Sept: Reading & Thinking

I don't have too much to write about these days as I'm spending a lot of my free time reading various books. I've been doing some reading on superconducting materials given all the attention in July and August on LK-99. Most the texts I'm reading on this topic are introductory so they don't cover the theory that well, which I would eventually like to have a good understanding of.

I've also been focusing on some computational neuroscience via the online course. Its pretty interesting how underlying model for neurons is some variation of an RC circuit. This is captured with the nobel prize winning model proposed by the Hodgkin-Huxley model, which is a gated version of a RC circuit and described by a set of nonlinear ordinary differential equations¹. The cool thing is its very easily to play around with this type of model using the SciML Julia language framework, specifically the DifferentialEquation.jl package. I'm in the process of creating a computational blog entry on it.

Finally, I've been thinking about some self-driving lab activity using a 3D printer. I would love to use a metal/ceramic FDM²/FFF³ than a polymer-based printer. It looks like there are some good options by Markforged and Desktop Metal. I don't know much about the technology used by these manufactures or the feasibility of doing materials development. What my dream would be is to tie a 3D printer like those into some filament synthesis and printed part testing apparatus. Then using concepts of self-driving labs, we can define a goal/policy for the system to work towards. For example find the pareto front⁴ for optimal tensile strength and minimal density. I've contributed to a awesome-self-driving-labs github repo that has some good resource materials to learn about this field.

Footnotes

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1. A nonlinear ordinary differential equation (ODE) is an equation involving a function and its derivatives in which the relationship between the variables is not proportional. In mathematical terms, if $F(x, y, y')$ represents the ODE, it is nonlinear if it can't be written as a linear combination of $y$ and its derivatives. These equations frequently emerge in complex systems such as biological processes. For example, the equation $\frac{dy}{dx} = y^2$ is nonlinear because of the $y^2$ term. Unlike linear ODEs, a small change in initial conditions can lead to substantial differences in outcomes, making them inherently more complex to solve and analyze. ↩

2. Stands for fused deposition modeling. Its a poor name if you ask me but I believe the name comes from the fact that STL files are used to print single printed object. ↩

3. Stands for fused filament fabrication. Almost the same as FDM with some minor differences. ↩

4. The pareto front or frontier is the set of solutions that provide a the best options between a multi-objective function. It captures the trade-offs. ↩

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Book Review: Building a Brain

I finally finished the "Self-assembling Brain" by Peter R. Hiesinger; it took me way to long. First of all, it's a pretty good book if you're interested in the competing views on how Genetics, Neuroscience, Robotics, and AI view intelligence and the human brain. The author really wants the reader to understand the vantage or viewpoints for each of these domain areas. The book is well-written and accessible to most people with a STEM background. However, at times it's hard to keep track of how these domains were developed by key individuals in the mid to end of the 20th century. For example, I really couldn't follow the author's presentation of what Roger W. Sperry was proposing for how a brain wired itself. 

In the book's second chapter, the author presents a dialogue between 4 different scientists/engineers. There is a roboticist, molecular geneticist, AI engineer, and neuroscientist. The playwright then carries on about the different perspectives about whether artificial neural networks are capable and mimic a human brain. The neuroscientist claims more needs to be drawn from biology in terms of function, i.e., how the brain does what it does. The AI engineer thinks we don't necessarily need to replicate the human brain works in order to achieve artificial general intelligence. The roboticist follows a similar thinking as the AI engineer. The interesting one is the geneticist who seems to propose that there is a significant need to understand how to grow a brain based on genetics, the other three push back.

At first, I thought this style would be a bit silly and somewhat gimmicky however it actually was one of my favorite part of the book because it provides context for someone who is on the outside of these fields. This style of discussion between the main actors continues throughout the book at actually makes it easy to follow the different approaches in the various domain areas. You get a clear image of the tension going on between different fields that focus on the human brain, conscience, and intelligence. The value of this is you see how there is no clear consensus on how the human brain does what it does and how artificial intelligence directly relates.

Overall it's an excellent book to be familiar with the different development and perspectives of how the brain may work. Here is the front cover of the book for reference and a link to where you can buy it.

Princeton Press

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