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4 questions to ask before building a computer vision model – TechCrunch

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It's since been an exciting time for startups as entrepreneurs continue to discover use cases for computer vision in everything from retail and agriculture to construction. With lower computing costs, greater model accuracy and rapid proliferation of raw data, an increasing number of startups are turning to computer vision to find solutions to problems. However, before founders begin building AI systems, they should think carefully about their risk appetite, data management practices and strategies for future-proofing their AI stack. TechCrunch is having a Memorial Day sale. You can save 50% on annual subscriptions for a limited time.


3 Free Machine Learning Courses You Should Take Right Now

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There are many ways to get started with studying machine learning. I have previously written a lot about how to design your own curriculum and roadmap as an alternative to taking courses. This approach allows you to pick and choose free, or low-cost, resources from across the internet that suit both your learning style and budget. However, when you are just starting out on the beginning of your journey into machine learning it can often be useful to follow at least a short course that will guide you through the basic concepts first. This will give you a good foundational overview of the field and it will make it easier to design your own learning path and then continue on with deeper self-directed learning.


Meet the Seattle-area teen geeks that just won awards at an international science fair

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The bleak and all-too-common spectacle of roadkill was upsetting to Vedant Srinivas -- particularly when his uncle and cousin's beloved German Shepherd-Rottweiler mix was fatally hit by a car. More importantly, the losses made the high school student wonder if he could do something about it. What if Srinivas could stop the pet owners' broken hearts, save wildlife and deflect the economic impacts caused by the collisions? This month his efforts were rewarded. The sophomore from Eastlake High School in Sammamish, Wash., brought home a $5,000, first place grand award for the category of Environmental Engineering from the Regeneron International Science and Engineering Fair (ISEF).


Data Science Roadmap

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It's easy to feel overwhelmed by the amount of tools and skills required to become a data scientist. While it can take years to master everything, there are clear steps you can take to get started towards your goal. As with any big goal, keep in mind that it might not be possible to get there overnight: much like climbing a mountain or running a marathon, becoming a data scientist will require patience, grit, and practice. But if you're motivated by the prospect of working with data for a living, let this guide serve as the map for the journey ahead. Programming is an important part of working as a data scientist.


Artificial Intelligence in App Creation: Beginners Edition - Coursemetry

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Note: 4.1/5 (122 notes) 39,729 students Welcome to experience the course "Artificial Intelligence in App Creation: Beginners Edition". Today, Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are used in diverse fields as part of the daily life of large organizations across the globe. The rapid speed of AI growth demonstrates that it is a groundbreaking technology designed to transform the way people use devices and conduct business: achievements in unmanned aerial vehicles, the ability to beat people in chess and sporting games, automated customer service, and analytical systems – of course. Talking about the business, development, or marketing field, for instance, it is worth noting that Artificial Intelligence does not apply in a pure form to real self-aware intelligence machines in this sense. Instead, it can be considered a generic term for the number of software powered by automation that is being used by developers of websites and smartphone apps. They include the recognition of images and speech, cognitive computing, automated processing, and machine learning – for that matter.


GitHub - jason718/awesome-self-supervised-learning: A curated list of awesome self-supervised methods

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Self-Supervised Learning has become an exciting direction in AI community. Predicting What You Already Know Helps: Provable Self-Supervised Learning. For self-supervised learning, Rationality implies generalization, provably. Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? FAIR Self-Supervision Benchmark [pdf] [repo]: various benchmark (and legacy) tasks for evaluating quality of visual representations learned by various self-supervision approaches.


Artificial Intelligence in China

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In the fifth of a series of blogs from our global offices, we provide a overview of key trends in artificial intelligence in China. What is China's strategy for Artificial Intelligence? In March 2021, the Chinese government released the Outline of the 14th Five-Year Plan of the National Economic and Social Development of the People's Republic of China and Vision 2035. This includes more than 50 references to "[artificial] intelligence", reflecting China aims to develop of a new generation of information technology powered by artificial intelligence. Specifically, China intends to drive industry through science and technology projects to develop cutting-edge fundamental theories and algorithms, create specialized chips and build open-source algorithm platforms such as deep learning frameworks.


Accused of Cheating by an Algorithm, and a Professor She Had Never Met

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A Florida teenager taking a biology class at a community college got an upsetting note this year. A start-up called Honorlock had flagged her as acting suspiciously during an exam in February. She was, she said in an email to The New York Times, a Black woman who had been "wrongfully accused of academic dishonesty by an algorithm." What happened, however, was more complicated than a simple algorithmic mistake. It involved several humans, academic bureaucracy and an automated facial detection tool from Amazon called Rekognition.


Free Workshop on AI Quality, Back By Popular Demand - KDnuggets

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Are you a data scientist or machine learning engineer interested in learning more about how to analyze and improve the performance and trustworthiness of your machine learning models? Then this live online course is for you! AI Quality: Driving ML Performance and Trustworthiness is a free course taught live by five experts from leading universities, including a professor from Carnegie Mellon University and Stanford University. This offer is exclusively for corporate and government practitioners. All students completing the course receive a certificate, limited edition shirt, and access to the Slack community.


How to develop machine learning skills in all of your company's employees

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Everyone loves Artificial Intelligence (AI) and data science (DS), and it's probably not going to change for the next decade or so. Still, most people only have a general idea of what data science is and what machine learning algorithms or AI can do. This is quite normal and a common phenomenon for all fields of expertise. Think about it: do you really know what DevOps, Support or NOC (Network Operation Center) actually do? Sure, as tech professionals we can probably explain it better than people who aren't part of the industry, but in most cases it's pretty hard to really understand what other people are doing if you've never done it yourself.