uriegas

The arts have always been influenced by the philosophy of their time.

However, there is a divide between vernacular and classical architecture. Vernacular is utilitarian and it is what most people build (you build your house to serve your needs) while classical is not only utilitarian but also communicates a message (authority in gov. buildings).

Most people find vernacular architecture pretty. For example, Florence, San Miguel de Allende, New Orleans, etc. All of them have different architecture caused by the locally available materials and the weather of the place. Contrary to popular belief most of their 'beauty' doesn't come from an old appreciation of the arts people, instead these buildings only met the needs with what was available and worked best.

For example, in the north of Mexico houses were built with thick limestone ('sillar de caliche'). The thickness helped as insulation (hot weather). The limestone was plastered with 'cal' in a light color with an 'aborregado' finish that helped reflect the sunlight thus keeping the house cooler. The houses were tall because hot air rises, windows looked almost like doors because people opened them during the night to let the air in. The buildings are beautiful but they are extremely utilitarian. Nowadays most houses in Mexico are built with cement and rebar, which is ugly.

The architect Leon Krier made the argument that all of this was a 20th century phenomenon caused by industrialization and fossil fuel dependency, which has not only affected buildings but how cities get built (urban planning). He believed this would end once fossil fuels reservoirs decreased.

I've had the same view since coding agents appeared. LLMs are good for summarizing, thus I tend to use them to understand code. However, the more I understand a code base the more I tend to be more picky with the agent. While the less I know about the code base, the more open I am to let it do its thing.

I've realized that the agent doesn't write 'good' code, instead my vague understanding of what I expect makes me think it does. When I look at the AI code, I see that there are more nuances and decisions that needed to be taken before writing the code. One would say that a spec would suffice, but then I remember TLA+, developed by Leslie Lamport to help developers think critically about their programs/algorithms. Sitting down and formally writing what I want to do helps me better understand the problem and come up with a real -not vaguely defined- solution.

I would have not thought that a work detector might be so useful

To be fair, even reading 'good' books won't make you smart. I think the key is to be critical, which should be thought at a young age. Ikram Antaki dedicated most of her last years in teaching this in Mexico.

Anecdote: When I started studying economics I really agreed with a lot of what I read from economists like David Ricardo, Marx, Smith, etc. Then, I studied what other economist had to say and I could see how they disagreed with the former. This made me realize that I agreed with those people because their arguments 'made sense' to me, but that doesn't mean that what they said is completely true. This is something that has stayed with me, I always wonder how can something be wrong.

Long ago we abstracted programming into a logical language which allow us to think at a higher level. IMO LLMs are another abstraction but a bad one as it is stochastic and we can't guarantee output quality (e.g. security, performance, etc). The dream has always been to tell the computer what to do in a simple language, and the challenge has always been finding out that we didn't even know what we wanted the computer to do. LLMs might help in the first one but not in the latter. At the end, human intelligence cannot be outsourced.

The idea of offshoring computing is good. However, the cloud developed as a centralized computing platform instead of a distributed one. This has created power dynamics that harm customers. The same happened with social media, and has happened to other industries. I think it would be better for customers if there were many small cloud providers and they could easily switch between them. But even migrating from one cloud provider to another is a huge endeavor these days.

I think AI labs are realizing that they no longer have any competitive advantage other than being the incumbents. Plus hardware improvements might render their models irrelevant for most tasks.

I don't really think it is turning into a guesswork. A lot of people wrote bad code before by pasting things from the internet they didn't understand. I think some people are using LLMs the same way, but it does not mean that programming has changed. But I do think that code quality is being neglected nowadays.

What does an AI degree provide? Is it really different than majoring in CS? AI has been pretty standard in CS undergrad programs AFAIK. At least in high school it seems that AI curricula is just learning to use LLMs and understand a few concepts. That does bring value at a society level but I am not sure if this makes sense as an undergrad degree (if that is what they are only teaching).

I do agree with his identification of the problem: sometimes agents fail because of the tools around it and not because of the model's reasoning. However, for the failing tests I think he is not making the distinction between a failed test due to a harness failure or due to a reasoning failure. It would be nice if someone analyzed that from the data set.

I don't think so, have you read 'The Bonobo and the atheist'? Humans are not the only ones using tools and in reality there isn't much difference between humans and animals. The conclusion I get from the book is that the only difference is religion. Although, I have a feeling that humans do have a more developed intellect (problem solving) but this was not explored in the book.

Yeah, there is also the work of primatologists which challenges some of our beliefs of what we think is human sciences (like politics). See Frans De Waal.

Yet, I believe there hasn't been much progress as compared with STEM. But it is just a belief at the end of the day. There might be some study about this out there.

I agree. Most of the time people think STEM is harder but it is not. Yes, it is harder to understand some concepts, but in social sciences we don't even know what the correct concepts are. There hasn't been so much progress in social sciences in the last centuries as there was for STEM.

I believe iMessage is only used in the USA. In Latin America almost everyone uses WhatsApp.