Trademarking language names is not allowed since a language is not considered a good but rather a standard, however what is being trademarked here is the software product which is the compiler of Rust. It just so happens that the language and the compiler software are called the same thing.
NeuroGEARS | Full-Time £50k-£70k+ | Open-Source Research Software Engineer, .NET Frontend Engineer and Technical Writer positions | London VISA or REMOTE (UK or EU preferred)
I am a computer scientist and software engineer by training but switched gears to do a PhD in neuroscience. I created [Bonsai](https://bonsai-rx.org/), a free and open-source visual programming language that currently is used to control thousands of Neuroscience experiments around the world. We founded NeuroGEARS after realizing the gap between the tinkering needs of basic neuroscience research and the rigidity of closed-source proprietary tools.
Our goal is to make technology that is accessible, understandable and modifiable. We want scientists to be able to take apart their instruments, understand how they work, and be able to quickly recombine and repurpose their technical toolkit to answer scientific questions at the edge of the unknown, while trusting the quality of their data.
We are looking for a talented .NET software engineer who can help us reimagine our frontend architecture to a modern and extensible cross-platform UI stack, and a technical writer or research scientist that can help us design and implement new outreach and training materials for the Bonsai community. We aim to cover a broad audience including not only scientists, but also artists and engineers with an interest in experimental lab activities, and open-source hardware and software.
We are based in London and can offer visa sponsorship. Contact details for applications are listed in each of the full positions linked below.
This is exactly where complexity hides. Simplicity of models relies on abstractions, which in the real world are invariably leaky. The complexity of making a robust NAND gate is very much observable at some level, and only goes away once you ignore the messy details. The more we look, the more this seems to hold for pretty much everything in our observable universe, from galaxies to quarks. The more you dig the more worms you find. There are thousands of sub-fields of molecular biology which try to understand how a single cell actually works, and we still are not done by a large margin. Of course we will always ignore what we can to make workable human models that we can actually reason about.
I would argue that it does matter for the brain. The large number of variations on the large number of different types of receptors means a great amount of variation in adaptability of the neural circuits to a great number of edge cases. But it also means there's a lot of possibility for maladaptation, such as with some presentations of mental and non-mental illnesses. Neural circuits can "remember" firing patterns through some of the varying adaptations, and not all circuit memories have the same function or the same effect.
The parent comment about varying transistor combinations was not quote correct in my opinion, as these variations in receptor makeups DO change how the neuron and circuits respond to stimuli.
This makes sense to me. It's like we're peering into a portion of the main logic in a function with one frozen global state and ignoring the idea that there are zillion global variables that can alter that logic.
Needless complexity has costs associated with building it and maintaining/running it, so I'd expect in the majority of cases it would be selected against strongly enough to disappear over time. Which implies the majority of complex systems are complex for a reason, because if a cheaper less complicated equivalent was equally good then that would win out.
Biological matter can't exactly opt out of being made of jiggly proteins immersed in water. And nerves can't opt out of the million things a cell needs to do to maintain itself. That's the kind of thing that adds immense complexity whether it's useful or not.
In my opinion TDD really only exploded in modern SW industry due to the shift to dynamic languages like Javascript and Python where you can’t even rely on the compiler to help you with types and typos.
Unit tests still help of course with static typed languages but you really don’t need to be afraid that refactoring can kill your entire code base as you have formal proof that types and names agree as part of your IDE and build process by default.
I've been developing a visual reactive programming language, Bonsai (https://bonsai-rx.org/) for the past 8 years and it's proven successful in neuroscience research. It is an algebra for reactive streams and addresses some of the issues raised in this thread about GPVPLs:
a) it's amenable to version-control;
b) you can type/edit a program with the keyboard without the need for drag-drop;
c) the auto-layout approach avoids readability issues with too much "artistic freedom" for freestyle diagramming languages like LabView;
d) because its mostly a pure algebraic language w/ type inference there is little complicated syntax to juggle;
e) the IDE is actually faster even though Bonsai is compiled because there is no text parsing involved, so you are editing the abstract syntax tree directly
It definitely has its fair share of scalability problems, but most of them seem to be issues with the IDE, rather than the approach to the language itself. I've never probed the programming community about it, so would be curious to hear any feedback about how bad it is.
