Each of these trucks is the size of a small two-story house. None has a driver or anyone else on board. Mining company Rio Tinto has 73 of these titans hauling iron ore 24 hours a day at four mines in Australia's Mars-red northwest corner. At this one, known as West Angelas, the vehicles work alongside robotic rock drilling rigs. The company is also upgrading the locomotives that haul ore hundreds of miles to port--the upgrades will allow the trains to drive themselves, and be loaded and unloaded automatically.

This ghost-like octopus was just discovered last year off the coast of Hawaii, but already scientists think it might be in danger. These octopods lay their eggs on one particular type of sponge that grows on top of manganese deposits. Manganese is currently a heavily-mined mineral because of its usefulness in producing mobile phones and other tech gadgets. For now, because of a sufficient amount of manganese on Earth's surface, these animals are safe. But scientists don't know for how long.

Machine learning and artificial intelligence are all the rage today in venture capital circles. We've seen spectacular exits in the past few years, from Google absorbing Deepmind in 2014 for $500 million, to Twitter buying TellApart in 2015 for $533 million, and Intel swallowing Nervana in 2016 for $400 million. But these were all IT plays. Berkeley-based Lygos is engineering and designing microbes that convert low-cost sugar into high-value, specialty chemicals. Ultimately, the ability to design and optimize microbes, or program them, is becoming faster and cheaper than ever before.

It is likely self-evident to many that the security industry's most overused buzzword of the year is "machine learning." Yet, despite the ubiquity of the term and its presence in company marketing literature, most people – including those working for many of the vendors using the term – don't actually know what it means. Scanning through industry sites and product descriptions, machine learning is often positioned as either a "new" tool or a "new" method – something that can provide additional capabilities or features. For many classes of threat detection, machine learning is positioned as "signatureless" detection by those that don't yet know the basic principles of the math or science behind it. The best way to understand what machine learning is and what it truly brings to the security industry is to compare it to a technology advance that kick-started two centuries ago – the steel age.

In a previous article, we had an overview of the applications of Deep Learning and touched upon some basic points to consider while creating a Deep Learning model. We also had an overview of what it is and methods to get started with deep learning. In this article, we jump straight into creating an anomaly detection model using Deep Learning and anomaly package from H2O. Readers who don't know what it is can view it as anything that occurs unexpected and is a rare event. It is a deviation from the standard pattern and does not confirm to the usual behavior of the data. Let's say we work in a steel manufacturing industry, and we see the quality of the steel suddenly drops down below the permissible limits. This is an anomaly; if not detected and resolved soon will cost the organization millions.

Mars is currently inhabited by an estimated 1 million microbes. They coat the surfaces and crowd the innards of our robotic landers and rovers, which international policy requires to be cleaned, but not fully sterilized. The bugs are dormant, but viable. If Mars warmed up and water began to flow again, these microorganisms would revive and reproduce. And it is within our power to make that happen.

Click to comment on this story. Click to comment on this story. Give your dinner party some gravitas by placing this stunning centerpiece among the sides and sauces. The beautiful jewel-toned object has a concave scoop in the center, providing a reflective surface that encourages deep thought--or offers an awesome place to toss your keys. It can control your entire home--commanding 380,000 devices in all, from TVs and game consoles to wireless speakers and streaming devices. Just try not to lose this one, OK?

Synthetic biology ("synbio") initially attracted high-profile venture capitalists and corporate partnerships for its products – bio-based chemicals and alternative fuels, for example. Today, synbio companies tools are attracting attention as well – from lab automation like DNA synthesis-as-a-service, to software (i.e. By integrating machine-learning, robotics, and high-throughput engineering, companies like Ginkgo Bioworks and Zymergen are helping customers dramatically reduce the timeline for developing new molecules in the flavor and fragrance, food ingredient, medical, agricultural, and specialty chemicals markets – a key reason that those two startups each raised more than $100 million in 2016. This podcast highlights Lux's thoughts on the impact of these tools in and beyond in synbio, and the rapidly developing ecosystem of startups within the space.

This is a great interview with Abhik Banerjee. Abhik is a Staff Data Scientist at Kohl's Technology (at the Kohl's departmental stores) where he leads a team on machine learning and data mining projects. He also provides strategic direction around these areas to senior executives at Kohl's. Abhik has an interesting role because he has to marry online and physical store analytics to provide insights to Kohl's. Abhik received his MS in computer science from the University of Cincinnati in 2012. Welcome to another episode of Flyover Labs. Today we are lucky enough to have Abhik Banerjee with us. And Abhik is a Staff Data Scientist at Kohl's Department Stores where he works on machine learning and data mining projects.

Health Catalyst has created Healthcare.ai, a website that offers free open source predictive analytics software for hospitals and other healthcare organizations. "Wherever you have a data set that you pull together, you can create a model based on that by using these tools," said Levi Thatcher, director of data science at Health Catalyst. Machine learning and predictive analytics to improve health outcomes has so far been limited to an elite group of data scientists, mostly in the nation's top academic medical centers, he pointed out. Healthcare.ai – open source predictive analytics software – is part of a mission to make machine learning accessible to the thousands of healthcare professionals with only basic technical skills, but who share an interest in using the technology to improve patient care, Thatcher explained. By making its central repository of proven machine learning algorithms freely available, Healthcare.ai opens the doors to a large, diverse group of technical healthcare professionals to quickly use machine learning tools to build accurate models.