Your 5G iPhone is unlikely to appear until 2020, an asteroid mining company gets some help from a new Blockchain owner, and drones get smarter at search and rescue. It's a match made in 2018. Planetary Resources just took an unusual turn on its path to asteroid mining -- selling itself to a blockchain company founded by Ethereum's Joe Lubin. Planetary Resources' Brian Israel said that Blockchain was a "natural solution" for commerce in space and an ideal way for people from various countries to coordinate efforts. It also adds some crucial funding to the space mining company, which had recently laid off employees.

Towards a Near Universal Time Series Data Mining Tool: Introducing the Matrix Profile by Chin-Chia Michael Yeh Doctor of Philosophy, Graduate Program in Computer Science University of California, Riverside, September 2018 Dr. Eamonn Keogh, Chairperson The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for text, DNA, and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. Surprisingly, however, little progress has been made on addressing this problem for time series subsequences. In this thesis, we have introduced a near universal time series data mining tool called matrix profile which solves the all-pairssimilarity-search problem and caches the output in an easy-to-access fashion. The proposed algorithm is not only parameter-free, exact and scalable, but also applicable for both single and multidimensional time series. By building time series data mining methods on top of matrix profile, many time series data mining tasks (e.g., motif discovery, discord discovery, shapelet discovery, semantic segmentation, and clustering) can be efficiently solved. Because the same matrix profile can be shared by a diverse set of time series data mining methods, matrix profile is versatile and computed-once-use-many-times data structure. We demonstrate the utility of matrix profile for many time series data mining problems, including motif discovery, discord discovery, weakly labeled time series classification, and vi representation learning on domains as diverse as seismology, entomology, music processing, bioinformatics, human activity monitoring, electrical power-demand monitoring, and medicine. We hope the matrix profile is not the end but the beginning of many more time series data mining projects.

Recently, locality sensitive hashing (LSH) was shown to be effective for MIPS and several algorithms including $L_2$-ALSH, Sign-ALSH and Simple-LSH have been proposed. In this paper, we introduce the norm-range partition technique, which partitions the original dataset into sub-datasets containing items with similar 2-norms and builds hash index independently for each sub-dataset. We prove that norm-range partition reduces the query processing complexity for all existing LSH based MIPS algorithms under mild conditions. The key to performance improvement is that norm-range partition allows to use smaller normalization factor most sub-datasets. For efficient query processing, we also formulate a unified framework to rank the buckets from the hash indexes of different sub-datasets. Experiments on real datasets show that norm-range partition significantly reduces the number of probed for LSH based MIPS algorithms when achieving the same recall.

California's largest lake, the Salton Sea, was created by accident in 1905 and was transformed into a vacation location by developers who built up the shoreline with resorts, hotels, yacht clubs and more. By the 1950s, thousands flocked to vacation there, including celebrities like Frank Sinatra and The Beach Boys. At one point, it was the most visited destination in the state - beating out Yosemite National Park. During the 1950s and 60s, celebrities flocked to the area to kick back and relax with some fun in the sun for vacations. Eventually, developers and officials saw an opportunity to bring tourism to the area so they began building fancy properties and yacht clubs around the Salton Sea.

Since the concept of "machines learning" was introduced in the 1950s, the field has gone from a cryptic domain understood by a few (Turing, Markov, Legendre, Laplace or Bayes) to a technology that every company must deploy. Every day we hear how data and automation improve our shopping experiences, our online searches and enables fraud prevention and cybersecurity routines to do more, faster and better for us. Now, the amalgamates created around Artificial Intelligence, Machine Learning and Big Data are bound to confuse industry observers or investors who aren't familiar with the technical details. If you're asking yourself: "What's the difference between Big Data and Machine Learning?", then for the sake of my piece, simply think about it this way: "Big Data is Machine Learning's great uncle". Machine Learning doesn't need Big Data to exist.

A few years ago I saw this headline news flashing all over the internet. Our dealers are missing up to $18 billion in easy sales. The Chairman and CEO of Caterpillar suggested that the company and its dealers were losing $9 - 18 billion in easy sales revenue as their sales, both internal and dealer networks, weren't monetising the real value of data. They are not tapping into the wealth of real-time customer data now at their fingertips; they are not communicating with each other; and they are not providing customers across the globe with a consistent experience when it comes to everything from e-commerce to parts and services pricing. Long story short, the whole idea was to convert the company's mentality from dumb iron sales to data-driven, machine learning-driven sales.

The mining industry continues to face volatile commodity prices, safety and environmental concerns, and decreasing productivity savings amongst others. Artificial intelligence (AI) related technologies have the potential to bring tangible benefits for mining organizations like enhanced operational efficiency and improved safety and health conditions. What are the key challenges that arise when deploying AI within the mining industry and how can mining companies prepare to adopt AI? Read the report to understand how mining companies are already using AI‑related technologies. There are also many useful lessons for other asset intensive organisations.

Perfumers look out: IBM Research partnered up with one of the top producers of flavors and fragrances, Symrise, to create an perfume-concocting AI. Named Philyra, after the Greed goddess of fragrance, it uses machine learning to sift through thousands of ingredients, formulas and industry trends to derive what IBM considers to be unique combinations. IBM is leveraging the AI to help perfumers design the next great scent rather than a machine that will replace experts of the human nose. Philyra looks at thousands of formulas and raw materials to identify patterns and new combinations to find a potential gap in the market and fill it with a new scent. It finds alternative raw materials, deduces the dosage based on human usage patterns and how humans tend to respond before comparing it to existing fragrances.

Skilled perfumers bring art and science together to design new fragrances, a talent that takes ten or more years to develop. Crafting a fragrance that leaves an impression is one of the most important components a consumer considers when forming a positive or negative opinion about everyday products like laundry detergent, deodorant, air freshener and, of course, cologne and perfume. What if artificial intelligence (AI) could learn from these professionals to augment the process of developing new fragrances or identify completely novel creative pathways? With this in mind, my team at IBM Research, together with Symrise, one of the top global producers of flavors and fragrances, created an AI system that can learn about formulas, raw materials, historical success data and industry trends. Building on previous IBM research using AI to pair flavors and for recipe creation, as well as our new IBM Research AI for Product Composition, we created Philyra.

Nowadays, the contemporary manufactured and small unmanned aerial vehicles (UAVs) known as drones are mostly electric-based, using electric engines for their flight power. The application of such propulsion systems need proper elaboration of efficient and light electric energy sources. The paper tends to shift our approach to drones towards one that will see efficient energy storage through the use of hydrogen – which is outlined in the following sections of this article. Speaking of, there are primarily two methods of on-board energy storing in today's drone system: The second method is one on which we are focusing in this article – mostly because of the complexity of the fuel cells and their constant need for the supply of hydrogen. Currently, hydrogen can be stored in compressed state in pressure bottles or in its liquid state (in cryogenic tanks).