Recognizing cross-industry interest in massive data ingest and analytics, we teamed up with O'Reilly Media on a new book: The Path to Predictive Analytics and Machine Learning. In this book, we share the latest step in the real-time analytics journey: predictive analytics, and a playbook for building applications that take advantage of machine learning. Chapter 1: Building Real-Time Data Pipelines We begin with a review our previous O'Reilly book: Building Real-Time Data Pipelines – Unifying Applications and Analytics with In-Memory Architectures. It covers the emergence of in-memory architectures and provides a framework for building real-time pipelines that serve as the foundation for machine learning applications. Chapter 2: Processing Transactions and Analytics in a Single Database This chapter details the shift from Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) to converged, multi-model systems designed for Hybrid Transaction/Analytical Processing (HTAP). Chapter 3: Dawn of the Real-Time Dashboard Data visualization is arguably the most powerful method for enabling humans to understand and spot patterns in a dataset.

After receiving her diploma, recent UT PGE graduate Katy Hanson went to work side-by-side the world's most recognized technology platform. In 2011 IBM's Artificial Intelligence (AI) platform, Watson, went up against the all-time most winning JEOPARDY! Within a few categories Watson's depth and breadth of knowledge surpassed the human brain providing Watson with the win. At that moment, Watson demonstrated to the world the powerful role AI will play in the 21st century. Watson hung up his game-show career, but has been incredibly busy over the past five years gaining vast amounts of insights on many fields, including oil and gas.

Based in New York and Seattle, Euclidean Technologies uses machine learning to evaluate individual companies as potential long-term investments. The company currently has about 100 million under management. Before Michael Seckler and John Alberg created Euclidean, they founded one of the first software-as-a-service (SaaS) companies. In 2006, their company was acquired by ADP for 160 million. They started Euclidean because they faced hard questions about how to manage their money.

As more and more organizations start harnessing artificial intelligence in their analytics projects, the vendor community is stepping up its efforts to address the trend. IBM led the charge last week by adding a new model to its Power LC server line that is specifically geared towards running machine learning algorithms. The two-socket server is marketed under the name S822LC and can be equipped with up to four of Nvidia Corp.'s Pascal P100 accelerators, which supposedly provide as much as three times better throughput than its previous-generation chips. The GPUs are linked to the machine's main processors using a technology called NVLink that allows for data to travel 5-12 faster than the PCIe interfaces included in traditional machines. As a result, Big Blue says that the server is able to provide 80 percent more performance per dollar in certain situations.

Custom computer vision is the promise of bootstrapping Barcelona-based b2b startup Restb.ai. The team is pitching its machine learning algorithms at businesses wanting to fix a pain-point or aiming to enhance what they can offer their customers by being able to automatically determine what is in a particular image. While tech giants like Facebook and Google are using their own data to train their own AI algorithms for their own ends, Restb.ai's The team launched back in March 2015 and has six customers at this point, according to Damia Garcia, the team's VP of growth and biz dev, speaking to TechCrunch from startup alley here at Disrupt SF 2016. He added it has been focusing its early acquisition efforts on winning larger customers in multiple verticals, and is now looking to raise a seed round.

For a deep dive into emerging AI techniques and technologies, join us September 26-27, 2016, for the O'Reilly Artificial Intelligence Conference in New York. If a figure be anyhow divided and the compartments differently coloured so that figures with any portion of common boundary line are differently coloured--four colours may be wanted, but not more--the following is the case in which four colours are wanted. Query cannot a necessity for five or more be invented. That is, you'll never need more than four colors on an ordinary two-dimensional map in order to color every country differently from the countries adjoining it. A proof for the four-color conjecture evaded mathematicians until 1976, when Kenneth Appel and Wolfgang Haken announced a solution.

There's no doubt that artificial intelligence (AI) is here and is rapidly gaining the attention of brands large and small. As I talk to customers and prospects, they are interested in understanding how AI and its subcomponents (cognitive computing, machine learning, or even deep learning) are being woven into various departments (marketing, sales, service and support) at organizations across industries. Here are some examples of cognitive computing and machine learning today at organizations, and how these capabilities will enhance customer experience in the future. I think it's important to start with a few foundational facts: Cognitive computing enables software to engaging in human-like interactions. Cognitive computing uses analytical processes (voice to text, natural language processing and text and sentiment analysis) to determine answers to questions.

We are a data analytics company using computer vision and robotics to automate data capture. Our core technology enables our own high-performance flying robots to operate fully autonomously in indoor environments with centimeter accuracy. Our first product uses these flying robots to automate inventory counting in warehouses.

Quadrotors are capable of doing some incredible stunts, like flying through narrow windows and thrown hoops. Usually, when we talk about quadrotors doing stuff like this, we have to point out that there are lots of very complicated and expensive sensors and computers positioned around the room doing all of the hard work, and the quadrotor itself is just following orders. Vijay Kumar's lab at the University of Pennsylvania is often responsible for some of the most spectacular quadrotor stunts, but their latest research is some of the most amazing yet: They've managed to get quadrotors flying through windows using only onboard sensing and computing, meaning that no window is safe from a quadrotor incursion. When you watch quadrotors flying indoors, if you look closely, you'll almost always see a motion-capture system in the background: Arrays of external cameras mounted on the walls that work together to collect very precise positional information hundreds of times every second. With the data that a system like this provides, a computer has no problem issuing very precise commands to a quadrotor flying under remote control to get it to do just about whatever you want.