It's funny because I was just starting my job-search today and, at the top of HackerNews, there was this article that seems to suggest that prospective employers look for applicants like me.
I wonder what sort of prospective employers might be looking for the sorts of things that this article describes?
How could someone find a recruiter like the author?
I mean, I'm just starting a job-search and the top HackerNews story's about how prospective employers ought to be looking for applications like mine! I'd love to talk to such prospective employers; I wonder how to make that connection?
It's difficult for me to appreciate how works in such fields might be "intellectual".
I feel like they're doing LLM-like opinion-pieces and acting as though the results are deeply meaningful. But the mechanics and results seem shallow and uninteresting.
In fields like Physics and Engineering, there're plenty of cranks who say crazy things. But in such fields, reality tears those people down -- their works fail; their perpetual-motion machines don't tend to generate infinite-energy; their snake-oil doesn't seem to open people's latent-psychic abilities; their mathematical theorems fall apart. Reality is a harsh blade that cuts them down without mercy. And people in those fields learn to be harsh/critical themselves, as to survive the constant assaults from reality's judgement.
But the softer fields lack such harshness -- they're comically tolerant. It's like they're all whimsy; there'd seem to be little incentive for an academic to even bother with the extreme costs associated with rationality, as they'd just get out-competed on the metrics that they're actually judged by.
I mean, I don't care to see what (the early versions of) ChatGPT have to say of math; while chatbots might spit out a lot of junk, their rantings would be shallow and disinteresting. Why ought we have any more regard for the same mindlessness in other fields?
Point being that it seems off-topic to discuss such matters in terms of intellectualism -- unless we're using "intellectual" so loosely as to include stuff like ChatGPT-generated content.
Seems inevitable enough that we may have to accept it and try to work within the context of (what we'd tend to think of today as) mass-spying.
I mean, even if we pass laws to offer more protections, as computation gets cheaper, it ought to become easier-and-easier for anyone to start a mass-spying operation -- even by just buying a bunch of cheap sensors and doing all of the work on their personal-computer.
A decent near-term goal might be figuring out what sorts of information we can't reasonably expect privacy on (because someone's going to get it) and then ensuring that access to such data is generally available. Because if the privacy's going to be lost anyway, then may as well try to address the next concern, i.e. disparities in data-access dividing society.
Title looks like misinformation. Sub-title says something different (and more plausible).
Title: "Meta will enforce ban on AI-powered political ads in every nation, no exceptions".
Sub-title: "With several nations expected to hold elections next year, Meta confirms its generative AI advertising tools cannot be used for campaigns targeting specific services and issues.".
The idea of preventing advertisers from using AI at all ("no exceptions") seems fairly absurd -- for example, if an advertiser asks ChatGPT to spell-check something a human wrote, how would Meta know that ChatGPT did the spell-checking? But if Meta's just trying to manage how people access its own tools, then that'd seem like a different scenario.
Presumably they mean that their tools couldn't be used directly, rather than not at all, though. For example, if Alice uses one of Meta's tools for some other declared purpose to spell-check a word that Alice'll then include in an ad that she'll ask Bob to deploy on Meta, then how would they detect such indirect usage? Though they might still try to detect their own tools' signatures on, say, images or longer bodies of text.
What makes some folks so attached to particular browsers?
Personally, I'm using a mix of Edge, Firefox, and Chrome. While in theory I could just use one of them (which would probably be Firefox), it's kinda like having 3 different super-profiles for a web-browser (where each browser is its own super-profile), so I'm mostly just using all 3 out of laziness.
I don't particularly trust Chrome nor Edge, so I just don't use them for anything important. Not that I'm 100% confident in Firefox, but if I've got to do something important, Firefox is the easy pick. Then I guess I end up favoring Chrome or Edge for everything else, since I don't want to junk up Firefox with nonsense (so Firefox'll remain solid for when it's appropriate). Between Chrome and Edge, I guess I favor Chrome for junk-level tasks since Chrome feels the most separated (being neither used for important stuff like Firefox nor being tied to the OS like Edge).
I get that some folks might have a business-critical app with compatibility-issues limiting their freedom-of-choice when it comes to certain tasks, but outside of such niche cases, what's the big deal?
> Net zero CO2 industrial-sector emissions are possible but challenging (high confidence). Energy efficiency will continue to be important. Reduced materials demand, material efficiency, and circular economy solutions can reduce the need for primary production. Primary production options include switching to new processes that use low-to-zero GHG energy carriers and feedstocks (e.g., electricity, hydrogen, biofuels, and carbon dioxide capture and utilisation (CCU) to provide carbon feedstocks). Carbon capture and storage (CCS) will be required to mitigate remaining CO2 emissions {11.3}. These options require substantial scaling up of electricity, hydrogen, recycling, CO2, and other infrastructure, as well as phase-out or conversion of existing industrial plants. While improvements in the GHG intensities of major basic materials have nearly stagnated over the last 30 years, analysis of historical technology shifts and newly available technologies indicate these intensities can be significantly reduced by mid-century. {11.2, 11.3, 11.4}
> From what I know, carbon capture over a field is not exactly a solved problem.
Doesn't appear to be capture-capture over a field. Instead:
> Navigator’s project would have laid pipelines across five US states—South Dakota, Nebraska, Minnesota, Iowa, and Illinois—to collect CO₂ from ethanol and fertilizer plants and pipe the gas to an underground storage site in Illinois.
Sounds like they were planning on point-source capture, which is generally a good bit more efficient than open-air capture.
If it were carbon-capture over a field, then it'd probably be under the category of "open-air capture" -- which is technically easy to do (as capture in general is; we've had CO2-capture technology since the 1930's), just more costly (since it's less thermodynamically efficient to capture from a low-concentration source like the atmosphere, relative to capture from a high-concentration source like the flue-gas from a plant).
Capturing CO2 from point-sources (like the flue-gas from plants) tends to be relatively efficient, which seems to work out better both economically and environmentally.
Yeah, the idea to use random-selection instead of keeping track of generation-history seems reasonable. The idea of guardrails from perfect-balancing seems less obvious to me.
For example, say someone wants to generate a "US President" -- what would the ideal range of outputs be?
The article checked for just two things: sex (male or female) and skin-tone (I, II, III, IV, V, or VI). To date, all US Presidents have been male, and they were probably mostly skin-tones I or II (not bothering to check), except for Obama who was probably.. like IV or something (still not bothering to check).
So if we run StableDiffusion for a "US President", what would a "perfectly balanced" output look like? Should there be any women? What about the skin-tone distribution?
Also, Obama was a 2-term President, so.. if his skin-tone should somehow affect the distribution, should it have a stronger effect because he was in office for longer than average? Or should all US Presidents have the same effect regardless of their time in office? And either way, why?
> (Is there some kind of universal law that you're citing, that claims that it's impossible to build a useful, unbiased system?)
Usually it'd be a trade-off, where you'd give up some quality at doing the main job to gain whatever the secondary objective would be, e.g. producing results according to whatever desired distribution.
That said, the bigger issue would be defining what "unbiased" means. There're some mutually-exclusive notions of what it'd take to be unbiased, so if "unbiased" requires not being biased according to any perspective, then, yeah, it wouldn't generally be possible.
For a quick example, say that we want to be unbiased in our representation of men-vs.-women in a profession. Then, is the proper ratio: (1) 50% men and 50% women; (2) population-weighted ratios (because men and women aren't generally equally represented in the population); (3) profession-weighted ratios (because men and women aren't generally equally represented in a profession); (4) observer-subjective ratio (this is, a realistic ratio for how often a viewer might actually see men-vs.-women of that profession); (5) something else? What about intersexed people -- should they be represented, and if so, at what ratio, and how should men/women be adjusted to account for them?
Then, what if the photo would be served by having a person of a certain height, to best fill the space while not covering up more -- then, should the algorithm be biased toward a man or a woman based on typical heights, to accurately represent sex-specific height-distributions? Or, should it deviate from that, and present men and women as having the same height-distribution? And if we do deviate from that to pretend that men and women have the same height-distribution, but that height-distribution more closely matches the actual height-distribution for one sex than the other, then is that itself a form of bias?
Then, same as above, but for weight. Then for body size.
Then, what if men and women have different fashions in the relevant culture, and one fashion would better fit the scene -- can that be a factor? And if so, what should be the logic for picking the relevant culture?
Then, how should interactions be handled? For example, if we're generating pictures of doctors who're also mothers, then should men be included in that, or is it okay to only have female doctors in that context? Or what if we're generating pictures of doctors at a conference for female doctors in specific but has some male attendees -- then what ratio would be correct?
Point being that, for someone designing algorithms that would be "unbiased", they'd presumably want to know what folks would accept as a valid solution to that objective. Without a clear definition of what's desired, it'd seem like any particular solution would be open to criticism from other perspectives.
The comment I'd responded to seemed to have thought that StableDiffusion picked what the sex of a person would be according to some internal odds that could be modified.
My point was that it doesn't actually think like that. For example, prompting StableDiffusion for a picture of a doctor doesn't necessarily get it to draw a human at all, much less a doctor of a pre-determined sex; instead, StableDiffusion de-noises the image until the result emerges, where that result would (ideally) contain a doctor of whatever sex it happened to come up with.
That said, you're right that we can add more code to try to guide things.
We could even just brute-force it by just re-generating images over-and-over, or tweaking them after generation, until they match exactly what we wanted. (Realistically, something like branch-and-bound would probably be preferred to blindly guess-and-check-ing.)
> Wouldn’t something like isMale*P(male=.66) work fine?
It doesn't think like that.
If it did, they could've just done `P(hasFiveFingersPerHand)=0.99999`.
But it doesn't even necessarily draw what you ask it to. Instead, it generally adopts a set of de-noising transforms that it's been trained to believe would tend to lead to what the prompt sounds like.. then whatever those transforms produce would, hopefully, be sorta like what was requested.
> For example, Carrefour said a bottle of sugar-free peach-flavoured Lipton iced tea, produced by PepsiCo, shrank to 1.25 litres (0.33 gallon) from 1.5 litres, resulting in a 40% effective increase in the price a litre.
That's only a 20% -- rather than 40% -- effective increase in the price-per-volume, right?
Trying to figure out what went on there. For example, maybe they meant that the price-per-volume did go up 40%, and they meant that the reduced-size contributed to (rather than "resulted in") that 40% increase? Or, maybe ChatGPT did the math?
> Participants were recruited through a number of mailing lists, Twitter and blog posts. [...] For all analyses, we use survey responses; for analysis (iii), we additionally use responses to a demographic survey that included a question on academic interests; and for analysis (iv), we additionally use the prediction market data.
So it sounds like they only used prediction-market data "for analysis (iv)", which they describe as:
> [...] (iv) whether survey-based aggregated forecasts and market-based forecasts are correlated.
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The last line of their paper:
> If our forecasts hold up, it will be interesting to investigate if specific factors (such as different methodologies and policies) can be identified that influence replication rates.
So, it doesn't sound like they're claiming that their forecasts should be assumed reliable.
In short, it looks like potential-respondents first had to apply (e.g., in response to [this Facebook post](https://www.facebook.com/Wristly.co/posts/apple-watch-owner-... )), and then they pre-screened potential-respondents.. and it sounds like respondents may have had some sort of a weekly commitment?, as the document includes:
> To finish, a big thank you to the 1,100+ strong members of the Wristly Inner Circle- we wouldn’t be able to learn so much so fast about the Apple Watch without your weekly contribution.
So it doesn't sound like a random sampling of Apple-product users.
Anyway, then apparently they ask if a respondent's satisfied with a product, giving 4 options: "Very Satisfied/Delighted", "Somewhat Satisfied","Neither Satisfied or Dissatisfied", and "Somewhat Dissatisfied".
Then, they add up the first 2 categories as their "key metric of customer satisfaction".
> When it came to overall customer satisfaction, iPhone X owners in our study gave the product an overall 97% customer satisfaction. [...] Just to contrast that with the original Apple Watch research with Wristly I was involved in, 66% of Apple Watch owners indicated they were very satisfied with Apple Watch, a product which also ranked a 97% customer satisfaction number in the first Apple Watch study we did.
Point being that, while the above PDF appears to talk about the Apple Watch, it sounds like they're strongly implying that the customer-satisfaction figures for the iPhone were largely done in a similar manner -- and, at least in the case of the above-quoted speaker, by some of the same people.
I mean, given that they were listing relative-abundances (by mass, count, or what?), it'd seem like they were giving the abundance of Mn alone, as a subset of the abundance of MnO. But since the numbers don't look right, not sure what they were trying to say.
This isn't the sort of writing that I'd expect from someone with a scientific background.
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PS: If the relative-abundance (by mass) of MnO were 2.109, then the relative-abundance of Mn alone would be about
1. A leaky house where positive-pressure causes flows to come out of various leaks all around.
2. A well-sealed house where positive-pressure causes flows to leave through a well-defined channel.
The first-case might be harder to recover heat/coolness from, as the (high/low)-temperature air might leak in a way that'd be harder to make use of it.
The second-case would seem to offer a stream of warmer/cooler air that could be used with a heat-exchanger or heat-pump or something.
To note it, well-sealed houses might have issues with relatively poor ventilation, making positive-pressure more desirable -- much like how the OP describes wanting to keep CO2-levels down with their positive-pressure system.
Have you explored the effects of a localized positive-pressure system?
Like, say the positive-pressure system has a duct that pumps outside-air directly to locations where a resident spends a lot of them time -- like their bed, a work desk, or a couch in a living-room. Could they get reasonable air-quality that way, even if the system isn't strong enough to clean up other areas of their home that they might spend less time in?
I wonder what sort of prospective employers might be looking for the sorts of things that this article describes?