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computer vision and machine learning


AI for AG: Production machine learning for agriculture

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How did farming affect your day today? If you live in a city, you might feel disconnected from the farms and fields that produce your food. Agriculture is a core piece of our lives, but we often take it for granted. The world's population is expected to grow to nearly 10 billion by 2050, increasing the global food demand by 50%. As this demand for food grows, land, water, and other resources will come under even more pressure. The variability inherent in farming, like changing weather conditions, and threats like weeds and pests also have consequential effects on a farmer's ability to produce food.


28 promising companies leading and disrupting industries with AI futureTEKnow

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Artificial Intelligence is moving at the speed of light, with multiple companies creating software, products and services in not just a vertical way – more of a horizontal disruption. Form Healthcare to Security, from Real Estate to Telecom, here is a look into 28 companies powering the disruption with AI – 1st Edition. Sherpa.ai was founded in 2012 after deep research into Artificial Intelligence, with the conviction of creating a personal assistant that would be not just useful, but indispensable for users. In order to do this, Sherpa brought together a team of experts in Artificial Intelligence who, coupled with a fantastic design, have been able to create the next generation of Digital Assistants which will help users make their life not just more exciting, but also more enjoyable. WellSaid Labs has developed state of the art text-to-speech technology that creates life-like synthetic voice, from the voices of real people.


Professor Amnon Shashua and Dr. Demis Hassabis Named Laureates of the International Dan David Prize for Outstanding Contributions in the Field of Artificial Intelligence Intel Newsroom

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The internationally renowned Dan David Prize, headquartered at Tel Aviv University, annually awards three prizes of $1 million each to globally inspiring individuals and organizations. The total purse of $3 million makes the prize not only one of the most prestigious, but also one of the highest-value prizes internationally. Laureates are selected on the basis of their outstanding achievements and contributions in the year's chosen fields, each representing a time category. This year's fields are Cultural Preservation and Revival (Past category), Gender Equality (Present category) and Artificial Intelligence (Future category). Professor Amnon Shashua, co-founder and CEO of Mobileye, and Dr. Demis Hassabis, co-founder and CEO of DeepMind, have been named the 2020 Dan David Prize laureates in the field of artificial intelligence (AI).


Explainable and Interpretable Models in Computer Vision and Machine Learning

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This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision.


Backstory - Commute Guardian

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The'why' of the DepthAI (that satisfyingly rhymes) is we're actually shooting for a final product which we hope will save the lives of people who ride bikes, and help to make bike commuting possible again for many. What we envisioned is a technology-equivalent of a person riding backwards on your bike holding a fog horn and an ambulance-LED strip, who would tap you on the shoulder when they noticed a distracted driver, and would use the LED strip and the horn judiciously to get the attention of distracted drivers - to get them to swerve out of the way. In working towards solving this problem, we discovered there was no solution on the market for the real-time situational awareness needed to accomplish this. So we decided to make it. In doing that, we realized how useful such an embeddable device would be across so many industries, and decided to build it as a platform not only for ourselves, but also for anyone else who could benefit from this real-time object localization (what objects are, and where they are in the physical world).


Research Scientist - Computer Vision and Machine Learning ai-jobs.net

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Lead research efforts in developing computer vision/machine learning capabilities for novel Human AI collaboration/interaction mechanisms Perform world-wide scouting around people-AI partnership research trends in academia and industries to shape Bosch's research efforts in human-centric machine intelligence Perform cutting-edge research to accelerate Bosch's data-driven AI efforts (e.g., utilize existing computer vision/machine learning algorithms themselves to further expedite large-scale crowdsourced data annotations for improving perception tasks such as semantic segmentation, pedestrian detection/tracking, object annotation on LIDAR for Autonomous Driving) Work with international research teams and business unit partners to elicit requirements, propose technical directions, develop/prototype solutions, and transfer the results to the business units as a next generation product Generate high quality patents and/or academic publications in the areas of applied ML, computer vision and human computer interaction (e.g. Perform world-wide scouting around people-AI partnership research trends in academia and industries to shape Bosch's research efforts in human-centric machine intelligence Perform cutting-edge research to accelerate Bosch's data-driven AI efforts (e.g., utilize existing computer vision/machine learning algorithms themselves to further expedite large-scale crowdsourced data annotations for improving perception tasks such as semantic segmentation, pedestrian detection/tracking, object annotation on LIDAR for Autonomous Driving) Generate high quality patents and/or academic publications in the areas of applied ML, computer vision and human computer interaction (e.g.


Chooch 6 Applications of Machine Learning for Computer Vision

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Artificial Intelligence is nothing new to anyone reading this blog, or most of the people on the planet. Siri, Alexa, and web chatbots have made AI commonplace. Yet, imagine what AI can do when you give it a pair of eyes and a training to analyze its surroundings. This is just what the combination of computer vision and machine learning offers to users. Machine learning is the application of statistical models and algorithms to perform tasks without the need to introduce explicit instructions.


Computer Vision and Machine Learning - Optics Beyond

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Machine learning and computer vision methods have recently received a lot of attention, in particular when it comes to data analytics. The success of deep neural networks that can help cars drive autonomously and make smartphones recognize speech and translate text attests to the value of using machine learning methods to tackle complex real-world problems. A further prominent example is the success of Google's AlphaGo AI that defeated the world champion Lee Sedol in playing Go. This is remarkable in particular since Go has previously been considered as one of the most complex games due to the larger number of game states. As the amount of data collected by cameras and scientific instruments continues to rise, automated analysis methods will become ever more important in the future.


Predicting Future Human Behavior with Deep Learning

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Carl Vondrick is a doctoral candidate and researcher at MIT, where he studies computer vision and machine learning. His research focuses include leveraging large-scale data with minimal annotation and its applications to predictive vision and scene understanding. Recently his work has received a lot of media attention, including features in Forbes, Wired, CNN and PopSci, and other media outlets worldwide. As part of his work with MIT CSAIL, Carl built a deep learning vision system for AI to learn and understand human behaviour and interactions, using popular TV shows like The Office, Desperate Housewives, and YouTube videos. The resulting algorithm analyzes videos, then uses what it learns to predict how humans will behave.