Chris X Edwards

Economics professor Bryan Caplan, an elite practitioner of formal education, has shit in the nest of formal education by writing a book excoriating almost everything about it. When it comes to formal education, I’m also an unusually harsh critic. For example, I have previously written about the futility of teaching calculus in the modern world. When I heard that someone had written a whole book calling bullshit on education, The Case Against Education was at the top of my reading list.

My years of formal "education" were painful. With decades of hindsight I am now more sure than ever that the problem was not simply my deficiencies. But go to university I did and I clearly remember sitting in my (required) economics classes and having a very profound stroke of insight: if this class is not bullshit, my time spent in it should be fungible with money. What I discovered over the years is that economics courses do not magically unlock secrets that increase personal wealth but that the magic piece of paper which these courses were required to obtain do. My degree, industrial engineering, is about bringing engineering methodology to bear on maximizing returns and minimizing investments. It didn’t seem unnatural at all to turn this ROI maximizing analysis back onto the educational process itself. It was hard not to notice the extensive room for improvement.

That there is immense room for improvement in modern education is basically the entire thesis of Caplan’s book. Like Caplan, I also have a lot to say about the topic of education and for the purpose of remembering this book I’ll say much of it here. I made a lot of notes while reading this so I apologize in advance if this is overly long and disjointed.

Rather than calling it the "The Case Against Education", a more accurate title would have been the phrase used on page 239: "The Case Against Actually Existing Education". Caplan uses "education" the way it is used in the phrase, "increased funding for education". This is the "provide schooling for" definition of education. He’s not talking about the particular dictionary definition of education that is "to develop mentally, morally, or aesthetically especially by instruction". What Caplan is arguing is that all of the ostensible goals of that second definition of education are thwarted by how the first is actually practiced.

I feel like the provocative title may actually detract from efforts to improve the situation. If people are so repulsed by the apparent argument against the idealistic notion of education as they imagine it, they may not investigate beyond the absurd sounding title. Buried in the book, Caplan does make it clear that he does truly care about real education. He makes a fuss because education humbug is actually detrimental to true education, something you could say he is making the case for. That is definitely how I feel about it.

The book asserts that current educational regimes are easier than they used to be and getting easier. Physics degrees, however, are never out of fashion because they are simply a proxy for any random instruction that is genuinely difficult. "As long as you avoid rare, demanding paths like engineering and premed in college, you bask in the warmth of a four-year vacation." As someone who completed one of those more challenging majors (some time ago) and who until recently provided engineering tutoring to computational molecular biophysics grad students professionally, I’ll have to take his word for it. He does provide data.

One damning point he correctly brings up is that the internet is the embarrassment of "education". What does formal education purport to achieve that the internet is not utterly upstaging? Here, for example, is an astonishingly good explanation of Fourier transforms which live instruction could not dream of bettering. Caplan points out that critics point out that this only serves self-motivated people. To which he says, to ask for more is petulant; I completely agree. This leads me to believe that if education were obvious, everyone would do it. I don’t just mean attend college, but also compulsively read Wikipedia, have a big library agenda, watch educational videos on-line more than TV, audit courses from MIT and Stanford, etc. But as far as I can tell from the popularity of general internet Prolefeed, most people are just not truly into education despite what they may claim.

Some problems with education seem obvious but they are far worse than you might imagine. For example, the staggering costs — if Caplan is to be believed, education costs vastly more to society to provide ($11e11) than defense ($7e11). Let that sink in. Read the book if you’re doubting the numbers. And there must certainly be similar or worse opportunity costs — students sitting there wasting potential en masse.

Part of what makes education so futile is that retention is almost nonexistent — he cites a study where 25 years after learning algebra and geometry in school, almost everybody had forgotten almost everything about them. He uses the idea that schools can’t be responsible for more than 100% of what people generally know about subjects they’ve been "taught", i.e. disturbing levels of post graduation ignorance show how sad the ultimate effects of education are.

Here is an email from my son’s math teacher (who I am sure is a hard working competent person using best practices). Note how they struggle to hold the house of cards together until they reach the safety of The Final Exam.

No mention of ensuring recall for the (basically nonexistent for most people) opportunities to apply this learning in one’s actual life. As you can see, this "education" is designed to be forgotten.

I’ve always felt that homework was an intellectual failure of the education process. It’s a bit like saying that flat pack furniture provides quality of life by giving you something to assemble. Worse, I remain unconvinced of homework’s efficacy. I am now not alone. On page 241 Caplan calls the majority of schooling "insipid busywork". I pretty much agree.

It is now easier than ever to properly criticize traditional education because there are now much better options. I hope that cost-effective, convenient (to everyone) on-line courses which are focused on functional useful skills that employers truly want employees to have (e.g.) will start to eclipse the fluff and bother of a misdirected, onerous, unpopular educational process in the traditional format that Caplan is criticizing.

Caplan’s main thesis is that education is mostly signaling. He thinks the ratio is around 80% signaling and 20% genuine "human capital", i.e. skills of value to society. He basically believes that current educational practices give kids all of the fun and joy of child labor with almost none of the productivity or practical vocational training. Kids today learn what is valuable in a perverse artificial marketplace of grades and other nonsense while kids working jobs in the past at least were educated about what society found truly valuable. That’s right, Caplan flirts with advocating for child labor and I have to say, he made a pretty good case (unless you hate children).

Caplan believes school in general is getting easier and that there is grade and degree "inflation" where students attain higher grades and degrees than in the past for the same amount of work. Here is a NYT article about how much good grades matter; short answer, they don’t. Caplan argues that this just wastes students' lives.

"Intellectual inbreeding" is Caplan’s term for the fact that professors teach what they have been taught, and "relevance" is only attended to in that academic context. Otherwise intelligent educators may be largely blind to the deficiencies of current educational practices because, as the saying goes, "It is difficult to get a man to understand something, when his salary depends upon his not understanding it!"

If most real and valuable learning is done on the job (and Caplan’s mountains of data say that is true), formal education is actually robbing us of valuable education.

Caplan really is convinced (again, mountains of data are provided) that 80% of the schooling experience is dedicated to signaling. Four days a week kids are figuratively growing peacock feathers at school.

Signalling is a zero-sum game (if I’m the best, you’re the worst) but real skill development is not (if I’m skillful and you’re very skillful, together we are even more skillful). Caplan points out, "Conventional education mostly helps students by raising their status, but average status cannot rise." That is an interesting thought.

On page 240 he reiterates exactly what I’ve said are the two essential ingredients for any effective presentation: a presenter who is genuinely interested in the material and an audience who is. Caplan adds a third essential ingredient — actual interesting content. While somewhat true, I would argue this mostly sorts itself out automatically with the other two. The important point here is that to say the typical classroom, at any level, is filled with people genuinely interested in learning (or teaching!) what is being presented is nonsensical.

Of course education does have some noticeable effects. For example, this article explores the question: is too much indoor time causing myopia? And in an astonishing and evolutionarily stupid twist, according to the book the main quantifiable result of education is to limit fertility. Yes, the truest clear actuarial thing that can be said about the way we practice education now is that if you subject your kids to it, you’ll have fewer grandchildren. Awesome.

Caplan does a pretty thorough job of thinking of ways that current education practices are deficient but I thought of some more. For example, I wonder if there is anti-signaling — normal people dumbing it down to gain status in some social strata that I am not a part of (perhaps related to this topic). Remember, some people do want grandchildren!

Another problem I easily perceive is what if you’re a generalist and want to octuple major in mechanical engineering, mechatronics, technical writing, urban planning, operations research, information theory, statistics, and computer science (with a "focus" on programming, graphics, networking, and machine learning)? Not only is the current configuration designed to not accommodate that, it is designed to eradicate any such thinking. To the education business, the idea of a generalist is literally worse than the idea of an ignorant person. Taking cues from education, how then does industry value these kinds of people — pretty much zero. (I can assure you!) Then consider the kinds of people who actually bring profound change to the world — they are often people who can fuse disparate fields like Ben Franklin, John Von Neumann, Paul Erdos, DaVinci, Bertrand Russell, Isaac Asimov, Leibniz, etc. Today any one of those guys would have been diagnosed as having a mental disorder and pumped full of magic beans to cure their ADHD. I don’t have any obvious solutions but the intense focus on a "major" with "high" achievement funnelled into absurdly specific PhD research certainly isn’t helping.

I’ve talked about the problems Caplan highlights in his book, but now let’s take a look at the problems with those problems. What are the reasonable counterarguments? First, here is an excellent critical reveiw of the book.

For me, it is too difficult to imagine that literacy is not helpful and attaining it is not important. Caplan is trying to shake such core beliefs but as a natural reader, that one is tough. But I’ve known illiterate people who got along just fine except for the stigma of illiteracy. The number of books those people read were pretty close to the national average of almost zero.

It is possible society needs to train 80% of kids to understand trig so that you can eventually produce society’s necessary 5% of engineers. Another thought I had is that since one of the best ways to learn something is to teach it, perhaps a captive audience of students is required to get grad student TAs up to the very top levels needed to sustain our technological civilization. That’s a messy trolley problem for you!

Caplan says, "And if we know anything about the future of work, we know that the demand for authors, historians, political scientists, translators, physicists, and mathematicians will stay low." I could argue for the (increasing) utility of these but let’s focus on the last two. It could be that very hard subjects like physics or math, besides signaling difficulty, are good at giving young people a practice domain that is broadly applicable. I know for certain that many very good practical programmers started out as physics and math majors. Caplan would ask, wouldn’t it be better to just have them study programming? Perhaps. However, given the programmer inbreeding problem, I could see how fresh perspectives might be valuable. Still, counting on that is tenuous.

One bonus of education that is not appreciated by the book — do not underestimate the confidence that you can do complicated stuff which comes from having done different complicated stuff in college, a relatively safe practice environment. Here’s an exact example between minute 1 and 2 of this talk. This may be borderline vocational though, which Caplan believes is perfectly valid and underappreciated.

A thought I had in defense of education was prompted by the question of whether reading itself was valuable. My readers (who are all highly literate and lovely and intelligent and good-looking and all around great people) may be shocked by the question but apparently it is very debatable. I wondered what I got out of reading. I certainly can completely forget everything about a book in a couple of years (hence reminders like this post!) but I rationalized that it might be more of a kind of neural network training than a "memorize these facts" kind of thing. Certainly you can read a humor book and find it funny and have memorized none of the gags. Reading does more broadly rewire your thinking in subtle ways that probably are beneficial. I think some of that reasoning applies to general education too. Of course some would argue that kids in school are getting their neural nets primed to submit to dull oppressive boring jobs as professional conformists.

I kind of get the feeling that the whole education circus might be a way to give natural nerds a chance to do a lot of reading and nerd stuff. But today, with near-compulsory signaling, people who do not enjoy reading (or writing long blog posts about the subtleties of education) or self-directed study of any kind are swept up in the "education" frenzy. That would be fine if it wasn’t such a futile waste of people’s lives.

A fun question I had — is Caplan trying to elucidate something with this book? Or is he merely signaling? I’ll arbitrarily give it a 80/20 split to highlight the arbitrariness of such a conclusion. ;-) Caplan has more support for his numbers, but not much more. Although I’m giving him a bit of a hard time, I did actually come to accept his 80% signaling as probably pretty close and certainly much closer to reality than 0% which is the ostensible figure. I don’t know if he properly does the math, but he certainly does quite a bit of math.

Education seems like unquestionable anti-evil magic. To just say the word puts everyone on the same page. People may quibble about how much to spend on the details of the education budget, but if cost were no object, it is taken for granted by pretty much everybody that any "education" is good education. Caplan is pretty brave to be the guy — in education no less — who thinks the matter is at least questionable. He questions it. He discovers that when looked at analytically the same way people make value propositions about most other facets of life, education comes out quite badly for both kids and society in general. He finds that taking the status quo for granted has produced muddled thinking about education and its worth. He finds that there are much better ways to invest our energies that better satisfy education’s presumed goals.

As someone who immediately suspected the same theme throughout my formal education, I am gratified to read such strong evidence that I wasn’t simply the one being petulant. Of course I am saddened by the current state of formal education which seems rote and somehow not genuine to me. I can only hope attitudes change and people start to question the true practical real-world effectiveness of schooling done in the ordinary way. The good news is that for those with an intrinsic motivation to learn who desire to be intellectually challenged thereby, there has never been a better time to be alive — despite the prevalence of schooling.

UPDATE 2019-07-16

Here’s an interesting article for people who have misgivings about school. Turns out that Steven Spielberg was rejected from two film schools despite being an obvious precocious prodigy. The reason: he simply hated school and got bad grades.

UPDATE 2019-07-22

I only read the abstract, but this paper seems to suggest there are diminishing returns to education: The Remarkable Unresponsiveness Of College Students To Nudging And What We Can Learn from It. I love the ironic "what we can learn from it" subtitle — might as well parenthetically add, "if we’re self-motivated."

I’ve now been in Buffalo for 11 months. The time has really flown by. After decades of no seasons in San Diego, having a proper one show up ever few months is quite a change, a pleasant change! Spring is quite a spectacular time of year. Living things suddenly burst into existence everywhere. Everything turns green. It’s quite a transformation.

Here are some photos that show some of what spring in western New York is like.

Here’s a snowman melting. This was April 3 and was the last snow that could have been made into a snowman. A couple weeks into April and it was safe to take my snow tires off my bicycle and car.

Here I am standing on the bridge to Tonawanda Island in the Niagara River. This is around the beginning of April. You can see big steel cylinders floating on the right. That is an ice boom and it is to protect the marina there from ice. There is a very large ice boom at the beginning of the Niagara River to keep Lake Erie ice out. It was up later than usual this year. I keep an eye on this because I don’t go boating on the river if it’s packed with ice (I go boating elsewhere).

Here’s my office. That beautiful tree reminded me of the ornamental tree in the apartment I lived in in San Diego. The reason is that it stinks! You’d expect such lovely flowers to not smell like uncleaned hamster cage, but nature is funny that way. Once the floweres dropped, it smelled fine again. To see the transformation, here’s what the office looked like in the winter.

The flowering trees were pretty spectacular all over. This is in my front yard.

After seeing Canadian geese everywhere all year long it was very fun to see how they get their start. The little goslings are everywhere and they are absurdly cute. Here they are getting swimming lessons.

Geese aren’t the only things repopulating. This is in my backyard. Here’s what this scene looked like in the winter.

Here’s the bike path behind my house. This photo is specifically composed to show the drainage problems but mostly the trail is in good shape and it’s extremely nice walking on the trails through the woods. Compare with the winter version.

Here’s another visitor to the backyard. We also put up a birdhouse that quickly became occupied.

Not just cute goslings but adorable ducklings too.

I don’t know if this is a frog or a toad, but he lives in our back yard.

A family of Canadians. Notice how they have carry their immigration documents on their right legs at all times. It’s the law.

Sometimes the geese set up a day care where a couple of adults watch a pretty huge group of little fuzz balls while the other parents take a break.

Yes, my wife and I like watching these things, hence many photos.

There are snapping turtles in the ponds. I see them in the Erie Canal sometimes too.

Here are a couple of ducks in the forest behind my house. The leaves are just coming in here.

And if for some strange reason I want to see palm trees and spiky plants that will seriously wound you, I can always come to the botanical gardens. There they cultivate the weird plants that can survive in deserts. To us, those exotic plants looked like normal desert stuff that’s all over San Diego County. I’d rather have those dangerous plants in a plant zoo with safe ones in the wild than the other way around.

After catching the end of summer last year and experiencing autumn, winter, and spring, we’re now back to summer. It’s too hot, but at least there’s usually a breeze and plenty of interesting things to do. And most importantly, in a couple of months, we’ll be getting our cool weather clothes back out.

I have several posts lined up but I’ve been super busy with my job as a professional recreational boater now that we’re deep into that activity’s prime season.

Enjoy this short clip of me at work.

As you can see, I wear many hats.

In a strange but fascinating dog and pony show that seemed staged to take the edge off of 2019Q1’s $700 million loss Tesla gave a masterclass on how to do computer science in the style of Tesla. This video was several hours long and astonishingly technical. As someone in the business of autonomous vehicles, I found much of it fascinating. This talk featured a complete computer vision lecture by super genius Andrej Karpathy which reminded me of his computer science lectures.

Let’s remember what I said about Andrej Karpathy two years ago when he was just a lowly Stanford CS professor:

I hope that makes it clear that if Andrej Karpathy says something about my vocation, I listen carefully and take it seriously.

Elon Musk on the other hand… Well, he’s a definitely an interesting guy and a colorful character; this seems to be what made the biggest impresssion. Specifically, Musk literally made headlines by openly mocking his competitors.

Here.

Musk is clearly a smart guy and he has accomplished a lot that I approve of but he is clearly not infallible. Let me go on record right now, for example, and predict that out of the flurry of projections Musk made in the video, not a single one will be completed by the stated schedule. People who keep an eye on Musk will know that it is not especially bold to say such a thing.

It is bold for a CEO to openly deride the technology of competitors. This is especially true when you’re basically on the frontier of technology doing primary research. You can have a hunch your competitors are wrong, but it wouldn’t be research if you were really sure. It’s also weird and risky to disparage people who are clearly very, very smart. Of course I’m weird and risky and I do exactly that all the time so maybe Musk has a worthwhile point to make.

This is my blog so before digging into the technical details, I want to share some personal background that establishes how important this topic is to me. A few years before Andrej Karpathy was busy being born, I owned the first touchpad called the KoalaPad. With very excruciating patience and dexterity, one could almost trace features in a photograph and thereby bring a tiny slice of the visual world into a computer.

This was in the days before digital photography when digital images were lovingly crafted pixel by pixel by hand. A few years later in my early career as an engineer, I had a 12x12 Calcomp tablet which strangely seems to still be for sale exactly as I remember them 25 years ago.

It was common to use these to digitize blueprints. I did not do that because with blueprints you know the ground truth (that is what the document is trying to communicate) so I would routinely resynthesize blue prints as proper accurate CAD models numerically. That made it clear to me what the tablet was good for which was artwork.

As a hobby, I was very interested in fine art sculpture at the time and professionally I was writing 3d contouring software for CNC milling machines. Combining those things, I felt that carving figurative sculpture out of solid steel was an interesting new possibility. The problem, which seems backwards today, was how could one get a fine enough computer model of the subject? I gave this problem a lot of thought. I remember using my Calcomp tablet to outline the profiles of objects taken from many different angled photographs which I then could reconstruct into a 3d model. This technique worked perfectly for convex shapes such as a model sailboat keel bulb I machined. This fails, however, for concave shapes such as eye sockets recessed into a face.

With that background I hope you can appreciate the plausibility of me asking and answering the following question before the advent of consumer digital photography.

Given the information of a 2d image, is it theoretically possible to mathematically reconstruct a 3d model of the scene?

And the answer that I asserted (in 1993) was yes. Today I have been pretty much proven correct but it is important to review why that assertion then was quite unintuitive. At that time, there was absolutely no conceivable technical path to making this transformation so it was reasonable to believe that it might well be impossible. I based my answer on my knowledge of sculpture — if a human sculptor can look at a photograph and create a sculpture from it, then it can not be impossible. I knew that we just merely lacked the imagination to think of a technical mechanism.

Karpathy makes the exact same argument in the presentation when he points out that humans can drive using (predominantly) visual sensory information, therefore it must not be impossible. Today, however, there are conceivable and even practical mechanisms. This miracle of our physiology, sculpting by sight, can now be recreated algorithmically using many techniques. I have been delighted to watch the progress of this technology mature — stereophotogrammetry, mocap, catadioptric imaging, structured light, structure from motion, CNN-based single image depth estimation, etc. Check out Tesla’s brilliant 3d reconstrcution from 2d video in the presentation at 2h19m42s.

My hope is that by understanding my motivation to solve this problem decades ago we can have a useful analogy for the important topic today. Ok, some talented humans can look at a photograph and then sculpt it, so what? Well, let’s ask what an alternative method would be. What if the sculptor had access to the live subject? One technique that suits less talented sculptors (like me) is to get out calipers and a tape measure and simply directly measure all of the important geometry and make sure the sculpted work matches.

The brain of an autonomous car must basically "sculpt" a little model diorama of the world around it and then use that toy to play out different possibilities so that it can confidently choose the best outcome. If the car’s toy model is very inaccurate, decisions it makes will likely be suboptimal. There are two competing theories about how that model should be created. The Tesla approach is to be like a virtuoso sculptor who can look at a scene and sculpt it perfectly. We know that sort of thing is possible, but note that even the most talented sculptors don’t ridicule direct measurement and often use it. The other approach, used by Waymo/Google and pretty much the entire field, is this direct measurement approach (in addition to vision). Some object looks 10 meters away; can we drive 10 meters before hitting it? Tesla will eyeball it and decide. Other players favor extending their measuring sticks and actually explicitly prodding and exploring the intermediate space before driving into it.

That measuring stick is lidar.

Musk did allow that some exotic applications require lidar, for example his own rocket docking adventures. Here’s a photo of me installing a lidar sensor for old-fashioned dock docking (a photo which will give nightmares to anyone who has ever accidentally dropped and lost a small piece of hardware while assembling something).

As someone who professionally studies this sensor, I have some basic knowledge about lidar. Lidar is very much like a measuring stick — you extend it out and when it hits something you check how far the stick is extended and that’s how far away it is. Or you could think of it like how a cane is used by a blind person. How does a blind person know where to direct their single cane to navigate through a complex world? I have no idea but it seems like a very sophisticated skill. Lidars avoid the need for that skill by using a shotgun approach — they send out measurement probes basically in all directions, all the time. The details are obviously more nuanced but that is a reasonable way to think of it.

Here is a visualization of the 3d lidar data, the explicit coordinates found by laser probes, of me sitting on my bike in a room outside my office.

The lidar is inside my office, its walls are at the bottom, and the interesting part of the scene is sensed through the open doorway. This image looks quite rough but remember, this data is not about how things look. It is about knowing how things are. It is about knowing with very high certainty what space is occupied by something that blocks lasers. What this means and what I think the most important point here is this: if the lidar says something is present then do not drive into it! There are some very minor exceptions like puffs of exhaust (which frankly, I’m fine with avoiding out of an abundance of caution) but generally, if the lidar says that something is in the way, you need to avoid hitting it!

And just to give lidar a bit more credit, consider this image mathematically constructed from the lidar’s 3d data points.

That is very helpful for humans who do not quite understand the world in explicit numerical coordinates. What’s remarkable about this image is that it is coded on depth changes which are very likely to be accurate.

Let’s now consider the alternative to a sensor that can reach out and explicitly check if something is in the way. You don’t hold your hands out in front of you when you walk through a doorway in case the door is closed (actually I do this every night in the dark). Tesla imagines the same confidence we have with our eyes being valid with computer analysis of camera feeds. This may be completely feasible and within spec for driving but let’s take a quick look at how computer vision can fail.

Here is a well-known general classifier I was playing with yesterday while testing a Jetson Nano (as described on the Computer Vision Hustler’s excellent website).

The best guess was a "white fox" which is pretty damn good given that "toy sheep" probably is not one of its known categories. But what struck me was the fact that "parachute" was number two. All the other low probability guesses were animals but why a parachute? Then I finally saw it in my mind. I’ve mocked it up so you don’t have to use your warped computer vision imagination.

Of course Tesla’s interesting vision claims include guessing depth values, not just object classifiers. What I’m trying to stress here is that neural networks do pretty well with the known unknowns but sometimes they produce unknown unknowns that are quite surreal. Basically computers have their own optical illusions and in life or death situations this is bad. It’s not an insurmountable problem. Neural net image processing can be really focused and leave much less room for error than human vision processing. However, it is something to be aware of and I think Tesla is.

I’m mostly giving you my personal reflections here, but if you want a good proper analysis of the issues, my always sensible resource for this technology, Brad Templeton, has a perfectly good article discussing the subtle points of lidar applied to autonomous vehicles. And to demonstrate his qualifications, here’s an article on the topic that is still quite useful from 2013.

Arguments Against Tesla’s Strategy

Arguments For Tesla’s Strategy

In summary I am completely aligned with Brad who offers this opinion. "I predict that cameras will always be present, and that their role will increase over time, but the lidars will not go away for a long time." That seems very fair. A lot will depend on just how much improvement lidars see. I can easily imagine a small $50 device that can tell you with 99% certainty high level things like the presence of a dog or motorcycle and their exact coordinates. And for that unknown 1% there would be a 99.99% chance that it is something you really, really should not hit with a car. We’ll see.

I was impressed with the Tesla presentation and strategy. It definitely seems like Musk has a fair amount of hubris, but I guess people who don’t have strong opinions are more comfortable killing each other in their status-quo cars. Tesla’s methodology is very clever and interesting but not necessarily correct. It might just work though. Time will tell.

I wish them the best of luck of course. You can’t say Musk is sitting back doing nothing. He’s definitely got a great team energized to give it their best effort. Although I disagree about the following comment made by Musk (only because consumers' demands are so disappointing) it is righteous enough for me to overlook any Tesla style faux pas. (3h23m50s)

If you can make that happen, Elon, you are my savior.

For a long time now I have been referring to a phenomenon in autonomous vehicle technology which I call The Tesla Paradox. Rather than my overly-complicated explanation, I’ll let Brad Templeton sum it up perfectly in one of his recent Forbes articles.

Consider also Cadillac’s Super Cruise, a pioneer in autonomus ADAS. Very fancy Mercedes was sure that you’d love extra fancy adaptive cruise control which they were first to sell using radar. Here’s a job ad for a "Senior Systems Engineer - Augmented Driving (Autonomy)" at McLaren. Etc.

On the other hand, Ferrari has understandably gone Amish. Perhaps a "Ferrari Paradox"? I.e. the most high performance (advanced?) sports car company refusing to acknowledge important new technology.

And now if you’ll excuse me, I need to continue my work making a fun-to-drive pleasure boat drive by itself.