my curiosity recently. Deep learning is an area of active research these days, and if you've kept up with the field of computer science, I'm sure you've come across at least some of these terms at least once. Deep learning can be an indimidating concept, but it's becoming increasingly important these days. Moreover, deep learning methods are beating out traditional machine learning approaches on virtually every single metric. So what exactly is deep learning? And most importantly, why should you even care? If you're new to computer science, and you've followed me up till this point, please stick with me. Certain optional sections of this article may get a little math heavy (marked with a *), but I want to make this subject accessible to everyone, computer science major or not.

Baidu, the world's No. 3 force in online advertising after Google and Facebook, sees artificial intelligence as the next big thing. And it's using AI to power advertising, voice recognition technology and self-driving cars. During its annual conference Thursday in Beijing, the search giant announced news on the autonomous vehicle front, saying it had won clearance from the California Department of Motor Vehicles to test its cars there. It also signed a partnership with U.S. chip-maker Nvidia to work on self-driving cars together. Despite all the investment on futuristic projects, Baidu got nearly 93% of its revenue in the last quarter from online marketing.

Most deep learning techniques are extensions or adaptions of ANN's, called deep nets. Different configurations of deep nets are suitable for different machine learning tasks: Restricted Boltzman Machines (RBM's) (Smolensky 1986; Hinton & Salakhutdinov, 2006) and Autoencoders (Vincent, Larochelle, Bengio, & Manzagol,2008) are the main deep learning techniques for finding patterns in unlabeled data. This includes tasks such as feature extraction, pattern recognition and other unsupervised learning settings.

Forget about Minority Report and its sexy gesture interface – predicting the future is very different from what you see in the movies. Applying the right statistical models allows you to gain insights from the information at your disposal. The hidden patterns unveiled by the process makes it possible to make predictions. This is what we call predictive analytics. This is how the retail industry is able to predict what customers buy according to the time of the month or other items they have just purchased.

There are lots of writings about how data analytics changes business and everything, but deep down it only matters how you put these analytics in action. This is a software engineering perspective. Following the success of AlphaGo, the machine learning software that defeated the Go master Lee Sedol, the Wired magazine released an article titled "The End of Code" with a provoking highlight This is a very interesting transition. In reality though, we are not going to train whole large systems like this, but more atomic functions. You see, training an ERP system like a dog would be one heck of an agility track.

What differentiates us from machines is not our ability to think, but our ability to reason and to make cross connections between two unrelated phenomenons. There is no doubt that machines have been getting smarter and now, there are concerns that they may take over work from humans. So, while Percy Spencer, inventor of the microwave, could figure out that micro waves from an active radar could be used for heating food, a machine would have just dismissed the accident as a black swan event. Although, there is increasing automation--especially for jobs that entail repetitive tasks, for instance, self-driving cars--there is still time before artificial evolution allows machines to turn our lives into a sci-fi movie gone horribly wrong. in that context, we need to address the question whether these are really as intelligent as their human masters. In order to delve into that question, one needs to understand artificial intelligence and its evolution.

We demonstrate a hybrid machine learning method to classify schizophrenia patients and healthy controls, using functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP) data. The method consists of four stages: (1) SNPs with the most discriminating information between the healthy controls and schizophrenia patients are selected to construct a support vector machine ensemble (SNP-SVME). The method was evaluated by a fully validated leave-one-out method using 40 subjects (20 patients and 20 controls). The classification accuracy was: 0.74 for SNP-SVME, 0.82 for Voxel-SVME, 0.83 for ICA-SVMC, and 0.87 for Combined SNP-fMRI. Experimental results show that better classification accuracy was achieved by combining genetic and fMRI data than using either alone, indicating that genetic and brain function representing different, but partially complementary aspects, of schizophrenia etiopathology.

As a technical founder at Siri, Sentient, and Viv Labs, Adam Cheyer has helped design and develop a number of intelligent systems. Drawing on specific examples, Adam reveals techniques he uses to maximize the impact of the AI technologies he employs. Follow O'Reilly on Twitter: http://twitter.com/oreilly

To ease the burden, a group of local entrepreneurs - some of whom are former lawyers - have designed a website that helps lawyers search faster, keep notes and organise their research better. Launched in January, Intelllex, meaning "intelligent law", has already attracted more than 1,000 users - about half of whom are lawyers and the rest law students. Lawyers said it has reduced their research time by 30 to 60 per cent, meaning they can handle more cases. "A junior litigation lawyer spends 35 per cent of his time every day doing research," said Mr Chang.

Legal research can be the bane of every lawyer and law student's existence. From poring over textbooks in law libraries to trawling through cases online and offline to prepare for submissions, it is a process that can take hours. To ease the burden, a group of local entrepreneurs - some of whom are former lawyers - have designed a website that helps lawyers search faster, keep notes and organise their research better. Launched in January, Intelllex, meaning "intelligent law", has already attracted more than 1,000 users - about half of whom are lawyers and the rest law students. The service is currently free, but a subscription fee is likely to be introduced next year.