When I read Andrej Karpathy’s endorsement of “context engineering” in a Twitter exchange with Shopify’s Tobi Lutke, I felt he tapped into something we all felt to some degree: tweet={"url":"https:\/\/twitter.com\/karpathy\/status\/1937902205765607626","author_name":"Andrej Karpathy","author_url":"https:\/\/twitter.com\/karpathy","html":"\u003Cblockquote class=\"twitter-tweet\" align=\"center\"\u003E\u003Cp lang=\"en\" dir=\"ltr\"\u003E+1 for

There’s a pervasive problem with semantics in artificial intelligence. It’s present at the creation – the term itself characterises the subject as a man-made simulacrum of something ‘natural’ the way we speak of artificial flavourings and artificial rubber.

How can we document software and computational analyses in such a way that others can convince themselves of their validity, and build on them for their own work? The question has been around for many years, and a number of attempts have been made to provide partial answers. This post provides a brief review and describes my own tentative answer, inviting you to play with it. Explainable AI is a hot topic today.

Thirty years after my first contact with computational (ir)reproducibility, I am happy to note that many things have improved. Reproducibility, computational and otherwise, is increasingly recognized as an important aspect of scientific quality control, and mostly considered worth striving for.

Reutilización CC BY 4.0

Can creativity and science go together?

Assembling and inferring missing semantic mappings is a timely problem in biomedical data and knowledge integration. I’ve been developing the Semantic Mapping Assembler and Reasoner (SeMRA) as a generic toolkit for this. In this blog post, I highlight its inference capabilities. SeMRA implements the chaining and inference rules described in the SSSOM specification.

Following the theme of my previous two posts, I’ve run into another typing conundrum where I want to unpack a pre-existing Callable into a class with Generic[P, T] where P is a parameter specification type (i.e. ParamsSpec) After figuring out the right way to declare a generic featuring a ParamSpec, I updated the class-resolver package to use the shiny new (and more accurate) annotations.

I’ve been working on applying strict static typing to my Python package class-resolver and ran into an interesting way of using generics in combination with parameter specification variables (i.e., ParamSpecs). Normally, if you want to type annotate a function, you use the Callable, which works like the following: from collections.abc import Callable #: the [int] represents a function that takes in a single integer, #: and returns a single

This post describes an issue I’ve had with writing correct types when using PEP-696 defaults in typing.TypeVar. I posted the exploration in a companion repository on GitHub. The motivation behind this comes from my work in biomedical data integration and the semantic web.

ChEMBL periodically curates clinical trial information into its DRUG_INDICATION table. However, there’s some weird inconsistencies in the way it references disease concepts in external vocabularies. This blog post is an exploration of that table.