Interesting Content in AI, Software, Business, and Tech- 05/8/2024
Content to help you keep up with Machine Learning, Deep Learning, Data Science, Software Engineering, Finance, Business, and more
Before we begin- amazing update. The cult is officially on the best-sellers list. Thank you all for your support of the chocolate milk cult. Your generosity is crucial to keeping our cult free and independent- and in helping me provide high-quality AI Education to everyone.
A lot of people reach out to me for reading recommendations. I figured I’d start sharing whatever AI Papers/Publications, interesting books, videos, etc I came across each week. Some will be technical, others not really. I will add whatever content I found really informative (and I remembered throughout the week). These won’t always be the most recent publications- just the ones I’m paying attention to this week. Without further ado, here are interesting readings/viewings for 04/18/2024. If you missed last week’s readings, you can find it here.
Reminder- We started an AI Made Simple Subreddit. Come join us over here- https://www.reddit.com/r/AIMadeSimple/. If you’d like to stay on top of community events and updates, join the discord for our cult here: https://discord.com/invite/EgrVtXSjYf.
Community Spotlight: Mohamed Aziz Belaweid
Mohamed Aziz Belaweid writes the excellent, “Aziz et al. Paper Summaries”, where he summarizes recent developments in AI. His articles are very similar to mine: they have great visualizations and use approachable language, but still maintain a good level of detail. They are also a lot shorter, which might be a pro depending on what you’re looking for. If you’re an AI engineer or researcher, then Aziz’s work would be a good match for you. I really loved his DoRA is The New LoRA! here.
If you’re doing interesting work and would like to be featured in the spotlight section, just drop your introduction in the comments/by reaching out to me. There are no rules- you could talk about a paper you’ve written, an interesting project you’ve worked on, some personal challenge you’re working on, ask me to promote your company/product, or anything else you consider important. The goal is to get to know you better, and possibly connect you with interesting people in our chocolate milk cult. No costs/obligations are attached.
Previews
Curious about what articles I’m working on? Here are the previews for the next planned articles-
How Pinterest Scaled to 11 Million Users With Only 6 Engineers
I’m a bit torn b/w two different topics. I was planning to do a look at KAN: Kolmogorov–Arnold Networks. But Google dropped AlphaFold 3 today. Which of the two is more interesting to you?
Highly Recommended
These are pieces that I feel are particularly well done. If you don’t have much time, make sure you at least catch these works.
AlphaFold 3 predicts the structure and interactions of all of life’s molecules
I don’t know enough about the challenges associated with AI for drug discovery and bio-tech, but this seems like a massive deal. I didn’t hear about AlphaFold 2 making significant impact, but if you know how it aided breakthroughs, I would love to hear more. Conversely, if it didn’t- I would love to know why it didn’t. What are the blockers? From a technical perspective- it’s pretty interesting that AlphaFold3 builds on Diffusion. Definitely worth keeping an eye out for it. Credit to Lior Sinclair for the find.
In a paper published in Nature, we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important categories of interaction we have doubled prediction accuracy.
We hope AlphaFold 3 will help transform our understanding of the biological world and drug discovery. Scientists can access the majority of its capabilities, for free, through our newly launched AlphaFold Server, an easy-to-use research tool. To build on AlphaFold 3’s potential for drug design, Isomorphic Labs is already collaborating with pharmaceutical companies to apply it to real-world drug design challenges and, ultimately, develop new life-changing treatments for patients.
LLM Paper Reading Notes — May 2024
Jean David Ruvini is a true expert in NLP. We are all very lucky that he shares his notes on important LLM-related papers on LinkedIn. Once again, if you are a technical person- his newsletter is a goldmine.
The May edition of my LLM reading note is out. In this edition, infinite context transformers, insights into the faithfulness of RAG, why not all tokens should be treated equal, evidence about how deceptive LLMs can be and more. Enjoy!
Musings on building a Generative AI product
Some really good insights on building Gen AI LinkedIn. I love reading these sorts of blog posts b/c they help me understand what kinds of challenges different projects face-
Over the last six months, our team here at LinkedIn has been working hard to develop a new AI-powered experience. We wanted to reimagine how our members go about their job searches and browse professional content.
The explosion of generative AI made us pause and consider what was possible now that wasn’t a year ago. We tried many ideas which didn’t really click, eventually discovering the power of turning every feed and job posting into a springboard to:
- Get information faster, e.g. takeaways from a post or learn about the latest from a company.
- Connect the dots, e.g. assess your fit for a job posting.
- Receive advice, e.g. improve your profile or prepare for an interview.
- And much more…
Was it easy to build? What went well and what didn’t? Building on top of generative AI wasn’t all smooth sailing, and we hit a wall in many places. We want to pull back the “engineering” curtain and share what came easy, where we struggled, and what’s coming next.
C*-Algebraic Machine Learning: Moving in a New Direction
Looks like more and more people are looking to integrate Complex numbers into Machine Learning. If it goes mainstream- just remember that y’all heard about it here first. The progress also seems to line up well with my estimated timelines. Exciting times ahead.
Machine learning has a long collaborative tradition with several fields of mathematics, such as statistics, probability and linear algebra. We propose a new direction for machine learning research: C∗-algebraic ML − a cross-fertilization between C∗-algebra and machine learning. The mathematical concept of C∗-algebra is a natural generalization of the space of complex numbers. It enables us to unify existing learning strategies, and construct a new framework for more diverse and information-rich data models. We explain why and how to use C∗-algebras in machine learning, and provide technical considerations that go into the design of C∗-algebraic learning models in the contexts of kernel methods and neural networks. Furthermore, we discuss open questions and challenges in C∗-algebraic ML and give our thoughts for future development and applications.
4 Software Design Principles I Learned the Hard Way
I recently built and designed a massive service that (finally) launched successfully last month. During the design and implementation process, I found that the following list of “rules” kept coming back up over and over in various scenarios.
These rules are common enough that I daresay that at least one of them will be useful for a project that any software engineers reading this are currently working on. But if you can’t apply it directly now, I hope that these principles are a useful thought exercise that you are free to comment on below or challenge directly too.
One thing I will note here is that of course — each “principle” has a time and place. Nuance is necessary, as always. These are conclusions that I find myself erring towards in general because oftentimes, the opposite that is the default that I see when I’m reviewing code or reading design docs.
Other Content
First Fractal Molecule in Nature Assembles Into a Sierpinski Triangle And We Don’t Know Why
Researchers from Germany, Sweden, and the UK have discovered an enzyme produced by a single-celled organism that can arrange itself into a fractal — not just any fractal, but a repeating pattern of triangles known as a Sierpiński triangle.
The enzyme is a form of citrate synthase produced by the cyanobacterium Synechococcus elongatus, and its evolution from non-fractal precursors suggests that such molecular patterns can emerge surprisingly quickly.
Human thought vs Human language: MIT psychologist explains | Edward Gibson and Lex Fridman
WTF Do Think Tanks Actually Do?
Think tanks are legitimately some of the most powerful organizations you have never heard of. They play a key role in influencing local, state, national and even global policy by hiring the smartest people in the world and paying them huge salaries to argue their point of view into the ears of people with power. Some of the biggest think tanks in America have more influence over legislation than our elected officials and have become so integrated into the political process that Washington would grind to a halt without them. It’s easy to see why people believe that think tanks are shadowy organizations pulling the strings of power, while insiders claim that they are just a cog in the machine keeping democracy intact…
But what the do these organizations actually do?
Neom — The Line — The Rise and Fall of Saudi Arabia’s Linear City.
Saudi Arabia’s plan to build a 170km long, 500m tall, mirrored city in the desert as one of their megaprojects filled with 9 million people has now been curtailed to 2.4km long.
According to Bloomberg, Saudi Arabia’s government has “scaled back its medium-term ambitions” for Neom, of which The Line is the most significant sub-project.
The Saudi government had hoped to have 9 million residents living in “The Line” by 2030, but this has been scaled back to fewer than 300,000, according to the new report.
This curtailment of plans comes as Saudi Arabia has not yet approved the 2024 budget for Neom, according to Bloomberg sources.
How to use SwiftData outside SwiftUI — Jacob Bartlett
If you’re an IOS developer, make sure you sign up to Jacob’s Tech Tavern.It’s got great deep dives into app development for IOS. I don’t build IOS apps, but I like to read his work occasionally just to get a better understanding of different tech ecosystems and the relevant discussions that happen there.
SwiftData.
The hip, all-new persistence framework, that totally isn’t just a wrapper on CoreData (shh!)
Resplendent with property wrappers such as @Environment\.modelContext) and @Query, SwiftData is convenient for accessing and modifying persistent data directly from your SwiftUI views!
This is great for the Gen Zs who grew up with MV architecture, but what about us boomers still clutching onto MVVM, who insist on unit testing our crufty old code?
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