On a block in San Francisco's SoMa district, near LinkedIn's headquarters and dozens of startups, a 180-year-old company best-known for making tractors has a gleaming new Silicon Valley office. But inside, instead of building the latest app, John Deere is focused on how to use artificial intelligence to make farming equipment that can meet modern sustainability and food production challenges. John Deere Labs, which opened its doors in the spring, made its first major deal on September 6. The company spent $305 million to acquire Blue River Technology, a startup with computer vision and machine learning technology that can identify weeds–making it possible to spray herbicides only where they're needed. The technology reduces chemical use by about 95%, while also improving yield.

In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuarcy) plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that can be quite costly for large scale screening purposes of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption, which do not demand the ab initio calculations. This pair of descriptors are then combined with machine learning methods, namely, artificial neural network (ANN) and kernel ridge regression (KRR), to allow large scale materials screenings. We show, for a toy set of 263 alloy systems, that the CO adsorption energy can be predicted with a remarkably small mean absolute deviation error of 0.05 eV, a significantly improved result as compared to 0.13 eV obtained with descriptors including costly d-band center calculations in literature. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV otherwise. As a practical application of this machine, we identified Cu3Y@Cu as a highly active and cost-effective electrochemical CO2 reduction catalyst to produce CO with the overpotential 0.37 V lower than Au catalyst.

The food and agriculture chain is one of the most promising industries where the Internet of Things (IoT) can bring about transformational changes. And this transformation includes building smart farm machines to manage crops at the plant level. To that end, John Deere Labs, which opened its doors earlier this year, made its first major deal on September 6, spending $305 million to acquire Blue River Technology, a startup with computer vision and machine learning technology that can identify weeds–making it possible to spray herbicides only where they're needed. Currently, on a global basis, $25 billion is spent each year on about 3.0 billion pounds of herbicides. This has resulted in over 250 species of weeds now considered resistant to herbicides.

During the Hands Free Hectare project, no human set foot on the field between planting and harvest--everything was done by robots. To make these decisions, robot scouts (including drones and ground robots) surveyed the field from time to time, sending back measurements and bringing back samples for humans to have a look at from the comfort of someplace warm and dry and clean. With fully autonomous farm vehicles, you can use a bunch of smaller ones much more effectively than a few larger ones, which is what the trend has been toward if you need a human sitting in the driver's seat. Robots are only going to get more affordable and efficient at this sort of thing, and our guess is that it won't be long before fully autonomous farming passes conventional farming methods in both overall output and sustainability.

America's agricultural sector faces an unprecedented crisis. Native honeybees, one of the most prolific pollinators in the animal kingdom, are dying off at an unprecedented rate from Colony Collapse Disorder (CCD), threatening an ecosystem service worth about $15 billion. Supported by the National Science Foundation (NSF), the "RoboBees" project looks to minimize the loss of this critical resource with remarkable microbots that can mimic the pollinating role of a honeybee. But the project has a number of challenges to overcome before these robots can take to the skies. The RoboBee is a microrobot inspired by the biology of a honey bee.

"AI is the new Electricity" – Andrew Ng* Since the advent of 20th century electricity became the main source of invention in every major industry ranging from transportation, manufacturing to healthcare, communications and many more. Today Artificial Intelligence (AI) is bringing the same big transformation across all the major industries. The part of AI that is rapidly growing and which is driving most of these transformations is Deep Learning. Today, Deep Learning has become one of the most sought after skills in the technology world. Agriculture is one industry where Deep Learning scientists and researchers are working with farmers to help them with their produce.

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John Deere, established in 1837 to manufacture hand tools, announced it had acquired Blue River Technology, founded in 2011, late Wednesday. John Stone, an executive in the company's intelligent-solutions group, says Blue River's computer-vision technology will help Deere's equipment view and understand the crops it is working with. Stone says that Blue River's technology can make a larger impact on productivity because it makes decisions up close, on the ground. That system can target weeds with squirts of herbicide no larger than a postage stamp.

EARTH AI are helping mineral explorers identify promising areas. They do this by analysing data from multiple sources and using a machine learning algorithm to identify areas where minerals are likely to be found.

– Any one working within industries like the mobility, fintech, mobile money, payments, banking or InsureTech with little knowledge of data science is actually sitting on gold mine to explore and show what Data Science / AI can do for that company. Today every company on this planet collect vast quantities of data on a daily basis or even per second. For example credit card issuers with every credit card swipe and completed transaction capture critical customer information, In case of mobile payments/money the same thing happen or even in banks same scenarios. However, the raw data alone does not generate the insights needed to drive business decisions or simply not good enough at all. It's the proper analysis of this data that unlocks its true value.