Customer reviews are a gold mine, and artificial intelligence is a fast and cost-effective way to turn them into essential insight. Learn how AI can help you turn good feedback into great product, uncover what really matters to customers, and more, in this VB Live event. Customers have higher-than-ever expectations, and 89% of consumers are more likely than ever to share positive or negative experiences. More importantly, behind every customer review is an important and personal story. Good or bad, there's a reason they took the time to search out your feedback form or Facebook page, compose a message, choose a rating, and share their thoughts with the world.

Researchers from Penn State and Carnegie Mellon University (CMU) have received a $1.2 million grant from the United States Department of Energy (DOE) to use machine learning -- a form of artificial intelligence -- and data science to design more effective catalysts for chemical processing. The grant is part of a new initiative by the DOE to provide $27.6 million in grants for data science research in chemical and materials sciences. A catalyst is a stable chemical substance that, when added to a chemical reaction, increases the rate of reaction without becoming part of the reaction. "It is important to recognize how widespread the use of catalysts is," said Michael Janik, Penn State professor of chemical engineering and principal investigator for the study. "About 90% of chemical products people are using every day, like gasoline and fine chemicals that are in shampoo, are going through some sort of catalytic process before they're used."

A nature-inspired robot using water and combustible powder can launch itself from water like a flying fish. The device, which can travel 26 metres through the air after take-off, could potentially be used to collect water samples in hazardous environments, such as floods. Researchers at Imperial College London created the system, which weighs just 160 grams and can'jump' multiple times after refilling its water tank. Furthermore, while similar robots often require calm conditions to leap from the water, the team's invention generates a force 25 times the robot's weight, giving it a greater chance of overcoming choppy waves. The water and the calcium-carbide powder combine in a reaction chamber, producing a burnable acetylene gas.

Machine learning (ML) techniques are increasingly applied to decision-making and control problems in Cyber-Physical Systems among which many are safety-critical, e.g., chemical plants, robotics, autonomous vehicles. Despite the significant benefits brought by ML techniques, they also raise additional safety issues because 1) most expressive and powerful ML models are not transparent and behave as a black box and 2) the training data which plays a crucial role in ML safety is usually incomplete. An important technique to achieve safety for ML models is "Safe Fail", i.e., a model selects a reject option and applies the backup solution, a traditional controller or a human operator for example, when it has low confidence in a prediction. Data-driven models produced by ML algorithms learn from training data, and hence they are only as good as the examples they have learnt. As pointed in [17], ML models work well in the "training space" (i.e., feature space with sufficient training data), but they could not extrapolate beyond the training space. As observed in many previous studies, a feature space that lacks training data generally has a much higher error rate than the one that contains sufficient training samples [31]. Therefore, it is essential to identify the training space and avoid extrapolating beyond the training space. In this paper, we propose an efficient Feature Space Partitioning Tree (FSPT) to address this problem. Using experiments, we also show that, a strong relationship exists between model performance and FSPT score.

Experimental effort and animal welfare are concerns when exploring the effects a compound has on an organism. Appropriate methods for extrapolating chemical effects can further mitigate these challenges. In this paper we present the efforts to (i) (pre)process and gather data from public and private sources, varying from tabular files to SPARQL endpoints, (ii) integrate the data and represent them as a knowledge graph with richer semantics. This knowledge graph is further applied to facilitate the retrieval of the relevant data for a ecological risk assessment task, extrapolation of effect data, where two prediction techniques are developed.

In this article, we list down 6 trending jobs in machine learning one can apply. Responsibilities: The responsibilities include developing highly scalable classifiers and tools leveraging machine learning, data regression and rule-based models, deep learning, create language models from petabytes of text data in different languages, suggest, collect and synthesize requirements and innovate to create next-generation feature sets. The candidate will work as part of the product team to implement algorithms that power user and developer-facing products reaching out to millions of users, adapt standard machine learning methods to best exploit modern parallel environments. Prerequisites: The candidate must have strong background in one or more of Machine Learning, Artificial Intelligence, Pattern Recognition, Natural Language, Deep Learning, DNNs, large scale Data Mining, experience with scripting languages such as Perl, Python, PHP, and shell scripts, experience with recommendation systems, targeting systems, ranking systems or similar systems, experience with any of Hadoop/Hbase/Pig or MapReduce/Bigtable or R/Matlab/AzureML or similar technologies. Responsibilities: The responsibilities for a Machine Learning Engineer – Lead include building common ML capabilities used across Corporate based on machine learning models, automate and streamline existing processes, procedures, and toolsets.

Syngenta Seeds announced the launch of a new digital platform to help farmers maximize profit potential through data-driven seed recommendations. The NK Seed Analyzer combines artificial intelligence, two decades of agronomic information and a simple user interface, extending the NK brand's focus on innovation by adding value beyond seed. The adaptability of the platform allows retailers and farmers to proactively plan for weather volatility, soil variability and planting specifications by seeing actual results from numerous sources. The tool complements retailers and agronomists' expertise with 18 years of data at no cost to the user. "The NK Seed Analyzer represents the best that NK has to offer," said Todd McRoberts, NK agronomy manager.

The Louisiana Economic Development department is using a computer model to help target countries for foreign investment in the state, and the agency says it has hit the mark on some projects. LED built the predictive investment computer model to help hone its strategy about two years ago, leveraging a $170,000 federal grant through the U.S. Economic Development Administration in 2017. The agency tapped into a customized database that parses through investments made across the U.S. by companies in industries suited for Louisiana's existing infrastructure. "You go fishing where the fish are biting," said Larry Collins, executive director of the Office of International Commerce. The fishing expedition produced a five-year snapshot of companies ranging from advanced manufacturing to chemical makers.

The simple bots that are destined to replace repetitive human activity are making inroads in almost all industries. A recent Gartner report suggests that RPA (Robotic Process Automation) is the fastest growing software segment. The reason for RPA adoption could be many – traditional businesses going after RPA for infrastructure and operations automation and new age businesses tapping RPA's it's continuous modernization capabilities. Most of the efforts are towards enabling and implementing automation processes, but many fail to recognize post-implementation pitfalls, which can crash or even stop an organization's RPA initiative. In large enterprises, bot production happens in Automation Factories and maintenance happens in ROCs (Robotic Operations Center).