Ultimate following list to keep updated in artificial intelligence
Everyone who is working with technologies knows the joy (and pain on the other hand) of rapid updates in the field. It doesn’t matter if you’re web developer who needs to learn new frameworks every couple of weeks, data scientist who has to know how to implement latest deep learning papers or financial manager who needs to know recent ways to manage assets better — in any case you have to read a lot and learn a lot to stay on the top. Artificial intelligence field is on fire today — hundreds of research papers, GitHub code releases, infrastructure updates are appearing every week. And it’s really difficult to know everything of that — from new Tensorflow Serving versioning updates to some another GAN architecture augmented with memory applied for graph data.
My personal opinion is that is impossible to know all novelties in AI unless you have a team of PhDs analyzing different fields and sources and bringing organized reports personalized for you. But it’s definitely possible to have birds eye view, see the whole picture and pick things that will be useful for you. For example you’re working on some computer vision project and most probably you’re already applying latest neural network architectures, use some good regularization algorithms and in-box gradient-based optimizing algorithms, but you can do much more than that. You can check codes released by Facebook, apply NLP-based losses from Einstein AI, use neuroevolution optimizers how Uber and OpenAI are doing now and check out more interesting regularization than L2 + Dropout. Maybe it won’t matter at all, but most probably it will bring your product to the next level in front of competitors, that are using pre-trained ResNets and are happy with their 85% of accuracy on some task, or worse, using some open API for that.
In this article I want to present a more organized approach on how to read AI-related news on different levels to make it really useful and impactful. Enough intros, let’s start :)
It’s not a secret that big companies are ruling the AI world right now. What’s interesting, they all have different topics and approaches they emphasize on and it’s worth to mention.
I think everyone knows OpenAI, I recommend to check all their research at least on high level, they open really cool problems, release code and will be super useful for those who does reinforcement learning.
OpenAI is a non-profit AI research company, discovering and enacting the path to safe artificial general intelligence.
DeepMind does much more diverse research from neural network architectures and reinforcement learning to generative models and symbolic reasoning, definitely worth to follow to make your picture much more broad.
News & Blog | DeepMind
Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental…
Uber is a dark horse in AI world, but they do really good research in time series, evolutional optimization and reinforcement learning as well. They also show good engineering practices in their blog.
Uber AI Labs Archives | Uber Engineering Blog
Uber AI Labs introduces Visual Inspector for Neuroevolution (VINE), an open source interactive data visualization tool…
Microsoft does much more fundamental applied research — they are discovering different interesting topics — NLP, optimization, neural program synthesis, biomedical data processing, robotics and much more.
Microsoft Research AI (MSR AI)
Microsoft Research AI (MSR AI) is a new organization that brings together the breadth of talent across Microsoft…
IBM has a lot of nice academic papers with applied flavor — I recommend to PhD students to check out how they organize research. What I also personally like (same as in Uber) — it’s how they separate engineering and pure research in different branches.
Artificial intelligence - IBM Research
IBM Research has been exploring artificial intelligence and machine learning technologies and techniques for decades…
Facebook releases a lot of very useful code for computer vision and NLP. Not so many mind blowing things as in OpenAI, but I’m sure you’ll always find something to apply in your projects.
At Facebook, research permeates everything we do. We believe the most interesting research questions are derived from…
If you’re working with speech — you simply must learn everything what Baidu does.
Co-located in Silicon Valley and Beijing, Baidu Research brings together top talent from around the world to focus on…
And… Google Research :) Very diverse, much more interesting things appearing in Google Brain and DeepMind, but still.
Well, universities also do some decent job, but unfortunately it’s difficult to tell about some specific emphasis of each school.
Well, I couldn’t not mention Stanford here.
Stanford Artificial Intelligence Laboratory |
Welcome to the Stanford AI Lab! The Stanford Artificial Intelligence Laboratory (SAIL) has been a center of excellence…
Berkeley lately is showing a lot of interesting things related to reinforcement learning.
Berkeley Artificial Intelligence Research Lab
The Berkeley Artificial Intelligence Research (BAIR) Lab brings together UC Berkeley researchers across the areas of…
MIT is like Stanford in a way — no specific emphasis.
And I also wanted to mention couple of European universities that are worth to follow:
Max Planck Institute for Intelligent Systems
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully…
Oxford Robotics Institute
Mobile Autonomy: A Pervasive Technology The Oxford Robotics Institute researches all aspects of land-based mobile…
If you like arxiv and academic papers there, you would like to switch to Arxiv-Sanity for… better user experience :)
I believe that alongside with big companies there are interesting individuals with their own points of view and impacts on the field. I follow some of them on Twitter (paragraph right after this one), but some of them have interesting personal blogs I read.
Ferenc Huszár, machine learning researching in Twitter is writing very nice about mathematics and explains topics very well:
posts on machine learning, statistics, opinions on things I'm reading in the space
Another mathematical blog, a bit restricted to optimization:
Sebastian Ruder is rather popular NLP researcher, I don’t miss his articles:
NLP News - Revue
NLP News - NLP News is a biweekly Natural Language Processing & Machine Learning newsletter from academia &…
The only English news aggregator I read is this Facebook group (I follow it just because of creator, voice recognition practitioner Arthur Chan and content is very useful):
Artificial Intelligence & Deep Learning
The definitive and most active FB Group on A.I., Neural Networks and Deep Learning. All things new and interesting on…
For Russian speaking readers I would like to mention Alexey Levenchuk’s blog, where he is showing his “bird’s eye” view on AI infrastructure time to time and popular VK page, which is an aggregator in fact, but I cannot not mention it.
What Jensen giveth, Jürgen taketh away Новая модель вычислительной архитектуры, наконец, начала как-то обсуждаться в…
Глубокое обучение (Deep learning) - это направление в области Искусственного Интеллекта (Artificial Intelligence) и…
While the above mentioned practitioners writing good posts in their blogs, they also discuss, tweet and retweet interesting things with other players in the field in Twitter and I personally find a lot of interesting ideas, research papers or GitHub codes there. Here is the small list of people you might start with:
Ilya Sutskever (@ilyasut) | Twitter
The latest Tweets from Ilya Sutskever (@ilyasut). @openai
Trask (@iamtrask) | Twitter
The latest Tweets from Trask (@iamtrask). Creator & Leader of @OpenMinedOrg, PhD Student @UniofOxford, Research…
Jeremy Howard (@jeremyphoward) | Twitter
The latest Tweets from Jeremy Howard (@jeremyphoward). Deep learning researcher & educator. Founder: fast.ai; Faculty…
Ian Goodfellow (@goodfellow_ian) | Twitter
The latest Tweets from Ian Goodfellow (@goodfellow_ian). Google Brain research scientist leading a team studying…
François Chollet (@fchollet) | Twitter
The latest Tweets from François Chollet (@fchollet). Deep learning @google. Creator of Keras, neural networks library…
Hugo Larochelle (@hugo_larochelle) | Twitter
The latest Tweets from Hugo Larochelle (@hugo_larochelle). Google Brain researcher, machine learning professor…
Andrej Karpathy (@karpathy) | Twitter
The latest Tweets from Andrej Karpathy (@karpathy). Director of AI at Tesla. Previously a Research Scientist at OpenAI…
Russ Salakhutdinov (@rsalakhu) | Twitter
The latest Tweets from Russ Salakhutdinov (@rsalakhu). Professor at Carnegie Mellon University, Director of AI Research…
Denny Britz (@dennybritz) | Twitter
The latest Tweets from Denny Britz (@dennybritz). Living in Tokyo. Into Startups, Deep Learning. Ex-Google Brain…
Fei-Fei Li (@drfeifei) | Twitter
The latest Tweets from Fei-Fei Li (@drfeifei). Prof (CS @Stanford), Director (Stanford AI Lab), Chief Scientist AI/ML…
Sebastian Raschka (@rasbt) | Twitter
The latest Tweets from Sebastian Raschka (@rasbt). Working on machine learning stuff. Author of 'Python Machine…
Okay, now we know what are the latest things in computer vision from Facebook research, we even launched some code their GitHub and even caught some coding ideas from Jeremy Howard’s Twitter, but it all stays in small programming/mathematics world and we would like to see how AI changes the real physical world we’re living in. I am used to read following resources (not strictly related to AI, but to technologies and startups):
MIT Technology Review
The mission of MIT Technology Review is to equip its audiences with the intelligence to understand a world shaped by…
Bloomberg - European Edition
Bloomberg delivers business and markets news, data, analysis, and video to the world, featuring stories from…
How to deal with all of this?
Well, I promised you organized way to stay updated in AI, but instead gave you a bunch of links. You might think that it’s better to go back to Arxiv, Reddit and Twitter / Facebook news aggregators. The problem is relying on mass opinions, but not on experts opinions… which is generally not the best thing to do. I recommend you to stop chaotic consuming of all information you see and develop some rules and system for this.
For example, I check Twitter feed every day — I see what people in AI are talking about, what papers they’re reading, what they mention and simply press “heart” on something what I find interesting:
Then I take couple of days per week (usually two) to sit couple of hours and read what I’ve saved on Twitter and what are the main updates in companies and universities blogs — I make my own noes, check papers, write down main ideas and study something in depth if I see how it will be helpful for me right now. Usually every week I see at least couple of released codes on Github that are worth to try or at least to check out (it can be implementations from twitter, OpenAI’s implementations or anything else) and I take couple of hours on my third day for some coding. If there is something I don’t know how to apply but I believe it may be useful to share with someone or simply interesting — I save it in a Google Docs file which I review and update monthly:
Let’s summarize things a bit:
- You can follow big players to understand general trends, see good code and practices
- You can follow some particular personalities to get insights in your fields or catch something what’s ignored in the “big world”
- You regularly save, review and try to apply or at least summarize things you’ve learned about
- You use public news aggregators on your own risk
Of course, you should mix this list with practitioners from your field, add more business people, or some specific researches, but the main idea is the above described system of catching high level trends, to see their successful applications and apply them in your business.
Good luck and stay tuned! Please, add people or sources who you think are worth to follow in comments!