murgindrag

I'd like to disagree, but I can't. What I can say is:

1) I know enough about molecular biology to tell you the vaccine is safe. You pick up novel stuff all the time. In abstract, we don't know a pebble in your backyard doesn't have some odd chemical which will end humanity, but the odds of that are astronomically low. There just isn't any voodoo in the vaccine.

2) In this case, the claims from "alternative news" are nonsense. With hundreds of millions of doses of vaccine administered, there will be instances of virtually everything happening after the vaccine. That's not to say they're always nonsense -- I read a variety of media -- but in this case, they are.

3) Doctors who fear vaccines are crackpots.

4) You're absolutely right about institutions lying. You're not only right about researchers not being able to get a message out contradicting the party line, but worse, their career would be destroyed. It's the story of the boy who cried wolf.

Your numbers are way off, as is your moral compass. I hope I never meet you, or anyone like you. To use your driving analogy, the way I respond to your message is if someone told me they drove around drunk and high, without headlights, and without really doing any car maintenance, and didn't accept any moral responsibility if they hit someone because car accidents happen, and sorry, that's life.

As a baseline number, about a half-million Americans died of COVID already. For comparison, there were about 40k car deaths in the US. For someone like yourself, taking no precautions, the odds of catching COVID19 are way more than 10x of a fatal car accident.

The more critical problem is a systemic one. If no one takes precautions, nearly 10 million people in the US will die. If everyone does, virtually no ones needs to die. It's like polluting or littering.

And COVID19 isn't flu. The flu doesn't leave a quarter of people with brain fog 9 months later.

I don't think OP's logic requires side effects of vaccines to be anywhere close to COVID19 causalities. It's a personal decision.

Without vaccines, at present, my risk of COVID19 would be marginal at best:

- I live in an area with a high vaccination rate, and a low incidence rate

- I take pretty strong precautions, including proper masks (which are probably more effective than vaccines), and pretty strong social distancing, which I can do working from home.

I'm vaccinated, but if I wasn't, my odds of catching COVID19 would be nominal at best.

That's different than, for example, a retail worker.

If I didn't believe vaccines were perfectly safe, OP's decision wouldn't be crazy. I am a general-purpose nerd, and I know enough biology to make an informed decision without relying on political authority figures. If that wasn't the case, I might make the same decision as OP.

Now, I can do the math on why, if many people make the same decision as OP, we'll have a serious problem, but again, most people (not OP specifically) can't do math.

You can have empathy without agreement.

I don't take issue with your conclusions, but I do with your arguments. I think precision and correctness are important, since a lot of vaccine skepticism comes from people making incorrect arguments.

> 1. I don't trust authorities to pull off a major conspiracy (and hide the data of bad vaccine effects).

I do trust the authorities to be incompetent enough not to collect such data.

> 2. I don't trust governments ability to effectively coordinate among themselves to pull off a conspiracy

I do trust governments to engage in groupthink. That's kind of like the idiot's version of coordinating a conspiracy.

In the process, I trust authorities to "message" and dishonestly manipulate information going out in ways which appear coordinated.

I empathize with most of what you wrote, but there are two errors:

"I also know what to expect from a respiratory virus, because I've had those a number of times."

No one whom I know who has had COVID describes it like any normal respiratory virus, and it attacks multiple systems. I'm terrified of brain damage, and before-and-after MRIs shows brain damage in >10% of people who contract COVID19.

"If I get covid, darn that sucks but people get sick and that's life"

And if you kill a loved one by sharing COVID19? Or give them brain fog? That's not something I could live with.

The deeper argument, though, is about herd immunity. If we don't stop the spread, really bad things happen.

I'll mention: Do you use proper masks? N95 and proper nanofiber masks are widely available, and appear to do more to stop the spread than the vaccine. They're also risk-free.

> 1) Did that 1957-58 vaccine use existing tech? My understanding is that other RNA vaccines and therapies were already under FDA trials with ~10 year time-frames since the method is new.

Our scientific knowledge in 1957 was limited at best. The double-helix was discovered just a half-decade before. Today, we understand in-depth most of the mechanisms of action involved. There just isn't a comparison here. The COVID19 vaccine is far safer than what we put together in 1957, no question.

> 2) Why is sequential better, because it is considered safer?

It's safer to the original research subjects, but not to the general public. For general safety, you don't find out more by going slowly. You generally learn strictly more from a human study than an animal one, and you learn more from a large study than from a small one.

The point of an animal study before first-in-man is to not endanger a human subject. Small groups come before big ones to pick off things which might e.g. harm 10% of subjects. And so on. The decision was made to skip that step, slightly raising the risk of the subjects, in order to save millions of lives.

Part of that is we had pretty darned good reason to believe the vaccine was safe, and part of that was the grave risk.

> 3) Yes, short-term we know it is extremely safe. But there are zero people that have had the vaccine much over 1 year. Long-term effects are unlikely but it's just not scientifically correct to say that this vaccine is better studied than any other just because of the number of doses administered. Duration matters a lot.

Not so much:

- We have historical evidence. Do you know of any vaccine in the history of mankind which had long-term effects not visible the first few months? I don't.

- We understand mechanisms of action pretty well at this point. Your body gets all sorts of junk thrown at it all the time -- random polens, plastics from your water bottle, randomly mutating cold viruses, and so on. There just isn't any reason to believe a new vaccine is any more dangerous than a random bug bite. Indeed, we understand the stuff going into the vaccine a lot better than we do a random bug.

Having been through an FDA process in a prior startup (although far from central at it; my role was at the outskirts), I can say the FDA is not ridiculously over-conservative. We did human pilots in another country, which was less bureaucratic. The processes there were both faster and more conservative in terms of safety. Doctors would look over our data and protocols and make reasonable decisions.

The FDA process took a long time, but wasn't all that effective at guaranteeing safety either. There was a ton of process.

I think you're attributing far too much rationality to an administrative government process.

Anecdotally, my experience is that the main reason is distrust of authority. It's usually warranted distrust too. Authorities aren't trustworthy.

To answer your question:

(1) Most vaccines don't have 10+ year trials. A year is not even the fastest; the flu on 1957-1958 has a vaccine developed in 4 months.

(2) Most medical trials are slower since everything is done sequentially, since there is no rush. You can do an animal trial, a first-in-man, a small group, then a medium group, and so on. It's a better way to go.

(3) This vaccine has had over 3 billion doses administered. It's far better studied and validated at this point than the vast, vast majority of medical trials.

If you believe "the risk of the vaccine greatly outweighs the risk of getting the virus," can you provide any back-of-the-envelope numbers to back up that statement? From my perspective, the virus already killed half a million Americans, and likely brain-damaged millions. We have enough data on the vaccine to know it doesn't do that.

That's part of the solution.

Another part is to somehow change incentive and organizational structures.

MIT, Harvard, Stanford, and similar schools made their reputations decades if not over a century ago, and the current people and structures aren't the same as the people who built those institutions. These are big institutions, with big money and big egos. The people at the top are politician types good at climbing corporate structures.

It's hard to overstate the distorting impact billions of dollars and those levels of brand recognition have.

It's also hard to overstate the degree to which Goodhart's Law is exaggerated when you have 1000:1 competition for faculty slots, among otherwise qualified applicants. It's Goodhart's law on steroids, turbo, and overdrive. You CANNOT get a faculty position at some labs without cheating, at least a little bit.

I don't see this changing unless federal and philanthropic funds are radically redistributed.

.... or the next best explanation:

- If I know nothing, I'm likely to defer to authority

- If I know a little, I know enough to at least distrust questionable authorities

The declining trust makes sense, with the replication crisis, grandstanding, intentional p-hunting, and all the other stuff happening today. I've been affiliated with an elite school for most of my life -- more weakly in recent years, and it just feels sleazy at this point. These things have gone from accidentally wrong results to games to win academia points (high impact factor publications, etc.) and get jobs and prestige.

The political split makes sense too. Trust in authority goes, in part, with representation. Conservatives are not welcome on elite university campuses. It's not just conservatives; other groups (like African Americans and some, although not many, immigrant groups) who have been excluded have historically had a hard time trusting those establishments. I'm from an ethnic group about a fifth of whom were killed based in part on eugenics research at Harvard.

Thanks Harvard!

That was in my parents' time, so the distrust from that has faded, but it at least give empathy for why skepticism is reasonable.

I think one of the things I find painful in reading an article like this one is how self-centered it is. It comes off as "Why don't they trust us?" with two presumptions:

- everything we believe is true

- so there's something wrong with them and not with us

If that were true, eugenics wouldn't have happened. The scientific establishment is just as susceptible to politics, biases, and everything else as any other establishment. I think it did pretty well around 1950-2000. Sometime in the nineties or early 00's, a gradual decline started, which is now a freefall.

Distrust in things like climate change and vaccines are very dangerous consequences of that. However, it takes a very high level of scientific literacy to sort truth from fiction in a post-truth era.

It's undermined by a history of very intentional dishonesty in both of those domains too. Throughout COVID19, the scientific establishment often exaggerated -- now obviously (see: masks) -- to bring about the behavior it wanted. Why would people believe them about vaccines? And climate change predictions had (knowingly) exaggerated timelines and impacts in the eighties and nineties, which are now being cited as reasons for distrust.

It's the boy who cried wolf.

Climate change is real, but if you have a history of lying about it, some people won't believe you.

I don't see this changing without fundamentally realigning incentives in academia.

I think the idea is to set it high enough that not too many people will look, but low enough that after a few decades of inflation (or a few weeks of hyperinflation), it will cover most reasonable transactions, beyond buying a burger.

That's my experience too.

As a student, I didn't really buy the "piracy increases sales" studies, since I went from buying one thing a year to zero. Clearly, sales were lost.

As an adult, I can casually swipe my credit card and buy similar things on a whim. How much less I bought as a student is a rounding error.

Most people or organizations are either rich enough to not bother with piracy, or poor enough not to buy especially much in the first place.

I'll mention this is doubly true with industrial software. Ain't no way an individual will drop $5k on a CAD program. On the other hand, ain't no way a serious business will pirate a $5k CAD program. Having people know how to use your tools, though, drives sales.

I want a linter where I can exclude specific things in some kind of configuration. Having linter hash tags in files is:

- Ugly and makes for less readable code.

- Doesn't apply the same exception in similar situations. If I made an exception to a rule in one place, I'd like it to apply everywhere I have similar contexts.

- Gives a binary good / bad. A linter ought to flag questionable choices to look at -- not just bad choices. At that point, I should be able to say "oops, let me fix that," or "I meant to do that" without leaving garbage around.

I'd like this stuff in a linter config/data file, ideally, as semantically as possible. If I tell a spell checker that yes, in fact, my company name is a misspelling of a common word, I don't need to tell it twice. This is not true for linters.

I'd also like all the linty stuff configured in one place, with one syntax. Not pylint + pycodestyle.

Examples of use-cases:

- A lot of the times when I'm building an API, I have unused arguments in interim versions of the code. That's okay.

- I occasionally break Python naming conventions, especially when either planning a code refactor, or when trying to comply with non-Python naming conventions

- I generally dislike lines > 80 characters, but often, that's the right way to do things, especially if the alternative is breaking up a long token like a URL.

And so on. I'd like all of these caught, and then I'd like to give the linter a pat on the back, say "Thank you, but here it's okay," and move on with my life.

There's actually a lot more stuff which could be put into a linter once you assume that being wrong even 80% of the time still makes for a helpful tool. If a linter flags give things and I change one as a result, that's well worth the little bit of hassle. If a linter keeps flagging those things forever, it's no longer worth the hassle.

GCP is nearly useless for any sort of regulated work.

Privacy, compliance, and stability aren't GCP's strong suite (by 'compliance', I mean several types, but to the greatest extent, being able to count on an API you use today existing tomorrow)

Rather oddly, Microsoft has historically gotten this nearly perfect.

I'm a bit sketched out by the increasingly malicious nature of Windows (spyware, adware, etc.), and if that same mentality translates into other offerings, this may be moot in a few years, but for now, Azure+Office 365 are the preferred vendor if you're dealing with data which really needs to proper controls.

(Although I do trust Amazon over Microsoft for the technical aspects of security)

I feel like there's an uncanny valley.

If you know a little bit of math, there's no benefit.

If you know enough math to jump to e.g. medical imaging, robotics, controls, simulators, image processing, ML, or similar, there's a ton of benefit.

An EE degree ought to give enough background to get there, although it may involve a year or two of study in a particular domain, and a side project to prove you have the skills.

I've used them all the time in my career, but:

1. I did learn them well, and so I did use Laplace quite often in EE.

2. I jumped careers not into IT, but into tracks which leveraged both programming and a mathematical skill set.

I'll mention: I'd be bored out of my wits doing just IT. The intersection of IT and math includes computer graphics, visualizations, robotics, machine learning, fintech, image processing, and a ton of other stuff I find much more fun and fulfilling.

That's not an implicit commentary on your path, by the way, just an explanation of mine. We all have different goals, desires, values, constraints, etc.

> There's a slant towards standardised EE-specific solutions for PDEs, but it's still much more abstract and mathematical than any field of CS, apart from maybe cryptography and data science.

Here's the thing, though: PDEs are NP-hard. There isn't a generalizable way to model dynamics. On the other hand, dynamics come up everywhere:

- How is the pandemic going to evolve?

- How will incentive structures skew cultures?

- How do I build a suspension for my car?

- How does heat leak from my house?

- How does my understanding evolve with learning?

... and so on.

What EE does -- and I think uniquely -- is given intuitive, graphical tools to think about differential equations, in tools like Laplace, Body, Nyquist, root-locus, and so on.

They also give a lot of applied experience in applying those, including in contexts with nonlinearities. An op amp will clip on both sides, which you model as a linear differential equation (which is easy enough to reason about) and a memoryless, time-invariant nonlinearity. You squint. You kinda ask yourself how it would work if it /were/ linear, and the nonlinearity just cut gain. And at some point, after doing it enough, you have intuition for what it will do.

With the EE-specific stuff, I can intuitively reason about these things think through to design.

EE is all about modeling -- building simpler equations which approximate more complex ones in ways which give intuition -- so this is also usually correct or almost correct. Indeed, if you go onto grad level courses in control theory, you'll see formalizations of this intuition, where for example, a time-variant system or a nonlinear system is modeled as a linear time-invariant system, together with a bounded error.

A lot of the mathy stuff -- which I've learned a fair bit of as well -- is in abstract more general, but in practice, gives much less intuition.

My experience with the real world is that there are rarely actual differential equations handed to me. I kinda get that we've set up some pricing structure, or some incentive design, or whatnot, but I can't model it formally. I know which way things push, and whether those integrate or not. I can draw a block diagram and reason about how it will behave, in a way the math side doesn't let me do.

An EE degree IS awesome. It gives a generalizable mathematical foundation which applies in just about any other field of work. It gives a huge competitive edge. For example:

* Circuits are physical implementations of differential equations, and EE gives a unique way to intuitively think about dynamics, which applies to finance, epidemiology, and just a really diverse range of domains.

* With a rigorous EE background, you can rapidly pick up most domains of engineering (think Elon Musk), since you've got all the mathematical foundations. The reverse isn't true. The way math is taught in EE is broader than e.g. mechanical, civil, or other engineering disciplines, where it tends to be more domain-specific. EE gives you a lot of depth in probability, error analysis, signal processing, controls, calculus, linear algebra, etc. I think the only things missing are statistics and discrete math, and I picked those up elsewhere.

High-performance board-level EE is insanely fun. Incredibly creative. You get to build stuff, design stuff, do math, and it's just a huge diversity of intellectually-fulfilling stuff.

IC design is a bit less fun, due to the many-month turn cycles (develop, wait months and hundreds of thousands dollars, and test/debug), but not bad.

However, the EE industry sucks:

- Pay is not bad, but much worse than other jobs you can get with an EE degree.

- Work culture has all the worst excesses of the eighties -- think of Office Space style cubicle farms, dress codes, conservative management, ISO processes, and paperwork.

- Yet it's somehow adopted some of the worst excesses of the 2010s; it no longer feels like work is a family or a community

- And it's hard to get into. There are virtually no jobs for junior-level EEs (which isn't just BSes -- in the Great Recession, I knew bright newly-minted Stanford/MIT/Caltech/etc. Ph.Ds who couldn't find jobs).

- Even at the senior-level, there's a bit too much of a boom-and-bust cycle, without the booms ever getting that boomy, but the busts being pretty busty.

I spent maybe five years doing EE work after my EE degree, and I think that was enough. I've been out for a long time now. I still do EE as a hobby, and I enjoy it, but the industry culture isn't one I remember with fondness.

I suspect a lot of this stuff will continue to disappear from the US into Asia; that transition is rapidly in progress. US firms maintain specialized knowledge in some areas (e.g. particular types of MEMS devices), but there are plenty of places we've fallen behind. I don't see us on the path to regain leadership. I think some of this is cyclical. Declining industries don't make for good employers, and poor employers don't make for growth industries.

There are plenty of errors in this, and you're confusing the four prongs, but I'll run with it for now, since it's good enough.

If I am doing code autocomplete, and copying three lines out of your program, those three lines are exceptionally unlikely to be "substantial ... in relation to the entire work."

Where you're confused is lots of places, but the biggest one is you're mixing up the prongs. Most of the reason your Citizen Kane example might work (it probably wouldn't) is Factor #1, character of use. Commentary and education are favorable.

I can come up with examples where 0.002% of an original work would not be okay, but they're pretty contrived.

For your benefit, a random link: https://guides.lib.utexas.edu/fairuse/fourfactor

Size definitely matters. It's not a hard-and-fast rule, but a factor.

>> Are you laundering 5 lines of code? Probably fair use.

>The amount of copying does not matter for a copyright claim.

This is incorrect. The amount of material used is the third factor in a fair use test. There are other ways it comes up as well (e.g. damage calculations).

>> Are you trying to launder, or is it unintentional?

> I'm not a lawyer but in the US, Copyright is a strict liability statute which means intent does not matter.

In this context, this is incorrect as well. People seem to be misinterpreting laws pretty badly, so instead of explaining how to apply here, I'll give a simpler, analogous context:

https://en.wikipedia.org/wiki/Online_Copyright_Infringement_...

In this context, it depends on a lot of things, such as direct versus contributory infringement. This would likely be a contributory infringement case, where intent is almost a requirement. https://www.legalmatch.com/law-library/article/what-is-contr...

Keep in mind these are all factors. They matter, so they'll help swing a case one way or the other, but they're not something you can bank on in isolation. That's what will make for interesting case law.

I'm okay with where it stands.

Where it stands: We have a new technology. We don't really understand it. There aren't well-defined ethical or legal bounds. We're figuring them out, blumbering along as we go. People are trying things, and are making all sorts of arguments, some of which will stand the test of time, and some of which won't.

I'm more concerned when we do this with weapons, medicine, or social science, but an autocomplete tool seems like a relatively benign place to start figuring this out.

It's more complex. Are you laundering 5 lines of code? Probably fair use. Are you laundering Windows? Clearly not okay. Are you trying to launder, or is it unintentional? Etc.

There's a line somewhere, but it's somewhere, not at either edge, and it's a complex, odd-shaped line we haven't yet defined. I don't know where it is. However, it's not where you're putting it, and there isn't a slippery slope to "IP is over." If you use five lines of my code in a different system, IP hasn't ended.

It's very different. Law looks at intent.

Napster was an easy litigation mostly because of intent. There was a document trail a mile long that the primary goal of Napster was to facilitate copyright infringement, although on paper, it could be used to share anything.

Cameras, xerox machines, and audio recorders can all be used for infringement, but aren't illegal since that's not their primary purpose. For even finer lines, see the Betamax case. You can also look at cases about different sorts of transformations and transmissions:

- I buy a DVD. It is transferred via USB cable from a DVD player connected to my computer, temporarily copied to computer memory, and then shown on my monitor.

- I buy a DVD. It is transferred via the internet from a DVD player in my data center, via the Internet, temporarily copied to your computer memory, and then shown on your monitor.

Can you see how these have nearly identical technology, yet different legally? If you ever take a law class, there are a lot of discussions about how to draw such lines (disclaimer: IANAL, but I've sat in on law classes before).

To be clear, I'm not taking a stance about where this ought to land legally. I don't know. But the analogy to bittorrent, limewire, or KaZaA is a faulty one.

If you have a legal argument, please make it. If you're going to make random, incorrect, unsubstantiated statements, this might not be the best forum for it.

I'm not going to engage in a flame war here. I clearly laid out references to relevant parts of the law: (1) What a license is and is not needed for under US copyright law (2) purpose of the GPL, and (3) distinction between copyright and data rights.

If you have any sort of citation for this nonsense, please post it. Otherwise, I'm checking out here.

Wrong.

What do you think the 'P' in GPL stands for?

Regardless:

1) You don't need a license to look at things. Copyrights reserve very specific rights: Reproduction, creating derivative works, distribution (limited to first sale), performance, display, and in some cases, transmission

2) And data can't be copyrighted.

US IP law is based on a clause in the US Constitution, and is fundamentally bounded in reach.

I care, but I don't deny the reality we have an enforcement issue. That's not just GPL. That's most businesses, schools, and many individuals.

I would guess 0.1% of GPL violations have as much as a legal demand letter, let alone a law suit.

It can be wiggled around. For example, if I ship a robot which can paint a famous painting, but not the painting itself, I would argue the copyright violation happens by the user of the robot when it paints the painting, not by the maker of the robot.

If that's something I'd planned on and intended, and you own the copyright to the painting, you have all sorts of legal tools to go after me -- contributory infringement, collusion, and so on. Still, contributing to a legal violation is not the same as engaging in one.

I suspect an argument can be made that github now incorporates AGPL code generated by co-pilot, and so is AGPL. A fair use argument might be made as well; we've all copied one-liners from blog posts and tutorials, and that's okay. I'm having a harder time seeing a reasonable argument that an ML model is directly infringing just because it can produce a copyrighted work, though.

So I don't think it's a cut-and-dry legal question.

I will give a caveat: Coders tend to read laws much too literally, like computer code. When I was was an obnoxious teenager, I thought I'd found all sorts of contract / license / etc. loopholes in all sorts of legal documents, and I considered myself profoundly clever, thinking I'd outsmarted the lawyers.

Nope.

In college I took law classes, got into the real world, did a few startups, and saw a few legal cases. Court systems have technical rules you need to be aware of, of course (e.g. if you miss a deadline....), but interpreting the law is really grounded in common sense. Common sense is culturally situated, and ours is situated in hundreds of years of case law.

FYI: Big companies which aren't Facebook can have energy, excitement, and sense-of-common-purpose. It's less common than in the sixties, when companies like HP were like families, but companies like that still do exist.

Indeed. And Clinton, let me ask you. What was your opinion of Anant?

Did he seem honest? Did he seem to care about the not-for-profit mission? Did the employees respect him? Or did he seem like a sleazy used car salesman?

If Anant were a highly-skilled, qualified executive acting in the interests of edX, I might have no problem with a high salary. Did Anant seem like that to you?

Did the teaching-and-learning on edX advance or regress since the MITx days? The focus on equity? The technology platform?

Did edX lead to major research breakthroughs? Did it impact the developing world?

And for that matter, how many URMs worked there? Were there leaders who had any knowledge of how less affluent people lived or who to build a platform for them?

Part of the reason lower compensation packages make sense is that people who care about initiatives like edX usually don't do it for the money. Paying Anant a megabuck a year exactly selected for the type of executive who worked his butt off to maximizes quarterly bonuses, rather than mission or long-term.

MIT and Harvard's top asset is in brand equity. Did you feel like the $800M -- let's call it $400M each -- which will in effect add 1% to Harvard's endowment and 2% to MIT's -- is worth the reputation hit?

Everyone at edX who helped define the future of education left about a half-decade ago. A third of the staff, including everyone who cared about learning, poor people, open, or much of anything else.

edX was overmonetized. If you want to see corruption on a grander scale, see where this $800M goes.