Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

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In the last few posts, where we discussed Logistic regression, we had a fair bit of discussions on classification problems. Classification problems are the most prevalent ones we encounter in the real world machine learning setting and it is important to deal with various facets of this problem.In the next few posts, we will decipher some of the popular algorithms used within the classification context. The first of those algorithms which we are discussing is called the Random Forest. It is one of the most popular and powerful algorithms, which is currently used in the classification setting. In addition to deciphering the dynamics of Random Forest, we will also be looking at a practical applications powered by Random Forest algorithm.

This week's top Machine Learning stories, including AI agents that both encrypt and explain themselves, and more! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!

One of the major announcements to come out of Microsoft's Build 2016 developer conference today was the bet the company is making on bots. Microsoft believes that bots are the new apps. Yesterday, they invited developers to build bots for Cortana, the company's virtual assistant. Cortana, for you non-gamers, is a virtual persona character from Microsoft's blockbuster first-person shooter Halo franchise. Microsoft is betting on the notion of "conversational computing," which is why the company is putting its voice recognizing virtual assistant front and center.

It provides operating system software for other manufacturers who make their phones. Google, in a way, is recognizing something that Apple has really based its business on for a long time, which is the close integration of hardware and software really has some big advantages. In what looked like a challenge to Apple, Google unveiled Pixel earlier this month and bragged its camera was superior to the iPhone's.

After years in the labs, artificial intelligence (AI) is being unleashed at last. Google, Microsoft and Facebook have all made their own AI APIs open source in recent months, while IBM has opened Watson (pictured above) for business and Amazon has purchased AI startup Orbeus. These announcements have not drawn much media attention, but are hugely significant. "In the long run, I think we will evolve in computing from a mobile-first world to an AI-first world," says Google CEO Sundar Pichai. What does the appearance of AI bots and machine learning on the open market mean for business, IT, big data, and for sellers of physical hardware? The AI APIs now opening up are essentially free platforms on which companies can build incredibly powerful analytics tools.

Amazon has crowned the latest champion in its robotic picking challenge -- an annual competition that looks for robots that could one day work in the company's warehouses. It's basically American Idol, but for robotic arms that can grab items off a shelf and put them back again. Competitors are asked to handle a range of products, from toiletries to clothes, and then scored on speed and accuracy in stocking shelves. This year's contest was won by a joint team from the TU Delft Robotics Institute in the Netherlands and the company Delft Robotics (both named after the city of Delft). The team's robot managed to pick items from a mock Amazon warehouse shelf at a speed of around 100 an hour, reports TechRepublic, with a failure rate of 16.7 percent.

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Machine learning will drop the cost of making predictions, but raise the value of human judgement. To really understand the impact of artificial intelligence in the modern world, it's best to think beyond the mega-research projects like those that helped Google recognize cats in photos. According to professor Ajay Agrawal of the University of Toronto, humanity should be pondering how the ability of cutting edge A.I. techniques like deep learning--which has boosted the ability for computers to recognize patterns in enormous loads of data--could reshape the global economy. Making his comments at the Machine Learning and the Market for Intelligence conference this week by the Rotman School of Management at the University of Toronto, Agrawal likened the current boom of A.I. to 1995, when the Internet went mainstream. Gaining enough mainstream traction, the Internet ceased to be seen as a new technology.