An interesting and (to this non-statistician) reasonable-looking paper on the effects of generative AI on exam scores (as a proxy for learning). As The Economist pithily puts it:

Homework scores once predicted exam performance; now those who score highest are, perversely, more likely to do worse in exams.

The paper’s own abstract reports:

AI adoption raises homework scores by 18% and reduces completion time by 30%, but lowers monthly exam scores by 20% within six months. High-stakes entrance-exam scores fall by 18 and 24%, with the full penalty emerging only after about two years.

But all is not lost:

Those who used AI but spent as long on assignments as non-users paid little penalty.

Perhaps the crucial ingredient for exam success is simply putting in the effort to become familiar with the material.

These figures from The Economist’s article reproduce figures A11 and A5 from the paper, respectively, and capture the core results well:

A concrete and formal definition of memory safety from (inter alia) Benjamin C. Pierce.

We give a rigorous characterization of what it means for a programming language to be memory safe, capturing the intuition that memory safety supports local reasoning about state.

Paper in the proceedings of the Principles of Security and Trust conference 2018.

Development of incremental improvements to a pure-functional quicksort implementation, eliminating multiple passes over the input and unnecessary append operations by introducing accumulators.

The OOPSLA 1997 paper that introduced Squeak, a Smalltalk implementation “written in itself”:

Squeak is an open, highly-portable Smalltalk implementation whose virtual machine is written entirely in Smalltalk, making it easy to debug, analyze, and change. To achieve practical performance, a translator produces an equivalent C program whose performance is comparable to commercial Smalltalks.

The paper discusses the bootstrapping process, the VM’s object format and garbage collector, the Smalltalk-subset-to-C translation process, the implementations’ approach to the BitBlt graphics primitive, performance, and the fledgling Squeak community. The latter is still going strong thirty-some years later.

A collection of 100 impossibility results and some discussion of commonalities and differences among them:

This talk is about impossibility results in the area of distributed computing. In this category, I include not just results that say that a particular task cannot be accomplished, but also lower bound results, which say that a task cannot be accomplished within a certain bound on cost.

You may ask yourself why impossibility proofs are important: my answer is that if you don’t know what’s impossible, you run the risk of wasting a lot of time trying to achieve the unachievable!