Materials


Materialize.X is using machine learning to disrupt the $300B engineered wood industry

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The startup also provides engineered wood factories with software that uses machine learning to optimize how their adhesive is used in the production process. For example, right now there's a standard formula for creating engineered wood – you take wood chips, add adhesive, and press them together until they are bonded into the shape you want. On the other hand, others may use legacy adhesive solutions like urea-formaldehyde, but use Materialize.X's machine learning algorithms to optimize their manufacturing process. And the machine learning optimization can be useful in other manufacturing processes unrelated to engineered wood – right now the startup is testing algorithms to improve production in the steel industry.


How John Deere's New AI Lab Is Designing Farm Equipment For A More Sustainable Future

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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. "What Blue River Technology allows us to do is move to the plant level, and start managing at that plant level," says Alex Purdy, director of John Deere Labs. Now, using computer vision tech to identify and spray only weeds, farmers can switch to other herbicides–including, potentially, organic herbicides that the weeds haven't evolved to resist (and that might otherwise kill the cotton, if they were sprayed everywhere). Computer vision and machine learning technology can also be used in every other step of farming: tilling soil, planting seeds in the optimal locations, spraying fertilizer or nutrients, and harvesting.


John Deere advancing machine learning in agriculture sector

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Blue River Technology The new technology will reduce the need for herbicides by almost 95 percent because computer vision and artificial intelligence will allow the machines to identify, and make management decisions about every single plant in the field, only applying an herbicide to those plants that need treating. "We are using computer vision, robotics, and machine learning to help smart machines detect, identify, and make management decisions about every single plant in the field." Using computer vision and artificial intelligence, smart machines can detect, identify, and make management decisions about every single plant in the field. Using computer vision and artificial intelligence, smart machines can detect, identify, and make management decisions about every single plant in the field.


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IEEE Spectrum Robotics Channel

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.


9 AgTech Startups Using AI to Grow Smarter - Nanalyze

@machinelearnbot

One of the big reasons we're rooting for the future is that the world's biggest tech fund, the SoftBank Vision Fund, planted $200 million in the biggest agtech funding round ever for San Francisco-based Plenty. Founded in 2015, Germany's PEAT has developed a free app called Plantix that uses machine learning and computer vision, technologies within the broader AI umbrella, to identify the problem with a plant from just a picture. We've seen the benefits of AI and computer vision on agriculture with another company called Blue River Technology, which has developed a system that can actually "see" weeds so that farmers can dramatically reduce the use of pesticides. This year's $200 million mega-round to Plenty and $305 million exit by Blue River show that the sector is drawing serious attention.


AgriAi-Deep Learning In Agriculture

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The resources for farming like water, fertilizers, agricultural land etc. Improved Efficiency of Farmer – Different factors like irrigation, seed, weather conditions, soil, fertilizers, weed, crop diseases etc. Identifying Crop Diseases – Deep Learning has been used for the identification of crop diseases. Peat Technology provides easy solutions for crop disease diagnosis and monitoring, automated disease detection and advanced tools for automated disease detection for precision farming.


Watson Virtual Agent Go viral for the right reasons

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IBM Watson 11,061 views Watson Virtual Agent Part 2 - Integrate with your own website - Duration: 9:39. Chris & Chris on Chatbots 33,605 views IBM Watson and Goldcorp accelerate the pace to reach proven ore reserves - Duration: 1:27. IBM Watson 7,520 views Watson Virtual Agent Part 1- Quick Tour - Duration: 9:49. IBM Watson and Goldcorp accelerate the pace to reach proven ore reserves - Duration: 1:27.


why-john-deere-just-spent-dollar305-million-on-a-lettuce-farming-robot

WIRED

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.


Data Science of Payments

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– 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. Artificial Intelligence and its sub areas like machine learning, deep learning and ai neural networks has their own threat Intelligence that will play a bigger role coupled with an evaluation of the driving factors and key capabilities required by convergent systems and requirements. Let me give some refresher to you from your school days about few useful terms like "Regression" which solves your the task for modeling continuous target variables, Classification for modeling categorical i.e "class" target variables and Clustering i.e most common unsupervised learning task, and it's for finding groups within your data. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy.


Healthcare Informatics

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As artificial intelligence (AI) gains more momentum in the healthcare sector, CIOs' use of these technologies has expanded. Emphasizing its growing importance of AI, more than four-fifths (84 percent) of healthcare executives surveyed as part of the research believe that AI will revolutionize the way they gain information from and interact with consumers, and nearly three-quarters (72 percent) of health organizations surveyed are already using virtual assistants to create better customer interactions." The company's co-founder and chief medical officer Jason Bhan, M.D., a family physician who previously worked at Clinovations, a company that helps hospitals implement health IT, recalls a project he was working on for a client in which the organization's CEO and chief medical officer turned and said to Bhan and his team, "We just spent $150 million on this [EHR] system, what did we do for ourselves?" He adds that the diagnostic information, however-- lab data, radiology, and tests that physicians are performing--is a gold mine, so that coupled with years of practice, and also years of making decisions based on looking at lab results, guided him towards thinking more about diagnostics in healthcare.