How does Amazon recommend products you might be interested in purchasing? OR How does Netflix decide which movies or TV shows you might want to watch? OR How does Facebook or LinkedIn decide who might you want to form a link with? OR How does Udemy decide what courses to market to you? OR How does New York Times decide which news you might be interested in reading? How does Amazon recommend products you might be interested in purchasing?

The acquisition will bring Zementis' predictive analytics to Software AG's real-time streaming analytics platform. Software AG has acquired California-based Zementis for an undisclosed sum in a move designed to bolster its internet of things capability. Zementis offers software for'deep learning' which plays a crucial role in the development of machine learning, data science and fundamental technology that drives artificial intelligence (AI) development. According to Software AG, the advances in machine learning and AI are being applied in the next generation Internet of Things (IoT) such as self-driving cars, personal digital assistants, medical diagnosis, predictive maintenance and robotics. Software AG has already employed Adaptive Decision and Predictive Analytics (ADAPA) from Zementis into its Digital Business Platform to offer its clients with comprehensive insights for real time business analytics.

Predictive analytics and machine learning working separately or together can be just what a company needs to succeed. But understanding how they work is key to figuring out how they can help businesses thrive. So, what is predictive analytics? Datafloq's Mark van Rijmenam uses the car metaphor, according to which traditional, descriptive analytics is like looking at the rear-view mirror to see what has happened, while predictive analytics is using a navigation system to tell you what will happen, and prescriptive analytics is a self-driving car that knows how to take you to your destination. This metaphor, while easy to comprehend, may also be deceptively simple. It certainly is open to interpretation, so it's a good starting point for discussion.

This project involved implementing machine learning methodologies to identify similarities in job skills contained in resumes. An organization presented the project to the New York City Data Science Academy to explore whether Academy students might be interested in working on it. The three authors of this post, all students at the Academy at the time, agreed to take the project on. In formulating the analysis described in this post, the authors collaborated with several representatives of the organization. While the organization has asked us to refrain from disclosing its name at this time, the authors wish to convey their gratitude to the organization for the opportunity to work on the project as part of our studies at the Academy. The general idea underlying this project was to uncover semantic similarity and relations behind skills that appear on resumes. A semantic-based approach to evaluating job skill similarity has many potential applications that flow from an understanding of the relationships between skills found in resumes. While there are certainly other approaches to identifying semantic connections between job skills, machine learning techniques create interesting and powerful possibilities.

In today's world of big data and the internet of things, it is common for a business to find itself sitting atop a mountain of data. Possessing it is one thing, but leveraging it for data driven decision making is a much different ball game. Gut-feelings and institutionalized heuristics have traditionally been used to guide development of protocol and decision making, but the world of artificial intelligence and big disparate data is changing that. Everyone is trying to make sense of, and extract value from, their data. Those that are not will be left behind.

Amazon has launched a real-life shop where people can just pick things up and leave. Amazon Go, as it is calling the shop, uses a range of technologies to watch over people and see what they take from the shop. When they leave – which is done simply by walking back out the door – they'll be charged through their Amazon account for everything that they've picked up. To shop at the store, people just sign in at the door with their Amazon app, by pressing their phone against a sensor. That signs them in – and then everything else is done automatically.

HyperScience delivers machine learning solutions for the enterprise, working with Fortune 500 companies. The HyperScience team is guided by the belief that AI is destined to be the biggest event in the history of human labor since the industrial revolution. HyperScience offers leading global businesses the tools to take advantage of this new technology and create innovative solutions ranging from predictions, automated classifications and anomaly detection in any domain. There are many examples of AI currently applied to everyday life, ranging from self-driving cars to medical software that diagnoses patients. The company already counts a number of businesses in the Fortune 500 as customers and their engagements start at the C-suite, solving these large businesses-- most challenging problems.

Leveraging the power of machine learning in healthcare to improve outcomes has primarily rested in the hands of data scientists--until now. Healthcare.ai--open source predictive analytics software--is on a mission to democratize machine learning--to make it accessible to everyone in healthcare (not just data scientists) with the right technical skillset and tools (e.g., BI developers, SQL developers, data architects, and project managers). Machine learning and artificial intelligence (AI) are transforming healthcare. Health systems are increasingly using predictive analytics to better prioritize at-risk patients and optimize care decisions. Healthcare.ai makes it easy to create predictive models on your healthcare data--and is unlike any other machine learning tool in the industry.

Uber made two big announcements on Monday: It is acquiring Geometric Intelligence, a New York-based startup, and using that 15-person team to help launch a new artificial intelligence division within Uber, called Uber AI Labs. Uber AI Labs will focus on improving both ride-hailing software and the company's self-driving car software, according to a blog post by Uber's product chief Jeff Holden. Geometric Intelligence, a 2-year-old artificial intelligence startup, will move its 15-person staff from New York to Uber's San Francisco headquarters to help build the lab. "In spite of notable wins with machine learning in recent years, we are still very much in the early innings of machine intelligence," Holden wrote in the blog post. "The formation of Uber AI Labs, to be directed by Geometric's Founding CEO Gary Marcus, represents Uber's commitment to advancing the state of the art, driven by our vision that moving people and things in the physical world can be radically faster, safer and accessible to all."

If Uber is going to make its dreams of self-driving ridesharing cars a reality, it's going to need a lot of expertise in artificial intelligence... and it's taking big steps to make that happen. The company has created Uber AI Labs to fuel its research, and it's getting the team started by acquiring AI startup Geometric Intelligence. It's a small 15-person outfit, but the newly purchased company stands out by resisting the urge to train AI by feeding it large data sets. As the New York Times notes, Geometric Intelligence prefers to have systems create their own rules from just a handful of examples -- while Uber ride data will help, the AI won't need a wealth of knowledge to make informed decisions. Autonomous driving will be the star of the show at the new labs, but Uber is promising that its AI work will shape a lot of its day-to-day business. It could improve the accuracy of predicted arrival times for your rides and UberEats deliveries, tackle fraud and improve the chances of UberPool matching your car with other travelers.