CharonY

Absolutely. Right now, I see them as an amplifier. That goes for both,competence and incompetence. In most cases, it is not equivalent to an expert, as the latter will more likely tell you why you are wrong. The carefully curated cases outperforming MDs tend to be edge cases where certain specialized abilities (e.g. pattern recognition, case matching etc.) outperform the average MD in controlled test. That being said, I also vaguely recall that some cases, radiologist underperform when they use AI. This could be down to who and how the tool is being used. I believe worst and best-performing radiologists, benefitted list from AI, suggesting that a certain level of competency is required to use it, but also that the boost has a ceiling. Typically there are some discussions on the economic system that in medieval times were based on agricultural/feudalist system. I would think that at least peripherally the importance of the Silk Road in shaping the ancient/medieval times as an early version of global trade. I also recall some discussion on the economics of pilgrimages and crusades, including building areas of worship and hospitality around often fake relics (i.e. early form of tourism). I guess history teachers do have some leeway to focus on what they want, but the one I had back in the day loved to talk more about the social science of history rather than wars.

Actually that is the part where I see the most convincing evidence for a good use- if they are properly curated and deployed in a specific setting (i.e. not the general chatbot for the masses). The reason is that medical knowledge is a mostly contained system, where MDs basically use established frameworks to make diagnoses. For that, if hallucinations can be kept in check, they are frightening good and outperform MDs in multiple contexts. That might be, but in research the big hurdle is to convince folks to give you money to test your ideas.

Well, I got a bigger mug. That is what I meant with "processing" i.e. generating ideas based on existing knowledge, but in my mind that process is finite as new physical discoveries are needed, in my mind. So that is the part I don't understand. Why provide them with power for them to generate the money to pay humans to do things, including discoveries. Why don't we pay folks right now to work on ideas that they are not getting realized because they don't get funding? I.e. doesn't AI seem to be an unnecessary middleman for that process?

So this is something that I am very curious about. On the software engineering side, this is very scary as it suggests to be capable to fully replace humans. In the broader discussion this is also shown as evidence that it is inevitable that it will surpass humanity's capabilities. What is your take on it? I may be entirely wrong, but sounds to me that the assumption is that software capabilities are virtually endless. I do not have the expertise to dispute that, but intuitively I would think that there is some limit. Even if it combines all the existing knowledge humanity has right now, and by being able to process it in a way no human can would discover things that humans either can't or would need to do it collectively and slowly, it does not follow for me that this expansion is limitless. At some point all the knowledge that can be generated based on existing one would hit some sort of boundary. At minimum it would require hardware (or people) to do additional discoveries to push boundaries further outward, and really that is where we are right now in my field, anyway. We do not have a sparsity of ideas or hypotheses, we lack manpower and funding to explore them (and ironically, the funding is tighter as quite a bit gets diverted to AI related fields). Or at least, that is my perspective, but I am curious how it looks like from other angles. A phrase from Gibson comes to mind. "The future is here but not evenly distributed." I think that would fall under cleaning up the mess once it is out? I am wondering a bit whether the paste is really all out, whether we really are thinking properly about cleaning up, or whether we are still at the process of squeezing really hard.

I get that. But perhaps because of the way I am trained to think about systems, I am always a bit baffled and disappointed that there is a kind of fatalism associated with that. I get what you are saying, it is there and we need to deal with it. But among decision-makers what I see is more akin to, the toothpaste is out, so there is nothing we can do. And then they are surprised that they keep accumulating toothpaste and that it doesn't in fact clean itself. I guess I am seeing a lot of parallels to other, much slower moving issues, like say global warming, where issues were not only predictable, but actually accurately predicted, a menu of actions were laid out, and then mostly ignored until the issue got so bad that folks then resigned to it, with no real game plan to address it meaningfully. Why not create a better tube? What about effective clean-up system. Or perhaps we can even develop improved metaphors? Increasingly I feel that we are using our brains mostly to please our egos and/or get rich, rather than solving real-life problems. Understood. I have no doubt that your industry is changing. From the outside, it reads to me like the industrial revolution on steroids. I am not entirely sure regarding my position, but I always used to be a tech enthusiast, both in private as in professional life, although technological changes in science undergo slightly different rhythms (typically more hardware than software, with quite a few notable exceptions). But with the cracks in systems and society I am seeing (and again, quite a bit I fear is the old man syndrome) I am increasingly drawn to the human side of things and getting increasingly skeptical regarding the impact of tech in our lives. Not in a Luddite sense of way, but I think one of dimming optimism which slowly turns into pessimism. Or as my wife put it, it feels like we are not aiming for Star Trek (next generation that is) but instead for the wost black mirror episodes. The way the system is moving, it feels that humanity plays a big role in diminishing its role. Which weirdly is also echoed by the US government, which makes me question my sanity before I have my sixth coffee.

There also seems to be a gold rush situation where communities are trying to draw in data centers by providing tax benefits and other things. It does seem a bit short-sighted to me, as I suspect it is not clear how much money they are going to ultimately bring to those communities (beyond the investment in building the infrastructure).

I think it is a matter of perspective. I have no doubt that the impact on coding is seismic. But in my corner of the world, it has been (so far) unable to accelerate the type of science that matters to me, but, in balance, has starting to create a host of kids who are increasingly useless. The folks I see and interact with, are those who are on the hype end. I.e. thinking that it can already replace critical thinking. It might come to that, but not yet. And this is where I see the hype. If I was a coder, I probably would already be switching jobs or try to be the guy who they keep until retirement to keep the agent army running. I guess, the point I am trying to make is that in certain areas, AI are cool (e.g. able to replace administrative assistants), and very impactful when things are mostly digital. In other areas, such as higher education, they are clearly disruptive to traditional learning, but so far have little positive impact. Those who do well might be doing better, for the rest the bottom is falling out. It is hard to be overly enthused in that regard. Research as a whole will have quite a bit of an impact, though most notably it is n computer science at some point and social science, where literature work is somewhat dominating. It is getting more reliable in things like cleaning data, which is important in many areas, but not doesn't free up the time you are wasting trying to teach college kids how to operate a book. I have no doubt that things will change, but at least for some of us it doesn't live up to what we have deal with right now.

So the argument against solar was from what I understand the ability to scale production up and down as needed. Solar was considered to be too tricky. But I have not seen really a quantitative argument. My guess is that there are more considerations and up-front costs that they would rather avoid, but this is only my gut feeling. I am sure there are some studies/report on cost/benefit somewehre. The closed loop argument is also a bit tricky, and I only skimmed some of the issues, so am not really knowledgeable on that front, either. But from what I understand, closed-loop system cost more energy to run, so depending on how the power gets in, the ecological costs are moved further upstream. Otoh there are ideas of also using the heat e.g., for greenhouses and other buildings. I am pretty sure that folks have or are currently doing heavy calculations on the overall burden (carbon emission, water usage etc.). Ultimately, someone has to pay the price.

Not sure whether I already brought it up but college kids are renting smart glasses to cheat in exams. Reports are mostly from China and Japan, but I have heard of cases elsewhere anecdotally.

They are not only popping up in the US. Canada and the EU is working hard on the idea of AI sovereignty (which would be the charitable way to to describe it- it is to me not clear what kind of agreements are in place to actually enforce it properly). Most that I am aware of will rely on LNG due to perceived flexibility and faster implementation. Some have proposed closed-loop cooling systems, but I have not seen details how exactly that works. The one in Utah will have a much more massive impact. However, given the rate they are popping, it is hard to overlook that the mid-sized ones will likely also have considerable issues. That being said, a positive element is that some of the data centers will connect their large capacity to the grid, and the promise here is that they can easily scale use up and down and either consume overcapacity or feed into the grid when needed, thus providing more stability.

The difference is that both are specific tools, whereas the AI is presented as an universal tool able to replace critical thinking capabilities. Ironically, those that have it, probably won't use it that way. Folks that lack it though... Well there are now lawsuits and it will be interested to see if that is going to change how the companies operate their models. It is also in their interest not to put proper safeguards on the system. What has been argued is that it would cripple their capabilities. Adding on top of that that the company spokespersons and leaders repeatedly mentioned how it will eventually be able to solve all our problems, it goes a little bit beyond a a viral moment of a neat tool, IMO. The hype at least feels endless, with the stated goal being AGI. Mechanistically it feels more that they want as much customer use data as possible to generate something that will become profitable rather than merely useful. And the move fast break things attitude, well, it breaks people on the way.

The scaling argument makes perfect sense, though I suspect there will be some nuance regarding what activities require the support of agriculture and which not. I am guessing that in most cases it wouldn't be a yes/no answer, but rather a matter of scale. We do have evidence of very early crafting and arts, but more complex arts really could only develop once food wasn't the key limiting factor of survival, I would guess. But regarding wars, there are (oral) records of First Nations in North America. While some have developed agriculture, others were largely dependent on hunting. I would suspect that the scopes of such conflicts were a bit more limited, but could be interesting to follow up. That being said, I suspect that it really depends on what we consider a war. If that is any large scale aggression between communities, that has likely happened throughout our history (well and our ancestors, considering that our chimpanzee cousins are doing that, too). Military specialization (e.g. making shields and building weapons specifically against humans) was also very prominent among First Nations, including hunter communities, as they developed a highly sophisticated system to sustain themselves rather successfully (which is one of the explanations why some First Nations didn't really develop large-scale agriculture).

There is one more thought on this, now that I think about it. I have been talking with researchers, who have collaborations with China. What I found interesting is that that it seems that in China, AI is intended to be used as a tool and they put a lot of money into operationalize AI, e.g. for robotics or to solve very specific questions. Even in the educational sector their implementation of AI seemed far more supporting learning (e.g. dedicated tools to reinforce training elements, rather than giving answers). Meanwhile, in the West AI is often framed as a thinking tool with the ultimate goal to develop it out into AGI. I found the perspective quite striking, and to me the Chinese approach seemed more grounded. Or at least I have an easier time to wrap my head around it without having layers of hype on top. I am curious, how do you see it? Edit: I should add that I am aware that the Chinese path could, at least in part be the result of the government being afraid that it could be a tool being used against then, but it still (to my mind) represents itself as more rational model, regardless of the underlying motivation.

Ah I read "supporting" in the text as a form prerequisite. My bad.

Intuitively I would have thought that language would predate agriculture. There are societies who largely live from hunting and have developed fairly complex societies. Though there are limits in community size and specialization, and associated forms of technology development, of course.