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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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Scientists are using bioelectronic medicine to treat inflammatory diseases, an approach that capitalizes on the ancient "hardwiring" of the nervous system.
- Bioelectronic medicine is an emerging field that focuses on manipulating the nervous system to treat diseases.
- Clinical studies show that using electronic devices to stimulate the vagus nerve is effective at treating inflammatory diseases like rheumatoid arthritis.
- Although it's not yet approved by the US Food and Drug Administration, vagus nerve stimulation may also prove effective at treating other diseases like cancer, diabetes and depression.
The nervous system’s ancient reflexes<p>You accidentally place your hand on a hot stove. Almost instantaneously, your hand withdraws.</p><p>What triggered your hand to move? The answer is <em>not</em> that you consciously decided the stove was hot and you should move your hand. Rather, it was a reflex: Skin receptors on your hand sent nerve impulses to the spinal cord, which ultimately sent back motor neurons that caused your hand to move away. This all occurred before your "conscious brain" realized what happened.</p><p>Similarly, the nervous system has reflexes that protect individual cells in the body.</p><p>"The nervous system evolved because we need to respond to stimuli in the environment," said Dr. Tracey. "Neural signals don't come from the brain down first. Instead, when something happens in the environment, our peripheral nervous system senses it and sends a signal to the central nervous system, which comprises the brain and spinal cord. And then the nervous system responds to correct the problem."</p><p>So, what if scientists could "hack" into the nervous system, manipulating the electrical activity in the nervous system to control molecular processes and produce desirable outcomes? That's the chief goal of bioelectronic medicine.</p><p>"There are billions of neurons in the body that interact with almost every cell in the body, and at each of those nerve endings, molecular signals control molecular mechanisms that can be defined and mapped, and potentially put under control," Dr. Tracey said in a <a href="https://www.youtube.com/watch?v=AJH9KsMKi5M" target="_blank">TED Talk</a>.</p><p>"Many of these mechanisms are also involved in important diseases, like cancer, Alzheimer's, diabetes, hypertension and shock. It's very plausible that finding neural signals to control those mechanisms will hold promises for devices replacing some of today's medication for those diseases."</p><p>How can scientists hack the nervous system? For years, researchers in the field of bioelectronic medicine have zeroed in on the longest cranial nerve in the body: the vagus nerve.</p>
The vagus nerve<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTYyOTM5OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY0NTIwNzk0NX0.UCy-3UNpomb3DQZMhyOw_SQG4ThwACXW_rMnc9mLAe8/img.jpg?width=1245&coordinates=0%2C0%2C0%2C0&height=700" id="09add" class="rm-shortcode" data-rm-shortcode-id="f38dbfbbfe470ad85a3b023dd5083557" data-rm-shortcode-name="rebelmouse-image" data-width="1245" data-height="700" />
Electrical signals, seen here in a synapse, travel along the vagus nerve to trigger an inflammatory response.
Credit: Adobe Stock via solvod<p>The vagus nerve ("vagus" meaning "wandering" in Latin) comprises two nerve branches that stretch from the brainstem down to the chest and abdomen, where nerve fibers connect to organs. Electrical signals constantly travel up and down the vagus nerve, facilitating communication between the brain and other parts of the body.</p><p>One aspect of this back-and-forth communication is inflammation. When the immune system detects injury or attack, it automatically triggers an inflammatory response, which helps heal injuries and fend off invaders. But when not deployed properly, inflammation can become excessive, exacerbating the original problem and potentially contributing to diseases.</p><p>In 2002, Dr. Tracey and his colleagues discovered that the nervous system plays a key role in monitoring and modifying inflammation. This occurs through a process called the <a href="https://www.nature.com/articles/nature01321" target="_blank" rel="noopener noreferrer">inflammatory reflex</a>. In simple terms, it works like this: When the nervous system detects inflammatory stimuli, it reflexively (and subconsciously) deploys electrical signals through the vagus nerve that trigger anti-inflammatory molecular processes.</p><p>In rodent experiments, Dr. Tracey and his colleagues observed that electrical signals traveling through the vagus nerve control TNF, a protein that, in excess, causes inflammation. These electrical signals travel through the vagus nerve to the spleen. There, electrical signals are converted to chemical signals, triggering a molecular process that ultimately makes TNF, which exacerbates conditions like rheumatoid arthritis.</p><p>The incredible chain reaction of the inflammatory reflex was observed by Dr. Tracey and his colleagues in greater detail through rodent experiments. When inflammatory stimuli are detected, the nervous system sends electrical signals that travel through the vagus nerve to the spleen. There, the electrical signals are converted to chemical signals, which trigger the spleen to create a white blood cell called a T cell, which then creates a neurotransmitter called acetylcholine. The acetylcholine interacts with macrophages, which are a specific type of white blood cell that creates TNF, a protein that, in excess, causes inflammation. At that point, the acetylcholine triggers the macrophages to stop overproducing TNF – or inflammation.</p><p>Experiments showed that when a specific part of the body is inflamed, specific fibers within the vagus nerve start firing. Dr. Tracey and his colleagues were able to map these relationships. More importantly, they were able to stimulate specific parts of the vagus nerve to "shut off" inflammation.</p><p>What's more, clinical trials show that vagus nerve stimulation not only "shuts off" inflammation, but also triggers the production of cells that promote healing.</p><p>"In animal experiments, we understand how this works," Dr. Tracey said. "And now we have clinical trials showing that the human response is what's predicted by the lab experiments. Many scientific thresholds have been crossed in the clinic and the lab. We're literally at the point of regulatory steps and stages, and then marketing and distribution before this idea takes off."<br></p>
The future of bioelectronic medicine<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTYxMDYxMy9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYzNjQwOTExNH0.uBY1TnEs_kv9Dal7zmA_i9L7T0wnIuf9gGtdRXcNNxo/img.jpg?width=980" id="8b5b2" class="rm-shortcode" data-rm-shortcode-id="c005e615e5f23c2817483862354d2cc4" data-rm-shortcode-name="rebelmouse-image" data-width="2000" data-height="1125" />
Vagus nerve stimulation can already treat Crohn's disease and other inflammatory diseases. In the future, it may also be used to treat cancer, diabetes, and depression.
Credit: Adobe Stock via Maridav<p>Vagus nerve stimulation is currently awaiting approval by the US Food and Drug Administration, but so far, it's proven safe and effective in clinical trials on humans. Dr. Tracey said vagus nerve stimulation could become a common treatment for a wide range of diseases, including cancer, Alzheimer's, diabetes, hypertension, shock, depression and diabetes.</p><p>"To the extent that inflammation is the problem in the disease, then stopping inflammation or suppressing the inflammation with vagus nerve stimulation or bioelectronic approaches will be beneficial and therapeutic," he said.</p><p>Receiving vagus nerve stimulation would require having an electronic device, about the size of lima bean, surgically implanted in your neck during a 30-minute procedure. A couple of weeks later, you'd visit, say, your rheumatologist, who would activate the device and determine the right dosage. The stimulation would take a few minutes each day, and it'd likely be unnoticeable.</p><p>But the most revolutionary aspect of bioelectronic medicine, according to Dr. Tracey, is that approaches like vagus nerve stimulation wouldn't come with harmful and potentially deadly side effects, as many pharmaceutical drugs currently do.</p><p>"A device on a nerve is not going to have systemic side effects on the body like taking a steroid does," Dr. Tracey said. "It's a powerful concept that, frankly, scientists are quite accepting of—it's actually quite amazing. But the idea of adopting this into practice is going to take another 10 or 20 years, because it's hard for physicians, who've spent their lives writing prescriptions for pills or injections, that a computer chip can replace the drug."</p><p>But patients could also play a role in advancing bioelectronic medicine.</p><p>"There's a huge demand in this patient cohort for something better than they're taking now," Dr. Tracey said. "Patients don't want to take a drug with a black-box warning, costs $100,000 a year and works half the time."</p><p>Michael Dowling, president and CEO of Northwell Health, elaborated:</p><p>"Why would patients pursue a drug regimen when they could opt for a few electronic pulses? Is it possible that treatments like this, pulses through electronic devices, could replace some drugs in the coming years as preferred treatments? Tracey believes it is, and that is perhaps why the pharmaceutical industry closely follows his work."</p><p>Over the long term, bioelectronic approaches are unlikely to completely replace pharmaceutical drugs, but they could replace many, or at least be used as supplemental treatments.</p><p>Dr. Tracey is optimistic about the future of the field.</p><p>"It's going to spawn a huge new industry that will rival the pharmaceutical industry in the next 50 years," he said. "This is no longer just a startup industry. [...] It's going to be very interesting to see the explosive growth that's going to occur."</p>
The first rule of Vulture Club: stay out of Portugal.
So you're a vulture, riding the thermals that rise up over Iberia. Your way of life is ancient, ruled by needs and instincts that are way older than the human civilization that has overtaken the peninsula below, and the entire planet.
"The Expanse" is the best vision I've ever seen of a space-faring future that may be just a few generations away.
- Want three reasons why that headline is justified? Characters and acting, universe building, and science.
- For those who don't know, "The Expanse" is a series that's run on SyFy and Amazon Prime set about 200 years in the future in a mostly settled solar system with three waring factions: Earth, Mars, and Belters.
- No other show I know of manages to use real science so adeptly in the service of its story and its grand universe building.
Credit: "The Expanse" / Syfy<p>Now, I get it if you don't agree with me. I love "Star Trek" and I thought "Battlestar Galactica" (the new one) was amazing and I do adore "The Mandalorian". They are all fun and important and worth watching and thinking about. And maybe you love them more than anything else. But when you sum up the acting, the universe building, and the use of real science where it matters, I think nothing can beat "The Expanse". And with a <a href="https://www.rottentomatoes.com/tv/the_expanse" target="_blank">Rotten Tomato</a> average rating of 93%, I'm clearly not the only one who feels this way.</p><p>Best.</p><p>Show.</p><p>Ever. </p>
Contrary to what some might think, the brain is a very plastic organ.
As with many other physicians, recommending physical activity to patients was just a doctor chore for me – until a few years ago. That was because I myself was not very active.