内容简介:If you have read one of our articles or went through one of our beginner tutorials for deep learning, then you have benefitted from open science.As our world transforms right in front of our eyes, we believe it’s important to adapt to different ways to lea
If you have read one of our articles or went through one of our beginner tutorials for deep learning, then you have benefitted from open science.
As our world transforms right in front of our eyes, we believe it’s important to adapt to different ways to learn and grow. What are we doing to help?
Over the span of two years, we have published hundreds of open articles in the form of tutorials, blog posts, articles, paper summaries, newsletters, etc.
To continue helping with these efforts, in the months that follow we will be working closely with authors, developers, organizations, students, and researchers from both academia and the industry.
We are thinking of ways to scale the distribution of open science and education, in particular as it relates to artificial intelligence (AI). We have shown that we are deeply committed as can be seen by our recent efforts .
We understand that some of our readers and learners struggle with paywalls and subscription-based websites for learning and we want to help with this situation. We want to create a community around transparency and accessibility. We want to be able to publish content that’s of high-quality but most importantly open to everyone.
Our mission is to democratize artificial intelligence research, education, and technologies. What better way to do this than to connect with our community and make a call for contributions to open science. There are different ways you can contribute but we are interested in scaling so as to reach as many learners are possible. Therefore, we have identified a few key areas where you can contribute to:
- Writing a scientific notebook explaining a fundamental concept in machine learning, natural language processing, or artificial intelligence more broadly. For instance, explaining the basics of reinforcement learning, unsupervised learning, supervised learning, etc. (See examples)
- We are aware that not everyone can speak English so one of our efforts to democratize AI educational content is through translations . You can help with translations of articles on our website, which include tutorials, paper summaries, newsletters, etc. ( See examples )
- If you are a researcher or a graduate student, you can contribute by writing and sharing paper summaries . These are very popular on our sites. They are a great way to get people started with certain aspects of the field. This is a very important project for us because we understand that learners need a starting point and these writings can help and inspire others in ways you couldn’t imagine. ( See examples )
- Sharing general expertise either through a virtual meetup, webinar, blog post or podcast. Yes, we are starting a podcast very soon! These experience sharing sessions help others to understand what to expect in our field and mistakes to avoid. This could be a more effective way to provide important leadership/guidance in our field. We are doing some reaching out to get interesting speakers but if you would love to be interviewed and have something to share, send me an email at ellfae@gmail.com. I will reach out with some more details.
- In the next coming months, we are going to start many research initiatives . Research is a very important skill to possess in the field of AI. I have learned this myself while doing both a master’s and Ph.D. in the field. Our idea is to help new researchers, guide them, and instill proper core values by allowing them to responsibly, efficiently and effectively work on research ideas, tasks, and projects that could have an important impact on the field. Tasks could include, collection of datasets, benchmarks, publishing, training a language model, reviewing, etc. We will provide more detail about this very soon. We will make the callhere and here .
These are key areas that can enable us to contribute to open science today and which will allow us to help educate the next wave of learners, researchers, and practitioners in the field of AI. There are many other ways to contribute, you can check out this list of GitHub issues for more details and ideas.
If you are interested in contributing to any of the areas above or have your own ideas you would love to contribute, let’s have a chat. Reach out to me directly via ellfae@gmail.co or send me a direct message on Twitter .
If you are a researcher, organization, startup, or developer and want to partner in any of these efforts, please reach out to me at ellfae@gmail.com.
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Making Things See
Greg Borenstein / Make / 2012-2-3 / USD 39.99
Welcome to the Vision Revolution. With Microsoft's Kinect leading the way, you can now use 3D computer vision technology to build digital 3D models of people and objects that you can manipulate with g......一起来看看 《Making Things See》 这本书的介绍吧!