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".

We've been giving this some serious thought over the past few months. How can we make prospecting relevant to a company's strategy and who in that organisation should we be targeting?? To solve this WDMP and our Science Dept. Our Data Scientists have found a way to now target businesses via their internal ambitions. Our machine learning algorithms and AI can read the content that businesses create and publish electronically (shareholder reports, blogs, published PR and web sites etc) in close to real time.

Google's neural networks invent their own encryption MIT scientists are training AI to scare people. 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.

Apple Inc. hired a prominent artificial intelligence researcher from Carnegie Mellon University as it seeks to regain lost ground against competitors such as Google, Microsoft Corp. and Amazon.com He posted a link to an Apple job application page seeking machine learning specialists. Apple is seeking scientists with "experience in Deep Learning, Computer Vision, Machine Learning, Reinforcement Learning, Optimization, and/or Data Mining," it said in the job listing. So you can sleep an extra five minutes. Travel with us, drive with us, eat with us – around the world.

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.