You know that feeling when you’re trying to stay current with AI research and it feels like trying to drink from a firehose? While blindfolded? In a hurricane? Yeah, me too. That’s why I created Paperpulse, a daily ArXiv AI/ML paper summarizer that helps me pretend I’m keeping up with the field.
What’s ArXiv and Why Should You Care?
For the uninitiated, ArXiv is a massive repository of scientific papers with over 2.4 million scholarly articles in fields ranging from physics to economics. In their own (more professional) words, it’s “a free distribution service and an open-access archive.” Think of it as Netflix for research papers, minus the “are you still reading?” prompts.
Paperpulse: Your AI Research TL;DR
Paperpulse is my attempt to tame the ArXiv beast. It automatically retrieves, analyzes, and summarizes the latest papers from the four main categories I’m most interested in:
- Machine Learning (cs.LG)
- Artificial Intelligence (cs.AI)
- Computer Vision (cs.CV)
- Computation and Language (cs.CL)
The code is available on Github, and you’re welcome to modify it for your own needs. Just change the ARXIV_SEARCH_QUERY in settings.py if you want to explore different categories. (Though fair warning: adding more categories might require upgrading your coffee consumption to industrial levels.)
What I’ve Learned After a Month of Running Paperpulse
Remember when I said drinking from a firehose? I was being optimistic. On average, there are 479 papers uploaded daily just in these four categories. And yes, there’s some overlap because papers can belong to multiple categories, but still - that’s a lot of research to digest.
After analyzing a month’s worth of papers, some clear themes emerged. Here’s what researchers are obsessing about these days:
- Theoretical Foundations & Algorithms (14 topics)
- Includes theoretical foundations, algorithmic innovations, learning algorithms, optimization techniques
- Combines topics like “Theoretical Foundations and Algorithmic Innovations”, “Advances in Learning Algorithms”, etc.
- Applications & Real-World Implementation (15 topics)
- Healthcare/Medical (8 topics)
- General Applications (7 topics)
- Combines “Applications in Healthcare”, “Applications of Machine Learning in Real-World Scenarios”, etc.
- Model Architecture & Efficiency (12 topics)
- Model compression, efficiency, optimization
- Neural network architectures
- Training techniques and scalability
- Ethics, Safety & Fairness (13 topics)
- Bias and fairness in AI
- Ethical considerations
- Safety and robustness
- Social implications
- Data & Learning Approaches (21 topics)
- Data augmentation and synthetic data
- Novel approaches to data utilization
- Knowledge extraction and representation
- Federated learning
- Specialized AI Domains (35 topics)
- Natural Language Processing (8 topics)
- Computer Vision & 3D (4 topics)
- Graph Neural Networks (7 topics)
- Multimodal Learning (8 topics)
- Reinforcement Learning (8 topics)
- Interpretability & Understanding (8 topics)
- Explainability and interpretability
- Causal inference
- Model trustworthiness
- Security & Privacy (4 topics)
- Privacy preservation
- Security in AI systems
- Federated learning security
The Bigger Picture: My Research Reading Pyramid
While Paperpulse is great for getting that 10,000-foot view of AI research, it’s just one tool in my staying-current toolkit. Here’s my full strategy, arranged from “drinking from the firehose” to “actually understanding things”:
- ArXiv (aka straight from the firehose approach)
- Paperpulse (my attempt at firehose filtering)
- Conference proceedings (where papers go to get dressed up)
- Bluesky/Mastodon (where researchers go to complain about reviewers)
- Blogs/newsletters (where people like me pretend to understand the papers)
- Meetups/online study sessions (where we collectively admit we don’t understand the papers)
- Talking to select researchers (where I finally understand I didn’t understand anything)
What’s Next?
It’ll take a few more months to account for the trends and number of papers to account for any seasonal variability (research often follows both the academic and the conference calendars). The trends so far show a strong emphasis on specialized AI domains, particularly in NLP, multimodal learning, and reinforcement learning. Healthcare appears to be the most popular application domain, probably because “AI for Netflix recommendations” doesn’t sound as impressive on a grant application.
In the meantime, I’m interested in training a simple ranked classifier on my personal choices of papers to see if I can select a subset of papers to summarize in more detail.
Disclaimer: The views expressed in this article are my own and do not necessarily represent the views of my employer.