On December 15, 2017, the United Nations Special Rapporteur on extreme poverty and human rights, Philip Alston, issued a damning report on his visit to the United States. He cited data from the Stanford Center on Inequality and Poverty, which reports that “in terms of labor markets, poverty, safety net, wealth inequality, and economic mobility, the US comes in last of the top 10 most well-off countries, and 18th amongst the top 21.” Alston wrote that “the American Dream is rapidly becoming the American Illusion, as the US now has the lowest rate of social mobility of any of the rich countries.” Just a few days before, on December 11, The Boston Globe's Spotlight team ran a story showing that the median net worth of nonimmigrant African American households in the Boston area is $8, in contrast to the $247,500 net worth for white households in the Boston area.

Clearly income disparity is ripping the nation apart, and none of the efforts or programs seeking to address it seems to be working. I myself have been, for the past couple of years, engaged in a broad discussion about the future of work with some thoughtful tech leaders and representatives of the Catholic Church who have similar concerns, and the notion of a universal basic income (UBI) keeps coming up. Like many of my friends who fiddle with ideas about the future of work, I’ve avoided actually having a firm opinion about UBI for years. Now I have decided it’s time to get my head around it.

Touted as an elegant solution to the problem of poverty in America and the impending decimation of jobs by automation, UBI is a hot topic today in the “salons” hosted by tech and hedge-fund billionaires. The idea of UBI in fact is an old idea, older than me even: Either through direct cash payments or some sort of negative income tax, we should support people in need—or even everyone—to increase well-being and lift society overall.

Interestingly, this notion has had broad support from conservatives like Milton Friedman and progressives such as Martin Luther King Jr. On the other hand, UBI also has been criticized by conservatives as well as liberals.

Conservative proponents of UBI argue that it could shrink a huge array of costly social welfare services like health care, food assistance, and unemployment support by providing a simple, inexpensive way to let individuals, rather than the government, decide what to spend the money on. Liberals see it as a way to redistribute wealth and empower groups like stay-at-home parents, whose work doesn’t produce income—making them ineligible for unemployment benefits. In addition, these UBI advocates see it as a way to eliminate poverty.

Nevertheless, just as many conservatives and liberals don’t like the concept. Conservatives against UBI worry that it will decrease incentives to work and cost too much, racking up a bill that those who do work will have to pay. Skeptical liberals worry that employers will use it as an excuse to pay even lower wages. They also fear politicians will offer it as a rationale to gut existing social programs and unwind institutions that help those most in need. The result is that UBI is a partisan issue that, paradoxically, has bipartisan support.

I was on a panel at a recent conference when the moderator asked audience and panel members what they thought of UBI. The overwhelming consensus of the 500 or so people in the room appeared to be “we're skeptical, but should experiment.” UBI sounds like a good or not-so-good idea to different constituents because we have so little understanding of either how we would do it, or how people would react. None of us really knows what we’re talking about when it comes to UBI, akin to being in a drunken bar argument before there were smartphones and Wikipedia. But there are a few basic principles and pieces of research that can help.

Universal Basic Income, In Theory

Much of the resurgent interest in UBI has come from Silicon Valley. Tech titans and the academics around them are concerned that the robots and artificial intelligence they’ve built will rapidly displace humans in the workforce, or at least push them into dead-end jobs. Some researchers say robots will replace the low-paying jobs people don’t want, while others maintain people will end up getting the worst jobs not worthy of robots. UBI may play a role in which scenario comes to pass.

Last year, Elon Musk told the National Governors Association that job disruption caused by technology was “the scariest problem to me,” admitting that he had no easy solution. Musk and other entrepreneurs see UBI as way to provide a cushion and a buffer to give humans time to retrain themselves to do what robots can’t do. Some believe that it might even spawn a new wave of entrepreneurs, giving those displaced workers a shot at the American Dream.

They may be getting ahead of themselves. Luke Martinelli, a researcher at the University of Bath Institute for Policy Research, has written that “an affordable UBI is inadequate, and an adequate UBI is unaffordable.” I believe that is roughly true.

One of the biggest problems with UBI is that a base sum that would allow people to refuse work and look for something better (rather than just allowing employers to pay workers less) is around $1000 per month, which would cost most countries somewhere between 5 percent to 35 percent of their GDP. That looks expensive compared with the cost to any developed country of eradicating poverty, so the only way a nation could support this kind of UBI would be to eliminate all funding for social programs. That would be applauded by libertarians and some conservatives, but not by many others.

Underpinning the Silicon Valley argument for UBI is the belief in exponential growth powered by science and technology, as described by Peter Diamandis in his book Abundance: The Future is Better Than You Think. Diamandis contends that technological progress, including gains in health, the power of computing, and the development of machine intelligence, among other things, will lead to a kind of technological transcendence that makes today’s society look like how we view the Dark Ages. He argues that the human mind is unable to intuitively grasp this idea, and so we constantly underestimate long-term effects. If you plot progress out a few decades, Diamandis writes, we end up with unimaginable abundance: “We will soon have the ability to meet and exceed the basic needs of every man, woman, and child on the planet. Abundance for all is within our grasp.” (Technologists often forget is that we actually already have enough food to feed the world; the problem is that it’s just not properly distributed.)

Many tech billionaires think they can have their cake and eat it too, that they are so rich and smart the trickle-down theory can lift the poor out of poverty without anyone or anything suffering. And why shouldn’t they think so? Their companies and their wealth have grown exponentially, and it doesn’t appear as though there is any end in sight, as Marc Andreessen prophetically predicted in his famous essay, “Why Software is Eating the World.” Most of Silicon Valley’s leaders gained their wealth in an exponentially growing market without having to engage in the aggressive tactics that marked the creation of wealth in the past. They feel their businesses inherently “do good,” and that, I believe, allows them to feel more charitable, broadly speaking.

Universal Basic Income, In Practice

If the technologists are correct and automation is going to substantially increase US GDP, then who better to figure out what to do about the associated problems than the technologists themselves—or so their thinking goes. Tech leaders are underwriting experiments and financing research on UBI to prepare for a future that will allow them and their companies to continue in ascendance while keeping society stable. (Various localities and organizations already have experimented with forms of UBI over the years. In some cases, they have produced evidence that people receiving UBI do in fact continue to work, and that UBI gives people the ability to quit lousy jobs and look for better ones, or complete or go back to school.) Sam Altman, president of Y Combinator, has a project to give people free money and see what happens to them over time, for instance.

Altman's experiment, prosaically named the Basic Income Project, will involve 3,000 people in two states over five years. Some 1,000 of them will be given $1,000 a month, and the rest will get just $50 a month and serve as a sort of control group. It should reveal some important information about how people will behave when given free money, providing an evidence-based way to think about UBI—we don’t have much of that evidence now. Among the questions hopefully to be answered: Will people use the cushion of free money to look for better work? Will they go back to school for retraining? Will neurological development of children improve? Will crime rates go down?

As with many ideas with diverse support at high levels, the particulars of execution can make or break UBI in practice. Take the recent, much heralded UBI experiment in Finland. A Finnish welfare agency, Kela, and a group of researchers proposed paying between 550 and 700 euros a month to both workers and nonworkers around that country. Finland’s conservative government then began tweaking the proposal, most importantly eliminating the part of the plan that paid people who had jobs, and only providing UBI for those receiving unemployment benefits instead. It had no interest in whether UBI would allow people to look for better jobs or to train themselves for the jobs of the future. The government declared that the “primary goal of the basic income experiment is related to promoting employment.” And so what started as a credible experiment in empowering labor and liberal values became a conservative program to get more people to go back to crappy jobs—and a great warning about the impact that politics can have on efforts to test or deploy UBI. (We must wait until 2019 to see the full extent of the outcome.)

Chris Hughes, a cofounder of Facebook and not-quite-billionaire, is the person I found with a plan for UBI that’s halfway between Silicon Valley’s techno-utopian vision and the vision held by the liberal East Coast types that I mostly hang out with these days.

His new book Fair Shot: Rethinking Inequality and How We Earn outlines his views on UBI, but here’s my brief version of what Hughes is thinking: He believes we can do UBI now. He says we can “provide every single American stability through cash” by providing a monthly $500 supplement to lower-middle income taxpayers through the Earned Income Tax Credit, or EITC. He would expand EITC to include child care, elder care, and education as types of work that would be eligible for EITC. (Currently if the jobs are unpaid jobs, they are not eligible.) Hughes contends that this would cut poverty in America by half. According to his numbers, right now the EITC costs the US $70 billion a year, and his UBI proposal would tack on an additional $290 billion. Citing research by Emmanuel Saez and Gabriel Zucman showing that less than 1 percent of Americans control as much wealth as 90 percent of Americans, Hughes' plan to pay for that expansion involves increasing the income tax for the top 1 percent, or people earning more than $250,000 a year, to 50 percent from 35 percent, and treating capital gains as income—moving long-term capital gains from 20 percent to 50 percent, hitting the wealthiest the hardest.

He’s putting his money where his mouth is too, underwriting a project that will give $500 a month to residents of Stockton, California.

Will UBI save America? Our Congress and president just passed a tax law that reduces taxes on the country’s wealthiest, but I still think Hughes' proposal is reasonable in part because EITC is a pretty popular program. My fear is that the current political climate and our ability to discuss things rationally are severely impaired, and that's without factoring in the usual challenges of turning rational ideas into law. In the meantime, it’s great that Silicon Valley billionaires have recognized the potential negative impact of their businesses and are looking at and funding experiments to provide better evidence-based understanding of UBI, even if evidence appears to have less and less currency in today’s world.

Am I optimistic? No. Should we get cracking on trying everything we can, and is UBI a decent shot on goal? Yes and yes.

On Friday, I spoke at the Elemental Excelerator Earth Day Energy Summit in Honolulu. The discussion was about the push for Hawaii to become 100% free of fossil fuels.

It reminded me of when my mother and I lived in Hawaii in the 80s and she was working with the late Senator Dick Matsuura and others to explore the idea. My mother and father worked for Energy Conversion Devices (ECD). (I got my first job working with computers as a 13-year-old at ECD. I would later join the board of directors from 1995 - 2000.) ECD was a pioneer in the field of solar power having created the first amorphous photovoltaic cells and the first roll-to-roll process for manufacturing them. ECD was founded by the late Stanford Ovshinsky who was a great mentor to me. I remembered how 30 years ago, solar in Hawaii seemed like an obvious idea, but a somewhat dreamy one.

It was truly exciting to see solar energy become a reality and the goal of a solar powered Hawaii within reach. Huge congrats to everyone who has gotten us so far.

Saturday, I participated in a board meeting of the Excelerator (as an advisor) which is doing an amazing job supporting renewable energy companies.

My mother loved Hawaii and when she died in 1995, we buried half of her ashes in our grave of 17 generations in Iwate at our family home. The other half of her ashes were released into the ocean off of Maui in a traditional Hawaiian ceremony. It was a full circle connection to my mother and her dreams of a solar powered Hawaii and my current role working on climate and energy issues with my friends at the Emerson Elemental.

Yesterday, I participated in a memorial symposium John Perry Barlow's at the Internet Archive in San Francisco. It was amazing to see so many old friends that I realized I had missed so dearly. It really felt like Barlow was in the room - he was the energy that united us. It also reminded me of the roots of the Internet and how different the culture of many of the founders was from the Silicon Valley. It gave me hope that we still have a fire in our belly to continue the fight for freedom and liberty that John Perry Barlow embodied and inspired everyone with.

I was allowed to make a few comments. The video of the whole event is worth watching. This is the speaker lineup in the order they appear:

Welcome
Brewster Kahle, Founder & Digital Librarian, Internet Archive

Co-Hosts
Cindy Cohn, Executive Director of the Electronic Frontier Foundation
Cory Doctorow, celebrated scifi author and Editor in Chief of Boing Boing

Speakers
Anna Barlow, daughter of John Perry Barlow
Mitch Kapor, Co-founder of EFF and Co-chair of the Kapor Center for Social Impact
Pam Samuelson, Richard M. Sherman Distinguished Professor of Law and Information at the University of California, Berkeley
Trevor Timm, Executive Director of Freedom of the Press
Edward Snowden, noted whistleblower and President of Freedom of the Press Foundation
Shari Steele, Executive Director of the Tor Foundation and former EFF Executive Director
John Gilmore, EFF Co-founder, Board Member, entrepreneur and technologist
Steven Levy, Wired Senior Writer, and author of Hackers, In the Plex, and other books
Joi Ito, Director of the MIT Media Lab
Amelia Barlow, daughter of John Perry Barlow

I've taken a bit of editorial license - below are my rough notes of what I was going to say which are roughly what I said or meant to say. :-)

---

I met Barlow in the summer of 1990 when my mother had moved to LA and we were installing my sister in college in Palo Alto. Timothy Leary, who I had met in Japan and who would later adopt me as a god son, drove us from LA to San Francisco to introduce us to his community there. (He didn't have a drivers license.) He threw a party for us at the Mondo 2000 House to introduce us to his SF community and Barlow was there.

This was 1990 - before WIRED, before the web. It was all about Cyberpunk - leather jackets, CDROMs, weird drugs, raves, VR. South Park was a needle park, and Toon Town used to have raves around there. I remember raves advertising "Free VR." Silicon Graphics computers were being used to make amazing rave flyers that eventually inspired the design for WIRED Magazine. All that started in South Park and and was the genesis of the gentrification that transformed the neighborhood to what it is now.

Cyberpunk was a sort of new punk rock - meets the hippies, meets computers and the proximity to Haight-Ashbury, Silicon Valley and Berkeley created this weird sub-culture where a lot of this Internet stuff started.

Timothy Leary and Barlow had many differences, but also had a lot of similarities. They were my mentors.

They both had an amazing sense of humor, optimism and hope. This wasn't the optimism of giddy investors during a bubble. Rather, it was the optimism and humor that I sense in the Dalai Lama and others who have become self-aware through meditation, mind-expanding drugs or whatever brings you close to understanding true nature and reality. It's that peculiar zone where you see all of the suffering, the injustice and just how fucked up the world can be - and you face this challenge with a fundamental confidence in human beings and a sense of humor.

Timothy Leary used to say, "Question Authority and Think for Yourself."

Barlow's manifesto, A Declaration of the Independence of Cyberspace, was a great example of that. It was a rallying cry for a new generation - for us. I remember when we were starting out, it felt like if we could just connect everyone and give them a voice, we'd have peace, love and fairness.

Today our dream of the world that Barlow wrote about seems like a distant dream. Barlow was obviously aware of the twists and turns that this path has taken.

Barlow said, "My belief in the virtues of giving all humanity a voice did not take into account what would happen if you gave every one of a billion people his own virtual soapbox and street corner. Everybody's talking and nobody's listening."

Barlow also said, "I'm not sorry I wrote it. One day, I still believe, it will seem true."

We're having to climb some mountains and suffer some bad weather. It almost feels like the winter of 1846 for the Donner Party. But he gave us a compass heading.

I also believe, as Barlow did, that one day it will seem true. But to make it true, it will require organizing, action and tenacity.

In addition to a compass heading, Barlow helped us organize, think and act, and he fueled us with hope, humor and optimism even in our darkest moments.

We are in one of the darkest moments in global and American history that I remember.

I was born in 1966. I don't remember 1967 because I was just a 1 year old. But in 1967, we had the Detroit Street Riots which some called a rebellion (I guess if you squash it, you get to name it). It the worst incident of its kind in US history killing 43 people and burning down 1,400 buildings as the National Guard was called in to stop it. It was also the year that The Grateful Dead's debut album came out and Barlow introduced them to Timothy Leary at Millbrook. 1967 was also the year of the Summer of Love that kicked off the Hippie movement.

The Hippies and the Grateful Dead fought against the Vietnam war and the racial tensions with songs, love and humor.

The Parkland kids and the collective movement they've inspired, the #meetoo and TimesUp movements are two of the most powerful movements of the day. The TimesUp movement is headed to overturn centuries of patriarchal power. There is another wave coming. It feels different from the Hippie movement, but it feels like we're once again on the following the compass heading Barlow gave us - to overthrow the established and ossified power structures and more importantly the paradigms that feed them. There is a feeling of rebellion and revolution in the air. I believe that now more than ever, it's important to remember Barlow's elegant balance of humor, love, optimism and kindness that so magically integrated with his activism, power, confidence and resolve.

I want to finish with the last two sentences from his manifesto.

"We will create a civilization of the Mind in Cyberspace. May it be more humane and fair than the world your governments have made before."

This is our compass heading.

One of the greatest things at MIT are the student run programs. One program is Tea with Teachers. It's a fun thing where they do short interviews with various "teacher" types at MIT and post them on YouTube. I got to do one with them in September last year and they just posted it last week.

They also let me "highjack" their Instagram feed for a week too.

And I'm sorry about the chicken.

I'm in the middle of trying to write a PhD thesis to complete a PhD at Keio University. I was working on this when I got my current job at the Media Lab and Nicholas Negroponte told me that I should dump the idea of finishing a degree because my not having an earned degree was a badge of honor at this point.

7 years later, people call me "the academic" on panels and while some people are still "impressed" that I don't have a degree, just as many students wonder whether I really understand their point of view having never gone through the process. Also, Jun Murai poked me the other day and urged me to think again about finishing the degree so I quietly started working on it awhile ago thinking, "I've got plenty of time..." Now my thesis is due on April 30. Step 1 in "how to feel like a student."

The degree that I am working on is a Thesis PhD which doesn't exist in the US. It's a process designed for people like me who aren't doing research for their degree, but instead, earn their degree by writing a thesis about stuff that they've done or are doing and "pitch" it to the university. Jun Murai, a Professor in the Department of Environment and Information Studies in the Graduate School of Media and Governance at Keio University, is my advisor.

So I have to finish writing the thesis, then I put together a committee, they review it, I defend, there is an exam and then, if I'm successful, I get a PhD and walk on September 18 in Japan. Jun and his team were kind enough to put "Congratulations!" on the commencement line of my proposed schedule.

So while this blog post is a bit of a break/procrastination ("how to feel like a student part 2!") it's also pressure for me to actually finish this or fail publicly. At least to the extent that anyone is reading this blog.

Which brings me to another point.

With my new Wired column and other more formal writing I'm doing these days, my blog has been getting neglected. Also, in doing research for my thesis, my blog has served as a great outboard memory for me to remember all of the things I've thought about or have been involved in with date stamps, photos and links. I realized that the original purpose - of journaling - might be a good reason to keep blogging. Writing to my future self to remind me of what I was thinking and doing today. Also, as a great procrastination method with slightly more long term value than browsing and liking random things on Facebook.

So there you go. I'm going to pivot my blog to be a bit more like personal journal to chronicle my journey than a soap box to pontificate from. Sort of like how it started.

When we look at a stack of blocks or a stack of Oreos, we intuitively have a sense of how stable it is, whether it might fall over, and in what direction it may fall. That’s a fairly sophisticated calculation involving the mass, texture, size, shape, and orientation of the objects in the stack.

Researchers at MIT led by Josh Tenenbaum hypothesize that our brains have what you might call an intuitive physics engine: The information that we are able to gather through our senses is imprecise and noisy, but we nonetheless make an inference about what we think will probably happen, so we can get out of the way or rush to keep a bag of rice from falling over or cover our ears. Such a “noisy Newtonian” system involves probabilistic understandings and can fail. Consider this image of rocks stacked in precarious formations.

Based on most of your experience, your brain tells you that it's not possible for them to remain standing. Yet there they are. (This is very similar to the physics engines inside videogames like Grand Theft Auto that simulate a player’s interactions with objects in their 3-D worlds.)

For decades, artificial intelligence with common sense has been one of the most difficult research challenges in the field—artificial intelligence that “understands” the function of things in the real world and the relationship between them and is thus able to infer intent, causality, and meaning. AI has made astonishing advances over the years, but the bulk of AI currently deployed is based on statistical machine learning that takes tons of training data, such as images on Google, to build a statistical model. The data are tagged by humans with labels such as “cat” or “dog”, and a machine’s neural network is exposed to all of the images until it is able to guess what the image is as accurately as a human being.

One of the things that such statistical models lack is any understanding of what the objects are—for example that dogs are animals or that they sometimes chase cars. For this reason, these systems require huge amounts of data to build accurate models, because they are doing something more akin to pattern recognition than understanding what’s going on in an image. It’s a brute force approach to “learning” that has become feasible with the faster computers and vast datasets that are now available.

It’s also quite different from how children learn. Tenenbaum often shows a video by Felix Warneken, Frances Chen, and Michael Tomasello, of the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, of a small child watching an adult walk repeatedly into a closet door, clearly wanting to get inside but failing to open it properly. After just a few attempts, the child pulls the door open, allowing the adult to walk through. What seems cute but obvious for humans to do—to see just a few examples and come up with a solution—is in fact very difficult for a computer to do. The child opening the door for the adult instinctively understands the physics of the situation: There is a door, it has hinges, it can be pulled open, the adult trying to get inside the closet cannot simply walk through it. In addition to the physics the child understands, he is able to guess after a few attempts that the adult has an intention to go through the door but is failing.

This requires an understanding that human beings have plans and intentions and might want or need help to accomplish them. The capacity to learn a complex concept and also learn the specific conditions under which that concept is realized is an area where children exhibit natural, unsupervised mastery.

Infants like my own 9-month-old learn through interacting with the real world, which appears to be training various intuitive engines or simulators inside of her brain. One is a physics engine (to use Tenenbaum's term) that learns to understand—through piling up building blocks, knocking over cups, and falling off of chairs—how gravity, friction, and other Newtonian laws manifest in our lives and set parameters on what we can do.

In addition, infants from birth exhibit a social engine that recognizes faces, tracks gazes, and tries to understand how other social objects in the world think, behave, and interact with them and each other. This “social gating hypothesis,” proposed by Patricia Kuhl, professor of speech and hearing sciences at the University of Washington, argues that our ability to speak is fundamentally linked to the development of social understanding through our social interactions as infants. Elizabeth Spelke, a cognitive psychologist at Harvard University, and her collaborators have been working to show how infants develop a “intuitive psychology” to infer people’s goals from as early as 10 months.

In his book, Thinking, Fast and Slow, Daniel Kahneman explains that the intuitive part of our brain is not so good at statistics or math. He proposes the following problem. A baseball bat and a ball together cost $1.10. The bat costs $1 more than the ball. How much is the cost of the ball? Our intuition wants to say, 10 cents, but that’s wrong. If the ball is 10 cents, and the bat is $1 more, the bat would be $1.10, which would make the total $1.20. The correct answer is that the ball is 5 cents and the bat is $1.05, bringing the total to $1.10. Clearly, you can fool our intuition about statistics, just like the stacked rocks existing in the natural world confuse our internal physics engine.

But academics and economists often use such examples as reasons to undervalue the role of intuition in science and academic study, and that’s a huge mistake. The intuitive engines that help us quickly assess physical or social situations are doing extremely complex computations that may not even be explainable; it may be impossible to compute them linearly. For example, an expert skier can’t explain what she does, nor can you learn to ski just by reading instructions. Your brain and your whole body learn to move, synchronize, and operate in a very complex way to enter a state of flow where everything works without linear thinking.

Your brain goes through a tremendous transformation in your infancy. Infant brains initially grow twice as many connections between neurons as adults have, and these are pruned back as a child’s brain matures. Their brains develop an intuitive understanding of the complex systems they interact with—stairs, mom, dad, friends, cars, snowy mountains. Some will learn the difference between dozens of types of waves, to help them navigate the seas, or the difference between many types of snow. As the brain develops, it prunes the connections that don’t appear to be important as we mature.

While our ability to explain, argue, and understand each other using words is extremely important, it is also important to understand that words are simplified representations and can mean different things to different people. Many ideas or things that we know cannot be reduced to words; when they are, the words do not transmit more than a summary of the actual idea or understanding.

Just as we should not dismiss the expert skier who cannot explain how they ski, we should not dismiss the intuition of the shamans who hear nature telling them that things are out of balance. It may be that our view of many of the sensibilities of indigenous people and their relationships with nature as “primitive”—because they can’t explain it and we can’t understand—is in fact more about our lack of an environmental intuition engine. Our senses may have pruned those neurons because they weren’t needed in our urban worlds. We spend most of our lives with our noses in books and screens and sitting in cubicles becoming educated so that we understand the world. Does our ability to explain things mathematically or economically really mean that we understand things such as ecological systems better than the brains of those who were immersed in a natural environment from infancy, who understand them intuitively?

Maybe a big dose of humility and an effort to integrate the nonlinear and intuitive understanding of the minds of people we view as less educated—people who have learned through doing and observing instead of through textbooks—would substantially benefit our understanding of how things work and what we can do about the problems currently unsolvable with our modern tools. It’s also yet another argument for diversity. Reductionist mathematical and economic models are useful from an engineering point of view, but we should be mindful to appreciate our limited ability to describe complex adaptive systems using such models, which don’t really allow for intuition and run the risk of neglecting its role in human experience.

If Tenenbaum and his colleagues are successful in developing machines that can learn intuitive models of the world, it’s possible they will suggest things that either they can’t initially explain or that are so complex we are unable to comprehend them with current theories and tools. Whether we are talking about the push for more explainability in machine learning and AI models or we are trying to fathom how indigenous people interact with nature, we will reach the limits of explainability. It is this space, beyond the explainable, that is the exciting cutting edge of science, where we discover and press beyond our current understanding of the world.