Interesting Content in AI, Software, Business, and Tech- 06/26/2024

Content to help you keep up with Machine Learning, Deep Learning, Data Science, Software Engineering, Finance, Business, and more

11 min readJun 27, 2024

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 06/26/2024. 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. Lastly, if you’d like to get involved in our many fun discussions, you should join the Substack Group Chat Over here: https://substack.com/chat/1315074.

Community Spotlight:

I’ve decided that I want to spend more time going over organizations solve ML Engineering problems, to add some more diversity to our currently research heavy focus. While studying important, up-coming, and new ideas is extremely important- it’s important to remember that a lot of ML Engineering is built from the combination of relatively simple ideas, combined in different ways. If you/your team have solved a problem that you’d like to share with the rest of the world, shoot me a message and let’s go over the details.

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-

Tech Made Simple

How To Make Less Dumb Mistakes When Programming (inspired by this video on adding rigor to programming).

AI Made Simple

The use of AI in Bio-Tech and drug-discovery space. We’ll be covering a very interesting organization I came across recently. If any of you have any insight into the drug discovery domain, I would love to talk to you to get additional context.

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.

Heuristics on the high seas: Mathematical optimization for cargo ships

A great share by the always insightful Barak Epstein . Shipping makes the world go round, so any optimizations done there are worth paying attention to.

Look around you. Chances are that something in your line of sight sailed on a cargo ship. 90% of the world’s goods travel over the ocean, often on cargo vessels mammoth in scale: a quarter mile long, weighing 250,000 tons, holding 12,000 containers of goods collectively worth a billion dollars. Unlike airplanes, trains, and trucks, cargo ships are in nearly constant operation, following cyclical routes across oceans.

But, what are the best, most efficient routes for these ships? To a computer scientist, this is a graph theory problem; to a business analyst, a supply chain problem. Done poorly, containers linger at ports, ships idle offshore unable to berth, and ultimately, products become pricier as the flow of physical items becomes slower and unpredictable. Every container shipping company needs to solve these challenges, but they are typically solved separately. Combining them multiplies the complexity, and, to the best of our knowledge, is a problem that has never been solved at the scale required by the largest container operations (500 vessels and 1500 ports).

Google’s Operations Research team is proud to announce the Shipping Network Design API, which implements a new solution to this problem. Our approach scales better, enabling solutions to world-scale supply chain problems, while being faster than any known previous attempts. It is able to double the profit of a container shipper, deliver 13% more containers, and do so with 15% fewer vessels. Read on to see how we did it.

Distributed constrained combinatorial optimization leveraging hypergraph neural networks

Scalable addressing of high-dimensional constrained combinatorial optimization problems is a challenge that arises in several science and engineering disciplines. Recent work introduced novel applications of graph neural networks for solving quadratic-cost combinatorial optimization problems. However, effective utilization of models such as graph neural networks to address general problems with higher-order constraints is an unresolved challenge. This paper presents a framework, HypOp, that advances the state of the art for solving combinatorial optimization problems in several aspects: (1) it generalizes the prior results to higher-order constrained problems with arbitrary cost functions by leveraging hypergraph neural networks; (2) it enables scalability to larger problems by introducing a new distributed and parallel training architecture; (3) it demonstrates generalizability across different problem formulations by transferring knowledge within the same hypergraph; (4) it substantially boosts the solution accuracy compared with the prior art by suggesting a fine-tuning step using simulated annealing; and (5) it shows remarkable progress on numerous benchmark examples, including hypergraph MaxCut, satisfiability and resource allocation problems, with notable run-time improvements using a combination of fine-tuning and distributed training techniques. We showcase the application of HypOp in scientific discovery by solving a hypergraph MaxCut problem on a National Drug Code drug-substance hypergraph. Through extensive experimentation on various optimization problems, HypOp demonstrates superiority over existing unsupervised-learning-based solvers and generic optimization methods.

Simulating 500 million years of evolution with a language model

A little bit skeptical about some of the things here, but still extremely exciting stuff. Can’t want to study more.

More than three billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here we show that language models trained on tokens generated by evolution can act as evolutionary simulators to generate functional proteins that are far away from known proteins. We present ESM3, a frontier multimodal generative language model that reasons over the sequence, structure, and function of proteins. ESM3 can follow complex prompts combining its modalities and is highly responsive to biological alignment. We have prompted ESM3 to generate fluorescent proteins with a chain of thought. Among the generations that we synthesized, we found a bright fluorescent protein at far distance (58% identity) from known fluorescent proteins. Similarly distant natural fluorescent proteins are separated by over five hundred million years of evolution

TechBio: Latest Trends 😺

We have a strong bio-tech focus this week b/c of all my reading into that space. And when it comes to TechBio/Bio-Tech (what is the difference), Marina T Alamanou, PhD is on the GOAT list for my favorite resources.

Why AI Companies are investing into DevRel

This is a piece we did on my sister publication, Tech Made Simple, on the rise of AI-Relations roles. Figured I’d weigh on this industry trend and speculate on how one might break into it.

Recently, I’ve noticed a very interesting trend in the messages I get on LinkedIn. A lot of the recent messages I get from recruiters come from people looking to hire me in an AI Relations Role-

The ratio of people who reach out to me for AIRel vs ML roles has gone up significantly over the last 2–3 months. AIRel is a very new idea, being itself a spin-off from the relatively new Developer Relations (DevRel) title. For those of you looking to either pivot, or break into AI- it might be an interesting alternative to the traditional Engineer, PM, or Researcher roles. In this piece, I will give you more detail on AI Relations role- including its background, why it’s so valuable to startups, and how you can break in. I think the relations path is worth exploring b/c it is very valuable across the board, and you will often find startups aggressively hiring for it. This can help you a lot in uncertain economic times.

Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models

Given all the talk about the coming of AGI, this paper is a good reality-check. Maybe Ilya left his job for nothing?

Large Language Models (LLMs) like closed weights ones GPT-3.5/4, Claude, Gemini or open weights ones like LLaMa 2/3, Mistral, Mixtral, and more recent ones Dbrx or Command R+ are often described as being instances of foundation models — that is, models that transfer strongly across various tasks and conditions in few-show or zero-shot manner, while exhibiting scaling laws that predict function improvement when increasing the pre-training scale. These claims of excelling in different functions and tasks rely on measurements taken across various sets of standardized benchmarks showing high scores for such models. We demonstrate here a dramatic breakdown of function and reasoning capabilities of state-of-theart models trained at the largest available scales which claim strong function, using a simple, short, conventional common sense problem formulated in concise natural language, easily solvable by humans. The breakdown is dramatic, as models also express strong overconfidence in their wrong solutions, while providing often non-sensical “reasoning”-like explanations akin to confabulations to justify and backup the validity of their clearly failed responses, making them sound plausible. Various standard interventions in an attempt to get the right solution, like various type of enhanced prompting, or urging the models to reconsider the wrong solutions again by multi step re-evaluation, fail. We take these initial observations to the scientific and technological community to stimulate urgent re-assessment of the claimed capabilities of current generation of LLMs, Such re-assessment also requires common action to create standardized benchmarks that would allow proper detection of such basic reasoning deficits that obviously manage to remain undiscovered by current state-of-the-art evaluation procedures and benchmarks

Scaling Instructable Agents Across Many Simulated Worlds

The results are okay, but this is an interesting idea by the crew at Deepmind. Can’t wait to never hear about it again (outside of maybe research discussions).

Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires learning to ground language in perception and embodied actions, in order to accomplish complex tasks. The Scalable, Instructable, Multiworld Agent (SIMA) project tackles this by training agents to follow free-form instructions across a diverse range of virtual 3D environments, including curated research environments as well as openended, commercial video games. Our goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment. Our approach focuses on language-driven generality while imposing minimal assumptions. Our agents interact with environments in real-time using a generic, human-like interface: the inputs are image observations and language instructions and the outputs are keyboard-and-mouse actions. This general approach is challenging, but it allows agents to ground language across many visually complex and semantically rich environments while also allowing us to readily run agents in new environments. In this paper we describe our motivation and goal, the initial progress we have made, and promising preliminary results on several diverse research environments and a variety of commercial video games.

Tech Bros Invented Trains And It Broke Me

It’s a bit weird how so many so-called revolutionary transport methods end up being worse versions of trains. I wonder why people don’t just skip all the additional steps and invest in more trains and buses.

Building a Perplexity AI clone

A great hands-on exploration by our man Alejandro Piad Morffis. If you’re looking for some clear and knowledgable analysis- he’s always a very good resource to turn to.

How Python Compares Floats and Ints: When Equals Isn’t Really Equal

If you want in-depth analysis of the coding systems- Abhinav Upadhyay is your guy. Every single article is like a chapter in a very advanced textbook.

Python compares the integer value against the double precision representation of the float, which may involve a loss of precision, causing these discrepancies. This article goes deep into the details of how CPython performs these comparisons, providing a perfect opportunity to explore these complexities.

So this is what we will cover:

Quick revision of the IEEE-754 double precision format — this is how floating-point numbers are represented in memory
Analyzing the IEEE-754 representation of the three numbers
The CPython algorithm for comparing floats and ints
Analyzing the three test scenarios in the context of the CPython algorithm

FOD#52: OpenAI’s new GPT-4o — what it can and cannot do

Meant to share this earlier, but things happened and I couldn’t. Regardless, another masterpiece by Ksenia Se .

trying out the new GPT-4o and — as always — providing you with the most relevant news, research papers and must-reads