This article is a low quality strawman. The owner of the laptop shop is one of the least credible witnesses I've ever seen - he never saw hunter biden drop off the laptop, and his story changes about 4 times in substanial ways during a single interview. He is an ardent trump supporter, and identifies it as hunters laptop because of a sticker from the beau biden foundation on the laptop. This is hardly a smoking gun.
Additionally, Hunter Biden is not up for president. Clearly its not cut and dry, but I'm not sure we should be hanging a father out to dry for the actions of his clearly struggling son, so there is lots here that's of shaky relevance to Joe or public interest in general.
This article says "we have no reason to believe the emails aren't genuine" which is the exact opposite stance to that a rational person should take. If there is substantiated evidence that these allegations are true, publish them, get them trending on twitter.
But this is the thing with misinformation. You simply cannot allow stuff to be widely shared until there is proof it is not true - the cost to making a false claim is 2 or 3 orders of magnitude as disputing it.
Conservatives are doing themselves a disservice here. This article stinks of vested interests, barely plausible narratives and incredibly poor witnesses. Is this what you want to attach your name to? Free speech has clearly not been violated - so the allegation is that this crackpot story wasn't amplified by twitter and "left leaning journalists". Come on, take a step back and think if this is really the hill you should be dying on.
The point is that the two are coupled. It is not clear why 0 false negatives is the aim. In almost all hard problems, you cannot have 0 false positives and 0 false negatives.
Normally, getting to 0 false negatives requires a large number of false positives. E.g. if I wanted a 0 false negative pregnancy test, the only feasible way would be to tell some very large proportion (maybe all) test takers they are pregnant.
If it requires 20 innocent people to be killed in order to achieve say a goal of 1 police officer failing to identify a threat, who says that is the right balance?
You want to take emotions out of it, I say the life officer of a police officer is no more important than an innocent person, and given a police officer has a) control of which situations they enter and b) presumably accepts some level of risk from the job the choose and c)
Killings by police are an externality that the police system is not incentivised to fix in a meaningful way , they should bear the burden of systemic risk from those interactions. Accepting no less than 1 innocent death for 1 police death seems like the rational baseline, and I think there are compelling points to suggest it should be less than one innocent death to police death.
I think this is touching on a key point about militarization of the police. I'm a non US veteran who went to Afghanistan.
The police in the U.S. seem to think like they are in the military , in their training and tactics. One big problem is the U.S. military is not exactly well regarded for is nuanced handling of conflict.
I once spoke to a marine who was involved in the invasion of Bagdad who describe their rules of engagement as "shoot any man woman or child holding a spade, a mobile phone, any kind of parcel or anything that might be a wire". These ROE are almost certainly a war crime, but the US is special so it gets away with it.
Now in the military you have a bunch of guys who actually have to deal with very dangerous, fluid situations that have a high likelihood of death. They mostly operate in areas where you have little room for anything other than binary control (obey or get shot). Whatever the details of the culture that was set down by the high ups before the Iraq invasion, I can somewhat get onboard. Casualties in a war zone are logistically hard, getting effective treatment often means at least some part of running them on a stretcher, potentially strapping them to the back of a vehicle and driving for an hour. If you aren't conservative in how you instruct people to respond, the effect can be highly non linear. One casualty take a 3 others out the fight, meaning casualties become more likely etc.
How police respond simply should not be modelled on the military. I entirely disagree with the idea that they are constantly primed to consider themselves one stop away from a body bag.
They almost certainly interact with more innocent members of the public than criminals. They are in largely stable situations. They may deal with bad people, but they do so in places that have good access to support, they will get timely care if something happens to them, and they almost certainly are well backed up if the situation gets out of hand.
My opinion is that the police basically suffer from a kind of dunning Kruger effect. Most would be woefully unprepared to handle an actual combat situation. You just have to compare the countless videos of about a dozen cops all unloading at the same car like the first to finish gets a prize.
Being a good solider is about maintaining discipline and composure under pressure. Most unit tactics involve some variant of your unit shooting over your head or off to your side whilst some of you push some kind of flanking manoeuvre. Our military even dropped the shoot from the hip on contact SOP because of the risk of friendly fire.
The police do not have anywhere near the same level of conditioning to operating under pressure from their training as any competent army gives it's soldiers. If they want to act like the military that's fine, but they should go through similar training before they do.
I think you are missing the key part of the appeal here (or framing it as a negative).
Let's look at your question.
Writing an equation for a cat is hard, actually really hard. Humans cannot reliably explain their decisions here. If I ask a person to tell me how they classify between cat and not cat, the answer will invariably be something along the lines of "well, it has the general shape of a cat". Which is actually just a huge combination of heuristics it took about 10 years to work out. There is quite a lot of work in neuroscience suggesting that the actual decision you make when you classify a cat happens before a rational is developed.
We could encode a function for that, but it relies on us knowing a lot about cats, which takes time and only works for toy examples.
If you use a convolution neural network, you can get close to human level performance on much more complex topics with little domain specific insight. There is no universal law for classifying hand written letters - they are an individual's interpretation of some symbols we made up. This task will always be 'non-rigorous' because the very underlying thing is not actually well defined. When does a 3 become an 8?
So we could have a person toil away and come up with a bunch of heuristics that we encode in a regression, but why is this better than having a machine learn those heuristics? Most problems are not life or death. What is the real added value in having people hand crafting features for predicting traffic jam delays or customer retention, when the end use is probably just to have a rough indication.
As somebody who does research using a huge range of models, I object that we should be guided by our intuition- our intuition is mostly wrong about non trivial problems.
Basically any "equation" somebody has discovered for what happens in a neutron star is "simple". There is a large amount of observational data, it is a consequence of some already well proven theorem, or relies on something well established to narrow the range of possible descriptions immensely, or (most commonly in my experience) the equation is basically a human version of deep learning, where grad students toil away making tweaks and heuristics until a point that the description fits the data somewhat well, and then there is some attempt to ascribe meaning after the fact.
For example, we can describe the trajectory of a comet using a "few" lines of high school level math. This means it is actually feasible for a person to have a reasonable intuition about what is happening, as the problem is actually dominated by a handful of important variables. Good luck getting anywhere near simple to describe cats (again, in a domain where the line of what is and isn't a cat is actually not even a property of it's physical attributes, so the problem is not properly defined under your requirement). To tell if something is or is not a cat, would require a DNA sequence. That is how we define the cat. So by your own definition, we do not have sufficient data in our dataset to properly do this classification.
I'm not sure you really understand the point you make about "statistical tests for bullshit".
Most statistical tests are themselves ivory towers of theory and assumption which nobody ever verifies in practice (which is as unsciency as anything you accuse machine learning of). And people do actually use well grounded ways of evaluating machine learning models. Cross validation is very common and predates most machine learning, and has various "correctness" results.
For any model we build, if we do not have data that encodes some pathological behaviour we can test it out on, there is no test, no statistical procedure to tell us that model is flawed. If we have that data, we can run the exact same test on a black box model.
You should not conflate science with formalism or complexity. Running a statistical test is pseudoscientific unless you do it correctly and appropriately.
Saying something is not scientific because the data may not contain enough information to fully answer the question is flat out wrong.
I bring out a coin; I tell you nothing, and ask you to guess what the probability of heads is. What do you guess?
Unless you have reason to believe I am trying to deceive you, it will be able 50% because you have a lot of knowledge from other contexts that tells you this is true.
The arrow is probably the other way round than you state -the brain probably isn't Bayesian; being Bayesian is modelled on how humans process and contextualise decisions.
I'm not even sure how a frequentist would construct a model to estimate an outcome with no observations.
It doesn't. But the workflow of Bayes forces you be explicit. If you try and cook the books, it will be shown for the world to see. Can you provide a paper that quoted a p value for a regression and also validated all the asymptotic conditions are close to being true in order for that p value to be even somewhat reliable?
This ignores the main strength of a Bayesian workflow. You can straight forwardly quantify the effect of your prior choice on your inference - pick a different prior; how much does that change the inference, etc etc. A good Bayesian workflow does not assume a prior to be true; it should be based on available evidence, and then stressed. To be a bit more concrete, let's say we wish to model the height of kangaroos. We come up with a model form, say regression, and a bunch of potential features. If we are Bayesian we might say; "I think nature prefers simple stable solutions, so I'll put a N(0,d) prior on my weights. We then compute a posterior and get a range of credible values. We can then say, "hey, what if I'm wrong and actually it's a student t, or it's flat prior or X or y or z", and use principled tools like marginal likelihood to say which family of models works best, do prior posterior comparisons to see how observations changed our prior etc etc.
If we do this under a frequentist framework we compute the regression coefficients, and can get some confidence bounds with some appeal to asymptotics (and nobody I've ever seen actually makes any attempt to validate these assumptions). And even when we are done, we get a confidence interval that has such a truly unintuitive definition that almost every person who is not a stats PhD fundamentally misinterprets.
To say frequentists make less assumptions is not true- they are just less explicit, and I consider it a strength not a weakness to highlight choices made by the statistician.
The OP you asked worked at an Investment Bank. This is not exactly the pinnacle of algo trading (at least in US/Europe) as they are hampered by regulations about prop trading etc.
I work at a big quant/algo prop trading firm. The reason you can't compete is that most successful algorithmic trading relies on economy of scale.
Yes capital to trade with is one aspect, but more importantly is the amount of knowledge needed to carry this off. Building a safe, robust strategy is hard even if you have a working idea.
It requires probably top 1% knowledge in distributed systems, Linux admin, networking, database administration, security and more.
The chance that any individual (or even team of less than 5 people) has those skills covered in addition to the quantitative know how to actually come up with a strategy is quite small.
There is a reason why algo trading is dominated by large firms. Working at somebody like Citadel or Optiver, if you say "oh damn, there's this weird effect on our server. When x happens I get a period of noticeable slowdown" they will turn around and say "Ok, well John here is a core dev on the Linux kernel, he'll come to your desk and help you figure it out".
Imagine that but with every problem pretty much. Running trading strategies you will hit problems. The level of work to overcome all of them satisfactorily whilst not degrading your edge is so much work for an individual I suspect you would burn out pretty sharply.
Additionally, Hunter Biden is not up for president. Clearly its not cut and dry, but I'm not sure we should be hanging a father out to dry for the actions of his clearly struggling son, so there is lots here that's of shaky relevance to Joe or public interest in general.
This article says "we have no reason to believe the emails aren't genuine" which is the exact opposite stance to that a rational person should take. If there is substantiated evidence that these allegations are true, publish them, get them trending on twitter. But this is the thing with misinformation. You simply cannot allow stuff to be widely shared until there is proof it is not true - the cost to making a false claim is 2 or 3 orders of magnitude as disputing it.
Conservatives are doing themselves a disservice here. This article stinks of vested interests, barely plausible narratives and incredibly poor witnesses. Is this what you want to attach your name to? Free speech has clearly not been violated - so the allegation is that this crackpot story wasn't amplified by twitter and "left leaning journalists". Come on, take a step back and think if this is really the hill you should be dying on.