When crowdsourced labor company CrowdFlower recently raised funding from Microsoft, co-founder Lukas Biewald told me his team was focused on technology that allows businesses to supplement algorithms and artificial intelligence with human judgment from crowdsourced labor pools. Now CrowdFlower bringing on more experts to shape the development of that technology. Specifically it's formed a three-person scientific advisory board, made up of Barney Pell (founder/co-founder of startups including Powerset, LocoMobi and Moon Express, who also led an artificial intelligence team at NASA), Anthony Goldbloom (founder and CEO of Kaggle) and Pete Warden (a staff research engineer at Google, where he's the technical lead on the TensorFlow Mobile machine learning project). "With all these different customers and all these different applications, we wanted them to be confident that they're going to get a high-quality algorithm," said Biewald. He's also a friend of mine from college --although we really only talk about CrowdFlower now, which is kinda sad when you think about it.) "One way to make sure all the product decisions we make really reflect the cutting edge was to get some of the world leaders come in and look at our product."

Advances in machine learning and deep learning systems are bring us much closer to developing true artificial intelligence (AI) than ever before. One major limitation to these systems, though, is the effort required to teach them, with most requiring thousands or even hundreds of thousands of examples before they can "learn" something new. Self-driving car systems absorb miles of traffic data to learn basic driving lessons, and this scary image generator had to be fed 200,000 images for it to recognize a normal face. However, a new development from the team at Google DeepMind may be the start of leveling out that steep learning curve for AI systems. To speed up the learning process, Google DeepMind researcher Oriol Vinyals added a memory component to a deep-learning system.

AI and machine learning changes the software paradigm computers have been based on for many decades. In the traditional computing domain, providing an input, we feed it into an algorithm to produce the desired output. This is the rule-based frameworkthe majority of the systems around us still work with. We set up our thermostat to a desire temperature (input) and a rule based programming (algorithm) will take care of reading a sensor and activating heating or AC machines to get to the room temperature we want (output). The industry has been working relentlessly for many years developing better hardware, software and apps to solve a gazillion problems and use cases around us with programmable solutions.

Ever wanted to develop your own artificially-intelligent programs? Microsoft is empowering everyone with the capability to create huge, intelligent data-processing systems with the release of the Cognitive Toolkit. The Cognitive Toolkit--previously known as CNTK--is a superfast deep-learning toolkit that brings commercial-grade quality and processing accuracy together with programming languages and algorithms you already use. It's not just for developers with a farm of servers and GPUs, though--hobbyists and modest users can be equally competitive because the Toolkit is flexible enough to run on a single laptop. Developers can also integrate into the Toolkit their own Python or C code.

Alice and Bob can keep secrets -- well, at least from Eve. These three are the neural networks (or neural nets) that a team from Google Brain, Google's research division for machine deep learning, developed to see just how well artificial intelligence (AI) can keep secrets. It turns out, they can do it pretty well. In a study published on arXiv, researchers Martín Abadi and David Andersen feature how neural nets can develop their own simple encryption techniques in order to keep messages from eavesdroppers, even without being given special cryptographic algorithms. In theory, neural nets "are generally not meant to be great at cryptography," the researchers said.

Machine learning has been part of the advanced analytics picture for decades, but the emergence of big data platforms and better tools for creating automated analytical algorithms is bringing it more front and center. As a result, growing numbers of IT and analytics teams face the challenges of making machine learning projects work. In many organizations, machine learning initiatives require big investments in IT infrastructure, often involving the deployment of Hadoop clusters, the Spark processing engine and other big data technologies. New data management and analytics processes are often also needed to get data sets ready for analysis and to develop the algorithms that will be run against them. In many cases, that means adding new skills through outside hiring or retraining of existing employees.

Marketing in the 2000s was dominated by search engine marketing and optimization (SEM and SEO). The early 2010s saw the rise of Facebook and social media marketing. Most recently, we've seen mobile marketing rise and plateau as users have stopped downloading new apps. Now, we are entering the era of messaging and chatbots. What is a "chatbot," you ask? Chatbots are computer programs that carry out conversations with people using a lightweight messaging app UI, language-based rules, or artificial intelligence.

Steele is a professional therapy dog and has been part of the Missouri Baptist Disaster Relief team, for two years. He was part of a team of 18 DR volunteers who provided free childcare to the messengers who attended the 2016 MBC Annual Meeting.

Machine learning as a service market grow at a CAGR of 43.7% to reach USD 3755.0 million by 2021

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".