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Best Machine Learning Blogs to Follow in 2020
Editorial   Machine Learning   Research

Best Machine Learning Blogs to Follow in 2022

Roberto Iriondo
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May 6, 2020
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Last Updated on January 1, 2022 by Editorial Team

Source: Image designed in Photoshop by Roberto Iriondo — you can find the full resolution image on Pexels
Source: Pixabay 

Keep up with the best and the latest machine learning (ML) research blogs through reliable sources.

Author(s): Roberto Iriondo

From researchers to students, industry experts, and machine learning (ML) enthusiasts — keeping up with the best and the latest machine learning research is a matter of finding reliable sources of scientific work.

While blogs usually update in a more informal and conversational style, we have found that the sources in this list are accurate, resourceful, and reliable sources of machine learning research. Fit for all of those interested in learning more about the scientific field of ML.

Please know that the blogs listed below are by no means ranked or in a particular order. They are all incredible sources of machine learning research. Please let us know in the comments or by emailing us if you know of any other reliable blog sources in machine learning.

Source: ML@CMU

Machine Learning Blog, ML@CMU, Carnegie Mellon University

The machine learning blog at Carnegie Mellon University, ML@CMU, provides an accessible, general-audience medium for researchers to communicate research findings, perspectives on the field of machine learning, and various updates, both to experts and the general audience. Posts are from students, postdocs, and faculty at Carnegie Mellon [1].

Amazon Science Machine Learning Blog Website Screenshot
Source: Amazon Science

Amazon Science Blog

Amazon Science has a fantastic scientific blog that allows you to filter by research area. Its blog encompasses work from Amazon’s science community, and its research areas include cloud and systems, computer vision, conversational AI, NLP, machine learning, robotics, search and information retrieval and security, privacy, and abuse prevention [13].

Source: Distill

Distill

Distill is an academic journal in the area of machine learning. The distinguishing trait of a Distill article is outstanding communication and a dedication to human understanding. Distill articles often, but not always, use interactive media. Most articles (if not all) published at Distill often take 100+ hours for publishing [2].

Source: Google AI

Google AI Blog

Google AI conducts research that advances the state-of-the-art in the field. Google AI (or Google.ai) is a division of Google dedicated solely to artificial intelligence. It was announced at Google’s conference I/O 2017 by CEO Sundar Pichai [3]. The Google AI blog has a section specifically for machine learning research [4].

Source: Neptune.AI

Neptune.AI

Neptune.AI provides a remarkable machine learning blog, offering tutorials on machine learning modeling, hyperparameter optimization, model evaluation, data exploration, generative models, machine learning tools, and many more [14]. Neptune.AI also offers a framework that makes it easier to track versions of your Jupyter notebooks, helps with managing your experimentation process, and integrates with your team’s workflow easily.

Source: Bair Berkeley

BAIR Berkeley

The BAIR blog provides an accessible, general-audience medium for researchers to communicate research findings, perspectives on the field, and various updates. Posts are from students, postdocs, and faculty in BAIR and intend to provide a relevant and timely discussion of research findings and results, both to experts and the general audience [5].

Source: Open AI

Open AI

OpenAI is a research laboratory based in San Francisco, California. Their mission is to ensure that artificial general intelligence benefits all of humanity [8]. The OpenAI blog brings state-of-the-art research in the field. Their mission is to discover and enact the path to safe artificial general intelligence (AGI) [8].

Source: Machine Learning (Theory)

Machine Learning (Theory) by John Langford

The Machine Learning (Theory) blog is an experiment in the application of a blog to academic research in machine learning and learning theory by machine learning researcher John Langford [6]. He has emphasized that the field of machine learning “is shifting from an academic discipline to an industrial tool” [7].

Source: DeepMind

DeepMind Blog

DeepMind works on some of the most complex and exciting challenges in AI. Their world-class research has resulted in hundreds of peer-reviewed papers, including in Nature and Science [9].

Source: MIT

Machine Learning at MIT

MIT often produces state-of-the-art research in the field of machine learning. This filtered news stream provides the latest news and research on what is happening in the field of machine learning at MIT.

Source: Colah’s Blog

Colah’s Blog

Christopher Olah describes himself as a wandering machine learning researcher, looking to understand things clearly and explain them well [10]. Olah is a researcher with Open AI and formerly at Google AI. His blog has complete and exciting articles for the machine learning researcher and enthusiast — a gold mine of free, open, machine learning research.

Source: Facebook AI

Facebook AI’s Blog

Facebook AI is known for working on state-of-the-art research in the field. Their research areas focus on computer vision, conversational AI, integrity, NLP, ranking and recommendations, systems research, machine learning theory, speech, and audio, along with human and machine intelligence. The Facebook AI Blog encompasses excellent content, from blog posts to research publications [12].

Source: AWS Machine Learning

Amazon AWS Machine Learning Blog

Amazon web services (AWS) is one of the most used cloud services around the world. They offer reliable, scalable, and accessible cloud computing services. Their research team publishes blog posts on machine learning state-of-the-art research and ML applications on the AWS blog [11].

If you happen to know of any other reliable machine learning blogs, please let me know in the comments. Thank you for reading!

Published by Towards AI

DISCLAIMER: The views expressed in this article are those of the author(s) and do not represent the views of Carnegie Mellon University nor other companies (directly or indirectly) associated with the author(s). These writings do not intend to be final products, yet rather a reflection of current thinking, along with being a catalyst for discussion and improvement.

Published via Towards AI

References

[1] Machine Learning Blog, ML@CMU About page| ML@CMU | https://blog.ml.cmu.edu/about/

[2] Publishing in the Distill Research Journal | Distill | https://distill.pub/journal

[3] Google AI | Wikipedia | https://en.wikipedia.com/wiki/google-ai

[4] Google AI Blog, Machine Learning | Google AI | https://ai.googleblog.com/search/label/Machine%20Learning

[5] Berkeley Artificial intelligence Research Blog, About page | BAIR | https://bair.berkeley.edu/blog/about/

[6] Machine Learning (Theory), about page | Machine Learning Theory | https://hunch.net/?page_id=122

[7] John Langford (computer scientist) | Wikipedia | https://en.wikipedia.org/wiki/John_Langford_(computer_scientist)

[8] About Open AI | Open AI | https://openai.com/about/

[9] DeepMind Blog | DeepMind | https://deepmind.com/research/

[10] Christopher Olah’s About Page | Colah’s Blog | https://colah.github.io/

[11] Amazon Web Services Blog | Amazon Web Services | https://aws.amazon.com/blogs/machine-learning/

[12] Facebook AI Blog | Facebook AI | https://ai.facebook.com/blog/

[13] Amazon Science | Amazon | https://www.amazon.science/blog

[14] Neptune.AI Blog | Neptune.AI | https://neptune.ai/blog

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Roberto Iriondo
Roberto Iriondo has established a venerable reputation in the domain of AI marketing, particularly as a Executive Marketing Advisor and consultant. His career trajectory commenced at Carnegie Mellon University under Manuela Veloso and Roni Rosenfeld's leadership, where he served as a front-end engineer and marketing lead, showcasing his proficiency in technical development by creating platforms such as the renowned CMU Machine Learning Research blog. As the Head of Content at Snorkel AI from 2021 to 2022, and subsequently at Cohere from 2022 to 2023, Roberto's expertise in shaping brand narratives and crafting effective content strategies became evident. Alongside these roles, he exhibited an astute understanding of web growth strategy, as manifested in his marketing agency's operations from 2017 to 2021. This agency catered to emerging businesses with a focus on SEO services. Furthermore, his involvement in strategic roles at Towards AI, both before and after its 2022 acquisition, underscores his multifaceted contributions to the AI marketing landscape. As a builder and strategist by heart, his work has helped several companies achieve business goals and significantly increase ROI, from Amazon Science, MBZUAI, Gather AI, Determined AI, Lambda Labs, Udacity, and many more.

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