As I've been focusing more and more on the Big Data and Machine Learning ecosystem, I've found Azure Databricks to be an elegant, powerful and intuitive part of the Azure Data offerings. Over my last 12 months at Slalom, I have had the incredible opportunity to travel across Canada and work hand in hand with the brilliant folks at Microsoft's Data & AI practice and Databricks experts to lead project engagements, deliver technical hands-on workshops, listen to the industry experts - the folks doing Data Science for a full time living - and absorb everything in between. There's a common theme across the industry verticals that's going to be our point of discussion today. The hot topic of 21st century tech is Machine Learning - some flavor of AI/ML is thrown into almost everything we find these days (I'm pretty sure I spotted a "genius" AI/ML toothbrush at Shoppers Drug Mart today). The reality is, the mathematical techniques that power Machine Learning models have been around for almost a century.

The recent progress in neural architectures search (NAS) has allowed scaling the automated design of neural architectures to real-world domains such as object detection and semantic segmentation. However, one prerequisite for the application of NAS are large amounts of labeled data and compute resources. This renders its application challenging in few-shot learning scenarios, where many related tasks need to be learned, each with limited amounts of data and compute time. Thus, few-shot learning is typically done with a fixed neural architecture. To improve upon this, we propose MetaNAS, the first method which fully integrates NAS with gradient-based meta-learning. MetaNAS optimizes a meta-architecture along with the meta-weights during meta-training. During meta-testing, architectures can be adapted to a novel task with a few steps of the task optimizer, that is: task adaptation becomes computationally cheap and requires only little data per task. Moreover, MetaNAS is agnostic in that it can be used with arbitrary model-agnostic meta-learning algorithms and arbitrary gradient-based NAS methods. Empirical results on standard few-shot classification benchmarks show that MetaNAS with a combination of DARTS and REPTILE yields state-of-the-art results.

Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc non systematic engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool ( ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated and real data sets and discuss possible difficulties in achieving satisfactory predictive power. 2 Introduction Metabolic engineering 1 enables us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels 2,3 or anticancer drugs.

Newswise -- Wood is a natural material that is lightweight and sustainable, with excellent physical properties, which make it an excellent choice for constructing a wide range of products with high quality requirements - for example for musical instruments and sports equipment. Unfortunately, as most natural products, wood has a very uneven material structure that extends over several length scales. Therefore, large safety margins are often required during processing, which limit the efficiency of material utilisation. With the help of science, this drawback could soon be resolved. A key technology for this is artificial intelligence.

Machine learning and artificial intelligence are becoming key components of mineral exploration programs as companies set exploration targets. Machine learning and artificial intelligence (AI) have the ability to solve two of the mining industry's biggest challenges: rising exploration costs and a lack of new discoveries. After a heavy downturn in the past few years, the mining and mineral exploration sector is finally starting to recover, but deep challenges remain. In an industry that thrives on new discoveries, today's resource companies are finding it harder and more expensive to locate new deposits. Gold provides one of the greatest examples of this dearth of new discoveries in the face of rising exploration costs.

Always in motion is the future." We've spread out towards the stars and colonized the solar system, from settlements orbiting the glittering rings of Saturn, to sprawling cities on the red hills of Mars built by nano insects invisible to the eyes. When their big bellies are filled to bursting, they rocket along invisible superhighways, delivering He3 to energy hungry fusion micro-reactors that power the interplanetary economy. Beyond the rings, deep space mining ships release clouds of drones like baby spiders into the wind and they digest asteroids hurtling in the endless void. The drones fuel an unprecedented building boom on nearly every planet circling the sun, as city after city goes up on barren rocks long hostile to organic life. The fastest transformations are taking place on Mars. The people who immigrated to Mars generations ago don't need oxygen at all.

By some estimates, the world generates 2.5 quintillion bytes of data every day. Yet only a sliver of that volume, much of it residing on enterprise servers, is fully leveraged to drive a deep understanding of the enterprise and how to improve it. What value lies untapped within all that data? As business leaders grapple with these questions, they rely increasingly on emerging cognitive technologies like artificial intelligence, machine learning and blockchain. When these technologies are coupled with cloud-based multi-enterprise networks, thought-leading companies are able to unearth, analyze and act upon critical insights across business lines and foster the emergence of intelligent enterprises.

Ever since the industrial chemist Leo Baekeland began synthesizing phenol and formaldehyde in 1907, the world has developed a love-hate relationship with the resulting polymer: plastic. While plastic is convenient, durable, and cheap, 50% of all plastics (about 150 million tons every year, worldwide) are used only once and then thrown away. Even for those who dutifully recycle our plastic water bottles and sandwich bags, we're only tackling a small part of the problem. "Considering the size of the problem, there's relatively limited infrastructure in place to capture and treat stormwater," says Tony Hale, program director for environmental informatics at the nonprofit San Francisco Estuary Institute (SFEI). That's where SFEI is looking to use research and data--and most recently, drones--to make a difference.

Ever since the industrial chemist Leo Baekeland began synthesizing phenol and formaldehyde in 1907, the world has developed a love-hate relationship with the resulting polymer: plastic. While plastic is convenient, durable, and cheap, 50% of all plastics (about 150 million tons every year, worldwide) are used only once and then thrown away. Even for those who dutifully recycle our plastic water bottles and sandwich bags, we're only tackling a small part of the problem. "Considering the size of the problem, there's relatively limited infrastructure in place to capture and treat stormwater," says Tony Hale, program director for environmental informatics at the nonprofit San Francisco Estuary Institute (SFEI). That's where SFEI is looking to use research and data--and most recently, drones--to make a difference.

A startup backed by billionaire Microsoft founder, Bill Gates, says a breakthrough in solar technology may revolutionize the way materials like steel and glass are created. The company, called Heliogen, says it uses artificial intelligence to help operate an array of mirrors capable of reflecting and focusing the sun's light and creating a type of solar oven. The system works so well that they report being able to create temperatures of 1,000 degrees Fahrenheit - about a quarter of the temperature found on the Sun's surface. That extreme heat is a first for solar-powered systems like Heliogens and, according to the company, could be used as an environmentally friendly way of creating crucial materials like cement, glass, and steel. As noted by CNN, Heliogen's system could drastically impact global emissions - roughly 7 percent of of C02 released into Earth's environment are from manufacturing cement alone according to the International Energy Agency.