More push back against AI. There is a realization that AI has severe limits in what it can do and that the field has a lot of hype. The idea that AI is going to replace large sections of the STEM workforce is simply not true. AI could replace bureaucratic or repetitive jobs but not jobs that require actual thinking. I think it could be a tool to aid in thinking and creative process but the big hope that it will "do" science, endangering, create new products on its own is not going to happen and in fact it could hinder or reduce the quality of science and engineering in some cases. A lot of LANL managers or ex managers have been saying the most naive things about AI and just unaware of where the field is actually heading.
https://medium.com/quantum-information-review/ai-has-a-critical-flaw-and-its-unfixable-06d6a5c294d4
AI Has a Critical Flaw — And it’s Unfixable.
"AI isn't intelligent in the way we think it is. It's a probability machine. It doesn't think. It predicts. It doesn't reason. It associates patterns. It doesn't create. It remixes. Large Language Models (LLMs) don't understand meaning -- they predict the next word in a sentence based on training data."
"The widespread excitement around generative AI, particularly large language models (LLMs) like ChatGPT, Gemini, Grok, and DeepSeek, is built on a fundamental misunderstanding. While these systems impress users with articulate responses and seemingly reasoned arguments, the truth is that what appears to be 'reasoning' is nothing more than a sophisticated form of mimicry.
These models aren't searching for truth through facts and logical arguments--they're predicting text based on patterns in the vast datasets they're 'trained' on. That's not intelligence--and it isn't reasoning. And if their 'training' data is itself biased, then we've got real problems.
I'm sure it will surprise eager AI users to learn that the architecture at the core of LLMs is fuzzy--and incompatible with structured logic or causality. The thinking isn't real, it's simulated, and is not even sequential. What people mistake for understanding is actually statistical association."
6 comments:
Amen. I have been watching AI making mistakes and what is said here fits. Note the disclaimer at the end of paragraphs constructed by AI in Google.
As more scientists start to use AI they are seeing major limitations. I am also seeing more discussions from faculty and on science blog about the negative effects of AI. This ranges from inexperienced or young scientists trusting it too much or leaning bad habits. AI incorporating bad information repeating it. There lots of bad science paper and bad results even in high level journals and scientists figure out what is what over time or who to trust. AI cannot do this.
There are also more and more signals that AI in general is a bubble.
https://www.reuters.com/markets/europe/is-todays-ai-boom-bigger-than-dotcom-bubble-2025-07-22/
ORLANDO, Florida, July 22 (Reuters) - Wall Street's concentration in the red-hot tech sector is, by some measures, greater than it has ever been, eclipsing levels hit during the 1990s dotcom bubble. But does this mean history is bound to repeat itself?
The growing concentration in U.S. equities instantly brings to mind the internet and communications frenzy of the late 1990s. The tech-heavy Nasdaq peaked in March 2000 before cratering 65% over the following 12 months. And it didn't revisit its previous high for 14 years.
6:33 -- that is an interesting statistic however not a diversified portfolio across even the US markets. There is a dubious claim associated with the Great Depression as well, that things took 25 years to recover as you know. There is an argument though to construct a lesser period of 7 years or so:
https://www.mymoneyblog.com/25-years-1929-stock-market-crash-myth.html
Beyond that, a market crash can create many bargains for an astute investor, focused on value or distressed assets. Those who are astute may also not overpay during periods of exuberance, and will retain cash or other liquidity as Warren Buffet is doing now as a portion of their assets.
I can recall in the late 1990's also, the gains kept going for many years during which time there were many claims a crash was imminent, a similar situation to now. The 14 year reference relates I think to someone who bought in at the peak and there was a huge run up prior to that.
There is some discussion on reddit on AI
I’m in an Ivy League AI research program, and our lab—like many others—is 70–80% international students and postdocs. These people are publishing state-of-the-art work, often on prestigious fellowships. But with tightening visa policies, general anti-immigrant sentiment, and increasing uncertainty, many are talking about leaving (or not coming at all, Fall apps are down by a lot, admissions office hasnt disclosed data but the inboxes are vacant)
At the same time, the U.S. is pouring billions into AI, robotics, chips, and biotech. Which is great! But who’s going to staff those projects if the international talent pipeline dries up?" The American Worker!"-I hear you say. But it takes years to train a top-tier researcher, and the U.S. education system—especially public STEM—hasn’t been receiving enough support(funding cuts and all that)
I'm struggling to see the long-term strategy here. Is there one? Or is this just policy contradiction from different arms of the government?
7:07 -- The US already attracts top-tier people from all over the world many of whom are already here and have gained residency or citizenship, that is we already have the most ambitious and educated people.
7:07 -- Also I asked one of those chatbots to explain it in more detail, and it gave the following. This is why while there is a need for "top-tier researchers" and so forth, you may personally experience an adversarial and toxic atmosphere:
In a post-labor economy, where automation and artificial intelligence have rendered most human labor obsolete, elite overproduction would manifest as a crisis of purpose and status among a highly educated and credentialed populace. With traditional avenues to elite status through professional careers diminished, a surplus of individuals with the intellectual capacity and ambition for leadership roles would find themselves without meaningful positions to fill. This surplus of aspiring elites, often unburdened by the necessity of labor but still driven by the desire for social standing and influence, would likely lead to intense competition for the few remaining positions of power and prestige in areas like governance, cultural production, and strategic oversight of automated systems. The resulting friction and frustration among these "elite-wannabes" could fuel social and political instability, as they challenge the established order and vie for relevance in a world where their advanced skills have been largely decoupled from economic necessity.
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