Replacing the legacy safeguards that have failed to protect networks and applications, machine learning is at the heart of IT's shift in mindset. It's no longer about preventing attacks or focusing on "known threats," but rather identifying potential threats early enough so that they don't have an opportunity to cause real damage. Without the proper tools, it can take companies months to discover a data breach and even more to resolve it. By failing to detect potential threats early, organizations like Home Depot, J.P. Morgan and Ashley Madison put their finances, reputation and relationship with valued customers at risk.

The industry is far from ready to take humans "out of the loop" on most security operations and let machines run our cyber-defenses autonomously. However, machine learning breakthroughs are already making huge strides in reducing compromises as well as easing the burden of security staff in investigating suspicious events. These under-staffed teams are often overwhelmed with massive volumes of alerts, making it tough to weed out false alarms and identify the subtle signs of more sophisticated attacks. Machine learning anti-malware systems draw on deep knowledge of malware from analyzing millions of malicious samples. Cloud computing horsepower and data science techniques help master the "DNA" and "family trees" of malware, so even completely new malware is accurately detected.

Some 250 billion billion (250 x1018) transistors were produced in 2014. That means that every second of that year, on average, eight trillion transistors were produced - about 25 times the number of stars in the Milky Way (this statistic is from 2014, so according to Moore's Law production should now have doubled). The enormous surge in computing power which we are witnessing heralds some other lapel-grabbing metrics: 58% of job activities can be automated; 47% of jobs will be lost to cognitive machines in the next ten years. Taking advantage of this exponential is a wave of machine learning, deep learning and AI specialists. One such company is Seldon, a talented start-up selected to join the Barclays Accelerator powered by Techstars.

Using artificial intelligence (AI) in your business may not be something that is on your agenda, but the chances are you may have already begun to use AI without even knowing it. The Airbnb application for instances uses Aerosolve to deliver its dynamic pricing feature. Amazon's Machine Learning – part of its AWS cloud services – allows businesses to analyse massive datasets to reveal patterns and also train its algorithm. And Google's Translate API uses machine learning to deliver much more accurate translations, as it assesses how words relate to each other. In a report into the possible impact of machine learning, Simon Raik-Allen, MYOB's Chief Technology Officer, said: "As machines get smarter, there will be a time when someone creates a machine that can learn.

IBM has updated its artificial intelligence (AI) product, IBM Watson, giving it the ability to recognise images. Watson, which relies on cognitive learning to help it process the world in a human-like manner, can now'guess' what's happening in images fed to it via URLs. IBM has created a'Visual Recognition Demo' to showcase Watson's latest trick, which allows users to feed Watson an image before it tells you what it believes it sees. For example, supplying Watson with the image of a tiger throws up the result 77 per cent tiger, 26 per cent wild cat and 63 per cent cat. As well as identifying objects, people or animals in photos, Watson is also fairly adept at guessing what's going on in the background of images such as sunsets and other outdoor scenes.

The technology industry has a problem with diversity. True to form, it thinks the solution is technological. Founders of the Future is a new venture that hopes to uncover the Mark Zuckerbergs of the future, using algorithms. "An AI doesn't know anything about your background - which school or university you went to," Tom Bowles told me. Mr Bowles, a machine learning expert, developed the software, which models the characteristics of successful entrepreneurs like Niklas Zennstrom, who set up Skype, and Natalie Massenet, who started Net-a-Porter.

After being away from the research on AI over the last few years, I took a deep dive over the last week into the current state of affairs. I downloaded and worked through Tutorials on Theano, Tensorflow and Torch. I read a number of scientific papers from arXiv and a book on a roadmap for machine learning, which you've probably come across. I tweeted a bunch of notes and thoughts while I was training my own Deep Learning model with new data (aka my human brain). And so what was I looking for as a result of all this research about learning?

Unexpected convergent consequences…this is what happens when eight different exponential technologies all explode onto the scene at once. This post (sixth in a series of seven) is a look at robotics. Be sure to read the first five posts if you haven't already: When the World Is Wired: The Magic of the Internet of Everything Where Artificial Intelligence Is Now and What's Just Around the Corner The Near Future of VR and AR: What You Need to Know Drones Have Reached at Tipping Point--Here's What Happens Next How 3D Printing Is Transforming the Way We Make Things An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., 3D Printing), but try to predict the future when AI, robotics, VR, drones, and computation are all doubling, morphing and recombining…You have a very exciting (read: unpredictable) future. This post is the result of an interview with Rodney Brooks on the top five recent robotics breakthroughs (2012-2015) and the top five anticipated robotics breakthroughs (2016-2018). Rodney is the Panasonic Professor of Robotics at MIT.

Twenty years ago, Louis Gerstner transformed IBM by emphasizing consulting and technology services -- not just technology -- to solve customer problems. Today, as the wave of digitization continues to grow and envelope all the world's enterprises, IBM is at a crucial juncture once again. CEO Ginni Rometty is leading the company into new areas, betting big on its Watson software and cloud computing. But these new services have yet to grow fast enough to supplant the profit declines in the company's eroding legacy products. This time, the transformation IBM faces is far more difficult, for two reasons.

But it could be a real threat, warn researchers at the recent World Economic Forum. Unlike today's drones, which are still controlled by human operators, autonomous weapons could potentially be programmed to select and engage targets on their own. "It was one of the concerns that we itemized last year," Toby Walsh, professor of artificial intelligence (AI) at the school of computer science and engineering at the University of New South Wales, told FoxNews.com. "Most of us believe that we don't have the ability to build ethical robots," he added. "What is especially worrying is that the various militaries around the world will be fielding robots in just a few years, and we don't think anyone will be building ethical robots."