Interesting Content in AI, Software, Business, and Tech- 8/16/2023 [Updates]
Content to help you keep up with Machine Learning, Deep Learning, Data Science, Software Engineering, Finance, Business, and more
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 8/16/2023. If you missed last week's readings, you can find it here.
Community Spotlight- Yannic Kilcher
A giant in the AI Research Discussion circles, Yannic needs no introduction. His paper breakdowns have helped millions, and his channel was my inspiration for writing (AI Made Simple would not exist without Yannic). Even if you're not interested in very in-depth Research Paper discussions, Yannic's channel has a lot of great discussions on AI Safety, recent developments in the space etc. I personally love his channel because he's knowledgeable, not scared to discuss controversial topics, and his use of sarcasm + dry humor is top-notch. To those looking for a good introduction to his work, Check out his most recent ML News here. It's thorough, informative, and agenda-free, which is rare in the space.
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 the community. No costs/obligations are attached.
AI Writeups
SPRING: GPT-4 Out-performs RL Algorithms by Studying Papers and Reasoning
Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexity limits its effectiveness in complex open-world games like Crafter or Minecraft. We propose a novel approach, SPRING, to read the game's original academic paper and use the knowledge learned to reason and play the game through a large language model (LLM). Prompted with the LaTeX source as game context and a description of the agent's current observation, our SPRING framework employs a directed acyclic graph (DAG) with game-related questions as nodes and dependencies as edges. We identify the optimal action to take in the environment by traversing the DAG and calculating LLM responses for each node in topological order, with the LLM's answer to final node directly translating to environment actions. In our experiments, we study the quality of in-context "reasoning" induced by different forms of prompts under the setting of the Crafter open-world environment. Our experiments suggest that LLMs, when prompted with consistent chain-of-thought, have great potential in completing sophisticated high-level trajectories. Quantitatively, SPRING with GPT-4 outperforms all state-of-the-art RL baselines, trained for 1M steps, without any training. Finally, we show the potential of games as a test bed for LLMs.
This paper has a lot of flaws. Edan Meyer did a great video covering the paper and critiquing it. Papers like this show us how important it has become to read between the lines in AI Research. Too much AI Research is becoming like clickbait, where the goal seems to be getting noticed over adding information to the discourse. Interesting paper regardless, but some very serious leaps were made here. Always make sure you apply critical thinking when interacting with content around AI, things are becoming very sensationalist.
Data is the Foundation of Language Models
This overview will study the role and impact of alignment, as well as the interplay between alignment and pre-training. Interestingly, these ideas were explored by the recent LIMA model [1], which performs alignment by simply fine-tuning a pre-trained LLM over a semi-manually curated corpus of only 1,000 high-quality response examples. We will learn that the alignment process, although critical, primarily teaches an LLM steerability and correct behavior or style, while most knowledge is gained during pre-training. As such, alignment can be performed successfully even with minimal training data. However, we will see that the impact of data quality and diversity on both alignment and other avenues of LLM training (e.g., pre-training, fine-tuning, etc.) is absolutely massive.
How NOT to apply Artificial Intelligence in your business
A great piece that explains the importance of truly understanding the problem and challenge instead of rushing in to use AI just for the sake. Loved this quote in particular, where he talks about why an entrepreneur replacing his customer support team with AI was not a great move, even if external metrics might make it seem so.
"First of all, he clearly states what were his drivers: the metrics he objectively measures and he thinks show he has made progress with the decision made.
Time to first response
Resolution time
Customer support costs
All of those metrics are important. But do you see the pattern there? Those are Customer Support metrics, not Customer Experience ones! All of them are inward facing (looking at the organization), instead of outward facing (looking at the customer). Yes, collecting Customer Experience data is harder than collecting Customer Support data. But it is indispensable, or you might end up shooting yourself in the foot. To do a proper analysis, we must follow the entrepreneur’s reasoning, so let’s keep going."
GPT-4 Can't Reason
GPT-4 was released in March 2023 to wide acclaim, marking a very substantial improvement across the board over GPT-3.5 (OpenAI's previously best model, which had powered the initial release of ChatGPT). However, despite the genuinely impressive improvement, there are good reasons to be highly skeptical of GPT-4's ability to reason. This position paper discusses the nature of reasoning; criticizes the current formulation of reasoning problems in the NLP community, as well as the way in which LLM reasoning performance is currently evaluated; introduces a small collection of 21 diverse reasoning problems; and performs a detailed qualitative evaluation of GPT-4's performance on those problems. Based on this analysis, the paper concludes that, despite its occasional flashes of analytical brilliance, GPT-4 at present is utterly incapable of reasoning.
A new partnership to promote responsible AI
Today, Anthropic, Google, Microsoft and OpenAI are announcing the formation of the Frontier Model Forum, a new industry body focused on ensuring safe and responsible development of frontier AI models. The Frontier Model Forum will draw on the technical and operational expertise of its member companies to benefit the entire AI ecosystem, such as through advancing technical evaluations and benchmarks, and developing a public library of solutions to support industry best practices and standards.
I'm personally skeptical of this partnership. Analysis on it coming soon.
Data Laundering: How Stability AI managed to get millions of copyrighted art works without paying artists
In this piece, I went over a key component of LLMs: Data Laundering and how Tech Companies use it to get around copyright to build their large datasets. We have seen many companies and organizations try to build their language models. Given the trend towards multi-modality- we will likely see more orgs use such techniques to work around copyright (and I’m sure many already have). Understanding how is important for building better regulations and procedures to ensure everyone is paid better.
Software Writeups
Position Paper: Bayesian Reasoning for Software Testing
Despite significant advances in software testing research, the ability to produce reliable software products for a variety of critical applications remains an open problem. The key challenge has been the fact that each program or software product is unique, and existing methods are predominantly not capable of adapting to the observations made during program analysis. This paper makes the following claim: Bayesian reasoning methods provide an ideal research paradigm for achieving reliable and efficient software testing and program analysis. A brief overview of some popular Bayesian reasoning methods is provided, along with a justification of why they are applicable to software testing. Furthermore, some practical challenges to the widespread use of Bayesian methods are discussed, along with possible solutions to these challenges
To those interested, we covered Bayesian Thinking for better Software here.
Time Pressure in Software Development
Abi Noda has a great newsletter for insights into developer productivity. His recent piece in particular was worth reading.
Time pressure, or “the perception that time is scarce in relation to the demands of the task,” is prevalent in software engineering and has been studied extensively. The authors of this paper aimed to synthesize existing research on the topic in a way that helps engineering leaders better understand what causes time pressure as well as how it may impact teams.
To conduct a systematic review of existing research, the researchers examined 5,414 sources found through repository searchers and snowballing. They narrowed their list to 102 relevant papers on time pressure in software engineering by applying inclusion and exclusion criteria. Then, they qualitatively coded and analyzed the selected papers to answer their research questions.
Videos
Innovation and Disruption through Software | Carlos Kelkboom | Beyond Coding Podcast #118
Some of you asked for great podcasts. Can't think of a better Tech Podcast than the Beyond Coding Podcast by 🎙Patrick Akil.
Better Llama 2 with Retrieval Augmented Generation (RAG)
James Briggs is one of the few people that makes actually useful LLM content online. In the above video, he covers Retrieval Augmented Generation. RAG allows us to keep our Large Language Models (LLMs) up to date with the latest information, reduce hallucinations, and allow us to cite the original source of information being used by the LLM.
He builds the RAG pipeline using a Pinecone vector database, a Llama 2 13B chat model, and wraps everything in Hugging Face and LangChain code.
Unlock creative genius like da Vinci and Richard Feynman | Tiago Forte
It’s never been easier for us to obtain information in today’s digital age. But at the same time, it’s never been more difficult for us to organize, synthesize, and make sense of all that information we have at our fingertips.
That’s why author Tiago Forte believes we need to build a “second brain,” or a personal system for knowledge management. To build up this system, Forte recommends using the CODE system: C for Capture, O for Organize, D for Distill, and E for Express.
Why the Rich World is Dying and How to Save It
Joeri Schasfoort is one of the most insightful people when it comes to Macroeconomics. Every video is packed with lots of insights. The one linked above covers fertility rates, why they are falling in rich countries and some ways different countries have improved their birth rates.
Raising Cane's - Expanding Near You
A great overview of the fast food chain Raising Cane's and some business lessons we can take from their rapid growth and amazing operations.
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