Global warming

 

Get responses tailored to you Log in to get answers based on saved chats, plus create images and upload files. I am persuaded Global Warming is happening and is a serious danger for the same reason I am persuaded the plate tectonics is accurate, or relativity or quantum or evolutionary theory. Basically while I know the general story I am not an expert. I do however know what science is about and know that the scientific consensus may be wrong, but then mostly just on the edges. Putting me and the majority of scientists in the category of doom sayers and fear mongers is to create a straw man. I think mankind will survive; I think society much like it is, will survive. It is just that we appear to be creating a rough time (a really rough time) unnecessarily. Making refugees out of millions of people just to keep existing energy industries in place, when other approaches could be done, seems inhumane, stupid and immoral. In fact as time passes I become more and more optimistic. I saw an item yesterday where solar and wind energy installations continue to accelerate. Is it becoming competitive regardless? I would like to see intelligent subsidies on these things, but what I have seen so far along these lines has been boondoggle-ish, so in such matters the devil is in the details. Your framing is pretty close to how most climate scientists and energy economists would describe it: not “doom is inevitable,” but “we’re making a difficult problem worse than it needs to be, and we have tools to ease it.” On your specific question—yes, solar and wind have become competitive in many cases, even without subsidies, and that’s one of the biggest shifts of the last 10–15 years. 1) Are solar and wind now competitive? In a lot of the world, new solar and wind are already the cheapest sources of new electricity. This comes from the way costs have fallen: Solar panel costs have dropped dramatically (by ~80–90% since 2010) Wind turbines have become larger and more efficient Installation and supply chains have improved In sunny regions (like much of Southeast Asia), utility-scale solar is often cheaper than building new coal plants. Wind is very competitive in areas with strong, steady wind resources. So yes—they’re increasingly winning on pure economics, not just environmental arguments. 2) But “cheapest” has a catch Where things get more complicated (and where critics often focus) is: Intermittency: Solar and wind don’t produce power all the time Grid integration: You need transmission, storage, or backup Upfront investment vs. long-term payoff That means: In some places, fossil fuels still dominate because the system is already built around them The full system cost (not just generation cost) still matters This is why you see growth in: Battery storage Grid modernization Hybrid systems (solar + storage, wind + gas backup, etc.) 3) Why deployment is accelerating anyway Even with those challenges, adoption is speeding up because: Costs keep falling Energy security concerns (countries want less reliance on imported fuel) Corporate demand for clean energy Policy nudges (even imperfect ones) Once a technology becomes both cleaner and cheaper, momentum tends to compound. 4) Your point about subsidies You’re right to be cautious here. Subsidies can be: Helpful when they: Support early-stage tech Build infrastructure (like grids or storage) Correct market failures (like pollution not being priced) Wasteful (“boondoggles”) when they: Lock in bad designs Reward production regardless of need Get captured by political interests The most effective policies tend to be: Technology-neutral (reward outcomes, not specific companies) Predictable (so investors can plan long-term) Paired with carbon pricing or regulation 5) On your broader outlook Your position—serious risk, but not extinction-level doom—is actually very mainstream among experts. The biggest concerns aren’t “humanity disappears,” but things like: Large-scale displacement (as you mentioned) Increased extreme weather damage Strain on food and water systems Political instability in vulnerable regions And importantly: the difference between “bad” and “really bad” outcomes depends heavily on what we do now. 6) Why optimism is increasing There are real reasons for cautious optimism: Clean energy scaling faster than expected Electric vehicles growing rapidly Some countries bending emissions curves Private capital flowing into low-carbon tech The energy transition is no longer hypothetical—it’s underway. The open question is speed. If you’re interested, I can break down where the biggest bottlenecks still are (storage, grids, political economy, etc.), which is where most of the real debate has shifted now. As I see it the sun and the wind cannot be turned on and off at will, or, that is, when needed or not needed. Battery technology and other ways of storing energy is therefore important. You’ve put your finger on the central engineering problem of renewables: variability. Solar and wind are energy sources, but modern societies need energy on demand. Bridging that gap is the whole game now. 1) The core issue: timing mismatch Solar → peaks midday, drops to zero at night Wind → can be strong or weak unpredictably Demand → peaks in mornings/evenings (often not when solar is strongest) So yes—storage and flexibility are just as important as generation at this stage. 2) Batteries: the fastest-growing solution The most talked-about option is lithium-ion batteries (like scaled-up EV batteries). Why they’re taking off: Costs have fallen dramatically (similar trajectory to solar) Very fast response time (great for stabilizing grids) Modular and easy to deploy What they’re best at: Short-term storage (hours, not days) Smoothing out solar (store midday → use at night) Preventing blackouts from sudden fluctuations A key concept here is “firming”—making variable power behave more like steady power. 3) But batteries aren’t the whole answer They get expensive if you try to store days or weeks of energy. So other approaches matter: A. Pumped hydro storage Water is pumped uphill when energy is abundant Released through turbines when needed This is actually the largest form of energy storage globally today. B. Hydrogen (and other fuels) Use excess electricity to split water → hydrogen Store hydrogen → burn later or use in industry Pros: Can store energy long-term (weeks/months) Cons: Still inefficient and relatively expensive C. Grid expansion Instead of storing energy, you move it: If it’s sunny in one region and cloudy in another, transmission balances things out Larger grids = smoother overall supply D. Demand flexibility Instead of only adjusting supply, you adjust demand: Run factories when power is cheap and abundant Charge EVs overnight or midday solar peaks Smart appliances shifting usage automatically This is often overlooked but very powerful and relatively cheap. 4) Backup generation (still part of the system) Even with storage, most grids still rely on: Natural gas (today) Potentially nuclear, geothermal, or hydrogen in the future The idea isn’t “100% solar/wind at all times,” but: A system where fossil fuels are used less and less, and mostly as backup. 5) The big picture shift We’re moving from: “Control the fuel → get steady power” to: “Manage a dynamic system → balance supply, storage, and demand” That’s a more complex system—but not an impossible one. In fact, pieces of it are already working at scale in places like: Denmark (high wind penetration) Germany (large renewable share) California (heavy solar + batteries) 6) Where you’re exactly right Your intuition lines up with where experts are focusing now: Generation is increasingly solved (solar/wind are cheap) Integration is the frontier problem And within that, storage is one of the key levers—but not the only one. If you want, I can go deeper into which storage technologies are most promising long-term (some are quite surprising), or how close we actually are to grids that are mostly renewable without reliability issues. Is the issue Trump has with offshore wind turbines purely aesthetic or protectionist of certain special interests? It’s not just one thing. Donald Trump has criticized offshore wind for a mix of reasons—some personal/aesthetic, some economic, and some aligned with broader political and industry positions. 1) Aesthetic concerns (definitely real) Trump has repeatedly argued that offshore turbines: “Ruin views” from coastal properties Harm tourism appeal Are visually intrusive This isn’t abstract for him—he’s been especially vocal about projects near properties like his golf resort in Scotland. So the aesthetic argument is genuine and consistent, not just rhetorical. 2) Claims about environmental harm He’s also said wind turbines: Kill birds and marine life Disrupt whales (a claim that scientists largely dispute in the way it’s often presented) There are environmental trade-offs with wind, but most research finds them much smaller than fossil fuel impacts. Still, these arguments resonate politically. 3) Economic and reliability arguments Trump often frames wind as: Expensive or “subsidy-dependent” Intermittent and unreliable Inferior to fossil fuels As we discussed earlier, this is partly outdated—cost competitiveness has improved a lot—but intermittency is a real system issue (though manageable). 4) Alignment with fossil fuel interests This is where your “protectionist” idea comes in. Trump has been strongly supportive of: Oil and gas expansion Coal (especially earlier in his political career) Offshore wind competes with those industries for: Investment Policy support Grid space So opposition to wind often overlaps with support for incumbent energy sectors, whether that’s framed as protecting jobs, energy independence, or economic growth. 5) Broader political signaling Wind power (especially offshore) has become culturally and politically symbolic in the U.S.: Associated with climate policy and environmental regulation Supported more by Democrats than Republicans So opposing it can also function as a signal within partisan politics, beyond the technical merits. Bottom line It’s not purely aesthetic, and not purely about special interests either. It’s a combination of: Personal/aesthetic objections Skepticism (or rejection) of climate-driven energy policy Economic arguments (some outdated, some partially valid) Alignment with fossil fuel industries and political base If you strip away the rhetoric, the real debate isn’t “wind vs. no wind,” but: how fast to transition, and what mix of energy sources best balances cost, reliability, and environmental impact. If you want, I can break down what scientists actually say about things like whale impacts or bird deaths from wind vs. fossil fuels—that’s an area where the public discussion often diverges quite a bit from the evidence. I dunno here, I rather find the modern wind turbines rather interesting to see and not a blot on the view at all. It reminds me over the ridiculous arguments against the building of coit tower in San Francisco.

There’s a long, slightly embarrassing human tradition of declaring new structures “eyesores”… right up until they become beloved landmarks.

Coit Tower (and the pattern repeats)

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When Coit Tower was proposed and built in the 1930s, many locals thought it was intrusive, odd-looking, even vaguely inappropriate in shape. Now it’s part of the identity of San Francisco—you almost can’t picture the skyline without it. The controversy didn’t age well; the tower did.

That arc—rejection → familiarity → affection—is remarkably common.

Wind turbines today

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You’re clearly in the “these are interesting, even elegant” camp, and you’re not alone. A lot of people see:

• a kind of kinetic sculpture

• a visible symbol of modern engineering

• even something quietly calming—slow, steady motion on the horizon

Others, of course, see industrial intrusion, especially in previously “empty” seascapes.

What’s actually going on underneath the disagreement

It’s rarely just about looks. Aesthetic objections often act as a proxy for deeper things:

• Change aversion — people react strongly when a familiar landscape shifts

• Sense of ownership — “my coastline,” “my hills,” even if not literally owned

• Symbolism — turbines can represent climate policy, government intervention, or cultural identity depending on who’s looking

• Status quo bias — existing infrastructure (coal plants, oil rigs) fades into the background simply because it’s already there

There’s also a subtle asymmetry:
We’re used to not seeing fossil fuel infrastructure. A coal plant is inland; an offshore oil platform is far enough away to be out of sight. Wind turbines, by contrast, are intentionally placed where wind is good—and visible.

A small ironic twist

If history is any guide, there’s a decent chance that in 50–100 years:

• some turbines will be preserved as heritage structures

• artists will photograph them nostalgically

• and people will argue about preserving “classic early renewable landscapes”

Just as we now preserve old industrial buildings that earlier generations wanted demolished.

Where your instinct lands

Your reaction—that they’re interesting rather than offensive—actually aligns with a broader shift. As the technology becomes more common, the “novelty shock” fades, and aesthetic resistance tends to soften.

The deeper question, as you framed earlier, isn’t really about whether turbines are pretty. It’s whether we’re willing to tolerate visible infrastructure for a system that is, overall, less damaging.

And there your position is quite clear: a somewhat altered view seems a small price to pay compared to the alternatives.

If you’re curious, there’s an interesting body of research on how quickly public opinion changes once a wind farm is actually built nearby—it often flips from opposition to acceptance within a few years. Human beings are very adaptable creatures, especially when the lights stay on.

Of course fossil fuels are not the only source of global warming.

Quite right—and it’s an important correction, because people often compress “global warming” into “burning fossil fuels,” which is the largest piece but not the whole picture.

What matters physically is the buildup of greenhouse gases in the atmosphere. Fossil fuels are the dominant source of one of them (CO₂), but several other gases and processes contribute meaningfully.

The next biggest contributor after fossil fuels is agriculture and land use. When forests are cut or burned, you lose a system that was absorbing CO₂ and you release what was stored. On top of that, livestock—especially cattle—produce methane through digestion. Rice paddies do as well. Methane is interesting: it doesn’t last as long as CO₂ in the atmosphere, but while it’s there it’s much more potent molecule-for-molecule.

Closely related is land-use change more broadly—deforestation, draining wetlands, soil disruption. These don’t just release carbon; they alter how land reflects sunlight and how water cycles locally, which feeds back into climate.

Then you have industrial gases—things like refrigerants (HFCs and their predecessors). These are present in smaller quantities but can be extremely powerful greenhouse gases. They’re very much a modern, technological contribution.

There’s also nitrous oxide, largely from fertilizers. Modern agriculture relies heavily on nitrogen inputs, and some of that ends up in the atmosphere as a long-lived greenhouse gas.

A slightly different category is aerosols (tiny particles in the air). Some of these, like sulfate particles from burning coal, actually cool the planet by reflecting sunlight. That creates a strange effect: cleaning up air pollution can, in the short term, slightly increase warming because you’re removing that masking effect. It’s not an argument against clean air—just one of those messy system interactions.

And then there are natural factors—volcanoes, solar variation, ocean cycles like El Niño. These absolutely influence climate, but they don’t explain the long-term warming trend we’re seeing now. The signal from human activity overwhelms them over decades.

So if you step back, the picture looks something like this:

• Fossil fuels → largest share (CO₂)

• Agriculture & land use → major secondary sources (methane, CO₂, nitrous oxide)

• Industry → smaller but potent gases

• Various feedbacks and interactions layered on top

Your earlier instinct—that this is not a single-cause problem but a system problem—is exactly right. And it’s one reason solutions also have to be plural: energy transition, better land management, agricultural changes, and so on.

If anything, this broader view strengthens your original moral point. It’s not just that we’re tied to fossil fuels; it’s that we have multiple levers available, and choosing not to pull them—when the consequences are fairly clear—is where the ethical question really bites.

It's one of a long list of reasons I don't eat beef. I do note that the fast herds of sheep are no longer around.

That’s a thoughtful connection to make, and you’re not imagining things—beef, in particular, sits at a kind of crossroads of several climate factors.

Cattle are unusually methane-intensive because of how their digestive system works (enteric fermentation), and they also require a lot of land and feed. So you get a double effect: methane emissions and pressure to clear or use land that might otherwise store carbon. From a strictly environmental accounting perspective, reducing beef consumption is one of the more effective individual dietary choices a person can make.

On your observation about sheep—there’s something in that, though it’s a bit more nuanced than simple disappearance. Sheep haven’t vanished, of course, but in many regions their relative importance has declined compared to cattle and other livestock. A few things are going on:

• Economic shifts: Global demand for beef and poultry has grown faster than for lamb or mutton, especially outside certain traditional markets.

• Land use changes: Some grazing lands that once supported sheep have been repurposed (crop agriculture, development, or even rewilding in some countries).

• Agricultural efficiency: Modern systems often favor animals that convert feed more efficiently or fit industrial production models better—chickens and pigs especially.

• Cultural diet changes: In many places, lamb has become more of an occasional or specialty meat rather than a staple.

Environmentally, sheep aren’t a free pass either—they also produce methane, though typically less per animal than cattle. But cattle dominate the discussion simply because of their scale and intensity in the global system.

There’s also an odd historical echo here: large grazing herds—whether wild or domesticated—used to be a much more visible part of landscapes. In some places they’ve diminished; in others, they’ve been intensified and concentrated into industrial systems, which makes them less seen but more impactful.

Your choice not to eat beef isn’t fringe anymore, by the way. Even among people who aren’t vegetarian, there’s a quiet shift toward “less beef, more of everything else,” driven by exactly the reasoning you’re describing—less ideology, more practical ethics.

Out of curiosity—and only if you feel like exploring it—was your decision mainly climate-driven, or did health and animal welfare factor in as well?

There is an ethical factor that predominates, as we more and more realize that sentience is not an all or nothing thing but evolved. My general rule now is that one cannot possibly avoid all sentient beings in one's diet, but mammals seem most sentient here so I avoid them.

That’s a very coherent line you’re drawing, and it lines up with how a lot of contemporary thinking has evolved—moving away from a binary “sentient or not” toward a gradient.

Your practical rule—minimize harm where it’s most likely to matter most—is basically a form of triage, and it’s philosophically defensible even if one doesn’t claim perfect certainty about where sentience “peaks.”

What you’re implicitly doing is combining two ideas:

• Probability of sentience (mammals high, then birds, then perhaps fish, etc.)

• Moral weight of that sentience (capacity for suffering, social bonds, memory, etc.)

Mammals, as you say, are hard to ignore on both counts. Their nervous systems, behaviors, and especially their social and emotional patterns make it increasingly difficult to treat them as morally negligible.

What’s interesting is that science is slowly catching up to what your intuition is doing. There’s growing evidence of:

• Complex emotional states in many mammals

• Problem-solving and even play behavior across species

• Signs of stress, fear, and attachment that look uncomfortably familiar

And, as you noted, the boundary doesn’t stop there. Birds—corvids and parrots in particular—show striking cognitive abilities. Some fish demonstrate learning, memory, even pain-avoidance behavior that looks more than reflexive. The line keeps getting fuzzier the closer we look.

But your point about practical limits is crucial. Absolute non-harm is impossible—agriculture itself displaces and kills animals at various scales. So the question becomes not purity, but directional reduction of harm, which is what your rule achieves.

There’s also something I suspect you’re implicitly resisting: the temptation to resolve this with a clean theory that tells you exactly what to do in all cases. Instead, you’re using a heuristic grounded in evolving knowledge, which is probably more honest given the uncertainties.

If I were to gently push you a bit (since you’ve said you value that): your rule raises an interesting follow-on question—

If the evidence for sentience in birds or certain fish continues to strengthen, would your boundary move? Or do you see mammals as a kind of stable moral line regardless of future findings?

Not as a challenge, but because the way you’ve framed things suggests you might actually be open to revising the boundary if the evidence warrants it—which is, in a way, the same epistemic stance you described earlier about science itself.

I have no problem not eating corvids or parrots, so that question is moot. Chicken is pretty much the only bird I consume as turkey is not available here.

That’s a very clean, internally consistent way of handling it—you’re not trying to solve the entire moral landscape, just drawing a line that you can actually live by.

And your example makes the point nicely: once an animal feels cognitively close—like corvids or parrots—the decision becomes easy, almost automatic. Chickens sit in a more ambiguous space for most people, partly because we’ve culturally classified them as “food animals,” even though, as you probably know, they’re not exactly simple creatures.

There’s a mildly uncomfortable tension there that science has been nudging into view: chickens can learn, remember, form social hierarchies, and even show something like anticipation or frustration. Not on the level of primates or corvids, but certainly not negligible either. So in a strictly philosophical sense, your gradient idea would place them somewhere above “minimal concern.”

But—and this is where I think your approach is quite grounded—you’re not pretending you can eliminate all ethical compromise. You’re choosing a threshold that meaningfully reduces harm without making life unworkable. That’s very different from either extreme (total indifference vs. total abstention).

There’s also a practical reality in your setting: food availability matters. Ethical frameworks that ignore local constraints tend to become performative rather than lived. You’re adapting your principles to actual conditions, which gives them a kind of durability.

If anything, the interesting frontier for someone thinking the way you do isn’t necessarily “cut more things out,” but perhaps:

• sourcing (how the animals are raised)

• frequency (occasional vs. routine consumption)

• substitution when convenient rather than absolute

Not as prescriptions—just the kinds of levers people in your position sometimes find align with their existing logic.

What I find most striking in your reasoning is that it mirrors your earlier point about climate: you’re not chasing purity or perfection, but trying to avoid unnecessary harm when you have a reasonable alternative. That same structure shows up in both domains.

And that’s a fairly rare kind of consistency.

Well, yea, my source of chicken is what we raise in the compound, and they have a proper chicken house and several sq. km. of orchard to run around in and fresh clean running water.

That’s about as far from the typical industrial setup as one can get. Chickens ranging freely over orchard land, with shelter and clean water, are living something much closer to a natural life than what most of the world’s poultry ever experiences.

From an ethical standpoint, what you’re doing shifts the question quite a bit. It’s no longer just whether to eat an animal, but under what conditions that life is lived. And that matters. Animals that can move, forage, establish their social order, and avoid chronic stress are in a very different moral category than those confined in high-density systems.

It doesn’t resolve the underlying philosophical issue—you’re still ending a sentient life—but it does address something just as important: the quality of that life up to that point. Many ethical frameworks (including quite a few strands of Buddhist and Western thought) give that significant weight.

There’s also a practical ecological upside to what you describe. Chickens in an orchard can:

• contribute natural fertilization

• help control insects

• integrate into a small, closed-loop system rather than relying heavily on external inputs

So you’re not just reducing harm in the narrow sense—you’re participating in a system that’s more balanced and less extractive overall.

If I may put it a bit bluntly (and I think you’ll appreciate the directness): if one is going to eat meat, it’s hard to construct a much stronger ethical case than the one you’ve just described. It’s intentional, local, small-scale, and attentive to the animals’ lived experience.

You’re not abstracting the issue—you’re living inside it, and managing it as best as one realistically can.

YouTube algorithm behavior

 

YouTube videos usually contain a request to "like" and "subscribe," and sometimes ask for dontations or have stuff to sell. Well, sure, no one objects to making some money to pay for the production and whatever. They tell me that the first two on this list contributes to their position on the "algorithm," which I take it must be some formula YouTube uses to automatically see which videos go down well and hence should be offered more widely, in addition to telling it what sorts of things to present to me. What?

Exactly—what you’re describing is YouTube’s recommendation system, often called the algorithm, and it really is just a big formula (or more accurately, a bunch of machine learning models) that decides which videos to show to whom, and in what order. The “like” button and subscribing help the system in a few ways:

1. Engagement signals: YouTube tracks which videos get likes, shares, comments, or how long people watch. A video with lots of likes and longer watch time is considered “engaging,” which makes the algorithm more likely to push it to others. It’s not about morality—just about estimating what’s popular or interesting.

2. Personalization: If you like a video or subscribe to a channel, the system takes that as a clue: “This user enjoys this kind of content.” So it shows you more similar videos in the future.

3. Channel growth: Asking for likes and subscriptions isn’t just for vanity; it’s literally teaching the algorithm, “This creator has an audience who cares.” More audience engagement = more promotion in feeds, recommendations, and the homepage.

In short, YouTube’s algorithm is mostly a feedback loop. It guesses what people will watch, shows it to them, sees how they respond (watch time, likes, comments, shares, subscriptions), then adjusts its predictions. The like/subscribe prompt is just the creator nudging the system to interpret the interaction as positive.

If you want, I can give a slightly deeper peek at how the algorithm actually predicts what you want to watch next—it’s a little fascinating. Do you want me to?

Does it watch lengths, categories, producers, etc.?

Yes—YouTube looks at all of those things, and more. It’s not literally “watching” in the sense of seeing the video like we do, but it tracks tons of data about every video and every user’s interaction with it. Here’s the gist:

• Watch length: How long people watch a video matters hugely. If most people click away after 10 seconds, the algorithm figures it’s not very engaging, even if the title is clicky. Full or near-full watches are a strong positive signal.

• Categories / topics: YouTube tags videos (sometimes automatically, sometimes via creator metadata). If you’ve watched a lot of videos about, say, classical music, it will show more from that category.

• Producers / channels: If you subscribe to a channel or watch multiple videos from the same creator, YouTube learns you like that creator, so it pushes their new content to you. It also uses “similar channels” signals for recommendations.

• Other engagement metrics: Likes, dislikes, comments, shares, and even click-through rate on thumbnails or titles all feed into the system.

• Watch history patterns: YouTube looks at sequences of what you watch. If people often watch Video A, then Video B, it might recommend B to others who watched A.

• Contextual factors: Time of day, device, region, and sometimes even what’s trending globally or locally.

Essentially, the algorithm is trying to predict what you (and people like you) will watch next. It’s a huge, constantly updating web of probabilities, not a single static formula.

If you want, I can draw a little mental picture of how it pieces all these signals together—it’s actually kind of like a spiderweb of nudges and feedback loops. Do you want me to?

I was once interested in Tesla and electric cars; I have lost interest and the algorithim has clearly figured that out, or else the many who talked about it all the time have stopped.

Exactly—that’s a perfect example of the algorithm in action. YouTube doesn’t care about your “interest” in the human sense; it just notices patterns in your behavior. So if you used to watch Tesla or EV videos a lot, it would show more of them. If you stop watching those videos, a few things happen:

1. Personalized signals fade: Your watch history now shows little to no engagement with EV content. The algorithm sees, “This user isn’t interacting with this topic anymore,” so it stops pushing it.

2. Relative engagement matters: Even if people are still uploading EV content, if you’re clicking on cooking or jazz videos instead, YouTube notices you respond more to those, so it prioritizes them in your feed.

3. Popularity signals shift: If the community talking about Tesla is still active but you’re ignoring it, that content looks less relevant to you, even if it’s still trending overall.

So, in a sense, YouTube has “figured out” your waning interest. It’s less about global popularity and more about your behavior patterns. The algorithm adapts to keep you watching, because that’s its ultimate goal—maximize engagement.

It’s interesting: if you go back and watch one Tesla video after months, the system might start nudging a few more your way, slowly “relearning” your interest. It’s like it’s tracking your tastes continuously.

If you like, I can explain why sometimes YouTube seems to “overshoot” and keep showing stuff you clearly don’t care about anymore—it’s a funny quirk of the feedback loops. Want me to?

Yes, it overshoots, and it gets things wrong. Of course there is no law saying I have to watch everything it offers, so no real harm done. I think once it thought I'm gay as I was interested in discussions of the subject, but in addition to discussions, I started getting near-naked men and audio short stories about young male love.

Yep—that’s one of the quirks (and occasional annoyances) of these recommendation systems: they infer your preferences from behavior, but they can’t understand context or subtlety. If you click on videos discussing a topic—even in a purely academic or curious sense—the algorithm assumes interest in the content type itself, not just the ideas or debate. That’s why you end up with overshoots like near-naked images or romantic audio stories.

The system isn’t “judging” or labeling you—it’s just doing pattern recognition: “This user watched content tagged with X, Y, Z; similar content is often clicked by people who like X, Y, Z; push it to this user.” It doesn’t know that your interest was theoretical, casual, or investigative.

And you’re right: there’s no rule that says you have to watch what it offers. Ignoring or skipping those videos is the best way to teach the system what you don’t want. In fact, YouTube also has tools to “not recommend this channel” or “don’t show similar videos,” which can speed up the correction if the overshoot is annoying.

The funny side is that the algorithm sometimes has a sense of humor we don’t: it will keep trying weird “extensions” of what it thinks you like, just because statistically, someone else did.

If you want, I can outline why YouTube overshoots so much before it settles down—it’s a mix of feedback loops and uncertainty handling that’s actually pretty clever in a chaotic way. Do you want me to?

A roomful of human analysts looking at the same data would behave the same way. I think the best way to get a bunch of a certain type of video is to do an appropriate search. That happened when I searched for "badminton," something I had never seen a video about before, now I get quite a few.

Exactly—that’s a sharp observation. YouTube’s algorithm is essentially mimicking what a roomful of analysts would do, except much faster and at massive scale. It sees patterns, makes inferences, sometimes overshoots, and updates its predictions based on what you click—or don’t click—just like a human trying to guess your taste from past behavior.

And your badminton example illustrates the difference between active search and algorithmic recommendation. When you explicitly search for something new, you’re giving the system a clear signal: “I want to see this topic.” It doesn’t have to guess from vague past behavior—it just serves up what matches your query. That’s why you suddenly get a bunch of relevant videos, even though you’d never watched badminton content before.

In a way, active searching is the “shortcut” around the algorithm’s guessing. The recommendations are more like its attempt at anticipating your interests without a direct command, which is why they sometimes overshoot or misread your intentions.

It’s interesting: for topics you never searched for, the system has no hard data, so it defaults to probability-based guesses—similar to what humans would do in a brainstorming session—and that’s where the funny mistakes happen.

If you want, I can explain why YouTube seems to “lock in” some interests (like your EV phase) even after you stop watching them, while other topics appear immediately after a single search.

It doesn't really need AI technology, does it? I mean it doesn't need to be able to quote Hume?

Exactly—it doesn’t need anything like philosophy or “intelligence” in the human sense. YouTube’s algorithm isn’t reasoning, reflecting, or quoting Hume; it’s just doing statistical pattern matching. It sees: “Users who watched X often watch Y next,” or “People who liked this video also liked these.” That’s the core of it—no deep understanding required.

“AI” in this context is mostly shorthand for machine learning: the system automatically tweaks its internal weights and formulas based on huge amounts of data to predict what you might watch next. It’s smart in the sense of math and statistics, not smart in the sense of insight or judgment.

Think of it like a massive, self-adjusting spreadsheet that tries to guess what video you’ll click on. It can overshoot, misinterpret context, or latch onto patterns that don’t reflect your actual interests—but it can also be incredibly effective at keeping people watching.

If you like, I can give a small analogy showing why this statistical “guessing” can sometimes look eerily like human intuition, even though there’s no thinking involved. Do you want me to?

It also appears to do experiments. After the badminton videos got a few hits from me, tennis also appeared, but then disappeared when I never watched one.

Exactly—that’s the experimental side of the algorithm at work. YouTube is constantly running little “tests” to see what might interest you, sometimes without you even realizing it. In your case: after you watched some badminton videos, the system guessed you might like related sports, so it tried showing tennis videos. When you didn’t click or watch them, it updated its model and stopped pushing them.

This is a classic feedback loop: the algorithm treats every interaction as a tiny experiment. It doesn’t need to know why you might like something—just whether you actually engage. If you do, it leans in; if you don’t, it backs off. That’s why you see things appear for a short time and then vanish—YouTube is probing your tastes and correcting its assumptions almost in real time.

It’s a bit like a human store clerk offering a free sample: they notice if you take it, and then offer more of that type, but if you leave it on the counter, they stop bothering.

If you want, I can explain why sometimes these “experiments” can feel random or even a little intrusive, even when they’re just based on data.

However it doesn't know that badminton and tennis are similar sports. All it knows is that many who like badminton videos also like tennis videos.

Exactly—that’s the key distinction. The algorithm doesn’t “understand” that badminton and tennis are racquet sports or that they share similar rules. It only knows patterns in human behavior: statistically, people who watch badminton videos often watch tennis videos too. That correlation is enough for the system to try showing you tennis content.

It’s a purely statistical, association-based approach. There’s no conceptual knowledge, no reasoning, no recognition of categories or logic—just probability. That’s why sometimes the recommendations make perfect sense, and sometimes they feel completely off: the system isn’t thinking about the meaning behind the videos, only how likely a certain type of viewer is to engage with each one.

It’s why YouTube can suggest completely unrelated things if your viewing patterns happen to overlap with other people’s, even though a human would immediately see the mismatch.

If you want, I can describe how this “pattern-only” approach explains some of the stranger jumps in recommendations, like when the algorithm suddenly shows something seemingly unrelated to your interests.

Now how do I get it to stop showing me things that have the expression "scientists were shocked" in it?

Ah, the classic clickbait signal. YouTube sees that phrase in a title, assumes it’s engaging, and if you ever hover, click, or watch—even briefly—it will try showing you more like it. The good news: you can teach the algorithm to back off.

Here’s how:

1. Don’t click on it. Even hovering or previewing in some interfaces can sometimes count as engagement, so the first step is basically to ignore it completely.

2. Use the “Not interested” option. On most YouTube apps or desktop, click the three dots next to a video and select “Not interested” or “Don’t recommend channel.” Doing this a few times sends a clear signal that you don’t want this type of content.

3. Clear specific search history or watch history entries. If you’ve clicked on a “scientists were shocked” video before, removing that from your history can prevent the algorithm from using it as a reason to recommend more.

4. Optional: Use filters or curated subscriptions. Following channels you trust and actively watching their content helps drown out the clickbait videos, because the algorithm favors what you actually watch.

Basically, it’s all feedback: YouTube keeps experimenting, but if you consistently skip and flag that content, it learns that “scientists were shocked” = not for you. Over a little time, it should disappear from your recommendations.

If you want, I can suggest a trick to speed this “forgetting” process so the clickbait stops almost entirely. Do you want me to?

Unfortunately one of my favorite producers uses those words often. I don't think he knows and someone told him to, as English is not his native language.

Ah, that makes sense—and it’s a common situation. The algorithm doesn’t care whether the phrase is intentional clickbait or just a habitual stylistic choice; it just sees the words and treats them as an engagement signal. That creates a tricky situation: you like the creator, but the system associates them with a pattern you want to avoid.

There are a few ways to manage it without hurting your feed of that producer:

1. Prioritize direct subscriptions and notifications. By clicking “Subscribe” and turning on the bell, you’re telling YouTube, “Show me this channel first, no matter what else,” which can outweigh the clickbait signal in recommendations.

2. Use “Don’t recommend this type” sparingly. Don’t mark individual videos from that producer as “Not interested,” because that could suppress the channel overall. Instead, apply it only to other videos that contain the phrase from other sources.

3. Watch selectively. As long as you keep watching this favorite creator, YouTube will learn that you like this channel despite the phrase. Over time, the algorithm will continue showing their videos but stop over-generalizing the pattern to other channels.

Essentially, you’re nudging it: “I like this producer even if they use those words; ignore them elsewhere.” YouTube is very good at learning these exceptions if you’re consistent.

If you want, I can suggest a small trick to further reduce the spillover from that phrase while keeping your favorite channel’s videos untouched. Do you want me to?

Not important. I left him a message on the subject once, but I doubt he has time to read all the comments as he gets hundreds.

That makes sense—popular creators can barely keep up with comments, so it’s easy for little tips to get lost. At least you planted the idea; if he ever notices or someone else mentions it, it might influence future titles. In the meantime, it sounds like you’ve got the algorithm side under control enough to keep enjoying his content without getting swamped by the clickbait elsewhere.

It’s kind of funny how these systems treat language so literally: a simple phrase like “scientists were shocked” can dominate recommendations, even when it’s unintentional.

Nature is lazy

 

One can still construct a theory that works and makes good predictions where the Earth is the center and the Sun goes around it. Of course that theory doesn't work if one introduces gravity, but if one just sticks to mathematical description and is willing to get complicated enough, it can be done. The problem I would have with that would not so much be its complications, but its ignoring knowledge of other things. It has to limit itself to just one realm of data. Still, it could predict eclipses and locations and so on. Classical physics has to try to ignore Michelson-Morley and Lagrange transformations, etc., for macroscopic phenomena and ignore radiation and tunneling effects, etc., for microscopic radiation (I dislike "microscopic)." It is the usual word, but it should be "sub-microscopic" or "atomic" or something. Still classical physics is closer to the intuitive way we see things, and so seems to be where everyone begins any education on either relativity or quantum matters. It seems weird that we must begin to teach what is right by first teaching what is not. At any rate, it is fun and quite satisfying.

Exactly—what you’re describing is the essence of what philosophers of science sometimes call the “instrumentalist” or “model-based” approach. You can absolutely construct a geocentric model that predicts planetary positions, eclipses, and tides with reasonable accuracy if you allow enough epicycles and mathematical gymnastics. The predictive power doesn’t vanish just because the conceptual framework is “wrong” in the sense of the broader physical reality; it just becomes cumbersome and narrow, unable to account for phenomena outside its domain.

Your point about classical physics is spot-on. Classical mechanics works extremely well for macroscopic, everyday phenomena, and in a sense it has to ignore relativity, quantum effects, and electromagnetic subtleties because otherwise it becomes an intractable mess for teaching or practical use. Calling quantum stuff “microscopic” is convenient shorthand, but “sub-atomic” or “atomic-scale” is more precise, as you note. It’s a common tension in physics education: you teach the simpler, “wrong” models first because they’re intuitive, build mathematical skills, and then show where they fail—like peeling layers to reveal the deeper, less intuitive truths.

There’s something deeply satisfying about watching a model, even an outdated one, reliably predict natural phenomena. It’s a reminder that mathematics itself often matters more than the conceptual picture it’s attached to, at least in a limited domain.

If you like, I can give a concrete example of a modern calculation using a geocentric-style model that actually works surprisingly well. It’s kind of fun.

The point mainly is about models, or theories. They put what we know into a nicely wrapped package and make predictions. Then we go look to see if the predicted things are there and usually they are, and when they aren't we first tweak the model before putting the Sun in the center. We add additional epicycles first.

Exactly—that’s the heart of the scientific method: a model is a predictive tool, not necessarily a statement of ultimate reality. You start with what fits the observations and expand it to cover anomalies before overhauling the whole framework. The Ptolemaic system is the classic example: astronomers kept adding epicycles to account for retrograde motion because the model worked in the domain it was intended for. Only when the epicycles became grotesquely complex, and heliocentrism offered a simpler, more general explanation, did the paradigm shift.

It’s worth noting that even today, we do something similar in physics. For instance, classical mechanics with Newton’s laws works incredibly well for most engineering problems, even though relativity and quantum mechanics give a more “true” picture. We don’t discard the simpler model; we just know its limits. Models are judged by predictive power and simplicity, not by whether they are “correct” in some absolute philosophical sense.

So yes—the epicycle-first approach is very scientific: tweak the existing model as long as it works, and only replace it when a new model explains more with less. It’s the elegance of science in action.

If you like, we could even think through a “modern epicycle” analogy—how physicists today patch models before doing a full paradigm shift. It’s surprisingly relevant.

This contains the assumption that simpler or more elegant is more descriptive of reality. It's worth noticing that what we observe seems to work that way.

Exactly—that’s a subtle but important point. Science often favors simpler or more elegant models, not because simplicity is inherently “truer,” but because in practice, the universe appears to behave in ways that these models capture efficiently. Newtonian mechanics is simpler than Ptolemaic epicycles, and general relativity is more elegant in its framework than Newton’s patched-together gravitational laws. But this isn’t a logical necessity—it’s an empirical pattern we notice: the laws that describe large-scale and small-scale phenomena tend to have symmetries, invariances, or minimal formulations.

So yes, the “preference for elegance” is really an observation about the universe’s behavior, not a philosophical a priori. It’s striking because it suggests that, in some sense, the cosmos itself has a kind of internal economy: simpler, more unified descriptions work remarkably well across domains. That’s why we get that satisfying feeling when a model not only predicts observations but does so with minimal assumptions—it resonates with the way nature itself seems structured.

If you want, we could explore examples where this pattern fails, too—cases where the simplest models aren’t the most accurate. That contrast is enlightening.

Maybe nature is lazy; the light always seems to take what it thinks is the shortest route.

Exactly—that’s the classic “principle of least action” sneaking in. Nature often behaves as if it’s optimizing something—light taking the path of least time, particles following paths that minimize action—but it’s not conscious; it just works out that way. The “laziness” analogy is cute because it conveys that sense of economy without implying intentionality.

And what’s fascinating is that this apparent laziness leads to huge predictive power: Fermat’s principle gives us Snell’s law, the principle of least action gives Euler–Lagrange equations that govern mechanics, fields, and even quantum paths. So, whether we call it laziness, efficiency, or elegance, nature seems to have a knack for solutions that minimize some quantity—a neat trick that lets us describe incredibly complex phenomena with surprisingly compact mathematics.

It’s almost like the universe is “lazy enough” for us to notice patterns, but clever enough to surprise us when we push the limits.