lemmsjid
Picard was much closer to a modern naval captain: his ship had a clear chain of command and it was part of the plot that his xo did the dangerous expeditions.
I would add that Eisenhower was a general of generals in d day. Generals closer to the action would sometimes find themselves in danger. And for ship captains that was quite a bit more true.
Picard the character was derived from the writers’ understanding of historical leaders. Saying the writing of him was solely for money or entertainment is like saying that a historian is only writing for a paycheck and prestige.
If you read the writings of the TNG writers, they were quite caught up in the ethical dilemmas they were presenting. Art that is purely for money is contrived and one dimensional.
Science fiction has an interesting and irreplaceable role in art: it can take a situation out of its immediate context and put it under another lens, clear of its modern cultural baggage. What makes science fiction interesting or not interesting is what it does with that ability: does it promote a simple good vs evil scenario, or does it explore the rich shades of grey that play out in real scenarios?
TNG was particularly good at showing real moral and ethical dilemmas that were quite thought provoking. Their head writers, particularly Michael Pillar and Ronald Moore and Jeri Taylor were deeply interested in exploring the implications and moral contradictions of the plots they presented.
Waving it all away as money grabbing is a reductive history at best.
My kid goes to a very liberal California school. The main difference between it and my school growing up is that it is no longer acceptable to ostracize or beat up lgbtq kids or kids who are different in other ways. Part of that is because, gasp, the school builds acceptance into the curriculum. I wish I grew up now, it’s such a nicer time to be a weird nerd.
I know nothing about Tesco beyond that quick google search, but I’ve been at several companies where I would read online comments claiming we could reduce our workload to a few servers, and I would think of our tens of thousands of fully loaded machines and roll my eyes.
I like your comment btw. I’d add Observability to CAP to incorporate what you’re saying.
Pretty much every view the user sees of data should include an understanding as to how consistent that data is with the source of truth. Issues with caching (besides basic bugs) often come up when a performance issue comes up and people slap in a cache without renegotiating how the end user would expect the data to look relative to its upstream state.
Tone is something that can be adopted intentionally or unintentionally. If you hear a pilot on a radio dryly say something in a calm and detached tone, it could be in the context of an emergency. Pilots are enculturated to adopt that tone (for various reasons). Meanwhile particular cultures have different levels of acceptability when it comes to tone: some cultures perceive other cultures as more angry, or detached, because of the norms of communication within those cultures.
In short, I think the tone of “calm, scientific detachment” is often weaponized to lend undeserved credibility to an argument, because people tend to believe people more when they adopt that tone.
Furthermore, tone does have a purpose if used alongside a well done argument. For example, in the article the OP linked to, there is a rather exhaustive refutation of the book in question. The tone of the author previews that their entire opinion on the book is negative, given all the arguments they put forth in their review. If the author of the review had adopted a calm and thoughtful tone, perhaps it would indeed have been more effective because the reader would decide. On the other hand, most people won’t read the entire review, so the tone of the author makes it clear what their opinion is.
That said I am not wholly disagreeing with you: would be interesting to do a study using some varying markers to identify tone, and identify, I don’t know, argumentative complexity, and see if snarkiness is associated with a lack of complexity. Assuming you can find markers with predictive power.
I’m not doubting your claim but I’m wondering how that very weird paper you’re citing bubbles up to the top, when there’s some very middle of the road meta analyses that don’t make outsized claims like access to objective truth.
When it comes to incompetence, people can hide that behind kindness, or behind bluster and bullying. Both are certainly unhealthy. I wouldn’t say one leads to issues over the other.
Look at Conway’s Law: “any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization's communication structure.”
The “kind” people are the people who optimize an organization’s communication structure by helping competent people to have a voice and not be impeded by political wrangling.
In short, I think it’s the ‘kind’ people who can help an organization realize an architecture that is less warped by political considerations and more true to the customer’s needs.
Of course an organization needs both kindness and competence. In my decades in tech, competence was over-valued in my early years (the worship of the trope of the rockstar-but-asshole programmer), so if there is an overindication towards kindness right now, it is probably a counterbalance.
I would also question your conclusion about the government. While I have not worked in the government myself, I come from a sort of “federal government family”, in that I have multiple close family who have spent decades in federal government roles, and they are full of stories of incompetent managers undermining their employees, politically fighting one another, etc. To your point, they also have plenty of stories of crass incompetence, Nepotism, etc. But I think it’s an easy and incorrect answer to say it’s simply due to “HR-values” as opposed to “engineering-values”: it’s multi-faceted in both directions.
Often times books turn abuse into the catalyst for a noble struggle and cathartic improvement, when in fact that often isn't the case. There's large swathes of human history where oppressive regimes were powerful enough to quickly and brutually suppress rebellions. When violence and abuse are normalized in a culture, it can take a long time and a lot of failed struggles to truly unwind the violence.
I think Jemisin is actively trying to explore that mentality, which is pretty brave because it does make the characters off putting. As the reader you want so badly for them to rise out of the muck and take on a noble struggle, but they're caught up in the cycle of violence.
I say this with respect for your viewpoint, I think you're being very fair about expressing your response, and I completely see where you're coming from.
On your second point: The Nebula Awards specifically target "science fiction" and "fantasy" genres. I'm quite widely read in science fiction. I took a look at the list of novels that have won or been finalists for the Nebula Awards. It turns out I've read quite a few of them, scattered over the decades the Nebulas have existed. The Fifth Season is far from the only set of books with fantastical elements to have won, and the ones I've read that have won have plenty of world building elements that would be incorrect by a fourth grade textbook (FTL? Reincarnation? Fire breathing dragons?). Yes, the Game of Thrones novels (a Song of Ice and Fire), though quite excellent, are quite firmly in the realm of fantasy, and they were often Nebula finalists! By your criteria, the Nebula Awards were "destroyed" a long time ago. Several books, including some from the very early years of the Nebula Awards, are now called "science fantasy", i.e. science fiction that leans into fantastical elements. The Fifth Season is actually typically labeled as "science fantasy".
If the Fifth Season "destroyed" the Nebula Awards, then you would see more recent winners being almost exclusively science fantasy. I haven't read as many of the newer winners as I have older winners, but two of my favorite novels of recent years won or were finalists: Network Effect by Martha Wells, which is pretty much a core science fiction novel, and Piranesi, by Susanna Clarke, which is even more fantastical than the Broken Earth.
One of my favorite recent-years science fiction novels, Children of Time, surprisingly didn't get a Nebula, but it did get a Hugo. Looking at its publishing year though, 2015, I see why! Three Body Problem was nominated, and Annihilation, which I absolutely adore (but can't spell), won that year.
Meanwhile Octavia Butler is absolutely one of my favorite authors regardless of genre. Her work stuck with me for quite a while, as has le Guin's (specifically, Left Hand of Darkness, I've actually had trouble getting into some of her other works, though I mean to try again). Butler to me is a visionary alongside my other favorites in the sci fi canon.
It may be that it hit me at a particular time in my life: I read it in my early teens and the way gender was expressed in the novel, the sort of tidal shift between masculine and feminine based on circumstance, really spoke to me at a time I was figuring all that out in my own psyche. I wonder if a lot of the gender exploration in the book may seem more trite and typical now.
Having understood that question, and built an understanding of what predicts fraud, you would then graduate to build models to understand the extent to which features predict fraudulence.
My point in context of the conversation is that it's useful in a business context to explore and understand that data.
- identify latent features of customers via their behavioral data, to be used for profiling customers or recommending products to them
- within a large amount of customer behavioral data, identify potentially fraudulent behavior
- identify causes of seasonality (e.g. temporal patterns) in the data in order to improve forecasting (sales, traffic, whatever)
In those cases part of the investigation is to initially take a hands-off (unsupervised) approach, so that we can compare our initial top-down hypotheses with actual patterns in the data.
In both of those cases there's considerable (and sometimes adversarial) noise in the data.
Maybe for people who use it negatively, "refactoring" or "unplanned improving" isn't "yak shaving" until you've gone beyond what's reasonable. E.g. if you're doing some recursive refactoring, and improving the code base, great, but yak shaving is when you've truly gone off course.
Either way, I think this is where healthy team dynamics are so useful, because it's hard for the yak shaver to know if they've gone off course or are doing something unplanned but useful. If I think I'm deep in a yak shaving exercise, but I think the results will be beneficial, I announce that I'm yak shaving and explain why. If the rest of the team thinks it isn't truly a useful exercise, they can guide me out of it.
Edit: Though good point to the parallel commenters, the knight shape is harder to differentiate and kind of throws me off. But maybe tweaks there.
It is useful to define terms. Merriam-Webster has what I believe is a quite useful definition of racism: "a belief that race is a fundamental determinant of human traits and capacities and that racial differences produce an inherent superiority of a particular race".
That is a useful definition because it helps to separate a racist interpretation of test scores from a non-racist interpretation. The racist interpretation is to jump to a conclusion of fundamental inferiority in academics. The non-racist interpretation is to acknowledge and emphasize the environmental factors that can lead an historically oppressed/displaced/enslaved minority to underperform academically, to acknowledge that African-born immigrants do quite well on standardized testing, and also to understand that standardized tests are normed on academic success, which creates a causality issue when measuring groups that have historically not been allowed in academic institutions. Perhaps all of those arguments don't hold water: or at least the pitcher holding them has holes of its own. But it is quite erroneous to jump to a conclusion of racial biological determinism, and that is what the racist conclusion would be.
There is a particular kind of fallacy where a person can, seemingly unconsciously, take what are perfectly valid observations, like the test-taking disparities, and spin them into non-evidential overarching beliefs. This happens all too commonly when it comes to scientific (or at least metrical) observations and racism. People were all too eager to embrace phrenology, IQ testing, evolutionary biology, etc. when it confirmed their cultural preconception that their race was superior. All of those areas, even phrenology, had some scientific merit to them (and still do in the case of IQ testing and evolutionary biology), but that merit is lost when the conclusions get spun into non-evidential belief systems like racist ideology. Seemingly (I can't peer into their heads, really) skepticism is lost in a chase for the personal gratification of a belief of fundamental superiority.
Discussions around academic standards are a case in point. Academic standards change quite frequently. The Oregon requirement we're discussing was only instituted in the early 2010's. When race is not part of the discussion, it is entirely normal for educators to say that a new standardized test doesn't really have evidence it serves the population and to remove it. There is a very lively debate amongst educators about how excessive measurement, i.e. standardized test taking, can interfere with the normal give-and-take of teaching. But, if the evidence is that non-native-English speakers are underserved, or that African-Americans are underserved, suddenly it is 'racist' to remove a requirement or hurdle. What I saw in the two articles is that those in favor of removing the requirement saw a lack of evidence it was beneficial, and saw evidence that it was not beneficial; while those opposed had no evidence other than it would graduate 'dumber' students; ignoring the fact that presumably dumber students were being graduated before the 2010's when the program was instituted.
In short, it is often a smokescreen to say that observations about race are racist, because that makes it impossible to act against inequality, or sometimes even to act at all. Once again, racism is the belief of one race's inherent superiority over another.
I guess for me the question of whether or not the model is lying or hallucinating is if it's correctly summarizing its source material. I find very conflicting materials on the density of rubber, and most of the sources that Google surfaces claim a lower density than water. So it makes sense to me that the model would make the inference.
I'm splitting hairs though, I largely agree with your comment above and above that.
To illustrate my agreement: I like testing AIs with this kind of thing... a few months ago I asked GPT for advice as to how to restart my gas powered water heater. It told me the first step was to make sure the gas was off, then to light the pilot light. I then asked it how the pilot light was supposed to stay lit with the gas off and it backpedaled. My imagining here is that because so many instructional materials about gas powered devices emphasize to start by turning off the gas, that weighted it as the first instruction.
Interesting, the above shows progress though. I realized I asked GPT 3.5 back then, I just re-asked 3.5 and then asked 4 for the first time. 3.5 was still wrong. 4 told me to initially turn off the gas to disappate it, then to ensure gas was flowing to the pilot before sparking it.
But that said I am quite familiar with the AI being confidently wrong, so your point is taken, I only really responded because I was wondering if I was misunderstanding something quite fundamental about the question of density.
Am I missing something?
I asked both Bard (Gemini at this point I think?) and GPT-4 why ducks float, and they both seemed accurate: they talked about the density of the material plus the increased buoyancy from air pockets and went into depth on the principles behind buoyancy. When pressed they went into the fact that "rubber"'s density varies by the process and what it was adulterated with, and if it was foamed.
I think this was a matter of the video being a brief summary rather than a falsehood. But please do point out if I'm wrong on the rubber bit, I'm genuinely interested.
I agree that hallucinations are the biggest problems with LLMs, I'm just seeing them get less commonplace and clumsy. Though, to your point, that can make them harder to detect!
Humanity has also had a great time making different kinds of plastics, only to discover that they are also penetrating our body at a microscopic scale.
While we don't have the smoking gun yet (i.e. radium jaw), it's at least scary to me, because platics are so pervasive across the population that if a smoking gun DOES emerge, it might be too late. I think the article itself brings up another good example: asbestos.
I get all the jokes about dihydrogen monoxide killing people who breathe it, i.e. drowning, but this is fundamentally different, because we are discovering a thing that is interacting with our bodies in a way that was not historically present throughout evolution. Presumably if we'd evolved in the mist of nano plastics there wouldn't be much cause for worry, because a priori we'd have survived and thrived in its presence.
He's applying a standard analytical lens to AI startups, e.g. looking for their moats through finding differentiators in economics, data, scalability, etc. He finds that "doing AI" is not a stable enough differentiator to compel him as a VC. He then lays out his reasons. There are plenty of startups selling themselves on their AI platform and/or acumen, so it's rather automatically relevant to a VC at least.