A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. Let's figure out how to end hunger forever.
Businesses across the world are rapidly leveraging the Internet-of-Things (IoT) to create new products and services that are opening up new business opportunities and creating new business models. The resulting transformation is ushering in a new era of how companies run their operations and engage with customers. However, tapping into the IoT is only part of the story. For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies, which enable'smart machines' to simulate intelligent behavior and make well-informed decisions with little or no human intervention. Artificial Intelligence (AI) and the Internet of Things (IoT) are terms that project futuristic, sci-fi, imagery; both have been identified as drivers of business disruption in 2017.
Agriculture is always modernizing, but most farmers struggle to collect data that's useful--or to analyze it in useful ways. That's changing: emerging tools for data collection and analysis are helping boost yields and make farming more sustainable, according to Sam Eathington, chief science officer at the Climate Corporation. In the next five to 10 years, "we're going to see an explosion of sensors and collection of data from the farm," Eathington said during his talk at MIT Technology Review's EmTech conference today. The Climate Corporation--originally founded in 2006 by a pair of former Google employees and now owned by German chemical giant Bayer--has developed tools to gather information from a variety of sources, including sensors on farming equipment as well as in the field. The data from these disparate sensors is then analyzed in the cloud.
In an industry such as mining where improving efficiency and productivity is crucial to profitability, even small improvements in yields, speed and efficiency can make an extraordinary impact. Mining companies basically produce interchangeable commodities. The mining industry employs a modest amount of individuals--just 670,000 Americans are employed in the quarrying, mining and extraction sector--but it indirectly impacts nearly every other industry since it provides the raw materials for virtually every other aspect of the economy. It's already been 10 years since the British/Australian mining company Rio Tinto began to use fully autonomous haul trucks, but they haven't stopped there. Here are just a few ways Rio Tinto and other mining companies are preparing for the 4th industrial revolutions by creating intelligent mining operations.
Researchers are paving the way to total reliance on renewable energy as they study both large- and small-scale ways to replace fossil fuels. One promising avenue is converting simple chemicals into valuable ones using renewable electricity, including processes such as carbon dioxide reduction or water splitting. But to scale these processes up for widespread use, we need to discover new electrocatalysts--substances that increase the rate of an electrochemical reaction that occurs on an electrode surface. To do so, researchers at Carnegie Mellon University are looking to new methods to accelerate the discovery process: machine learning. Zack Ulissi, an assistant professor of chemical engineering (ChemE), and his group are using machine learning to guide electrocatalyst discovery.
Connor Coley, currently pursuing his graduate degree in chemical engineering at MIT, has been selected as one of 2018's "Talented Twelve" by Chemical and Engineering News (C&EN), the weekly magazine of the American Chemical Society. Coley was recognized for his work in "reprogramming the way chemists design drugs." Currently a member of the Klavs Jensen and William Green research groups, Coley is focused on improving automation and computer assistance in synthesis planning and reaction optimization with medicinal chemistry applications. He is more broadly interested in the design and construction of automated microfluidic platforms for analytics (e.g. Coley's work is an integral part of the new MIT-industry consortium, Machine Learning for Pharmaceutical Discovery and Synthesis.
Kespry announced the availability of the pulp and paper industry's first drone-based aerial intelligence solution. The new industry-specific solution improves the profitability of pulp and paper operations by delivering more accurate and timely supply chain material inventory data, while improving site operations and safety. "Measuring chip piles at a pulp mill has always been a challenge. In the past, a team of surveyors would climb onto the chip pile and arrive at a manual measurement," said Mitch Dunlop, Accounting Manager, Celgar, a leading North American pulp and paper organization. "This method is slow, poses safety concerns and is not very accurate.
That the modern world is a complex place will not have escaped your notice. We are all dimly, unsettlingly aware that our lives are enmeshed in systems we can't fully comprehend. The last meal you ate probably contained produce grown in another country that was harvested, processed, packaged, shipped, then sold to you. The phone in your hand is the end-product of an even more convoluted chain; one that relies on human labor from mines in Africa, assembly lines in China, and standing desks in San Francisco. Explaining how these systems connect and the effect they have on the world is not an easy task.
This paper is concerned with a molecular optimization framework using variational autoencoders (VAEs). In this paradigm, VAE allows us to convert a molecular graph into/from its latent continuous vector, and therefore, the molecular optimization problem can be solved by continuous optimization techniques. One of the longstanding issues in this area is that it is difficult to always generate valid molecules. The very recent work called the junction tree variational autoencoder (JT-VAE) successfully solved this issue by generating a molecule fragment-by-fragment. While it achieves the state-of-the-art performance, it requires several neural networks to be trained, which predict which atoms are used to connect fragments and stereochemistry of each bond. In this paper, we present a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to address the issue. Our idea is to develop a novel graph grammar for molecular graphs called molecular hypergraph grammar (MHG), which can specify the connections between fragments and the stereochemistry on behalf of neural networks. This capability allows us to address the issue using only a single VAE. We empirically demonstrate the effectiveness of MHG-VAE over existing methods.
The digital revolution has brought with it a new way of thinking about manufacturing and operations. Emerging challenges associated with logistics and energy costs are influencing global production and associated distribution decisions. Significant advances in technology, including big data and analytics, AI, Internet of Things, robotics and additive manufacturing, are shifting the capabilities and value proposition of global manufacturing. In response, manufacturing and operations require a digital renovation: the value chain must be redesigned and retooled and the workforce retrained. Total delivered cost must be analyzed to determine the best places to locate sources of supply, manufacturing and assembly operations around the world.