We introduce the Formally Verified Automated Programming Progress Standards, or FVAPPS, a benchmark of 4715 samples for writing programs and proving their correctness, the largest formal verification benchmark, including 1083 curated and quality controlled samples. Previously, APPS provided a benchmark and dataset for programming puzzles to be completed in Python and checked against unit tests, of the kind seen in technical assessments in the software engineering industry. Building upon recent approaches for benchmarks in interactive theorem proving, we generalize the unit tests to Lean 4 theorems given without proof (i.e., using Lean's "sorry" keyword). On the 406 theorems of 100 randomly selected samples, Sonnet correctly proves 30% and Gemini correctly proves 18%. We challenge the machine learning and program synthesis communities to solve both each general purpose programming problem and its associated correctness specifications. The benchmark is available at https://huggingface.co/datasets/quinn-dougherty/fvapps.

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A while back, I was on Twitter and I saw the following exceptional take on Twitter. This is a very popular idea online, and I had meant to write about this sooner. But thanks to all the insanity happening in the ML research domain, I got side-tracked. However, now that I have the time I can finally cover this in-depth. In this post, I will cover "Why you absolutely need Math for Machine Learning."

Large Language Models have taken the world by storm recently. The capabilities shown by these models, combined with the way it seems like they can do everything have gotten the AI community very excited (and some AGI doomers terrified, lol). As LLMs become more powerful, we will naturally see them serve as foundation tasks for all kinds of applications. The impact they will have can't be overstated. However, it's crucial to ensure that these models are safe and don't come with seriously problematic biases/cases.

Unless you’re living under a rock, you know of Google’s Pathways Language Model (PaLM), “a 540-billion parameter, dense decoder-only Transformer model trained with the Pathways system”. People are…

Analogies are very helpful for explaining ideas. I was recently talking to somebody from my boxing gym about coding interview prep. I explained the stages of preparation in terms of different boxing stages (shadow boxing, pad work, sparring, fights, etc). This analogy helped my friend understand the process, and even made the prep more fun for him (his words). In this article, I will share my experiences with Brazilian Jiu-Jitsu, A.K.A Murder Yoga.

I believe the performance benefits, ability to generalize, and robustness are all too good to ignore. Thus, I've written/talked a lot about it throughout my content. Recently, a reader of mine reached out with an interesting question. He wanted to know why it was that randomness in aspects such as Data Augmentation, but not in selecting features (Garbage In, Garbage Out). I figured this would make for a good topic since I stress the integration of noise and randomness into machine learning pipelines, but haven't covered why it works so well.

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