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Can you step in the same river twice? Wittgenstein vs. Heraclitus
Imagine Heraclitus spending an afternoon down by the river...
These problems that he claims to see from a religious point of view tend to be technical matters of logic and language. Wittgenstein trained as an engineer before he turned to philosophy, and he draws on mundane metaphors of gears, levers and machinery. Where you find the word 'transcendent' in Wittgenstein's writings, you'll likely find 'misunderstanding' or 'nonsense' nearby.
When he does respond to philosophers who set their sights on higher mysteries, Wittgenstein can be stubbornly dismissive. Consider: 'The man who said one cannot step into the same river twice was wrong; one can step into the same river twice.' With such blunt statements, Wittgenstein seems less a religious thinker and more a stodgy literalist. But a close examination of this remark can show us not only what Wittgenstein means by a 'religious point of view' but also reveal Wittgenstein as a religious thinker of striking originality.
'The man' who made the remark about rivers is Heraclitus, a philosopher at once pre-Socratic and postmodern, misquoted on New Age websites and quoted out of context by everyone, since all we have of his corpus are isolated fragments. What is it that Heraclitus thinks we can't do? Obviously I can do a little in-and-out-and-back-in-again shuffle with my foot at a riverbank. But is it the same river from moment to moment – the water flowing over my foot spills toward the ocean while new waters join the river at its source – and am I the same person?
One reading of Heraclitus has him conveying a mystical message. We use this one word, river, to talk about something that's in constant flux, and that might dispose us to think that things are more fixed than they are – indeed, to think that there are stable things at all. Our noun-bound language can't capture the ceaseless flow of existence. Heraclitus is saying that language is an inadequate tool for the purpose of limning reality.
What Wittgenstein finds intriguing about so many of our philosophical pronouncements is that while they seem profoundly important, it's unclear what difference they make to anything. Imagine Heraclitus spending an afternoon down by the river (or the constantly changing flux of river-like moments, if you prefer) with his friend Parmenides, who says that change is impossible. They might have a heated argument about whether the so-called river is many or one, but afterwards they can both go for a swim, get a cool drink to refresh themselves, or slip into some waders for a bit of fly fishing. None of these activities is in the least bit altered by the metaphysical commitments of the disputants.
Wittgenstein thinks that we can get clearer about such disputes by likening the things that people say to moves in a game. Just as every move in a game of chess alters the state of play, so does every conversational move alter the state of play in what he calls the language-game. The point of talking, like the point of moving a chess piece, is to do something. But a move only counts as that move in that game provided a certain amount of stage-setting. To make sense of a chess game, you need to be able to distinguish knights from bishops, know how the different pieces move, and so on. Placing pieces on the board at the start of the game isn't a sequence of moves. It's something we do to make the game possible in the first place.
One way we get confused by language, Wittgenstein thinks, is that the rule-stating and place-setting activities happen in the same medium as the actual moves of the language-game – that is, in words. 'The river is overflowing its banks' and 'The word river is a noun' are both grammatically sound English sentences, but only the former is a move in a language-game. The latter states a rule for using language: it's like saying 'The bishop moves diagonally', and it's no more a move in a language-game than a demonstration of how the bishop moves is a move in chess.
What Heraclitus and Parmenides disagree about, Wittgenstein wants us to see, isn't a fact about the river but the rules for talking about the river. Heraclitus is recommending a new language-game: one in which the rule for using the word river prohibits us from saying that we stepped into the same one twice, just as the rules of our own language-game prohibit us from saying that the same moment occurred at two different times. There's nothing wrong with proposing alternative rules, provided you're clear that that's what you're doing. If you say: 'The king moves just like the queen,' you're either saying something false about our game of chess or you're proposing an alternative version of the game – which might or might not turn out to be any good. The trouble with Heraclitus is that he imagines he's talking about rivers and not rules – and, in that case, he's simply wrong. The mistake we so often make in philosophy, according to Wittgenstein, is that we think we're doing one thing when in fact we're doing another.
But if we dismiss the remark about rivers as a naive blunder, we learn nothing from it. 'In a certain sense one cannot take too much care in handling philosophical mistakes, they contain so much truth,' Wittgenstein cautions. Heraclitus and Parmenides might not do anything different as a result of their metaphysical differences, but those differences bespeak profoundly different attitudes toward everything they do. That attitude might be deep or shallow, bold or timorous, grateful or crabbed, but it isn't true or false. Similarly, the rules of a game aren't right or wrong – they're the measure by which we determine whether moves within the game are right or wrong – but which games you think are worth playing, and how you relate to the rules as you play them, says a lot about you.
What, then, inclines us – and Heraclitus – to regard this expression of an attitude as a metaphysical fact? Recall that Heraclitus wants to reform our language-games because he thinks they misrepresent the way things really are. But consider what you'd need to do in order to assess whether our language-games are more or less adequate to some ultimate reality. You'd need to compare two things: our language-game and the reality that it's meant to represent. In other words, you'd need to compare reality as we represent it to ourselves with reality free of all representation. But that makes no sense: how can you represent to yourself how things look free of all representation?
The fact that we might even be tempted to suppose we can do that bespeaks a deeply human longing to step outside our own skins. We can feel trapped by our bodily, time-bound existence. There's a kind of religious impulse that seeks liberation from these limits: it seeks to transcend our finite selves and make contact with the infinite. Wittgenstein's religious impulse pushes us in the opposite direction: he doesn't try to satisfy our aspiration for transcendence but to wean us from that aspiration altogether. The liberation he offers isn't liberation from our bounded selves but for our bounded selves.
Wittgenstein's remark about Heraclitus comes from a typescript from the early 1930s, when Wittgenstein was just beginning to work out the mature philosophy that would be published posthumously as Philosophical Investigations (1953). Part of what makes that late work special is the way in which the Wittgenstein who sees every problem from a religious point of view merges with the practical-minded engineer. Metaphysical speculations, for Wittgenstein, are like gears that have slipped free from the mechanism of language and are spinning wildly out of control. Wittgenstein the engineer wants to get the mechanism running smoothly. And this is precisely where the spiritual insight resides: our aim, properly understood, isn't transcendence but a fully invested immanence. In this respect, he offers a peculiarly technical approach to an aspiration that finds expression in mystics from Meister Eckhart to the Zen patriarchs: not to ascend to a state of perfection but to recognise that where you are, already, in this moment, is all the perfection you need.
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Scientists discover what our human ancestors were making inside the Wonderwerk Cave in South Africa 1.8 million years ago.
- Researchers find evidence of early tool-making and fire use inside the Wonderwerk Cave in Africa.
- The scientists date the human activity in the cave to 1.8 million years ago.
- The evidence is the earliest found yet and advances our understanding of human evolution.
One of the oldest activities carried out by humans has been identified in a cave in South Africa. A team of geologists and archaeologists found evidence that our ancestors were making fire and tools in the Wonderwerk Cave in the country's Kalahari Desert some 1.8 million years ago.
A new study published in the journal Quaternary Science Reviews from researchers at the Hebrew University of Jerusalem and the University of Toronto proposes that Wonderwerk — which means "miracle" in Afrikaans — contains the oldest evidence of human activity discovered.
"We can now say with confidence that our human ancestors were making simple Oldowan stone tools inside the Wonderwerk Cave 1.8 million years ago," shared the study's lead author Professor Ron Shaar from Hebrew University.
Oldowan stone tools are the earliest type of tools that date as far back as 2.6 million years ago. An Oldowan tool, which was useful for chopping, was made by chipping flakes off of one stone by hitting it with another stone.
An Oldowan stone toolCredit: Wikimedia / Public domain
Professor Shaar explained that Wonderwerk is different from other ancient sites where tool shards have been found because it is a cave and not in the open air, where sample origins are harder to pinpoint and contamination is possible.
Studying the cave, the researchers were able to pinpoint the time over one million years ago when a shift from Oldowan tools to the earliest handaxes could be observed. Investigating deeper in the cave, the scientists also established that a purposeful use of fire could be dated to one million years back.
This is significant because examples of early fire use usually come from sites in the open air, where there is the possibility that they resulted from wildfires. The remnants of ancient fires in a cave — including burned bones, ash, and tools — contain clear clues as to their purpose.
To precisely date their discovery, the researchers relied on paleomagnetism and burial dating to measure magnetic signals from the remains hidden within a sedimentary rock layer that was 2.5 meters thick. Prehistoric clay particles that settled on the cave floor exhibit magnetization and can show the direction of the ancient earth's magnetic field. Knowing the dates of magnetic field reversals allowed the scientists to narrow down the date range of the cave layers.
The Kalahari desert Wonderwerk CaveCredit: Michael Chazan / Hebrew University of Jerusalem
Professor Ari Matmon of Hebrew University used another dating method to solidify their conclusions, focusing on isotopes within quartz particles in the sand that "have a built-in geological clock that starts ticking when they enter a cave." He elaborated that in their lab, the scientists were "able to measure the concentrations of specific isotopes in those particles and deduce how much time had passed since those grains of sand entered the cave."
Finding the exact dates of human activity in the Wonderwerk Cave could lead to a better understanding of human evolution in Africa as well as the way of life of our early ancestors.
If you ask your maps app to find "restaurants that aren't McDonald's," you won't like the result.
- The Chinese Room thought experiment is designed to show how understanding something cannot be reduced to an "input-process-output" model.
- Artificial intelligence today is becoming increasingly sophisticated thanks to learning algorithms but still fails to demonstrate true understanding.
- All humans demonstrate computational habits when we first learn a new skill, until this somehow becomes understanding.
It's your first day at work, and a new colleague, Kendall, catches you over coffee.
"You watch the game last night?" she says. You're desperate to make friends, but you hate football.
"Sure, I can't believe that result," you say, vaguely, and it works. She nods happily and talks at you for a while. Every day after that, you live a lie. You listen to a football podcast on the weekend and then regurgitate whatever it is you hear. You have no idea what you're saying, but it seems to impress Kendall. You somehow manage to come across as an expert, and soon she won't stop talking football with you.
The question is: do you actually know about football, or are you imitating knowledge? And what's the difference? Welcome to philosopher John Searle's "Chinese Room."
The Chinese Room
Searle's argument was designed as a critique of what's called a "functionalist" view of mind. This is the philosophy that argues that our mind can be explained fully by what role it plays, or in other words, what it does or what "function" it has.
One form of functionalism sees the human mind as following an "input-process-output" model. We have the input of our senses, the process of our brains, and a behavioral output. Searle thought this was at best an oversimplification, and his Chinese Room thought experiment goes to show how human minds are not simply biological computers. It goes like this:
Imagine a room, and inside is John, who can't speak a word of Chinese. Outside the room, a Chinese person sends a message into the room in Chinese. Luckily, John has an "if-then" book for Chinese characters. For instance, if he gets <你好吗>, the proper reply is <我还好>. All John has to do is follow his instruction book.
The Chinese speaker outside of the room thinks they're talking to someone inside who knows Chinese. But in reality, it's just John with his fancy book.
What is understanding?
Does John understand Chinese? The Chinese Room is, by all accounts, a computational view of the mind, yet it seems that something is missing. Truly understanding something is not an "if-then" automated response. John is missing that sinking in feeling, the absorption, the bit of understanding that's so hard to express. Understanding a language doesn't work like this. Humans are not Google Translate.
And yet, this is how AIs are programmed. A computer system is programmed to provide a certain output based on a finite list of certain inputs. If I double click the mouse, I open a file. If you type a letter, your monitor displays tiny black squiggles. If we press the right buttons in order, we win at Mario Kart. Input — Process — Output.
Can imitation become so fluid or competent that it is understanding.
But AIs don't know what they're doing, and Google Translate doesn't really understand what it's saying, does it? They're just following a programmer's orders. If I say, "Will it rain tomorrow?" Siri can look up the weather. But if I ask, "Will water fall from the clouds tomorrow?" it'll be stumped. A human would not (although they might look at you oddly).
A fun way to test just how little an AI understands us is to ask your maps app to find "restaurants that aren't McDonald's." Unsurprisingly, you won't get what you want.
The Future of AI
To be fair, the field of artificial intelligence is just getting started. Yes, it's easy right now to trick our voice assistant apps, and search engines can be frustratingly unhelpful at times. But that doesn't mean AI will always be like that. It might be that the problem is only one of complexity and sophistication, rather than anything else. It might be that the "if-then" rule book just needs work. Things like "the McDonald's test" or AI's inability to respond to original questions reveal only a limitation in programming. Given that language and the list of possible questions is finite, it's quite possible that AI will be able to (at the very least) perfectly mimic a human response in the not too distant future.
What's more, AIs today have increasingly advanced learning capabilities. Algorithms are no longer simply input-process-output but rather allow systems to search for information and adapt anew to what they receive.
A notorious example of this occurred when a Microsoft chat bot started spouting bigotry and racism after "learning" from what it read on Twitter. (Although, this might just say more about Twitter than AI.) Or, more sinister perhaps, two Facebook chat bots were shut down after it was discovered that they were not only talking to each other but were doing so in an invented language. Did they understand what they were doing? Who's to say that, with enough learning and enough practice, an AI "Chinese Room" might not reach understanding?
Can imitation become understanding?
We've all been a "Chinese Room" at times — be it talking about sports at work, cramming for an exam, using a word we didn't entirely know the meaning of, or calculating math problems. We can all mimic understanding, but it also begs the question: can imitation become so fluid or competent that it is understanding.
The old adage "fake it, 'till you make it" has been proven true over and over. If you repeat an action enough times, it becomes easy and habitual. For instance, when you practice a language, musical instrument, or a math calculation, then after a while, it becomes second nature. Our brain changes with repetition.
So, it might just be that we all start off as Chinese Rooms when we learn something new, but this still leaves us with a pertinent question: when, how, and at what point does John actually understand Chinese? More importantly, will Siri or Alexa ever understand you?
With the rise of Big Data, methods used to study the movement of stars or atoms can now reveal the movement of people. This could have important implications for cities.
- A treasure trove of mobility data from devices like smartphones has allowed the field of "city science" to blossom.
- I recently was part of team that compared mobility patterns in Brazilian and American cities.
- We found that, in many cities, low-income and high-income residents rarely travel to the same geographic locations. Such segregation has major implications for urban design.
Almost 55 percent of the world's seven billion people live in cities. And unless the COVID-19 pandemic puts a serious — and I do mean serious — dent in long-term trends, the urban fraction will climb almost to 70 percent by midcentury. Given that our project of civilization is staring down a climate crisis, the massive population shift to urban areas is something that could really use some "sciencing."
Is urbanization going to make things worse? Will it make things better? Will it lead to more human thriving or more grinding poverty and inequality? These questions need answers, and a science of cities, if there was such a thing, could provide answers.
Good news. There already is one!
The science of cities
With the rise of Big Data (for better or worse), scientists from a range of disciplines are getting an unprecedented view into the beating heart of cities and their dynamics. Of course, really smart people have been studying cities scientifically for a long time. But Big Data methods have accelerated what's possible to warp speed. As "exhibit A" for the rise of a new era of city science, let me introduce you to the field of "human mobility" and a new study just published by a team I was on.
Credit: nonnie192 / 405009778 via Adobe Stock
Human mobility is a field that's been amped up by all those location-enabled devices we carry around and the large-scale datasets of our activities, such as credit card purchases, taxi rides, and mobile phone usage. These days, all of us are leaving digital breadcrumbs of our everyday activities, particularly our movements around towns and cities. Using anonymized versions of these datasets (no names please), scientists can look for patterns in how large collections of people engage in daily travel and how these movements correlate with key social factors like income, health, and education.
There have been many studies like this in the recent past. For example, researchers looking at mobility patterns in Louisville, Kentucky found that low-income residents tended to travel further on average than affluent ones. Another study found that mobility patterns across different socioeconomic classes exhibit very similar characteristics in Boston and Singapore. And an analysis of mobility in Bogota, Colombia found that the most mobile population was neither the poorest nor the wealthiest citizens but the upper-middle class.
These were all excellent studies, but it was hard to make general conclusions from them. They seemed to point in different directions. The team I was part of wanted to get a broader, comparative view of human mobility and income. Through a partnership with Google, we were able to compare data from two countries — Brazil and the United States — of relatively equal populations but at different points on the "development spectrum." By comparing mobility patterns both within and between the two countries, we hoped to gain a better understanding of how people at different income levels moved around each day.
Mobility in Brazil vs. United States
Socioeconomic mobility "heatmaps" for selected cities in the U.S. and Brazil. The colors represent destination based on income level. Red depicts destinations traveled by low-income residents, while blue depicts destinations traveled by high-income residents. Overlapping areas are colored purple.Credit: Hugo Barbosa et al., Scientific Reports, 2021.
The results were remarkable. In a figure from our paper (shown above), it's clear that we found two distinct kinds of relationship between income and mobility in cities.
The first was a relatively sharp distinction between where people in lower and higher income brackets traveled each day. For example, in my hometown of Rochester, New York or Detroit, the places visited by the two income groups (e.g., job sites, shopping centers, doctors' offices) were relatively partitioned. In other words, people from low-income and high-income neighborhoods were not mixing very much, meaning they weren't spending time in the same geographical locations. In addition, lower income groups traveled to the city center more often, while upper income groups traveled around the outer suburbs.
The second kind of relationship was exemplified by cities like Boston and Atlanta, which didn't show this kind of partitioning. There was a much higher degree of mixing in terms of travel each day, indicating that income was less of a factor for determining where people lived or traveled.
In Brazil, however, all the cities showed the kind of income-based segregation seen in U.S. cities like Rochester and Detroit. There was a clear separation of regions visited with practically no overlap. And unlike the U.S., visits by the wealthy were strongly concentrated in the city centers, while the poor largely traversed the periphery.
Data-driven urban design
Our results have straightforward implications for city design. As we wrote in the paper, "To the extent that it is undesirable to have cities with residents whose ability to navigate and access resources is dependent on their socioeconomic status, public policy measures to mitigate this phenomenon are the need of the hour." That means we need better housing and public transportation policies.
But while our study shows there are clear links between income disparity and mobility patterns, it also shows something else important. As an astrophysicist who spent decades applying quantitative methods to stars and planets, I am amazed at how deep we can now dive into understanding cities using similar methods. We have truly entered a new era in the study of cities and all human systems. Hopefully, we'll use this new power for good.
A small percentage of people who consume psychedelics experience strange lingering effects, sometimes years after they took the drug.