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There are (or were) tax time penalties for failing to have healthcare coverage. Possible USA laws have changed recently.
If benchmarks across the board keep trending up and you still don't notice a difference, that's not evidence the model stopped improving. More likely your tasks aren't hard enough to expose the gains, or the model has passed the point where you're able to judge it.
You can only tell a good answer from a great one up to your own ceiling. Once the model clears that, both look the same to you, and the extra capability is real whether or not you can see it.
Regarding that last option: speculation helps max concurrency when it replaces many memory-expensive serial decode rounds with fewer verifier rounds, and the proposer is cheap enough. It hurts when you are already compute-saturated or the acceptance rate is too low. Good idea to benchmark a workload with and without speculative decoding.
A congregation trained to say “amen” on cue.
That’s demoralizing if you’re doing party-building. But the flip side is: it means citizens do have leverage, just not always in the form of “replace the government.”
Even if they never spend that wealth on luxury, they use it to direct the flow of human effort and raw materials. Giving it away for free would mean surrendering their remote control over global resources. At this scale, it is not about wanting more stuff. It is about the ability to organize the world. Whether those most efficient at accumulating capital should hold such concentrated power remains the central tension between growth and equality.
Kids are not only getting classroom time. They inherit a whole baseline that previous taxpayers built: safer streets, clean water, courts that mostly function, vaccines, roads, libraries, stable money, and the accumulated tech and culture that makes modern jobs even possible. That bundle is huge, and it starts paying out long before anyone is old enough to “owe” anything.
Also, adults are not literally trapped. People can move, downshift, opt out of a lot, or choose different communities. Most don’t, even when they complain loudly, and to me that’s a pretty strong signal the deal is at least somewhat reasonable. Not perfect. Not fair for everyone. But not a cartoon pyramid scheme either.
If there’s a real fight worth having, it’s making the burdens and benefits less lopsided across generations, not pretending the whole social investment in kids is fake.
What about giving the LLM a narrowly scoped role as a hostile reviewer, while your job is to strengthen the write-up to address any valid objections it raises, plus any hallucinations or confusions it introduces? That’s similar to fuzz testing software to see what breaks or where the reasoning crashes.
Used this way, the model isn’t a source of truth or a decision-maker. It’s a stress test for your argument and your clarity. Obviously it shouldn’t be the only check you do, but it can still be a useful tool in the broader validation process.
FYI: Over time, this repeated pressure + rubbing (especially with dust or grit in between) can leave permanent key-shaped marks or “ghosts” on the screen. Thin laptops and bags that are tightly packed or bulging make this a lot more likely, since there’s less rigidity and more pressure on the lid.
Temperature 0 does not inherently improve “quality”. It just means you always pick the highest probability token at each step, so if you run the same prompt n times you will essentially get the same answer every time. That is great for predictability and some tasks like strict data extraction or boilerplate code, but “highest probability” is not always “best” for every task.
If you use a higher temperature and sample multiple times, you get a set of diverse answers. You can then combine them, for example by taking the most common answer, cross checking details, or using one sample to critique another. This kind of self-ensemble can actually reduce hallucinations and boost accuracy for reasoning or open ended questions. In that sense, somewhat counterintuitively, always using temperature 0 can lead to lower quality results if you care about that ensemble style robustness.
One small technical nit: even with temperature 0, decoding on a GPU is not guaranteed to be bit identical every run. Large numbers of floating point ops in parallel can change the order of additions and multiplications, and floating point arithmetic is not associative. Different kernel schedules or thread interleavings can give tiny numeric differences that sometimes shift an argmax choice. To make it fully deterministic you often have to disable some GPU optimizations or run on CPU only, which has a performance cost.
On who controls investment, it is fair to worry about political influence, but it does not follow that highly concentrated private wealth is mostly idle or socially useless. Large fortunes are usually claims on productive assets that employ people and produce goods and services. Capital markets already involve broad and diverse mechanisms like index funds, pensions, and institutional investors that pool the savings of millions of non wealthy people and allocate them based on expected returns. That is not perfect, but it is not simply a handful of billionaires directing everything according to whim.
The equation of exchange point also overstates the role of velocity in judging what is good for the economy. A dollar that flows into an asset is not disappearing from the real economy. It is funding someone else who is selling equity or debt and who will use that for wages, R&D, or capital spending. Lower velocity at the cash register can be consistent with higher long run output if more of today’s income is channeled into projects that raise productivity tomorrow. High velocity tied to fragile consumption and low investment can look good in the short term but leave people poorer over time.
When we treat everything as a zero sum status game, especially when it comes to other human beings, we tend to slide into ugly, destructive dynamics (up to and including wars) where everyone burns resources just to keep their relative place. Cooperation and innovation, on the other hand, are how we turn a fixed pie into a growing one.
So yes, relative position matters in some contexts, but the whole reason our species has gotten this far is that we are not limited to fighting over scraps. We can change the size and shape of the pie together.
What actually increases long-run prosperity isn’t consumption itself, but efficient use of resources and investment - choices that expand the stock of tools, knowledge, and productive capacity in the future. New wealth is created when resources are not immediately consumed, but are instead used to boost productivity.
That’s why wealthy people who don’t spend all their wealth aren’t “hoarding” in the economic sense. Their capital is usually invested - financing factories, startups, research, tools, and infrastructure that generate more output. And when they die with wealth still invested, the state captures a chunk through estate and capital-gains taxes.
A better way to think about wealth is as decision-making power over how resources are allocated - toward current consumption (including luxuries) or toward future production (investment). Capitalism tends to concentrate that decision power in the hands of those who are best at growing capital, which raises total prosperity but also increases inequality.
Consumption uses up wealth; investment grows it. People like Musk have a lot of wealth because they’ve been good at growing it. We should absolutely guard against wealth hijacking politics - but it would be shortsighted to treat their continued investment as a net negative for society.
If it’s the patterns that matter, do you think it’s actually impossible for those patterns to be transmitted across interstellar distances? Just like a cup of ocean water is packed with DNA, it’s at least conceivable that what we call “cosmic background noise” could, in principle, hide extremely compressed life-patterns that only an advanced civilization could recognize and reconstruct back into something we’d meaningfully call “alive.” And of course, the more efficiently you code that information, the more it statistically has to look like random noise.
Not saying this is likely -- just that if the essence of life is informational rather than chemical, "traveling" could look very different for any life that is suitably advanced.
https://chatgpt.com/share/69156fa7-6314-800c-8ffc-9d6aa14847...
Findings are summarized but you are free to double check each summary by following the links to research articles.
> Anything that happens to you after reproductive age does not get affected by natural selection because the selection pressure of reproduction is gone.
You are mostly correct but must also consider traits that affect the odds children will fail to reproduce. People can for example be genetically predisposed to depression or impulsive anger or substance abuse or ... any of which can impact the survival of their children thus selection pressure does not entirely disappear after a child is born.
Yes that is capitalism however if inflation cuts value of money in half and in the same time your revenue doubles, did you actually double your revenue? Do you even need to change your service or product to justify raising prices when the currency is being devalued? For both these questions there is a strong case that the answer is no.
On reasoning, I see LLMs and classic algorithms as complements. LLMs do robust manifold following and associative inference. Traditional programs do brittle rule following with guarantees. The promising path looks like a synthesis where models use tools, call code, and drive search and planning methods such as MCTS, the way AlphaGo did. Think agentic systems that can read, write, execute, and verify.
LLMs are strongest where the problem is language. Language co evolved with cognition as a way to model the world, not just to chat. We already use languages to describe circuits, specify algorithms, and even generate other languages. That makes LLMs very handy for specification, coordination, and explanation.
LLMs can also statistically simulate algorithms, which is useful for having them think about these algorithms. But when you actually need the algorithm, it is most efficient to run the real thing in software or on purpose built hardware. Let the model write the code, compose the tools, and verify the output, rather than pretending to be a CPU.
To me the risk is not that LLMs are a dead end, but that people who do not understand them have unreasonable expectations. Real progress looks like building systems that use language to invent and implement better tools and route work to the right place. If a paper lands tomorrow that shows pure next token prediction is not enough for formal reasoning, that would be an example of misunderstanding LLMs, not a stop sign. We already saw something similar when Minsky and Papert highlighted that single layer perceptrons could not represent XOR, and the field later moved past that with multilayer networks. Hopefully we remember that and learn the right lesson this time.
Everyday reasoning is full of heuristics, analogies, and pattern matches: we jump to conclusions, then backfill justification afterward. Psychologists call this “post hoc rationalization,” and there’s plenty of evidence that people form beliefs first and then search for logical scaffolding to support them. In fact, that’s how we manage to think fluidly at all; the world is too noisy and underspecified for purely deductive inference to function outside of controlled systems.
Even mathematicians, our best examples of deliberate, formal thinkers, often work this way. Many major proofs have been discovered intuitively and later found to contain errors that didn’t actually invalidate the final result. The insight was right, even if the intermediate steps were shaky. When the details get repaired, the overall structure stands. That’s very much like an LLM producing a chain of reasoning tokens that might include small logical missteps yet still landing on the correct conclusion: the “thinking” process is not literal step-by-step deduction, but a guided traversal through a manifold of associations shaped by prior experience (or training data, in the model’s case).
So if an LLM doesn’t collapse under contradictions, that’s not necessarily a bug; it may reflect the same resilience we see in human reasoning. Our minds aren’t brittle theorem provers; they’re pattern-recognition engines that trade strict logical consistency for generalization and robustness. In that sense, the fuzziness is the strength.
Many consider "thinking" something only animals can do, and they are uncomfortable with the idea that animals are biological machines or that life, consciousness, and thinking are fundamentally machine processes.
When an LLM generates chain-of-thought tokens, what we might casually call “thinking,” it fills its context window with a sequence of tokens that improves its ability to answer correctly.
This “thinking” process is not rigid deduction like in a symbolic rule system; it is more like an associative walk through a high-dimensional manifold shaped by training. The walk is partly stochastic (depending on temperature, sampling strategy, and similar factors) yet remarkably robust.
Even when you manually introduce logical errors into a chain-of-thought trace, the model’s overall accuracy usually remains better than if it had produced no reasoning tokens at all. Unlike a strict forward- or backward-chaining proof system, the LLM’s reasoning relies on statistical association rather than brittle rule-following. In a way, that fuzziness is its strength because it generalizes instead of collapsing under contradiction.
Access to immense intelligence and power could elevate humanity to extraordinary heights -- or it could lead to outcomes we can no longer recognize or control. That uncertainty is what makes superintelligence both a potential blessing and a profound existential risk.
> I'm not sure what would motivate an artificial intelligence.
Those who give it orders hence your concern about how AI will be used as a tool is spot on.
So even if there’s no “malfunction”, a feedback loop of constant analysis and reflection could still lead to unpredictable - and potentially catastrophic - outcomes. In a way, the Fermi Paradox might hint at this: it is possible that very intelligent systems, biological or artificial, tend to self-destruct once they reach a certain level of awareness.
Why not frame this as challenge for AI? When the intelligence gap between a fully aligned system and a not-yet-aligned one becomes very large, control naturally becomes difficult.
However, recursive improvement — where alignment mechanisms improve alongside intelligence itself — might prevent that gap from widening too much. In other words, perhaps the key is ensuring that alignment scales recursively with capability.
Today yes but extrapolate GPU/NPU/CPU improvement by a decade.