Wednesday, June 10, 2026

Functional Analysis Primer

An introduction to functional analysis for science and engineering"

This is a tutorial introduction to the functional analysis mathematics needed in many physical problems, such as in waves in continuous media. Functional analysis takes us beyond finite matrices, allowing us to work with infinite sets of continuous functions. It resolves important issues, such as whether, why and how we can practically reduce such problems to finite matrix approximations. It is, however, difficult to find a readable introduction that is efficient and comprehensible for scientists and engineers. Here, I have selected only the topics necessary for the most important results, but the argument is mathematically complete and self-contained. 

The article starts from sets and sequences of real numbers. It then develops spaces of vectors or functions, introducing the concepts of norms and metrics that allow us to consider how these can converge. Adding the inner product, it introduces Hilbert spaces, and the key forms of operators that map within or between such spaces. This leads to the concept of compact operators, which allows us to resolve many difficulties of working with infinite sets of vectors or functions. We then introduce Hilbert-Schmidt operators, which are compact operators encountered extensively in physical problems, such as those involving waves. 

Finally, it introduces the eigenfunctions for major classes of operators, and their powerful properties, and ends with singular-value decomposition of operators. This article is written in a style that is complementary to that of standard mathematical treatments; by relegating longer proofs to a separate section, 

I have attempted to retain a clear narrative flow and motivation in developing the mathematical structure. Hopefully, the result is useful to a broader readership who need to understand this mathematics, especially in physical science and engineering.

Monday, June 08, 2026

AI Bootstrapping

"SoftBank's Masayoshi Son says AI is already designing OpenAI's next model"

New Octopus

"A Tiny Bright-Blue Octopus Found in the Galápagos Is Completely New to Science". (Via H.R.)

Friday, June 05, 2026

Your Own AI Stock Trader

"Robinhood launched agentic trading and an agentic credit card today that will allow AI agents to trade equities and make credit card purchases on customers' behalf."

Thursday, June 04, 2026

Wednesday, June 03, 2026

Using AI To Sell Your House

NYT: "I Tried to Sell My House With a Chatbot

In the end, using A.I. netted me more than $90,000. That includes the premium over the asking price, plus the roughly $36,000 in fees I didn’t pay. I'm not sure how replicable my experience would be for anyone less versed than me in technology.

Tuesday, June 02, 2026

Less Drag

"A Fundamental Principle of Aeronautical Engineering Has Been Overturned"
Aiko Yakino, associate professor at Tohoku University's Institute of Fluid Science, and her research group were the first in the world to demonstrate that aerodynamic drag can be reduced by up to 43.6 percent simply by applying distributed micro-roughness (DMR), a surface roughness so fine and irregular that it cannot be distinguished by the naked eye. 
This technology is fundamentally different from the rivulet (“shark skin”) process, which is a known air-drag-reduction technology. The rivulet process mimics the fine longitudinal grooves in shark skin, and by carving grooves approximately 0.1 millimeter wide along the direction of airflow, it aligns the vortices that occur near the wall surface of turbulent airflow areas. DMR, on the other hand, delays the switch from laminar to turbulent flow by means of random and minute irregularities. The flow zones it affects and the mechanisms it employs are based on completely different concepts.

Our Latest Overlords

"Scientists Build a Living AI Device Using Real Brain Cells". (Via H.R.)

Monday, June 01, 2026

Do LLMs Need Sleep?

Maybe you don't want your LLMs to work 24/7: "Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference".  Abstract: 

Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. To handle this, we study a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache. During sleep, the model performs offline recurrent passes over the accumulated context and updates the fast weights in its state-space model (SSM) blocks through a learned local rule. During inference, this shifts extra computation to sleep while preserving the latency of wake-time prediction. We test our method on controlled synthetic tasks, including cellular automata and multi-hop graph retrieval, as well as a realistic math reasoning task, on which a regular transformer as well as SSM-attention hybrid models fail. We then show that increasing sleep duration for our models improves performance, with the largest gains on examples that require deeper reasoning. 

(Via S.S.)