Technology companies are hurriedly snapping up startups in the field of artificial intelligence, and Intel Corp. INTC 0.48 % is the latest to join a buying spree fueled by one of the hottest trends in the tech sector. The chip maker on Tuesday announced plans to pay an undisclosed amount for Nervana Systems, a 48-employee company working on semiconductors, software and services to exploit a popular AI technique called deep learning. Intel's move follows a deal disclosed Friday by Apple Inc. AAPL -0.59 % to purchase Turi Inc., a Seattle-based specialist in the field. The two acquisitions add to a string of 31 purchases since 2011 of AI startups by large companies, according to venture-capital research firm CB Insights. Factoring in smaller acquirers, PricewaterhouseCoopers LLP counts 29 related acquisitions so far this year, suggesting the total deal count for 2016 will top the 37 deals announced last year.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Forget ideology, liberal democracy's newest threats come from technology and bioscience

Predictive data analytics and machine learning are becoming necessities for businesses that wish to succeed in today's market. The right machine learning strategy can put your business ahead of the competition, reduce your TCO, and give you the edge your business needs to succeed. First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems. Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with artificial intelligence (AI) to avoid these problems and challenges in the first place.

This project uses deep learning to upscale 16x16 images by a 4x factor. The resulting 64x64 images display sharp features that are plausible based on the dataset that was used to train the neural net. Here's an random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16x16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth. As you can see, the network is able to produce a very plausible reconstruction of the original face.

Through the journey of getting familiar with Azure Machine Learning, cloud based machine learning platform of Microsoft, we discussed about the very first steps of getting started. When you open up the online studio through your favorite web browser, you'll directed to create a blank experiment. In your left hand side of the studio, you can see the pre-built modules that you can use to develop your experiments. If they are not enough for your case, you can use R or Python scripts in your experiment. With Azure ML Studio, you get the ability to deploy models for almost all the machine learning problem types.

Machine learning is nipping at the heels of conventional physical modeling of air quality predictions in more and more places. The latest is Johannesburg, South Africa, where computer engineer Tapiwa M. Chiwewe at the newly opened IBM Research lab is adapting IBM's air quality prediction software to local needs and adding new capabilities. The work is an expansion of the so-called Green Horizons initiative, in which IBM researchers partnered with Chinese government researchers and officials, starting two years ago. Last month, Chiwewe presented some of the Johannesburg lab's first results, involving ground-level ozone level predictions, at the 14th International Conference on Industrial Informatics in Poitiers, France. "You can do a lot of physics to understand how ozone is found in different places," he says, "but what we did is we just collected a lot of data and trained these machines on it and they were able to predict [local ozone levels] without any knowledge of how ozone works in the atmosphere."

Apple wants you to know it's been working on AI for years now -- you just didn't know it. In a new feature by Stephen Levy in Backchannel, some of the company's top execs and machine learning experts hammer home this message, pointing out all the ways in which AI is used in Apple's products today. But, they also point out that artificial intelligence isn't the "final frontier" for tech products "despite what other companies say." Apple has cause to push this message. The perception of the iPhone-maker in the wider AI community is that it's been behind the game, despite having launched Siri, its virtual assistant, back in 2011.

Opportunity Scoring for Dynamics CRM & Sales Force consists of predictive and prescriptive service models. These compute a probability of success for each sales opportunity in the sales pipeline and recommend what action should be taken to improve probability of winning. Opportunity Scoring provides a solution reference architecture that a IT developer can customize to build customized end-to-end solutions.

It is hard to miss the warnings. In the race to make computers more intelligent than us, humanity will summon a demon, bring forth the end of days, and code itself into oblivion. Instead of silicon assistants we'll build silicon assassins. The doomsday story of an evil AI has been told a thousand times. But our fate at the hand of clever cloggs robots may in fact be worse - to summon a class of eternally useless human beings.