Content Recommendations- 1/22/2024 [Updates]

What you should know in AI, Software, Business, and Tech- 1/22/2025

Jan 23, 2025

Hey, it’s Devansh 👋👋

In issues of Updates, I will share interesting content I came across. While the focus will be on AI and Tech, the ideas might range from business, philosophy, ethics, and much more. The goal is to share interesting content with y’all so that you can get a peek behind the scenes into my research process.

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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 1/22/2025. 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.

Community Spotlight: bycloud

bycloud is a pretty interesting YouTube channel that covers recent research and developments in AI Research. He’s a pretty engaging dude, and explains recent developments in a way that it’s pretty interesting upto an intermediate level. I like to play his videos in the background when I’m not doing anything too attention intensive to keep myself updated with the news. You won’t always learn something completely new (especially if you’re advanced), but it’s a good source just to keep up with AI developments and find ideas that you should dig into by yourself.

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

Bayesian Stats

AI Made Simple-

Deepseek Breakdown

I provide various consulting and advisory services. If you‘d like to explore how we can work together, reach out to me through any of my socials over here or reply to this email.

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.

Rewiring the Internet: Commerce in the Age of AI Agents

Sahar Mor did a fantastic job talking about the infrastructure changes we might see as Agents become more mainstream on the internet. By and large, the highest returns in implementing AI come when you make your system more AI Friendly (enabling better data fed into the model, and less computational heavy lifting for the model). I would love to hear more about what are the systemic/infrastructural issues that hold back the deployment of AI in different industries, and what we can do to fix them.

The revolution in web infrastructure we discussed in previous posts isn’t just theoretical — it’s enabling fundamental changes in how commerce, marketing, and customer service function. As agent passports and trust protocols become standardized, we’re witnessing the emergence of entirely new commercial paradigms.

With the recent release of Tasks by OpenAI, which equips ChatGPT — its consumer-facing AI — with the ability to perform tasks behind the scenes on behalf of users, it’s now easier than ever to envision a future where ChatGPT seamlessly navigates the web and handles complex operations for us.

Today, we’ll explore how an agent-first internet will reshape domains like payments, marketing, support, and localization.

DeepSeek-R1

Needs no introduction. I’m pretty interested in exploring their reasoning chains and sampling methods, which I think would be big performance drivers.

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.

Why Companies Invest in Open-Source Tech and Research

Now that Deepseek is open sourcing everything, people are once again asking how this benefits them. A while back, I did an exploration of the economics of OSS (why and how companies can profit from the OSS community by contributing to it). It will be relevant now.

Recently, I had dinner with Eric Flaningam, an investor, (excellent) writer, and one of our cult members in NYC. We talked about a bunch of interesting topics, including open-source software (OSS) in AI and why companies invest so much in it. After all, how does a company gain from spending so much time and effort into R&D for a tool only to give it to the public for free, especially since it exposes possible trade secrets/competitive advantages to their competitors?

This article will look to answer that question from a purely business perspective. To do so, we will look at various kinds of stakeholders in the Tech Ecosystem (focusing on AI) and how each can leverage OSS for their benefit. Once that is covered, we will review the different strategies companies can use Open Source to increase business adoption.

Accelerated enzyme engineering by machine-learning guided cell-free expression

People don’t realize that AI is wayy beyond Language Models and Tech Applications. This is a great use of solving a problem in a field that’s not traditionally seen as “AI” (laundry detergents)-

Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions. We apply this platform to engineer amide synthetases by evaluating substrate preference for 1217 enzyme variants in 10,953 unique reactions. We use these data to build augmented ridge regression ML models for predicting amide synthetase variants capable of making 9 small molecule pharmaceuticals. Over these nine compounds, ML-predicted enzyme variants demonstrate 1.6- to 42-fold improved activity relative to the parent. Our ML-guided, cell-free framework promises to accelerate enzyme engineering by enabling iterative exploration of protein sequence space to build specialized biocatalysts in parallel.

LB-KBQA:Large-language-model and BERT based Knowledge-Based Question and Answering System

Good combination of multiple methods. As discussed yesterday, Encoder heavy models are incredibly powerful for a lot of NLU tasks and should not be overlooked.

Generative Artificial Intelligence (AI), because of its emergent abilities, has empowered various fields, one typical of which is large language models (LLMs). One of the typical application fields of Generative AI is large language models (LLMs), and the natural language understanding capability of LLM is dramatically improved when compared with conventional AI-based methods. The natural language understanding capability has always been a barrier to the intent recognition performance of the Knowledge-Based-Question-and-Answer (KBQA) system, which arises from linguistic diversity and the newly appeared intent. Conventional AI-based methods for intent recognition can be divided into semantic parsing-based and model-based approaches. However, both of the methods suffer from limited resources in intent recognition. To address this issue, we propose a novel KBQA system based on a Large Language Model(LLM) and BERT (LB-KBQA). With the help of generative AI, our proposed method could detect newly appeared intent and acquire new knowledge. In experiments on financial domain question answering, our model has demonstrated superior effectiveness.

Lithium Batteries and Water: Separating Facts from Fear-Driven Misinformation

Another great demonstration of how much media misrepresents more “scientific” cases for clicks and misinformation. There should be stronger fact-checking standards imposed.

While drinking some coffee and waiting for Abe to wake up, I pulled up Google and did a search for lithium news. Right off the bat, I see MSN manipulating google’s algorithm to push a “lithium is dangerous” article.

They all link back to the same video of a guy taking a AA lithium battery apart and tossing the anode into water to watch it react. It’s a powerful media tool that paints lithium batteries as highly dangerous, but there’s no context. The battery in the video is a primary battery that has a lithium metal anode, and it’s this type of lazy and careless — dare I say journalism — from entities like MSN that has led people worldwide to think that if an EV is exposed to any amount of water, it will explode.

Sliding Spectrum Decomposition for Diversified Recommendation

After TikTok ban talks, I picked up an interested in Rec Systems. This one was pretty interesting

Content feed, a type of product that recommends a sequence of items for users to browse and engage with, has gained tremendous popularity among social media platforms. In this paper, we propose to study the diversity problem in such a scenario from an item sequence perspective using time series analysis techniques. We derive a method called sliding spectrum decomposition (SSD) that captures users’ perception of diversity in browsing a long item sequence. We also share our experiences in designing and implementing a suitable item embedding method for accurate similarity measurement under long tail effect. Combined together, they are now fully implemented and deployed in Xiaohongshu App’s production recommender system that serves the main Explore Feed product for tens of millions of users every day. We demonstrate the effectiveness and efficiency of the method through theoretical analysis, offline experiments and online A/B tests.

Four pitfalls to product-market fit

After working with 300+ early-stage startups, I see entrepreneurs get stuck in four common areas: exploring ideas, not doing a pilot, not asking for money, and lack of marketing.