How To Stay Updated With Machine Learning and Computer Vision Advances In 2023?

Author(s): Hasib Zunair

Originally published on Towards AI.

Are you overwhelmed by the recent progress in machine learning and computer vision as a practitioner in academia or in the industry? Know what youtube channels, newsletters, podcasts, and platforms to follow to stay up-to-date while keeping your sanity!

Motivation

Recent updates in machine learning (ML) and computer vision (CV) are a mouthful, from Stable Diffusion for generative artificial intelligence (AI) to Segment Anything as foundation models. Let’s not forget large language models like Llama 2 and ChatGPT. It is getting increasingly difficult to stay up-to-date in the ML or CompVis community. In this article, I’ll share with you some newsletters, youtube channels, podcasts and platforms that you can follow to stay buckled up on this hype train that we are in right now.

This article is organized as follows:

1. Newsletters
2. YouTube channels
3. Podcasts
4. Other platforms

Let’s get started!

Newsletters

Ground Truth: Currently one of my favorites right now! It gives you the latest and greatest breakthroughs happening in the computer vision space. Additionally, it also shares news on recent techniques and tools, as well as best practices. This can be really helpful in you’re someone who is working in the industry.

The Neuron: While this is not specifically for computer vision, it is a really good newsletter to follow, which gives you a relevant, interesting, and, more importantly, digestible compilation of what’s going on in AI, both trends and tools. For me, it takes only about five minutes to read and it points to links that you can go through later if you want to know further details.

The Batch: It is also a generic AI newsletter that shares recent news and insights happening in AI. At the beginning of the newsletter, Andrew Ng shares some of his opinions on the topics. Compared to the previous ones, this is a rather comprehensive newsletter.

Import AI: It is a weekly newsletter that shares news on cutting-edge AI in research as well as in the industry. It also analyzes the implications it has on real world and talks about safety and ethical concerns in the field of AI.

Davis Summarizes Papers: This is not exactly a newsletter, but the writer basically summarizes 10 to 20 ML research papers that come out on arXiv each week. I find this really helpful as it provides a compressed understanding of the papers all together. Of course, you can read the paper in detail if you find it really interesting. If you’re a grad student or a researcher, you should definitely have a look!

YouTube Channels

Yannic Kilcher: I guess you know his channel; if not, just subscribe already! I like to think of Yannic’s channel as the BBC News of AI. As he mentions in about section: “I make videos about machine learning research papers, programming, and issues of the AI community, and the broader impact of AI in society.” If you had only a YouTube channel to follow to keep track of AI stuff, this would be it.

Two Minute Papers: For short summaries of AI research papers and news in general, this is a really good channel to follow. Think of Davis Summarizes Papers, but a video walkthrough. Some videos are longer than two minutes haha. Again, grad students and researchers take note!

Abhishek Thakur: A new ML algorithm came out? Abhishek’s channel makes tutorials to build them. Think of if this channel as not only keeping up-to-date with new ML algorithms but also learning how to implement those and build projects! This channel reminds me of my undergrad days (*nostalgic*) when I used to follow youtube tutorials for hobby projects.

Some other awesome channels you could consider following are AssemblyAI, What’s AI, and AI Coffee Break.

Podcasts

Lex Fridman Podcast: As Lex says: “Conversations about nature of intelligence, consciousness, love, and power”. My tops ones are this and this.

The Robot Brains Podcast: Pieter Abbeel aims to discuss with leading experts in AI with a focus on robotics how to build robots with brains! This is my favorite one so far.

Machine Learning Street Talk: Currently the top AI podcast on Spotify and is inspired by academic research. The podcast dives deep in AI technical analysis with pre-eminent figures in AI along with substantial scope and rigor by covering the main ideas in the field.

TWIML AI Podcast: Through the podcast, they bring a diverse set of ML and AI researchers, practitioners, and innovators with the goal of making ML and AI more accessible to communities. It is targeted toward ML/AI researchers, data scientists, engineers, and tech-savvy business and IT leaders.

Jay Shah Podcast: Jay interviews people in ML and AI from both industry and academia with the goal of getting advice from the interviewees on how to get started with it. Also, discuss their journey, insights, as well as latest research topics.

Other platforms

Websites: Consider following paperswithcode and papers.labml.ai for recent papers in ML. You can sort according to date and time and also find papers by topic.

GitHub: I like to follow 52CV to stay updated with papers and their code published in top vision conferences. For example, here are the papers along with code (mostly) for CVPR 2023, which is also categorized by specific topics like Object Detection and Continual Learning etc..

Twitter: I have used Twitter to stay up-to-date with ML/CV for a few years now. There are a bunch of people I follow who work on ML/CV in both industry and in academia. You can find them here.

Conclusion

In this article, I talked about ways to stay updated with the machine learning and computer vision field for practitioners in academia or on the industry. I listed and described YouTube channels, newsletters, podcasts and other platforms which you could use to keep track of recent progress in ML/CV both in industry and academia. You can pick among the ones mentioned here and see what works for you instead of following all of them and getting overwhelmed!

About the author

I am a Ph.D. candidate at Concordia University in Montreal, Canada, working on computer vision research. I am also an Applied ML Scientist at Décathlon, where I help build new ML systems that transform sports images and videos into actionable intelligence. If you’re interested to learn more about me, please visit my webpage here.

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Published via Towards AI