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Why A.I. is a big fat lie
The Dr. Data Show is a new web series that breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.
- All the hype around artificial intelligence misunderstands what intelligence really is.
- And A.I. is definitely, definitely not going to kill you, ever.
- Machine learning as a process and a concept, however, holds more promise.
A.I. is a big fat lie
A.I. is a big fat lie. Artificial intelligence is a fraudulent hoax — or in the best cases it's a hyped-up buzzword that confuses and deceives. The much better, precise term would instead usually be machine learning – which is genuinely powerful and everyone oughta be excited about it.
On the other hand, AI does provide some great material for nerdy jokes. So put on your skepticism hat, it's time for an AI-debunkin', slam-dunkin', machine learning-lovin', robopocalypse myth-bustin', smackdown jamboree – yeehaw!
3 main points
1) Unlike AI, machine learning's totally legit. I gotta say, it wins the Awesomest Technology Ever award, forging advancements that make ya go, "Hooha!". However, these advancements are almost entirely limited to supervised machine learning, which can only tackle problems for which there exist many labeled or historical examples in the data from which the computer can learn. This inherently limits machine learning to only a very particular subset of what humans can do – plus also a limited range of things humans can't do.
2) AI is BS. And for the record, this naysayer taught the Columbia University graduate-level "Artificial Intelligence" course, as well as other related courses there.
AI is nothing but a brand. A powerful brand, but an empty promise. The concept of "intelligence" is entirely subjective and intrinsically human. Those who espouse the limitless wonders of AI and warn of its dangers – including the likes of Bill Gates and Elon Musk – all make the same false presumption: that intelligence is a one-dimensional spectrum and that technological advancements propel us along that spectrum, down a path that leads toward human-level capabilities. Nuh uh. The advancements only happen with labeled data. We are advancing quickly, but in a different direction and only across a very particular, restricted microcosm of capabilities.
The term artificial intelligence has no place in science or engineering. "AI" is valid only for philosophy and science fiction – and, by the way, I totally love the exploration of AI in those areas.
3) AI isn't gonna kill you. The forthcoming robot apocalypse is a ghost story. The idea that machines will uprise on their own volition and eradicate humanity holds no merit.
Neural networks for the win
In the movie "Terminator 2: Judgment Day," the titular robot says, "My CPU is a neural net processor, a learning computer." The neural network of which that famous robot speaks is actually a real kind of machine learning method. A neural network is a way to depict a complex mathematical formula, organized into layers. This formula can be trained to do things like recognize images for self-driving cars. For example, watch several seconds of a neural network performing object recognition.
What you see it doing there is truly amazing. The network's identifying all those objects. With machine learning, the computer has essentially programmed itself to do this. On its own, it has worked out the nitty gritty details of exactly what patterns or visual features to look for. Machine learning's ability to achieve such things is awe-inspiring and extremely valuable.
The latest improvements to neural networks are called deep learning. They're what make this level of success in object recognition possible. With deep learning, the network is quite literally deeper – more of those layers. However, even way way back in 1997, the first time I taught the machine learning course, neural networks were already steering self-driving cars, in limited contexts, and we even had our students apply them for face recognition as a homework assignment.
The achitecture for a simple neural network with four layers
But the more recent improvements are uncanny, boosting its power for many industrial applications. So, we've even launched a new conference, Deep Learning World, which covers the commercial deployment of deep learning. It runs alongside our long-standing machine learning conference series, Predictive Analytics World.
Supervised machine learning requires labeled data
So, with machines just getting better and better at humanlike tasks, doesn't that mean they're getting smarter and smarter, moving towards human intelligence?
No. It can get really, really good at certain tasks, but only when there's the right data from which to learn. For the object recognition discussed above, it learned to do that from a large number of example photos within which the target objects were already correctly labeled. It needed those examples to learn to recognize those kinds of objects. This is called supervised machine learning: when there is pre-labeled training data. The learning process is guided or "supervised" by the labeled examples. It keeps tweaking the neural network to do better on those examples, one incremental improvement at a time. That's the learning process. And the only way it knows the neural network is improving or "learning" is by testing it on those labeled examples. Without labeled data, it couldn't recognize its own improvements so it wouldn't know to stick with each improvement along the way. Supervised machine learning is the most common form of machine learning.
Here's another example. In 2011, IBM's Watson computer defeated the two all-time human champions on the TV quiz show Jeopardy. I'm a big fan. This was by far the most amazing thing I've seen a computer do – more impressive than anything I'd seen during six years of graduate school in natural language understanding research. Here's a 30-second clip of Watson answering three questions.
To be clear, the computer didn't actually hear the spoken questions but rather was fed each question as typed text. But its ability to rattle off one answer after another – given the convoluted, clever wording of Jeopardy questions, which are designed for humans and run across any and all topics of conversation – feels to me like the best "intelligent-like" thing I've ever seen from a computer.
But the Watson machine could only do that because it had been given many labeled examples from which to learn: 25,000 questions taken from prior years of this TV quiz show, each with their own correct answer.
At the core, the trick was to turn every question into a yes/no prediction: "Will such-n-such turn out to be the answer to this question?" Yes or no. If you can answer that question, then you can answer any question – you just try thousands of options out until you get a confident "yes." For example, "Is 'Abraham Lincoln' the answer to 'Who was the first president?'" No. "Is 'George Washington'?" Yes! Now the machine has its answer and spits it out.
Computers that can talk like humans
And there's another area of language use that also has plentiful labeled data: machine translation. Machine learning gobbles up a feast of training data for translating between, say, English and Japanese, because there are tons of translated texts out there filled with English sentences and their corresponding Japanese translations.
In recent years, Google Translate – which anyone can use online – swapped out the original underlying solution for a much-improved one driven by deep learning. Go try it out – translate a letter to your friend or relative who has a different first language than you. I use it a lot myself.
On the other hand, general competence with natural languages like English is a hallmark of humanity – and only humanity. There's no known roadmap to fluency for our silicon sisters and brothers. When we humans understand one another, underneath all the words and somewhat logical grammatical rules is "general common sense and reasoning." You can't work with language without that very particular human skill. Which is a broad, unwieldy, amorphous thing we humans amazingly have.
So our hopes and dreams of talking computers are dashed because, unfortunately, there's no labeled data for "talking like a person." You can get the right data for a very restricted, specific task, like handling TV quiz show questions, or answering the limited range of questions people might expect Siri to be able to answer. But the general notion of "talking like a human" is not a well-defined problem. Computers can only solve problems that are precisely defined.
So we can't leverage machine learning to achieve the typical talkative computer we see in so many science fiction movies, like the Terminator, 2001's evil HAL computer, or the friendly, helpful ship computer in Star Trek. You can converse with those machines in English very much like you would with a human. It's easy. Ya just have to be a character in a science fiction movie.
Intelligence is subjective, so A.I. has no real definition
Now, if you think you don't already know enough about AI, you're wrong. There is nothing to know, because it isn't actually a thing. There's literally no meaningful definition whatsoever. AI poses as a field, but it's actually just a fanciful brand. As a supposed field, AI has many competing definitions, most of which just boil down to "smart computer." I must warn you, do not look up "self-referential" in the dictionary. You'll get stuck in an infinite loop.
Many definitions are even more circular than "smart computer," if that's possible. They just flat out use the word "intelligence" itself within the definition of AI, like "intelligence demonstrated by a machine."
If you've assumed there are more subtle shades of meaning at hand, surprise – there aren't. There's no way to resolve how utterly subjective the word "intelligence" is. For computers and engineering, "intelligence" is an arbitrary concept, irrelevant to any precise goal. All attempts to define AI fail to solve its vagueness.
Now, in practice the word is often just – confusingly – used as a synonym for machine learning. But as for AI as its own concept, most proposed definitions are variations of the following three:
1) AI is getting a computer to think like a human. Mimic human cognition. Now, we have very little insight into how our brains pull off what they pull off. Replicating a brain neuron-by-neuron is a science fiction "what if" pipe dream. And introspection – when you think about how you think – is interesting, big time, but ultimately tells us precious little about what's going on in there.
2) AI is getting a computer to act like a human. Mimic human behavior. Cause if it walks like a duck and talks like a duck... But it doesn't and it can't and we're way too sophisticated and complex to fully understand ourselves, let alone translate that understanding into computer code. Besides, fooling people into thinking a computer in a chatroom is actually a human – that's the famous Turing Test for machine intelligence – is an arbitrary accomplishment and it's a moving target as we humans continually become wiser to the trickery used to fool us.
3) AI is getting computers to solve hard problems. Get really good at tasks that seem to require "intelligence" or "human-level" capability, such as driving a car, recognizing human faces, or mastering chess. But now that computers can do them, these tasks don't seem so intelligent after all. Everything a computer does is just mechanical and well understood and in that way mundane. Once the computer can do it, it's no longer so impressive and it loses its charm. A computer scientist named Larry Tesler suggested we define intelligence as "whatever machines haven't done yet." Humorous! A moving-target definition that defines itself out of existence.
By the way, the points in this article also apply to the term "cognitive computing," which is another poorly-defined term coined to allege a relationship between technology and human cognition.
The logical fallacy of believing in A.I.'s innevitability
The thing is, "artificial intelligence" itself is a lie. Just evoking that buzzword automatically insinuates that technological advancement is making its way toward the ability to reason like people. To gain humanlike "common sense." That's a powerful brand. But it's an empty promise. Your common sense is more amazing – and unachievable – than your common sense can sense. You're amazing. Your ability to think abstractly and "understand" the world around you might feel simple in your moment-to-moment experience, but it's incredibly complex. That experience of simplicity is either a testament to how adept your uniquely human brain is or a great illusion that's intrinsic to the human condition – or probably both.
Now, some may respond to me, "Isn't inspired, visionary ambition a good thing? Imagination propels us and unknown horizons beckon us!" Arthur C. Clarke, the author of 2001, made a great point: "Any sufficiently advanced technology is indistinguishable from magic." I agree. However, that does not mean any and all "magic" we can imagine – or include in science fiction – could eventually be achieved by technology. Just 'cause it's in a movie doesn't mean it's gonna happen. AI evangelists often invoke Arthur's point – but they've got the logic reversed. My iPhone seems very "Star Trek" to me, but that's not an argument everything on Star Trek is gonna come true. The fact that creative fiction writers can make shows like Westworld is not at all evidence that stuff like that could happen.
Now, maybe I'm being a buzzkill, but actually I'm not. Let me put it this way. The uniqueness of humans and the real advancements of machine learning are each already more than amazing and exciting enough to keep us entertained. We don't need fairy tales – especially ones that mislead.
Sophia: A.I.'s most notoriously fraudulent publicity stunt
The star of this fairy tale, the leading role of "The Princess" is played by Sophia, a product of Hanson Robotics and AI's most notorious fraudulent publicity stunt. This robot has applied her artificial grace and charm to hoodwink the media. Jimmy Fallon and other interviewers have hosted her – it, I mean have hosted it. But when it "converses," it's all scripts and canned dialogue – misrepresented as spontaneous conversation – and in some contexts, rudimentary chatbot-level responsiveness.
Believe it or not, three fashion magazines have featured Sophia on their cover, and, ever goofier and sillier, the country Saudi Arabia officially granted it citizenship. For real. The first robot citizen. I'm actually a little upset about this, 'cause my microwave and pet rock have also applied for citizenship but still no word.
Sophia is a modern-day Mechanical Turk – which was an 18th century hoax that fooled the likes of Napoleon Bonaparte and Benjamin Franklin into believing they'd just lost a game of chess to a machine. A mannequin would move the chess pieces and the victims wouldn't notice there was actually a small human chess expert hidden inside a cabinet below the chess board.
In a modern day parallel, Amazon has an online service you use to hire workers to perform many small tasks that require human judgement, like choosing the nicest looking of several photographs. It's named Amazon Mechanical Turk, and its slogan, "Artificial Artificial Intelligence." Which reminds me of this great vegetarian restaurant with "mock mock duck" on the menu – I swear, it tastes exactly like mock duck. Hey, if it talks like a duck, and it tastes like a duck...
Yes indeed, the very best fake AI is humans. In 1965, when NASA was defending the idea of sending humans to space, they put it this way: "Man is the lowest-cost, 150-pound, nonlinear, all-purpose computer system which can be mass-produced by unskilled labor." I dunno. I think there's some skill in it. ;-)
The myth of dangerous superintelligence
Anyway, as for Sophia, mass hysteria, right? Well, it gets worse: Claims that AI presents an existential threat to the human race. From the most seemingly credible sources, the most elite of tech celebrities, comes a doomsday vision of homicidal robots and killer computers. None other than Bill Gates, Elon Musk, and even the late, great Stephen Hawking have jumped on the "superintelligence singularity" bandwagon. They believe machines will achieve a degree of general competence that empowers the machines to improve their own general competence – so much so that this will then quickly escalate past human intelligence, and do so at the lightning speed of computers, a speed the computers themselves will continue to improve by virtue of their superintelligence, and before you know it you have a system or entity so powerful that the slightest misalignment of objectives could wipe out the human race. Like if we naively commanded it to manufacture as many rubber chickens as possible, it might invent an entire new high-speed industry that can make 40 trillion rubber chickens but that happens to result in the extinction of Homo sapiens as a side effect. Well, at least it would be easier to get tickets for Hamilton.
There are two problems with this theory. First, it's so compellingly dramatic that it's gonna ruin movies. If the best bad guy is always a robot instead of a human, what about Nurse Ratched and Norman Bates? I need my Hannibal Lecter! "The best bad guy," by the way, is an oxymoron. And so is "artificial intelligence." Just sayin'.
But it is true: Robopocalypse is definitely coming. Soon. I'm totally serious, I swear. Based on a novel by the same name, Michael Bay – of the "Transformers" movies – is currently directing it as we speak. Fasten your gosh darn seatbelts people, 'cause, if "Robopocalypse" isn't in 3D, you were born in the wrong parallel universe.
Oh yeah, and the second problem with the AI doomsday theory is that it's ludicrous. AI is so smart it's gonna kill everyone by accident? Really really stupid superintelligence? That sounds like a contradiction.
To be more precise, the real problem is that the theory presumes that technological advancements move us along a path toward humanlike "thinking" capabilities. But they don't. It's not headed in that direction. I'll come back to that point again in a minute – first, a bit more on how widely this apocalyptic theory has radiated.
A widespread belief in superintelligence
The Kool-Aid these high-tech royalty drink, the go-to book that sets the foundation, is the New York Times bestseller "Superintelligence," by Nick Bostrom, who's a professor of applied ethics at Oxford University. The book mongers the fear and fans the flames, if not igniting the fire in the first place for many people. It explores how we might "make an intelligence explosion survivable." The Guardian newspaper ran a headline, "Artificial intelligence: 'We're like children playing with a bomb'," and Newsweek: "Artificial Intelligence Is Coming, and It Could Wipe Us Out," both headlines obediently quoting Bostrom himself.
Bill Gates "highly recommends" the book, Elon Musk said AI is "vastly more risky than North Korea" – as Fortune Magazine repeated in a headline – and, quoting Stephen Hawking, the BBC ran a headline, "'AI could spell end of the human race'."
In a Ted talk that's been viewed 5 million times (across platforms), the bestselling author and podcast intellectual Sam Harris states with supreme confidence, "At a certain point, we will build machines that are smarter than we are, and once we have machines that are smarter than we are, they will begin to improve themselves."
Both he and Bostrom show the audience an intelligence spectrum during their Ted talks – here's the one by Bostrom:
What happens when our computers get smarter than we are? | Nick Bostrom
You can see as we move along the path from left to right we pass a mouse, a chimp, a village idiot, and then the very smart theoretical physicist Ed Witten. He's relatively close to the idiot, because even an idiot human is much smarter than a chimp, relatively speaking. You can see the arrow just above the spectrum showing that "AI" progresses in that same direction, along to the right. At the very rightmost position is Bostrom himself, which is either just an accident of photography, or proof that he himself is an AI robot.
Oops, that was the wrong clip – uh, that was Dr. Frankenstein, but, ya know, same scenario.
A falsely conceived "spectrum of intelligence"
Anyway, that falsely-conceived intelligence spectrum is the problem. I've read the book and many of the interviews and watched the talks and pretty much all the believers intrinsically build on an erroneous presumption that "smartness" or "intelligence" falls more or less along a single, one-dimensional spectrum. They presume that the more adept machines become at more and more challenging tasks, the higher they will rank on this scale, eventually surpassing humans.
But machine learning has us marching along a different path. We're moving fast, and we'll likely go very far, but we're going in a different direction, only tangentially related to human capabilities.
The trick is to take a moment to think about this difference. Our own personal experiences of being one of those smart creatures called a human is what catches us in a thought trap. Our very particular and very impressive capabilities are hidden from ourselves beneath a veil of a conscious experience that just kind of feels like "clarity." It feels simple, but under the surface, it's oh so complex. Replicating our "general common sense" is a fanciful notion that no technological advancements have ever moved us towards in any meaningful way.
Thinking abstractly often feels uncomplicated. We draw visuals in our mind, like a not-to-scale map of a city we're navigating, or a "space" of products that two large companies are competing to sell, with each company dominating in some areas but not in others... or, when thinking about AI, the mistaken vision that increasingly adept capabilities – both intellectual and computational – all fall along the same, somewhat narrow path.
Now, Bostrom rightly emphasizes that we should not anthropomorphize what intelligent machines may be like in the future. It's not human, so it's hard to speculate on the specifics and perhap it will seem more like a space alien's intelligence. But what Bostrom and his followers aren't seeing is that, since they believe technology advances along a spectrum that includes and then transcends human cognition, the spectrum itself as they've conceived it is anthropomorphic. It has humanlike qualities built in. Now, your common sense reasoning may seem to you like a "natural stage" of any sort of intellectual development, but that's a very human-centric perspective. Your common sense is intricate and very, very particular. It's far beyond our grasp – for anyone – to formally define a "spectrum of intelligence" that includes human cognition on it. Our brains are spectacularly multi-faceted and adept, in a very arcane way.
Machines progress along a different spectrum
Machine learning actually does work by defining a kind of spectrum, but only for an extremely limited sort of trajectory – only for tasks that have labeled data, such as identifying objects in images. With labeled data, you can compare and rank various attempts to solve the problem. The computer uses the data to measure how well it does. Like, one neural network might correctly identify 90% of the trucks in the images and then a variation after some improvements might get 95%.
Getting better and better at a specific task like that obviously doesn't lead to general common sense reasoning capabilities. We're not on that trajectory, so the fears should be allayed. The machine isn't going to get to a human-like level where it then figures out how to propel itself into superintelligence. No, it's just gonna keep getting better at identifying objects, that's all.
Intelligence isn't a Platonic ideal that exists separately from humans, waiting to be discovered. It's not going to spontaneously emerge along a spectrum of better and better technology. Why would it? That's a ghost story.
It might feel tempting to believe that increased complexity leads to intelligence. After all, computers are incredibly general-purpose – they can basically do any task, if only we can figure out how to program them to do that task. And we're getting them to do more and more complex things. But just because they could do anything doesn't mean they will spontaneously do everything we imagine they might.
No advancements in machine learning to date have provided any hint or inkling of what kind of secret sauce could get computers to gain "general common sense reasoning." Dreaming that such abilities could emerge is just wishful thinking and rogue imagination, no different now, after the last several decades of innovations, than it was back in 1950, when Alan Turing, the father of computer science, first tried to define how the word "intelligence" might apply to computers.
Don't sell, buy, or regulate on A.I.
Machines will remain fundamentally under our control. Computer errors will kill – people will die from autonomous vehicles and medical automation – but not on a catastrophic level, unless by the intentional design of human cyber attackers. When a misstep does occur, we take the system offline and fix it.
Now, the aforementioned techno-celebrity believers are true intellectuals and are truly accomplished as entrepreneurs, engineers, and thought leaders in their respective fields. But they aren't machine learning experts. None of them are. When it comes to their AI pontificating, it would truly be better for everyone if they published their thoughts as blockbuster movie scripts rather than earnest futurism.
It's time for term "AI" to be "terminated." Mean what you say and say what you mean. If you're talking about machine learning, call it machine learning. The buzzword "AI" is doing more harm than good. It may sometimes help with publicity, but to at least the same degree, it misleads. AI isn't a thing. It's vaporware. Don't sell it and don't buy it.
And most importantly, do not regulate on "AI"! Technology greatly needs regulation in certain arenas, for example, to address bias in algorithmic decision-making and the development of autonomous weapons – which often use machine learning – so clarity is absolutely critical in these discussions. Using the imprecise, misleading term "artificial intelligence" is gravely detrimental to the effectiveness and credibility of any initiative that regulates technology. Regulation is already hard enough without muddying the waters.
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Evolution doesn't clean up after itself very well.
- An evolutionary biologist got people swapping ideas about our lingering vestigia.
- Basically, this is the stuff that served some evolutionary purpose at some point, but now is kind of, well, extra.
- Here are the six traits that inaugurated the fun.
The plica semilunaris<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8xOTA5NjgwMS9vcmlnaW4ucG5nIiwiZXhwaXJlc19hdCI6MTYxMTgyMzg1NX0.ZY8qmhtoZfbRMAqrNnmbgyk7GLabglx_9lBq3PKcy7g/img.png?width=980" id="99882" class="rm-shortcode" data-rm-shortcode-id="68e8758894b0359c6ef61b2c158832b2" data-rm-shortcode-name="rebelmouse-image" />
The human eye in alarming detail. Image source: Henry Gray / Wikimedia commons<p>At the inner corner of our eyes, closest to the nasal ridge, is that little pink thing, which is probably what most of us call it, called the caruncula. Next to it is the plica semilunairs, and it's what's left of a third eyelid that used to — ready for this? — blink horizontally. It's supposed to have offered protection for our eyes, and some birds, reptiles, and fish have such a thing.</p>
Palmaris longus<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8xOTA5NjgwNy9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYzMzQ1NjUwMn0.dVor41tO_NeLkGY9Tx46SwqhSVaA8HZQmQAp532xLxA/img.jpg?width=980" id="879be" class="rm-shortcode" data-rm-shortcode-id="970e9c15f3c3d846dde05e2b2c6ebf12" data-rm-shortcode-name="rebelmouse-image" />
Palmaris longus muscle. Image source: Wikimedia commons<p> We don't have much need these days, at least most of us, to navigate from tree branch to tree branch. Still, about 86 percent of us still have the wrist muscle that used to help us do it. To see if you have it, place the back of you hand on a flat surface and touch your thumb to your pinkie. If you have a muscle that becomes visible in your wrist, that's the palmaris longus. If you don't, consider yourself more evolved (just joking).</p>
Darwin's tubercle<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8xOTA5NjgxMi9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY0ODUyNjA1MX0.8RuU-OSRf92wQpaPPJtvFreOVvicEwn39_jnbegiUOk/img.jpg?width=980" id="687a0" class="rm-shortcode" data-rm-shortcode-id="b38a957408940673ccc744f0f6828d18" data-rm-shortcode-name="rebelmouse-image" />
Darwin's tubercle. Image source: Wikimedia commons<p> Yes, maybe the shell of you ear does feel like a dried apricot. Maybe not. But there's a ridge in that swirly structure that's a muscle which allowed us, at one point, to move our ears in the direction of interesting sounds. These days, we just turn our heads, but there it is.</p>
Goosebumps<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8xOTA5NzMxNC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyNzEyNTc2Nn0.aVMa5fsKgiabW5vkr7BOvm2pmNKbLJF_50bwvd4aRo4/img.jpg?width=980" id="d8420" class="rm-shortcode" data-rm-shortcode-id="f735418322b34382dcd882299c9ccc48" data-rm-shortcode-name="rebelmouse-image" />
Goosebumps. Photo credit: Tyler Olson via Shutterstock<p>It's not entirely clear what purpose made goosebumps worth retaining evolutionarily, but there are two circumstances in which they appear: fear and cold. For fear, they may have been a way of making body hair stand up so we'd appear larger to predators, much the way a cat's tail puffs up — numerous creatures exaggerate their size when threatened. In the cold, they may have trapped additional heat for warmth.</p>
Tailbone<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8xOTA5NzMxNi9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxMDMzMDc3N30.p9BEtkf3-PV3EtDSQMUGUeopsimiCHUagx97P4f8IBw/img.jpg?width=980" id="e8ab8" class="rm-shortcode" data-rm-shortcode-id="0063ce99bdd22fbebe1279244b87935c" data-rm-shortcode-name="rebelmouse-image" />
Coccyx. Image source: decade3d-anatomy online via Shutterstock<p>Way back, we had tails that probably helped us balance upright, and was useful moving through trees. We still have the stump of one when we're embryos, from 4–6 weeks, and then the body mostly dissolves it during Weeks 6–8. What's left is the coccyx.</p>
The palmar grasp reflex<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8xOTA5NzMyMC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYzNjY0MDY5NX0.OSwReKLmNZkbAS12-AvRaxgCM7zyukjQUaG4vmhxTtM/img.jpg?width=980" id="8804c" class="rm-shortcode" data-rm-shortcode-id="45469ca5ee5f43433a782f7d4ac0a440" data-rm-shortcode-name="rebelmouse-image" />
Palmar reflex activated! Photo credit: Raul Luna on Flickr<p> You've probably seen how non-human primate babies grab onto their parents' hands to be carried around. We used to do this, too. So still, if you touch your finger to a baby's palm, or if you touch the sole of their foot, the palmar grasp reflex will cause the hand or foot to try and close around your finger.</p>
Other people's suggestions<p>Amir's followers dove right in, offering both cool and questionable additions to her list. </p>
Fangs?<blockquote class="twitter-tweet" data-conversation="none" data-lang="en"><p lang="en" dir="ltr">Lower mouth plate behind your teeth. Some have protruding bone under the skin which is a throw back to large fangs. Almost like an upsidedown Sabre Tooth.</p>— neil crud (@neilcrud66) <a href="https://twitter.com/neilcrud66/status/1085606005000601600?ref_src=twsrc%5Etfw">January 16, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
Hiccups<blockquote class="twitter-tweet" data-conversation="none" data-lang="en"><p lang="en" dir="ltr">Sure: <a href="https://t.co/DjMZB1XidG">https://t.co/DjMZB1XidG</a></p>— Stephen Roughley (@SteBobRoughley) <a href="https://twitter.com/SteBobRoughley/status/1085529239556968448?ref_src=twsrc%5Etfw">January 16, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
Hypnic jerk as you fall asleep<blockquote class="twitter-tweet" data-conversation="none" data-lang="en"><p lang="en" dir="ltr">What about when you “jump” just as you’re drifting off to sleep, I heard that was a reflex to prevent falling from heights.</p>— Bann face (@thebanns) <a href="https://twitter.com/thebanns/status/1085554171879788545?ref_src=twsrc%5Etfw">January 16, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> <p> This thing, often called the "alpha jerk" as you drop into alpha sleep, is properly called the hypnic jerk,. It may actually be a carryover from our arboreal days. The <a href="https://www.livescience.com/39225-why-people-twitch-falling-asleep.html" target="_blank" data-vivaldi-spatnav-clickable="1">hypothesis</a> is that you suddenly jerk awake to avoid falling out of your tree.</p>
Nails screeching on a blackboard response?<blockquote class="twitter-tweet" data-conversation="none" data-lang="en"><p lang="en" dir="ltr">Everyone hate the sound of fingernails on a blackboard. It's _speculated_ that this is a vestigial wiring in our head, because the sound is similar to the shrill warning call of a chimp. <a href="https://t.co/ReyZBy6XNN">https://t.co/ReyZBy6XNN</a></p>— Pet Rock (@eclogiter) <a href="https://twitter.com/eclogiter/status/1085587006258888706?ref_src=twsrc%5Etfw">January 16, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
Ear hair<blockquote class="twitter-tweet" data-conversation="none" data-lang="en"><p lang="en" dir="ltr">Ok what is Hair in the ears for? I think cuz as we get older it filters out the BS.</p>— Sarah21 (@mimix3) <a href="https://twitter.com/mimix3/status/1085684393593561088?ref_src=twsrc%5Etfw">January 16, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
Nervous laughter<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">You may be onto something. Tooth-bearing with the jaw clenched is generally recognized as a signal of submission or non-threatening in primates. Involuntary smiling or laughing in tense situations might have signaled that you weren’t a threat.</p>— Jager Tusk (@JagerTusk) <a href="https://twitter.com/JagerTusk/status/1085316201104912384?ref_src=twsrc%5Etfw">January 15, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
Um, yipes.<blockquote class="twitter-tweet" data-conversation="none" data-lang="en"><p lang="en" dir="ltr">Sometimes it feels like my big toe should be on the side of my foot, was that ever a thing?</p>— B033? K@($ (@whimbrel17) <a href="https://twitter.com/whimbrel17/status/1085559016011563009?ref_src=twsrc%5Etfw">January 16, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
So far, 30 student teams have entered the Indy Autonomous Challenge, scheduled for October 2021.
- The Indy Autonomous Challenge will task student teams with developing self-driving software for race cars.
- The competition requires cars to complete 20 laps within 25 minutes, meaning cars would need to average about 110 mph.
- The organizers say they hope to advance the field of driverless cars and "inspire the next generation of STEM talent."
Indy Autonomous Challenge<p>Completing the race in 25 minutes means the cars will need to average about 110 miles per hour. So, while the race may end up being a bit slower than a typical Indy 500 competition, in which winners average speeds of over 160 mph, it's still set to be the fastest autonomous race featuring full-size cars.</p><p style="margin-left: 20px;">"There is no human redundancy there," Matt Peak, managing director for Energy Systems Network, a nonprofit that develops technology for the automation and energy sectors, told the <a href="https://www.post-gazette.com/business/tech-news/2020/06/01/Indy-Autonomous-Challenge-Indy-500-Indianapolis-Motor-Speedway-Ansys-Aptiv-self-driving-cars/stories/202005280137" target="_blank">Pittsburgh Post-Gazette</a>. "Either your car makes this happen or smash into the wall you go."</p>
Illustration of the Indy Autonomous Challenge
Indy Autonomous Challenge<p>The Indy Autonomous Challenge <a href="https://www.indyautonomouschallenge.com/rules" target="_blank">describes</a> itself as a "past-the-post" competition, which "refers to a binary, objective, measurable performance rather than a subjective evaluation, judgement, or recognition."</p><p>This competition design was inspired by the 2004 DARPA Grand Challenge, which tasked teams with developing driverless cars and sending them along a 150-mile route in Southern California for a chance to win $1 million. But that prize went unclaimed, because within a few hours after starting, all the vehicles had suffered some kind of critical failure.</p>
Indianapolis Motor Speedway
Indy Autonomous Challenge<p>One factor that could prevent a similar outcome in the upcoming race is the ability to test-run cars on a virtual racetrack. The simulation software company Ansys Inc. has already developed a model of the Indianapolis Motor Speedway on which teams will test their algorithms as part of a series of qualifying rounds.</p><p style="margin-left: 20px;">"We can create, with physics, multiple real-life scenarios that are reflective of the real world," Ansys President Ajei Gopal told <a href="https://www.wsj.com/articles/autonomous-vehicles-to-race-at-indianapolis-motor-speedway-11595237401?mod=e2tw" target="_blank">The Wall Street Journal</a>. "We can use that to train the AI, so it starts to come up to speed."</p><p>Still, the race could reveal that self-driving cars aren't quite ready to race at speeds of over 110 mph. After all, regular self-driving cars already face enough logistical and technical roadblocks, including <a href="https://www.bbc.com/news/technology-53349313#:~:text=Tesla%20will%20be%20able%20to,no%20driver%20input%2C%20he%20said." target="_blank">crumbling infrastructure, communication issues</a> and the <a href="https://bigthink.com/paul-ratner/would-you-ride-in-a-car-thats-programmed-to-kill-you" target="_self">fateful moral decisions driverless cars will have to make in split seconds</a>.</p>But the Indy Autonomous Challenge <a href="https://static1.squarespace.com/static/5da73021d0636f4ec706fa0a/t/5dc0680c41954d4ef41ec2b2/1572890638793/Indy+Autonomous+Challenge+Ruleset+-+v5NOV2019+%282%29.pdf" target="_blank">says</a> its main goal is to advance the industry, by challenging "students around the world to imagine, invent, and prove a new generation of automated vehicle (AV) software and inspire the next generation of STEM talent."
A new Harvard study finds that the language you use affects patient outcome.
- A study at Harvard's McLean Hospital claims that using the language of chemical imbalances worsens patient outcomes.
- Though psychiatry has largely abandoned DSM categories, professor Joseph E Davis writes that the field continues to strive for a "brain-based diagnostic system."
- Chemical explanations of mental health appear to benefit pharmaceutical companies far more than patients.
Challenging the Chemical Imbalance Theory of Mental Disorders: Robert Whitaker, Journalist<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="41699c8c2cb2aee9271a36646e0bee7d"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/-8BDC7i8Yyw?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>This is a far cry from Howard Rusk's 1947 NY Times editorial calling for mental healt</p><p>h disorders to be treated similarly to physical disease (such as diabetes and cancer). This mindset—not attributable to Rusk alone; he was merely relaying the psychiatric currency of the time—has dominated the field for decades: mental anguish is a genetic and/or chemical-deficiency disorder that must be treated pharmacologically.</p><p>Even as psychiatry untethered from DSM categories, the field still used chemistry to validate its existence. Psychotherapy, arguably the most efficient means for managing much of our anxiety and depression, is time- and labor-intensive. Counseling requires an empathetic and wizened ear to guide the patient to do the work. Ingesting a pill to do that work for you is more seductive, and easier. As Davis writes, even though the industry abandoned the DSM, it continues to strive for a "brain-based diagnostic system." </p><p>That language has infiltrated public consciousness. The team at McLean surveyed 279 patients seeking acute treatment for depression. As they note, the causes of psychological distress have constantly shifted over the millennia: humoral imbalance in the ancient world; spiritual possession in medieval times; early childhood experiences around the time of Freud; maladaptive thought patterns dominant in the latter half of last century. While the team found that psychosocial explanations remain popular, biogenetic explanations (such as the chemical imbalance theory) are becoming more prominent. </p><p>Interestingly, the 80 people Davis interviewed for his book predominantly relied on biogenetic explanations. Instead of doctors diagnosing patients, as you might expect, they increasingly serve to confirm what patients come in suspecting. Patients arrive at medical offices confident in their self-diagnoses. They believe a pill is the best course of treatment, largely because they saw an advertisement or listened to a friend. Doctors too often oblige without further curiosity as to the reasons for their distress. </p>
Image: Illustration Forest / Shutterstock<p>While medicalizing mental health softens the stigma of depression—if a disorder is inheritable, it was never really your fault—it also disempowers the patient. The team at McLean writes,</p><p style="margin-left: 20px;">"More recent studies indicate that participants who are told that their depression is caused by a chemical imbalance or genetic abnormality expect to have depression for a longer period, report more depressive symptoms, and feel they have less control over their negative emotions."</p><p>Davis points out the language used by direct-to-consumer advertising prevalent in America. Doctors, media, and advertising agencies converge around common messages, such as everyday blues is a "real medical condition," everyone is susceptible to clinical depression, and drugs correct underlying somatic conditions that you never consciously control. He continues,</p><p style="margin-left: 20px;">"Your inner life and evaluative stance are of marginal, if any, relevance; counseling or psychotherapy aimed at self-insight would serve little purpose." </p><p>The McLean team discovered a similar phenomenon: patients expect little from psychotherapy and a lot from pills. When depression is treated as the result of an internal and immutable essence instead of environmental conditions, behavioral changes are not expected to make much difference. Chemistry rules the popular imagination.</p>