Issue #518
October 26 2023

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And now…let's dive into some interesting links from this week

Editor's Picks

• Building LLM-Powered Web Apps with Client-Side Technology
  It’s no secret that for a long time machine learning has been mostly a Python game, but the recent surge in popularity of ChatGPT has brought many new developers into the field. With JavaScript being the most widely-used programming language, it’s no surprise that this has included many web developers, who have naturally tried to build web apps. There’s been a ton of ink spilled on building with LLMs via API calls to the likes of OpenAI, Anthropic, Google, and others, so I thought I’d try a different approach and try to build a web app using exclusively local models and technologies, preferably those that run in the browser!…
  

• What Every Developer Should Know About GPU Computing
  Most programmers have an intimate understanding of CPUs and sequential programming because they grow up writing code for the CPU, but many are less familiar with the inner workings of GPUs and what makes them so special. Over the past decade, GPUs have become incredibly important because of their pervasive use in deep learning. Today, it is essential for every software engineer to possess a basic understanding of how they work. My goal with this article is to give you that background…
  

• Is AI alignment on track? Is it progressing... too fast?
  This is not an essay bashing AI alignment. I love alignment. I spent most of 2023 writing about it and working on it, getting some neat results, e.g. a partially automated process for jailbreaking GPT-4 and Claude, and even getting an academic paper to cite my essay on jailbreaks. …all of this in the breaks from yelling at AI researchers at parties about them not doing enough about alignment. Sorry kipply and Jacob!I wish this was a silly joke, but it’s not. Why else would I be sitting in a tiny dark room on a Saturday evening writing this essay and not at a party?…

  
  

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Data Science Articles & Videos

• Joining CSV Data Without SQL: An IP Geolocation Use Case
  Given an IP address associated with some network traffic, can we deduce from where on earth the traffic originated? There’s plenty of software and services out there to help provide this information, but a familiar one is the GeoLite2 data from MaxMind. Not only is it free, it’s provided in the form of downloadable data sets. This allows us to pick it apart in interesting ways…
  

• Contractors who are called Data Scientists but can't do what I'd expect [Reddit]
  I was hired as a senior member of a pre-existing data science team. I now manage a few other team members (who were there before me). They are all contractors and their day rate is HIGH. They are all 'Data Scientists' and graduates. I'm older. I've done lots of technical roles and I'm not really sure what my official title is. I can do data science but I really just build stuff. I've done Data Engineering in the past, MLOps, DevOps, Cloud etc. I'm a jack of all trades, master of none. Now, I know what I think a 'Data Scientist' should be able to do…
  

• Why Now? Malloy Data Deep Dive
  “If we knew all the things we know about data, and about programming with data, and about programming languages in general, and we were designing a query language today, what would it look like?”…Malloy is a language for describing data relationships and transformations within SQL databases. It:

  • Compiles to SQL optimized for your database.

  • Has both a semantic data model and query language.

  • Excels at reading and writing nested data sets.

  • Seamlessly handles what are complex/error-prone queries in SQL.

  …As always within Why Now, we’ll build towards an understanding of the definition above via breaking it into its components…
  

• Embeddings: What they are and why they matter
  Embeddings are a really neat trick that often come wrapped in a pile of intimidating jargon. If you can make it through that jargon, they unlock powerful and exciting techniques that can be applied to all sorts of interesting problems…I gave a talk about embeddings at PyBay 2023. This article represents an improved version of that talk, which should stand alone even without watching the video. If you’re not yet familiar with embeddings I hope to give you everything you need to get started applying them to real-world problems…
  

• Bayesian Regression Markets
  Machine learning tasks are vulnerable to the quality of data used as input. Yet, it is often challenging for firms to obtain adequate datasets, with them being naturally distributed amongst owners, that in practice, may be competitors in a downstream market and reluctant to share information. Focusing on supervised learning for regression tasks, we develop a regression market to provide a monetary incentive for data sharing. Our proposed mechanism adopts a Bayesian framework, allowing us to consider a more general class of regression tasks. We present a thorough exploration of the market properties, and show that similar proposals in current literature expose the market agents to sizable financial risks, which can be mitigated in our probabilistic setting…

• Multi-scale Generalized Hamiltonian Monte Carlo with Delayed Rejection
  In this talk, I will demonstrate how we can combine two ideas, generalized Hamiltonian Monte Carlo and delayed rejection, to derive a sampler that is as efficient as Hamiltonian Monte Carlo, but is able to adapt its step size to deal with multi-scale distributions, much like a standard integrator for ordinary differential equations…
  

• Generative AI Use Cases Companies Can Implement Today
  Where do most companies actually start when it comes to incorporating generative AI? What generative AI use cases are realistic, achievable, and actually worth the ROI? We dug deep into the early adopters’ strategies to learn how companies are putting this technology to use today — and what it takes for a data team to implement Gen AI at scale…
  

• Empirical Design in Reinforcement Learning
  This manuscript represents both a call to action, and a comprehensive resource for how to do good experiments in reinforcement learning. In particular, we cover: the statistical assumptions underlying common performance measures, how to properly characterize performance variation and stability, hypothesis testing, special considerations for comparing multiple agents, baseline and illustrative example construction, and how to deal with hyper-parameters and experimenter bias. Throughout we highlight common mistakes found in the literature and the statistical consequences of those in example experiments. The objective of this document is to provide answers on how we can use our unprecedented compute to do good science in reinforcement learning, as well as stay alert to potential pitfalls in our empirical design…

• Should, a data engineer, uses Pandas in his production code? [Reddit]

  Pandas is a fantastic library for reading datasets on the go and performing daily data analysis tasks. However, is it advisable to use it in our Python production code?…
  

• lea - 🏃‍♀️ Minimalist alternative to dbt
  lea is a minimalist alternative to tools like dbt, SQLMesh, and Google's Dataform. lea aims to be simple and opinionated, and yet offers the possibility to be extended. We happily use it every day to manage our data warehouse. We will actively maintain it and add features, while welcoming contributions. Right now lea is compatible with BigQuery and DuckDB….
  

• Learning Sensorimotor Agency in Cellular Automata
  Finding robust self-organizing “agents” with gradient descent and curriculum learning: individuality, self-maintenance and sensori-motricity within a cellular automaton environment…
  

• Some things I learned about GAN training
  For several months now, I've been working on a adversarial network that takes synthetic audio as input and enhances it to sound more natural. I know that this is different from the usual way you would use a GAN, which would be passing noise as input to the generator, turning it into a generative network. But I still think a lot of the things I learned during this project, especially about training stability, can be applied to other GANs, which is why I'm making this post!…

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Jobs

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Training & Resources

• financial-machine-learning
  A curated list of practical financial machine learning tools and applications…
  

• Deep dive into AutoGPT: A comprehensive and in-depth step-by-step guide to how it works
  I've recently started experimenting with AI agents and stumbled upon AutoGPT…To gain a better understanding of AutoGPT's inner workings, I embarked on a journey of practical experimentation, working with various examples and meticulously recording inputs and outputs at each step. This hands-on exercise significantly enhanced my understanding of AutoGPT. What follows are my observations on AutoGPT's inner workings and what occurs after the user enters a task…
  

• The Ancient Art of Data Management (Inaugural lecture of Professor Hannes Mühleisen)
  Inaugural lecture of DuckDB's co-founder, Professor Hannes Mühleisen, given at Radboud University in Nijmegen on 28 September 2023….
  

Last Week's Newsletter's 3 Most Clicked Links

• Kalman Filter For Dummies

• Understanding Moments

• You've just joined a new company who do everything in Excel, but.... [Reddit]

* Based on unique clicks.
** Find last week's issue #517 here.

Cutting Room Floor

• The Killer Use Case for LLMs Is Summarization

• What happens when you vectorize wide PyTorch expressions?

• Evaluating Math Reasoning in Visual Contexts with GPT-4V, Bard, and Other Large Multimodal Models
  

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