GloamingNiblets

This is exactly what I eat every day and I am phenomenally happy and successful.

It might feel good but spiking your blood sugar isn't healthy for you, and the crashes afterwards will get worse over the years. Improving metabolic health might be a better long term solution; have you explored how endurance or high intensity exercise affects your focus?

The nature of the content is an important variable to control for in future work, but the primary negative impact appears to be via the devastating effect on human attention.

From the paper: "repeated exposure to highly stimulating, fast-paced content may contribute to habituation, in which users become desensitized to slower, more effortful cognitive tasks such as reading, problem solving, or deep learning. This process may gradually reduce cognitive endurance and weaken the brain’s ability to sustain attention on a single task... potentially reinforcing impulsive engagement patterns and encouraging habitual seeking of instant gratification".

My experience is from another prominent AV company; I do not have Waymo insider knowledge.

The better part of a decade as a SWE at another AV company. In practice the latency is a not a concern, I was just sharing some trivia.

I don't have any specific knowledge about Waymo's stack, but I can confidently say Waymo's reaction time is likely poorer than an attentive human. By the time sensor data makes it through the perception stack, prediction/planning stack, and back to the controls stack, you're likely looking at >500ms. Waymos have the advantage of consistency though (they never text and drive).

Isn't this ruling out outlier observations given our model, rather than evaluating our model given outlier observations?

The von Neumann architecture is not ideal for all use cases; ML training and inference is hugely memory bound and a ton of energy is spent moving network weights around for just a few OPs. Our own squishy neural networks can be viewed as a form of in-memory computing: synapses both store network properties and execute the computation (there's no need to read out synapse weights for calculation elsewhere).

It's still very niche but could offer enormous power savings for ML inference.

Given the elegance of the wave equation, I like to imagine there are solutions with some sort of symmetry and structure. We are unfortunately missing the tools and knowledge to find these solutions today!

Thank you for the excellent writeup of some extremely interesting work! Do you have any opinions on whether binary networks and/or differentiable circuits will play a large role in the future of AI? I've long had this hunch that we'll look back on current dense vector representations as an inferior way of encoding information.

Out of curiosity, what degree and level is this class? Super cool for a final project