It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen. My lecturer is a full-time Applied Math and CS professor at the Technical University of Denmark, in which his research areas are logic and artificial, focusing primarily on the use of logic to model human-like planning, reasoning and problem solving.

Leaders should always be asking themselves What's new?, What's next? and What's better?; that's where the future is. What technologies will drive the biggest changes in industries over the next 10 to 20 years and create the next economy? There are many that in combination will drive massive change across enterprises and all size of business. Specifically, I think 10 are essential, and shaping the industries of the future: drones, blockchain, big data, augmented reality, virtual reality, 3D printing, artificial intelligence, robots, internet of things, genetics. We will see them both in the consumer and enterprise domain; specifically in how we get stuff done, how we hire and how we collaborate. How might the way we work, hire and collaborate change in the future?

IBM is teaming up with Slack Technologies Inc. to make it easier for companies to build custom chatbots into the startup's workplace-messaging systems, the latest move by Big Blue to add more diverse business cases for its Watson artificial-intelligence technology. The two companies will release a developer toolkit that includes Watson technologies and can integrate easily into Slack, they said in a statement Wednesday. International Business Machines Corp. will also build a chatbot -- a conversation-based application -- that will help IT departments identify and resolve issues without having to leave the Slack platform. The startup's own customer-service bot will incorporate the Watson Conversation system, which includes technologies such as speech-to-text conversion and natural-language processing, or the ability for a computer to understand what a person is saying. "Right now, if you say something to Slackbot in Slack, it will kind of blindly take what you ask it and do a search," Slack Chief Executive Officer Stewart Butterfield said.

Artificial intelligence isn't just being used to automate cars... it's finding a home in conventional cars, too. GM has unveiled a partnership with IBM that will see the Watson cognitive computing platform power OnStar Go, its latest in-car service offering. The AI technology will suggest stores and services based on your location, your decisions and your habits. If you're driving home from work, for example, OnStar can remind you to pick up shopping on the way back. It can also recommend restaurants when you arrive in a new city, or tell you that a store order is ready for pickup.

MIT explains the motives for the project on its site: "Creating a visceral emotion such a fear remains one of the cornerstones of human creativity. This challenge is especially important in a time where we wonder what the limits of Artificial Intelligence are: Can machines learn to scare us?"

At present, predictive analytics is the most used form of AI in enterprise and companies are focusing on innovation, patenting their AI developments at a faster rate than ever before. Join us as we explore the rise of artificial intelligence in six charts including the top investors in AI and the most used AI enterprise solutions. As of June 2016, artificial intelligence received $974m of funding. This year's funding is set to surpass 2015's total and CB Insights suggests that 200 AI-focused companies have raised nearly $1.5 billion in equity funding. AI isn't limited to the business sphere, in fact the personal robot market, including'care-bots', could reach $17.4bn by 2020.

The smart energy startup Verdigris announced today that it has raised $6.7 million to scale production of its Einstein smart sensor and frequency detectors. The sensors are used to predict the failure of machines and improve energy efficiency. Factories, manufacturing facilities, and other large buildings using Verdigris technology reduce energy use 8 to 22 percent, CEO Mark Chung told VentureBeat in a phone interview. The Einstein frequency detector from Verdigris made its debut in August. "Rather than take a big data approach where we study thousands of motors and this is the failure pattern, we instead take a physics based model which is looking at a signal through our sensors," Chung said.

The'Insight Economy': What will the world look like when ads have conversations? Has a Black Mirror episode predicted the future of video games? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

"The best way to build capacity for addressing the longer-term speculative risks is to attack the less extreme risks already seen today, such as current security, privacy, and safety risks, while investing in research on longer-term capabilities and how their challenges might be managed," the White House suggests, reinforcing the idea that addressing issues of information governance, patient privacy, and provider workflows as soon as possible will prepare healthcare for an AI-driven future.

A team from Google Brain, Google's deep learning project, has shown that machines can learn how to protect their messages from prying eyes. Researchers Martín Abadi and David Andersen demonstrate that neural networks, or "neural nets" – computing systems that are loosely based on artificial neurons – can work out how to use a simple encryption technique. In their experiment, computers were able to make their own form of encryption using machine learning, without being taught specific cryptographic algorithms. The encryption was very basic, especially compared to our current human-designed systems. Even so, it is still an interesting step for neural nets, which the authors state "are generally not meant to be great at cryptography".