Chemical process industries (CPI) companies are entering a critical stage in the movement toward digitalization (Industry 4.0), in which the majority of organizations are now initiating pilot projects aimed at improving operations with advanced digital tools. This includes a wide range of technologies, including data analytics, cloud computing, machine learning, artificial intelligence and many others. As the digitalization transformation of the CPI gains momentum, it has become clear that the movement is as much about people as it is about technology. The acceptance and involvement of workers is critical to the successful adoption and expansion of digital tools, as they are asked to adapt to new work practices. He emphasizes: "Companies don't adopt new technologies; people do."

The Marshall Liberal Government will be the first government globally to host a $250,000 crowd sourced open data competition to fast-track the discovery of mineral deposits in South Australia. ExploreSA: The Gawler Challenge partners with open innovation platform, Unearthed, in a world-wide call for geologists and data scientists to uncover new exploration targets in the state's Gawler Craton region. Using the Geological Survey of South Australia's historical records, primary data and research, the competition combines geological expertise with new mathematical, machine learning and artificial intelligence to increase the number of potential drill targets across central South Australia. "This state-of-the-art competition has the potential to unearth the next Olympic Dam or Carrapateena by encouraging global thinkers and innovators to interrogate our open-file data and generate new exploration models and ideas for targeting," said Minister van Holst Pellekaan. "The Marshall Liberal Government is thinking outside the square to drive investment and jobs in South Australia's vital resources sector. "Mining is one of the pillars of the South Australian economy and this competition should add to the pipeline of projects in the resources and minerals processing sector.

There's no question that artificial intelligence and data analytics are reshaping the resources sector. These new technologies bring new challenges in the way companies consider risk, adapt to new ways of working and the skills needed for the future. These issues were the topic of conversation at a business roundtable lunch hosted by professional services firm Accenture in its new Perth Innovation Hub this week as part of the Resources Technology Showcase program of events. Invited guests, including leading policymakers and industry heavyweights, heard former SAS commander and Mettle Global managing partner Ben Pronk speak about the need to take calculated risks in the battlefield. He explained how the resources industry could adopt that philosophy to take advantage of the fourth industrial revolution.

Rio Tinto's boss of ports and rail Ivan Vella says the increasing bank of data the industry is generating is the greatest untapped "enabler and disrupter" available to the sector as he reveals expanding artifical intelligence across the global miner's business. Mr Vella, managing director of port, rail and core services at Rio Tinto, told the Resources Technology Showcase today that as the miner had moved to remote operations and asset automation it had generated a huge amount of data across its business. "Today, we track everything, our team is swimming in an ocean of data, which will be crucial to ensuring the efficiency and ongoing health of autonomous assets and systems," he said. "Without a doubt, it is the greatest untapped enabler and disrupter available to our industry." Mr Vella highlighted in his speech, 'Project Tempo', which Rio developed with EY data and analytics, Monash University and Strukton Rail.

The firm has also developed a drone able to fly around the underground corridors of coal mines to detect gas emissions and other potential threats. Marcin Dziekanski, coordinator of the drone project of the Silesian metropolis, an alliance of more than 40 cities in the coal-mining Katowice region, said they use drones to monitor the smoke produced by coal-heated individual houses. "They fly over Katowice, over the buildings, as well as over other cities, enabling us to intervene, in cooperation with the city police, showing that we are monitoring our space, our environment," he told AFP, adding that "we are creating a set of good practices that we are sharing with others." Spartaqs considers itself above all a research firm looking into new technologies, though it has already sold a dozen drones--at an average price of 50,000 euros ($55,000) a pop--in Poland and Georgia. But the company has realised that buyers like the Saudis and the Americans, who are very interested in certain models, want to see "the plant where they are produced." So they have begun looking for investors, including abroad, who would like to participate in the development of a serial production line.

Hetal Yagnik Associate Director – Strategy & Operations Polymerupdate Abstract The presentation will cover how artificial intelligence has gained prominence in petrochemical industry. The increased volatility in polymer prices increases the difficulty of forecasting accurately, making the simple methods less reliable. To overcome these limitations, machine learning (ML) models were used as an alternative to conventional models. He has over 18 years of experience in Sales & Marketing, Market Research & Analytics, Competitive Intelligence, Consulting & General Management area. He has experience in delivering clients with actionable research reports in various industries including pharmaceuticals, chemical, petrochemical, automobile, agriculture equipments.

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.