We are entering the era of talking machines--and it's about more than just asking Amazon's Alexa to turn down the music. General Electric has built a digital assistant into its cloud service for managing power plants, jet engines, locomotives, and the other heavy equipment it builds. Over the internet, an engineer can ask a machine--even one hundreds of miles away--how it's doing and what it needs. Fast Company got an exclusive demonstration of the technology before its debut at GE's Minds Machines conference in San Francisco. Voice controls are built on top of GE's Digital Twin program, which uses sensor readings from machinery to create virtual models in cyberspace.

We have all heard, if not aware of IBM's cognitive system, Watson. It enables a link between people and computers enabling them to partner mutually. In order to strengthen this symbiotic relationship between human and a computer, IBM has come up with Watson Conversations. This application helps to deploy messaging platform on any device by using natural language algorithms. The best part about this app is, one need not be conversant in technology but few simple clicks can build, test and deploy bots across messaging platforms, physical robots or even mobile devices.

Do you remember ten years ago when phones used to be the prime point of contact for customer-related issues? Businesses could rely on call centre agents to respond to customers in a couple of days. It took a while, and the communication options were limited. Today the rise of e-commerce, multiple communication channels and the proliferation of mobile devices has meant that consumer behaviour has significantly shifted. We now demand access to immediate information and want our issues to be solved instantly at the click of a button. However, while advancements such as the move to mobile and e-commerce have undoubtedly propelled the customer service industry forward, it's the technology that is still evolving, like machine learning and AI, which will have a much larger impact on the future of businesses' customer relationships.

Ford will pour $1 billion over the next five years into an artificial intelligence company tasked with developing the technology that will one day drive its autonomous vehicles. The technology could also be licensed to other automakers in the future, executives said. Pittsburgh-based Argo AI was founded late last year by Bryan Salesky and Peter Rander, who previously worked on self-driving car initiatives at Google and Uber, respectively. The company will include the staff inside Ford that has been developing its virtual driver system for the past several years. In a phone call last week, CEO Mark Fields said the investment will help Ford bring its self-driving cars to market by its previously stated goal of 2021.

"Mandy Monroe" works relentlessly to reach out to customers of Lee Kia of Greenville in North Carolina to schedule service appointments. A consistent revenue generator, Mandy doesn't complain, gossip, make personal phone calls on company time or pop chewing gum. Mandy doesn't even take bathroom breaks. That's because Mandy is a cyberstaffer driven by artificial intelligence. The virtual assistant, created by the software provider Conversica, conducts email conversations with dealership service customers.

Recent breakthroughs in machine learning and artificial intelligence mean AI-enabled technologies are gaining traction. The billion-dollar cybersecurity industry is no exception, as vendors begin to scale and automate their processes intelligently - all while locked into the early stages of a security arms race with professional hackers. A recent report from analyst firm ABI Research estimates that machine learning in cybersecurity will enormously bolster spending in big data, intelligence and analytics, reaching as much as $96 billion (£71.9 billion) by 2021. Vendors are likely to find buyers in large enterprises, and more than likely, across industries that are especially prone to attack: think government and defence, banking, and across the technology sector. At the moment, ABI's report says, User and Entity Behavioural Analytics - using machine learning for threat detection by analysing data at scale - is the driving force. "Using static machine learning models to detect previously unknown malware is the only use case I'm aware of that offers clear evidence of effective results," says cybersecurity analyst at 451 Research, Adrian Sanabria.

Everyday, most people around the world are using artificial intelligence (AI) in their day-to-day lives. That is the suggestion from Japp Zuiderveld, VP of EMEA at NVIDIA. "It is already out there, people are already using it, or you are already using it, everybody is using it without knowing it and it facilitates the services you have today." In the interview he referred to Google Translate as the very simplest example of AI that people use on a daily basis. See also: Automation will transform marketing but brands need to allay'consumer fears' Despite this, however, he explained that it is still in its infancy and that common preconceptions of the technology are wrong.

Will artificial intelligence get more aggressive and selfish the more intelligent it becomes? A new report out of Google's DeepMind AI division suggests this is possible based on the outcome of millions of video game sessions it monitored. The results of the two games indicate that as artificial intelligence becomes more complex, it is more likely to take extreme measures to ensure victory, including sabotage and greed. The first game, Gathering, is a simple one that involves gathering digital fruit. Two DeepMind AI agents were pitted against each other after being trained in the ways of deep reinforcement learning.

As Maurice Conti has said in one of his presentations, we have arrived to the augmented era, in which Artificial Intelligence gains a new role. Until now all our tools were passive: we told them what to do and they did it. But the future holds a new era where artificial intelligence will come up with its own designs, its own ideas, its own products. Many tasks could soon be executed with the cooperation of robots, AI and humans, robots providing perfect execution, AI delivering the design the design and humans making the necessary decisions. But to get to this point it is crucial to build a nervous system for robots through sensors.

Episode Summary: There's been lot of hype around AI and ML in business over the past five years. Even among investors exist a lot of misconceptions about using ML in a business context, and how to get up to speed on and learn machine learning as it applies to utility in industry. Recently, I talked with Benjamin Levy of BootstrapLabs in San Francisco, whom I met through an investment banking friend in Boston. BootstrapLabs invests in Bay area companies, and Levy also travels around the world speaking about investing in AI companies and raising funds for new ventures. In this episode, Levy gives his perspective on what investors and executives get wrong about ML and and AI, and discusses how they can get up to speed and leverage the applications for these technologies and related expertise to really make a difference (i.e.