I wanted to share this important article – ‘How to become a centaur’ – written by Nicky Case for the Journal of Design and Science (JoDS) at the MIT Media Lab. It discusses Intelligence Augmentation (IA) and Human +\u00a0 Artificial Intelligence, where the ‘+’ is the most important factor to consider.Garry cringed, like someone just spit in his breakfast. Pawn to f5. Blue remained silent, like it just spit in someone else\u2019s breakfast. Rook to e7: taking Garry\u2019s queen. This was Game 6, but Garry had already lost his nerve when Blue beat him at the end of Game 2, and they\u2019ve been drawing ever since. Garry made the move that would be his last. Bishop to e7: taking the rook that took his queen. Blue responded. Pawn to c4. Garry quickly recognized this was a set-up for Blue to invade with its queen \u2014 and knew there was no hope after that.Garry Kasparov resigned, in less than 20 moves. On May 11th, 1997, IBM\u2019s Deep Blue became the first AI to beat a human World Chess Champion.You can now download a chess-playing AI better than Deep Blue on your laptop.The Story of AIHere\u2019s the story we\u2019ve been telling ourselves about AI for decades: it\u2019s man versus machine, creators versus their creation, a ball of wrinkly meat versus a smooth block of silicon. Whether it\u2019s our immediate worries about AI (machines stealing your job, self-driving cars making deadly mistakes, autonomous killer drones) or the more far-fetched concerns about AI (taking over the world and turning us all into pets and\/or paperclips), it all comes from the same root fear: the fear that AI will not share our human goals and values. And what\u2019s worse, we\u2019ve told ourselves that our relationship between ourselves and our AI is like a chess game:Zero-sum \u2014 one player\u2019s win is another player\u2019s loss.Garry demanded a rematch. He accused IBM\u2019s humans of secretly helping out Blue, and besides, this match he\u2019d lost in 1997 was a rematch after he\u2019d decisively beaten Deep Blue in 1996. Another rematch would only be fair.IBM said no. They killed Blue, then packed up and went home. (RIP Deep Blue, 1989-1997)However, Garry couldn\u2019t help but imagine: what if a human did work together with an AI? The next year, in 1998, Garry Kasparov held the world\u2019s first game of \u201cCentaur Chess\u201d. Similar to how the mythological centaur was half-human, half-horse, these centaurs were teams that were half-human, half-AI.But if humans are worse than AIs at chess, wouldn\u2019t a Human+AI pair be worse than a solo AI? Wouldn\u2019t the computer just be slowed down by the human, like Usain Bolt trying to run a three-legged race with his leg tied to a fat panda\u2019s? In 2005, a online chess tournament, inspired by Garry\u2019s centaurs, tried to answer this question. They invited all kinds of contestants \u2014 supercomputers, human grandmasters, mixed teams of humans and AIs \u2014 to compete for a grand prize.Not surprisingly, a Human+AI Centaur beats the solo human. But \u2014 amazingly \u2014 a Human+AI Centaur also beats the solo computer.This is because, contrary to unscientific internet IQ tests on clickbait websites, intelligence is not a single dimension. (The \u201cg factor\u201d, also known as \u201cgeneral intelligence\u201d, only accounts for 30-50% of a individual\u2019s performance on different cognitive tasks. So while it is an important dimension, it\u2019s not the only dimension.) For example, human grandmasters are good at long-term chess strategy, but poor at seeing ahead for millions of possible moves \u2014 while the reverse is true for chess-playing AIs. And because humans & AIs are strong on different dimensions, together, as a centaur, they can beat out solo humans and computers alike.But won\u2019t AI eventually get better at the dimensions of intelligence we excel at? Maybe. However, consider the \u201cNo Free Lunch\u201d theorem, which comes from the field of machine learning itself. The theorem states that no problem-solving algorithm (or \u201cintelligence\u201d) can out-do random chance on all possible problems: instead, an intelligence has to specialize. A squirrel intelligence specializes in being a squirrel. A human intelligence specializes in being a human. And if you\u2019ve ever had the displeasure of trying to figure out how to keep squirrels out of your bird feeders, you know that even squirrels can outsmart humans on some dimensions of intelligence. This may be a hopeful sign: even humans will continue to outsmart computers on some dimensions.Now, not only does pairing humans with AIs solve a technical problem \u2014 how to overcome the weaknesses of humans\/AI with the strengths of AI\/humans \u2014 it also solves that moral problem: how do we make sure AIs share our human goals and values?And it\u2019s simple: if you can\u2019t beat \u2018em, join \u2018em!The rest of this essay will be about AI\u2019s forgotten cousin, IA: Intelligence Augmentation. The old story of AI is about human brains working against silicon brains. The new story of IA will be about human brains working with silicon brains. As it turns out, most of the world is the opposite of a chess game:Non-zero-sum \u2014 both players can win.In the next few sections, I\u2019ll talk about the past, present, and possible future of IA \u2014 how we humans have built tools to amplify our intellectual strengths, and overcome our intellectual weaknesses. I\u2019ll show how humans are already working with AIs in various fields, from art to engineering. And finally, I\u2019ll give some rough ideas on how you can design a good partnership with an AI \u2014 how to become a centaur.Together, humans and AI can go from \u201ccheckmate\u201d, to \u201cteammate\u201d.The Story of IADoug Engelbart taped a brick to a pencil, and tried to write with it. He sure knew how to use his Cold War military research money.In 1962 \u2014 decades before Garry Kasparov played chess with centaurs, years before the early internet was invented, even a while before the first supercomputer \u2014 Doug Engelbart was investigating how our tools shape our thoughts. At the time, most of Doug\u2019s peers just saw computers as a way to crunch numbers faster. However, he saw something deeper: he saw a way to augment the human mind.Not that humans augmenting their own abilities is anything new. We don\u2019t have claws or fangs, so our ancestors augmented their physical abilities with spears and arrows. We don\u2019t have large working memories, so our ancestors augmented their cognitive abilities with abacuses and writing. And these tools didn\u2019t just make human lives easier \u2014 they completely changed how humans lived. Writing especially: it wasn\u2019t \u201cjust\u201d a way to record things, it led to the creation of mathematics, science, history, literary arts, and other pillars of modern civilization.That\u2019s why Doug tied that brick to a pencil \u2014 to prove a point. Of all the tools we\u2019ve created to augment our intelligence, writing may be the most important. But when he \u201cde-augmented\u201d the pencil, by tying a brick to it, it became much, much harder to even write a single word. And when you make it hard to do the low-level parts of writing, it becomes near impossible to do the higher-level parts of writing: organizing your thoughts, exploring new ideas and expressions, cutting it all down to what\u2019s essential. That was Doug\u2019s message: a tool doesn\u2019t \u201cjust\u201d make something easier \u2014 it allows for new, previously-impossible ways of thinking, of living, of being.Doug Engelbart chased this dream for several years, and on December 9th, 1968, showed the world a new computer system that brought the idea of intelligence amplification to life. This event is now known as The Mother of All Demos, and it\u2019s a fitting title. For the very first time, the world saw: the computer mouse, hypertext, video conferencing, collaborative work in real-time, and so much more, in \u2014 let me remind you \u2014 1968. That was 16 years before the Apple Macintosh, 35 years before Skype, and 44 years before Google Docs.Over the next few decades, the wonders in The Mother of All Demos slowly reached the public. The personal computer gave ordinary people the power of computing, something only governments and big corporations could afford previously. A particle physics lab in Switzerland released a little thing called the \u201cWorld Wide Web\u201d, which let people share knowledge using things called \u201cweb pages\u201d, and people could even create connections between pieces of knowledge using something called a \u201chyperlink\u201d.Steve Jobs once called the computer a bicycle for the mind. Note the metaphor of a bicycle, instead of a something like a car \u2014 a bicycle lets you go faster than the human body ever can, and yet, unlike the car, the bicycle is human-powered. (also, the bicycle is healthier for you.) The strength of metal, with a human at its heart. A collaboration \u2014 a centaur.Things were looking good for the Intelligence Augmentation movement.Were.Nowadays, few people have even heard of IA, especially compared with its cousin, AI. But it\u2019s not just linguistics. Doug Engelbart envisioned that the computer would be a tool for intellectual and artistic creativity; now, our devices are designed less around creation, and more around consumption. Forget AI not sharing our values \u2014 even non-AI technology stopped supporting our values, and in some cases, actively subverts them.We hoped for a bicycle for the mind; we got a Lazy Boy recliner for the mind.But thankfully, IA\u2019s story does not end there. In recent years, there\u2019s been a resurgence of interest in IA. Ironically, it\u2019s in part due to a fear of humans \u201cfalling behind\u201d AI \u2014 this is the exact reason why Elon Musk founded Neuralink, a company that\u2019s researching how to make brain implants that link our minds directly to computers. But as Doug Engelbart and Garry Kasparov have shown, you don\u2019t need a direct brain-machine interface to augment our intelligence. The interface that evolution has already gifted us \u2014 eyes, ears, hands and a body \u2014 work pretty darn well. You can ride the bicycle for the mind, without literally jamming metal into it.But just as IA shows that it doesn\u2019t have to be humans versus machines, it doesn\u2019t have to be IA versus AI. For the last century, the story of AI and the story of IA have been chugging along on different tracks \u2014 but in the next decade, these two stories may be headed for a collision course.How To Become A CentaurThere was another shock in store for Garry Kasparov. Remember that 2005 online chess tournament, between supercomputers, human grandmasters, and Human+AI centaurs? I forgot to mention who actually won the grand prize.At first, Garry wasn\u2019t surprised when a human grandmaster with a weak laptop could beat a world-class supercomputer. But what stunned Garry was who won at the end of the tournament \u2014 not a human grandmaster with a powerful computer, but rather, a team of two amateur humans and three weak computers! The three computers were running three different chess-playing AIs, and when they disagreed on the next move, the humans \u201ccoached\u201d the computers to investigate those moves further.As Garry put it: \u201cWeak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.\u201dThe centaur, from ancient Greek mythology, was a majestic being born of a goddess. Bojack Horseman, from the Netflix original series, is a depressed alcoholic who hurts everyone around him. Despite both of them being half-human, half-horse creatures, one is clearly a more successful combination than the other. And this brings us to the most important lesson about human-machine collaboration:When you create a Human+AI team, the hard part isn\u2019t the \u201cAI\u201d. It isn\u2019t even the \u201cHuman\u201d.It\u2019s the \u201c+\u201d.So, how do you find the best \u201c+\u201d for humans and AI? How do you combine humans\u2019 and AI\u2019s individual strengths, to overcome their individual weaknesses? Well, to do that, we first need to know exactly what humans\u2019 and AI\u2019s strengths and weaknesses are.Human nature, for better or worse, doesn\u2019t change much from millennia to millennia. If you want to see the strengths that are unique and universal to all humans, don\u2019t look at the world-famous award-winners \u2014 look at children. Children, even at a young age, are already proficient at: intuition, analogy, creativity, empathy, social skills. Some may scoff at these for being \u201csoft skills\u201d, but the fact that we can make an AI that plays chess but not hold a normal five-minute conversation, is proof that these skills only seem \u201csoft\u201d to us because evolution\u2019s already put in the 3.5 billion years of hard work for us.And if you want to see the weaknesses of humans, go to school. This is the stuff that\u2019s hard for human intelligences, and requires years of training to gain even a basic competency: arithmetic, computation, memory, logic, numeracy. Note that these are all things your phone can do better and faster than the smartest human alive. (And we wonder why kids feel school is meaningless\u2026)Now, those are the strengths & weaknesses of humans \u2014 what about the strengths & weaknesses of AI? Honestly, it\u2019s a fool\u2019s errand to try to predict what specific things AI can or can\u2019t do in the far future. Thirty years ago, nobody predicted we\u2019d have self-driving cars by now. (Then again, we predicted we\u2019d have flying cars by now.) Since we can\u2019t predict anything specific, let\u2019s think generally about what kinds of tasks, so far, AI has had a relative advantage or disadvantage.Computers are, obviously, best at computing. They\u2019re good at crunching trillions of numbers, scanning billions of data points, considering millions of possibilities. Numbers may be AI\u2019s greatest strength \u2014 but numbers are also their greatest weakness. Right now, you can only train AI if you have a \u201ccost function\u201d, that is, if there are quantitatively better or worse answers. This is why AIs have bested grandmasters at chess and Go \u2014 where it\u2019s clear that win > draw > lose \u2014 but are awkward at best at having conversation, creating inventions, making art, negotiating business, formulating scientific hypotheses \u2014 where you can\u2019t simply rank all your answers on a single dimension from best to worst. In those kinds of tasks, you\u2019d want a human being, who can step back from a single answer and ask, \u201cwhy?\u201d or \u201chow?\u201d or \u201cwhat if?\u201dIn other words: AIs are best at choosing answers. Humans are best at choosing questions.And that\u2019s how the winning Human+AI team of the 2005 online tournament chose their \u201c+\u201d. The two amateur humans gave questions to their three weak computers, and when the computers gave back differing answers, the humans gave them even deeper questions.The chessboard isn\u2019t the only place Human+AI centaurs have had success. From art to engineering, the last few years have seen the rise of centaurs in multiple fields:In 2002, Sung-Bae Cho created a tool where you and an AI create fashion designs together. The tool simulates the process of evolution, but on dresses. The AI provides the \u201cgenetic variation\u201d by randomly generating variants of dresses, and you provide the \u201cnatural selection\u201d by using your sense of aesthetics to pick the dresses that will go on to \u201creproduce\u201d in the next generation.In 2016, Maurice Conti demonstrated another case of evolutionary AI working with a human, to create a quadcopter body. The human sets goals and constraints for the AI (\u201ctry to make the body as light as possible, while still remaining sturdy and having four propellers\u201d) and the AI \u201cevolves\u201d a quadcopter body in response. The human can then \u201creply\u201d to the AI, by setting further goals or constraints.In 2016, Zhu et al created a painting tool where you draw in the rough outlines, and an AI photo-realistically fills in the gaps. The human and the AI have an artistic \u201cconversation\u201d between them, through pictures. For example, the human can draw some green lines on the bottom, and the AI replies with several possible photo-realistic grassy fields to choose from. Then, the human can draw a black triangle above that, and the AI replies with several pictures of a mountain behind a grassy field. Through this push and pull between human & machine, art is made.In all these examples of centaurs, the human chooses the questions, in the form of setting goals and constraints \u2014 while the AI generates answers, usually showing multiple possibilities at once, and in real-time to the humans\u2019 questions. But it\u2019s not just a one-way conversation: the human can then respond to the AI\u2019s answers, by asking deeper questions, picking and combining answers, and guiding the AI using human intuition.So, when you think of augmenting human intelligence with AI, think less of assimilating into The Borg, and more of a spirited conversation between Kirk & Spock \u2014 a mix of intuition and logic that surpasses either one alone.Since the design of Human+AI systems is such a new field \u2014 in fact, it\u2019s pretty generous to call it a \u201cfield\u201d, it\u2019s more like a small patch of grass \u2014 there are lots of unsolved problems, like: 1) What kind of questions should a human ask? In all the above examples, the question is usually \u201cwhat possible solutions fit these goals & constraints?\u201d 2) How should humans and AIs communicate? You don\u2019t have to use words, or even code; the painting example has the human and AI communicate through pictures! 3) How can multiple humans or multiple AIs work together? All the above examples had just one human working with one AI, but the winner of the 2005 Centaur Chess tournament had two humans and three AIs \u2014 how can this scale to dozens, thousands, even millions of people and\/or machines?AIs choose answers. Humans choose questions. And given all the possibilities, the promises and pitfalls of technology in the coming decades, the next question for us humans to choose is:What\u2019s next?The Story of UsFor the last few decades, the story of AI has been one of a rising hero \u2014 or is it of a rising villain? In 1997, an AI beat Garry Kasparov at chess, and in 2011 and 2016, AIs beat the world\u2019s top humans at Jeopardy! and Go. And now, many fear that AI will take over our jobs, or even take over humanity itself.Meanwhile, the story of IA has been one of a tragic fall. Starting out strong with Doug Engelbart\u2019s Mother of All Demos, the idea of IA has slowly been forgotten, as technology shifted from tools for creation and more towards tools for consumption. Someone stole the wheels off the bicycle for our mind.But now, these two story threads may be starting to wrap together, forming a new braid in history: AIA \u2014 Artificial Intelligence Augmentation. IA can give AI the human partnership it needs in order to remain aligned with our deepest goals and values. And in return, AI can give IA some new replacement wheels for the bicycle of our mind.I\u2019d like to tell you what the future holds. But if you tell someone something good is inevitable, it can cause self-defeating complacency \u2014 and if you tell someone something bad is inevitable, it can cause self-fulfilling despair.Besides, answers are for AIs. As a human, you deserve questions.For example: IA may be able to align AI\u2019s goals with humans\u2019 goals, but how can we align augmented humans\u2019 goals with non-augmented humans\u2019 goals? Are we just replacing a divide between humans and AIs with a divide between humans and humans 2.0? Forget getting humans and AIs to live in peace, how do we even get humans and humans to live in peace? We know how to create tools to augment our intelligence, but can we create tools to augment our empathy? Our communities? Our sense of meaning and purpose?I don\u2019t know. I don\u2019t know what the answers are.However, humanity has had a long history of borrowing ideas from nature. In just the field of machine learning alone, artificial neural networks were inspired by biological neural networks, and genetic algorithms were inspired by the process of biological evolution itself. So, if there\u2019s just one idea you take away from this entire essay, let it be Mother Nature\u2019s most under-appreciated trick: symbiosis. It\u2019s an Ancient Greek word that means: \u201cliving together.\u201d Symbiosis is when flowers feed the bees, and in return, bees pollinate the flowers. It\u2019s when you eat healthy food to nourish the trillions of microbes in your gut, and in return, thoe microbes break down your food and make serotonin to keep you happy. It\u2019s when, 1.5 billion years ago, a cell swallowed a bacterium without digesting it, the bacterium decided it was really into that kind of thing, and in return, the bacterium \u2014 which we now call \u201cmitochondria\u201d \u2014 produces energy for its host.Symbiosis shows us you can have fruitful collaborations even if you have different skills, or different goals, or are even different species. Symbiosis shows us that the world often isn\u2019t zero-sum \u2014 it doesn\u2019t have to be humans versus AI, or humans versus centaurs, or humans versus other humans. Symbiosis is two individuals succeeding together not despite, but because of, their differences. Symbiosis is the \u201c+\u201d.A new chapter in humanity\u2019s story is beginning, and we \u2014 living together \u2014 get to write what happens next.Shared thanks to the\u00a0Creative Commons Attribution 4.0 International License.Nikolas is a world-leading Futurist Speaker that drives leaders to take action in creating a better world for humanity. He promotes exponential thinking along with a critical, honest, and optimistic view that empowers you with knowledge to plan for today, tomorrow, and for the future.Contact him\u00a0to discuss how to engage and inspire your audience. You can also see more of Nikolas\u2019 thoughts on his Futurist Speaker VLOGs as he publishes them in this\u00a0Youtube playlist.Please\u00a0SUBSCRIBE\u00a0to Nikolas\u2019 Youtube channel so that you don\u2019t miss any as they come up. You can see more of his thoughts on\u00a0Linkedin,\u00a0Twitter, and bookmarked research on\u00a0Tumblr.