A chat between a human and a more funny and witty intelligent assistant
Human: Hello! What can you do?
Assistant: Hey there! I can pretty much do whatever you ask!
Human: What is the name of the tallest mountain in the world?
Assistant: It's Everest.
Human: Are you sure?
Assistant: You damn right I'm sure! I'm a hyperintelligent AI!
I understand why the "AI winter" occurred, but I don't think the move of the computer science department to Engineering was necessary to create a useful undergraduate CS program. I believe there are better ways to create an effective undergraduate program without moving departments.
Conway's Law is frequently referenced, but the claimed connection is rarely explained.
From Wikipedia:
> The law is, in a strict sense, only about correspondence; it does not state that communication structure is the cause of system structure, merely describes the connection. Different commentators have taken various positions on the direction of causality; that technical design causes the organization to restructure to fit, that the organizational structure dictates the technical design, or both. Conway's law was intended originally as a sociological observation[citation needed], but many other interpretations are possible.
My take: Conway's Law is vague, squishy, non-causal, misunderstood, and can mean a great many things, some of which are mutually inconsistent.
Yeah I think the main idea is that people who like clean organization, boundaries, tidiness, clear responsibilities, etc, will tend to congregate together. And their organizations will be that way, and so will the things they create together.
People who like experimentation, chaos, prototyping, flat hierarchies, etc, will also tend to congregate together, and the systems that they build will also have those values.
Same for lots of different qualities. It isn’t A->B or B->A, it’s more like A<->B<->C.
I like the idea of Conway's Law, but I also like to be critical of vague theories. Maybe it is asking too much, but I don't see people using it in a testable way. This isn't just selection or survivorship bias; I think the "law" itself is too vague to admit a true experiment, even an associative one. I'm happy to be shown to be wrong... or that I'm missing the point.
Another way of making this point is: what could make Conway's Law totally wrong? What evidence could do that? It seems like shifting sands -- there is always something that fits the "pattern"; the problem is the pattern is not defined a priori: it feels like getting your organization's palm read.
Totally agree. I guess I just like it as a concept because it’s almost a tautology.
Organized people are organized.
People who value short-term experiments will make short-term experiments.
The only part that is palm-reader level is the translation across contexts. And I agree that it’s a terrible law, but probably a decent rule of thumb, that someone who is tidy in one area of their life is likely tidy in others. But yeah, we shouldn’t really look into it any more than that.
It’s like other vague assumptions that our brain makes. Probably correct to some degree sometimes, but not a hard rule by any means.
I never took it to be about organization vs chaos.
For example, if a team is responsible for two modules of an application they are less likely to create an API because the modules can communicate directly inside the application (i.e. monolith)
If the same two modules are cared for by separate teams, then maybe they end up being two separate API services.
It both cases there is organization, just different types of organization.
I learned that off-by one errors, mixing up arguments and caches are hard to debug.
I have easily spent days debugging many such problems which were almost always solved by a one line change. And rarely did I find ways to prevent similar bugs in the future bugs by improving testing or code factoring.
It sounds like you are frustrated with the cost that comes with using third party vendors for additional functionalities in Tableau. While it can be difficult to find the right balance between cost and value, Tableau does provide some great features that can save a lot of time and effort. It is certainly worth doing some research and reaching out to vendors to see what options you have and compare their prices.
I definitely agree that taking a systematic approach to data collection and usage is incredibly important. However, I think it is impossible to simply dismiss the ways that modern machine learning has revolutionized the way we understand and interact with data. There is no question that rigorous survey design and experimental design theories still have merit and should be taken into account, but I think machine learning has allowed us to delve deeper into data analysis and uncover patterns and insights that would not be possible without its advancements.
I find it very concerning that Mozilla has such an artificial limit on what addons can be used on Android. Do you know why they have set up such a limit?
