In recent years, interest in monitoring air quality has been growing. Traditional environmental monitoring stations are very expensive, both to acquire and to maintain, therefore their deployment is generally very sparse. This is a problem when trying to generate air quality maps with a fine spatial resolution. Given the general interest in air quality monitoring, low-cost air quality sensors have become an active area of research and development. Low-cost air quality sensors can be deployed at a finer level of granularity than traditional monitoring stations. Furthermore, they can be portable and mobile. Low-cost air quality sensors, however, present some challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Some promising machine learning approaches can help us obtain highly accurate measurements with low-cost air quality sensors. In this article, we present low-cost sensor technologies, and we survey and assess machine learning-based calibration techniques for their calibration. We conclude by presenting open questions and directions for future research.

LOS ALTOS, CALIFORNIA and LEMONT, ILLINOIS – Cerebras Systems, a company dedicated to accelerating artificial intelligence (AI) compute, and the Argonne National Laboratory, a multidisciplinary science and engineering research center, today announced that Argonne is the first national laboratory to deploy the Cerebras CS-1 system. Unveiled today at SC19, the CS-1 is the fastest AI computer system in existence and integrates the pioneering Wafer Scale Engine, the largest and fastest AI processor ever built. By removing compute as the bottleneck in AI, the CS-1 enables AI practitioners to answer more questions and explore more ideas in less time. The CS-1 delivers record-breaking performance and scale to AI compute, and its deployment across national laboratories enables the largest supercomputer sites in the world to achieve 100- to 1,000-fold improvement over existing AI accelerators. By pairing supercompute power with the CS-1's AI processing capabilities, Argonne can now accelerate research and development of deep learning models to solve science problems not achievable with existing systems.

Schlumberger and Dataiku have entered into an exclusive technology partnership that will enable the E&P industry to build and deploy their own artificial intelligence (AI) solutions across the full breadth of their upstream workflows within the DELFI cognitive E&P environment. The partnership will deliver capabilities to petrotechnical domain experts in response to global demand for AI, bridging the gap between machine learning and domain expertise to enable better insights. The industry will have access to a platform where data scientists--experts in the development of AI solutions--can accelerate the deployment of new solutions across their organizations. In addition, making Dataiku technology available in the DELFI environment equips petrotechnical experts to build and extend workflows by leveraging machine learning and data science capabilities that are supported by a rich algorithm library. Combining existing Schlumberger digital tools and solutions with the proven enterprise AI technology from Dataiku means a ready-to-deploy full machine learning solution will be available at enterprise scale to the E&P industry.

Welcome to the November edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted articles about AI that can identify who wrote each scene in Shakespeare's Henry VIII, and teach non-native speakers how to pronounce English words! Let's start, as usual, with the comic of the month: In a recent article researchers describe how they trained machine-learning algorithms to predict what features in a song would impact people's emotional responses. They predicted brain and heart activities as well as physiological response using features based on music dynamics such as timbre, harmony, etc...

China is planning to upgrade its naval power with unmanned AI submarines that aim to provide an edge over the fleets of their global counterparts. A report by the South China Post on Sunday revealed Beijing's plans to build the automated subs by the early 2020s in response to unmanned weapons being developed in the US. The subs will be able to patrol areas in the South China Sea and Pacific Ocean that are home to disputed military bases. While the expected cost of the submarines has not been disclosed, they're likely to be cheaper than conventional submarines as they do not require life-supporting apparatus for humans. However, without a human crew, they'll also need to be resilient enough to be at sea without onboard repairs possible. The XLUUVs (Extra-Large Unmanned Underwater Vehicles) are much bigger than current underwater vehicles, will be able to dock as any other conventional submarine, and will carry a large amount of weaponry and equipment.

In the financial world, neural networks are helping investigators find and stop fraud by uncovering trends across millions of transactions. ERGO, a German insurance company, is using predictive analytics from SAS to find unjustified claims. Customers also use neural networks in their buildings to optimize power usage and predict mechanical failures. Through the use of decision trees, we've helped rapidly growing Wake County, North Carolina, make property tax assessments fairer and more accurate. We are working with many tax authorities to uncover tax fraud and find citizens that have underdeclared their income.

When it recently launched a new weather model called IBM GRAF, The Weather Company took a big supercomputing step forward. IBM GRAF, with its ability to process weather data from a variety of sources worldwide, enables The Weather Company, an IBM Business, to deliver high-resolution, hourly-updated forecasts around the globe--particularly to regions that have never had them before. But the full power of IBM GRAF to generate local forecasts for the entire world depends on technology that The Weather Company already had in place and is continually refining: artificial intelligence (AI) and cloud computing. When a series of winter storms recently lashed much of the United States, millions of people used The Weather Channel mobile app and weather.com Sophisticated AI algorithms from The Weather Company turn troves of current and historic weather data into recommendations, for example, that can tell an electric utility company where to trim trees to prevent blackouts before the next storm hits.

Incorporating AI and machine learning technologies into everyday workflows isn't as easy as the testimonials would have you believe. That's the top-level finding from a survey of 750 business decision makers conducted by Algorithmia, which found that while machine learning maturity in the enterprise is generally increasing, the majority of companies (50%) spend between 8 and 90 days deploying a single machine learning model (with 18% taking longer than 90 days). Most peg the blame on failure to scale (33%), followed by model reproducibility challenges (32%) and lack of executive buy-in (26%). "The findings of our 2020 [State of Enterprise Machine Learning] study are consistent with what we're hearing from customers," said Algorithmia CEO Diego Oppenheimer. "Companies are growing their investments in machine learning, and machine learning operationalization is maturing across all industries, but significant room for growth and improvement remains. The model deployment lifecycle needs to continue to be more efficient and seamless for ML teams. Nevertheless, companies with established ML deployment lifecycles are benefiting from measurable results, including cost reductions, fraud detection, and customer satisfaction. We expect these trends to continue as ML technologies and processes arrive to market and are adopted."

Incorporating AI and machine learning technologies into everyday workflows isn't as easy as the testimonials would have you believe. That's the top-level finding from a survey of 750 business decision makers conducted by Algorithmia, which found that while machine learning maturity in the enterprise is generally increasing, the majority of companies (50%) spend between 8 and 90 days deploying a single machine learning model (with 18% taking longer than 90 days). Most peg the blame on failure to scale (33%), followed by model reproducibility challenges (32%) and lack of executive buy-in (26%). "The findings of our 2020 [State of Enterprise Machine Learning] study are consistent with what we're hearing from customers," said Algorithmia CEO Diego Oppenheimer. "Companies are growing their investments in machine learning, and machine learning operationalization is maturing across all industries, but significant room for growth and improvement remains. The model deployment lifecycle needs to continue to be more efficient and seamless for ML teams. Nevertheless, companies with established ML deployment lifecycles are benefiting from measurable results, including cost reductions, fraud detection, and customer satisfaction. We expect these trends to continue as ML technologies and processes arrive to market and are adopted."

"You must unite behind the science. You must do the impossible. Because giving up can never ever be an option" – Greta Thunberg Most woke Millenials have updated their vocabulary to use terms that more accurately describe the environmental crises facing the world. 'Climate change' has now turned to'climate emergency' – but there are others who haven't yet understood how the situation has worsened over the years. According to a report, seven million people have been displaced globally due to natural disasters including storms and floods between January and June 2019 and the number is estimated to grow more than triple by the end of the year.