[CONTENT WARNING: liberal spoilers for Star Wars: Rise of Skywalker]
[CONTENT WARNING: liberal spoilers for Star Wars: Rise of Skywalker]
I hate visiting the same place in the mountains twice, yet I’ve been to Lake O’Hara…. shoot, nine times? I’ve lost count. That should tell you something about the place. [Read more…]
Things have gotten quiet over here, due to SIGGRAPH. Picture a giant box of computer graphics nerds, crossed with a shit-tonne of cash, and you get the basic idea. And the papers! A lot of it is complicated and math-heavy or detailing speculative hardware, sprinkled with the slightly strange. Some of it, though, is fairly accessible.
This panel on colour, in particular, was a treat. I’ve been fascinated by colour and visual perception for years, and was even lucky enough to do two lectures on the subject. It’s a ridiculously complicated subject! For instance, purple isn’t a real colour.
Ok ok, it’s definitely “real” in the sense that you can have the sensation of it, but there is no single wavelength of light associated with it. To make the colour, you have to combine both red-ish and blue-ish light. That might seem strange; isn’t there a purple-ish section at the back of the rainbow labeled “violet?” Since all the colours of the rainbow are “real” in the single-wavelength sense, a red-blue single wavelength must be real too.
It turns out that’s all a trick of the eye. We detect colour through one of three cone-shaped photoreceptors, dubbed “long,” “medium,” and “short.” These vary in what sort of light they’re sensitive to, and overlap a surprising amount.
Your brain determines the colour by weighing the relative response of the cone cells. Light with a wavelength of 650 nanometres tickles the long cone far more than the medium one, and more still than the short cone, and we’ve labeled that colour “red.” With 440nm light, it’s now the short cone that blasts a signal while the medium and long cones are more reserved, so we slap “blue” on that.
Notice that when we get to 400nm light, our long cones start becoming more active, even as the short ones are less so and the medium ones aren’t doing much? Proportionately, the share of “red” is gaining on the “blue,” and our brain interprets that as a mixture of the two colours. Hence, “violet” has that red-blue sensation even though there’s no light arriving from the red end of the spectrum.
To make things even more confusing, your eye doesn’t fire those cone signals directly back to the brain. Instead, ganglions merge the “long” and “medium” signals together, firing faster if there’s more “long” than “medium” and vice-versa. That combined signal is itself combined with the “short” signal, firing faster if there’s more “long”/”medium” than “short.” Finally, all the cone and rod cells are merged, firing more if they’re brighter than nominal. Hence where there’s no such thing as a reddish-green nor a yellow-ish blue, because both would be interpreted as an absence of colour.
I could (and have!) go on for an hour or two, and yet barely scratch the surface of how we try to standardize what goes on in our heads. Thus why it was cool to see some experts in the field give their own introduction to colour representation at SIGGRAPH. I recommend tuning in.
Starting out with a lie probably isn’t a good idea, but it’s the best summary I’ve got. I finally have a stretch of free time, and I’ve given myself explicit orders to kick back and relax. That includes poking away at this blog again, as poor ol’ Proof of God has suffered a fair bit of neglect and my list of rant topics is worth a rant in and of itself. But kicking back also means kicking out stuff like this:
It’s not my best work, but I’m also coming back into photo processing after a few year’s absence. I’ve never really been happy with my forest scenes (they always feel way too busy and unfocused), so I thought I’d crush the busy bits into blackness and draw your eye to the really cool bits: the texture of the snow. The desaturated high-detail look is practically a cliché nowadays, but I like it here (and I was hip to it before most, dammit!)
This is like a tangent of a tangent of a tangent. Remember that Mythcon boondoggle? Not everyone agreed it was bad, for instance Melissa Chen thought it was a defining moment for her. Stephanie Zvan agreed, but in a cursed-monkey’s-paw sort of way. In the process, Zvan linked to two tweets by someone in attendance, who grabbed some of Chen’s slides. [Read more…]
Confession time: not too long ago, I probably would have been standing next to Science Headquarters. I never would have called philosophy useless, and I thought Harris in particular was underplaying how difficult it would be to create a moral system from science, but I did buy into things like this.
Science is the best method humankind has devised for understanding causality. Therefore the scientific method is our most effective tool for understanding the causes of the effects we are confronted with in our personal lives as well as in nature. There are few human traits that most observers would call truly universal. Most would consent, however, that survival of the species as a whole, and the achievement of greater happiness of individuals in particular, are universals that most humans seek. We have seen the interrelationship between science, rationality, and rational skepticism. Thus, we may go so far as to say that the survival of the human species and the attainment of greater happiness for individuals depend on the ability to think scientifically, rationally, and skeptically.
In the handful of years since then, I’ve realized that science is both a business and a career. That alone is enough to warp the scientific record and induce false results. But the rot extends even further, right into the scientific method itself, and the only way out is through philosophy. If you’d prefer the short version (emphasis mine):
The above derivation is one reason why the frequentist confidence interval and the Bayesian credible region are so often confused. In many simple problems, they correspond exactly. But we must be clear that even though the two are numerically equivalent, their interpretation is very different.
Recall that in Bayesianism, the probability distributions reflect our degree of belief. So when we computed the credible region above, it’s equivalent to saying
“Given our observed data, there is a 95% probability that the true value of μ falls within CRμ” – Bayesians
In frequentism, on the other hand, μ is considered a fixed value and the data (and all quantities derived from the data, including the bounds of the confidence interval) are random variables. So the frequentist confidence interval is equivalent to saying
“There is a 95% probability that when I compute CIμ from data of this sort, the true mean will fall within CIμ.” – Frequentists
Note the difference: the Bayesian solution is a statement of probability about the parameter value given fixed bounds. The frequentist solution is a probability about the bounds given a fixed parameter value. This follows directly from the philosophical definitions of probability that the two approaches are based on.
That question has been central to [John] Ioannidis’s career. He’s what’s known as a meta-researcher, and he’s become one of the world’s foremost experts on the credibility of medical research. He and his team have shown, again and again, and in many different ways, that much of what biomedical researchers conclude in published studies—conclusions that doctors keep in mind when they prescribe antibiotics or blood-pressure medication, or when they advise us to consume more fiber or less meat, or when they recommend surgery for heart disease or back pain—is misleading, exaggerated, and often flat-out wrong. He charges that as much as 90 percent of the published medical information that doctors rely on is flawed. His work has been widely accepted by the medical community; it has been published in the field’s top journals, where it is heavily cited; and he is a big draw at conferences. Given this exposure, and the fact that his work broadly targets everyone else’s work in medicine, as well as everything that physicians do and all the health advice we get, Ioannidis may be one of the most influential scientists alive. Yet for all his influence, he worries that the field of medical research is so pervasively flawed, and so riddled with conflicts of interest, that it might be chronically resistant to change—or even to publicly admitting that there’s a problem.
Come to think, that could explain why I read the comics I do.
Ugh, I wish I had more time to sink into this blog. What’s worst, most of what I’m focused on can’t or is too boring to share over here.
Most. There are always exceptions, of course.