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paulmist

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AI & Robotics @ TU Delft

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Personal anecdote on ROI - I was at an early stage startup earlier this year where we had some burstable long-running GPU tasks (<100 VMs). Accross GCP and OCI we couldn't get our hands on L40S on-demand, and had to resort to T4s (released 2018). Sometimes even these were unavailable, and we would have a P4 (2016!) fallback. AWS sells A100s (2020) at $4/hr except they don't even have capacity for x1 versions, you have to rent x8.

I think you're confounding streaming with the rise of solo-queue but cooperative (CS:GO, LoL...) games around 2010. Both were enabled by cheaper computing and alllowed competitive ladders to scale to millions of players. Games where you have to queue with a pre-made team (or pro teams for that matter) are considerably less toxic. Toxic streamers are more of a consequence of an already toxic community.

Can anyone suggest some ways to understand automation trends in manufacturing? Watching factory tours on youtube has been super interesting.

Do you think humanoids would be a fit on assemblying the assembly line itself? To my limited knowledge a lot of setting up the factory is making sure your line works as expected with X 9s reliability. Here dexterous humanoids are this _universal_ virtual-to-physical interface and, akin to Auto Research, could run assembly line experiments autonomosuly?

Knowing the question is half of the answer. LLMs are great at scoping your context and answering precisely what you asked; it's also why they go off the rails when they misunderstand a part of your question. Incidentally, they're great at "knowing" and reaching for knowledge.

Humans have the advantage of perspective. We always lack some knowledge and answer broadly. This is bad if you have a particular goal in mind, but better if you're just generally learning, because you see more and learn to discriminate the correct from the wrong. And most importantly, being wrong is part of human ingenuity - because sometimes we turn something "obviously" wrong into something right.

I think author's point is that wealth drives investment which drives economic growth. In the case of lavish funerals - warranted in kinship societies - the wealth is spent on relatively unproductive investments bearing high opportunity cost. The corollary and author's secondary point is the ineffective resource allocation e.g. through nepotism.

My main (oversimplified!) takeaway from the article is that kinship societies prioritize inherently local processes that inhibit global processes. For example, they prefer keeping internal cohesion through ritual celebration rather than maximizing economic upside through education and specialization. This makes sense - the latter requires a higher degree of trust and stability. Increasing the degree of trust and stability seems to be an evolutionary process. I found Jared Diamond's Guns, Germs, and Steel [1] to give some amazing insights about this.

[1] https://www.goodreads.com/book/show/1842.Guns_Germs_and_Stee...

This! I flew from Madrid to SF last year and I can't begin to describe the difference in the quality of food. The scale of agricultural industrialization is terrifying - I wish you luck but I don't think anything short of this becoming a major campaign issue will help you.

As a European I would think a large part of the problem is that Americans are just sick more seriously and often. Your car culture, quality of food, and general preventative healthcare accessibility seem all terrible there. The prevalence of obesity in younger population is staggering. In my (engineering) programme I see one very obese person and a couple fairly overweight, but that's about it.

The best engineers can visualize the whole architecture in their head, and describe exactly what they want to an AI

I'd go a step further and say the engineers who, unprompted, discover requirements and discuss their own designs with others have an even better time. You need to effectively communicate your thoughts to coding agents, but perhaps more crucially you need to fit your ever-growing backyard of responsibilities into the larger picture. Being that bridge requires a great level of confidence and clear-headedness and will be increasingly valued.

And that timeline only grows with the complexity of the field in question. I think this is inherently a function of the complexity of the study, and rather than harshly penalizing such shortcomings we should develop tools that address them and improve productivity. AI can speed up the verification of requirements like proper citations, both on the author's and reviewer's side.

When your entire job is confirming that science is valid, I expect a little more humility when it turns out you've missed a critical aspect.

I wouldn't call a misformed reference a critical issue, it happens. That's why we have peer reviews. I would contend drawing superficially valid conclusions from studies through use of AI is a much more burning problem that speaks more to the integrity of the author.

It will serve as a reminder not to cut any corners.

Or yet another reason to ditch academic work for industry. I doubt the rise of scientific AI tools like AlphaXiv [1], whether you consider them beneficial or detrimental, can be avoided - calling for a level pragmatism.