Caught in the snow

As Louis Armstrong liked to say, one of those old-time good ones. Not old, but surely good, yes?

Amazon court

Josh Dzieza, Prime and Punishment: Dirty Dealing in the $175 Billion Amazon Marketplace, The Verge:

Amazon’s judgments are so severe that its own rules have become the ultimate weapon in the constant warfare of Marketplace. Sellers devise all manner of intricate schemes to frame their rivals, as Plansky experienced. They impersonate, copy, deceive, threaten, sabotage, and even bribe Amazon employees for information on their competitors.

And what’s a seller to do when they end up in Amazon court? They can turn to someone like Cynthia Stine, who is part of a growing industry of consultants who help sellers navigate the ruthless world of Marketplace and the byzantine rules by which Amazon governs it. They are like lawyers, only their legal code is the Amazon Terms of Service, their court is a secretive and semiautomated corporate bureaucracy, and their jurisdiction is an algorithmically policed global bazaar rife with devious plots to hijack listings for novelty socks and plastic watches. People like Stine are fixers, guides to the cutthroat land of Amazon, who are willing to give their assistance to the desperate — for a price, of course. [...]

JC Hewitt, whose law firm frequently works with Amazon sellers, calls the system’s mandatory guilty pleas, arbitrary verdicts, and obscure language “a Kafkaesque bureaucracy with bad writing.” Inscrutable rulings emerge as if from a black box. The Performance team, which handles suspensions, has no phone number; there’s no one to ask for clarification. The only way to interact with them is by filing an appeal, and when it’s rejected, sellers often have no idea why. Sellers can call another Amazon department, Seller Support, but those workers can’t provide information about the Performance team and can offer only generic advice about what the seller might have done wrong.

The secrecy can be so frustrating that sellers have traveled to Seattle or Amazon’s London office to try to find a human, to no avail. One seller flew to Seattle from Shengzhou, China, and lived out of a Honda Pilot he bought on Craigslist while he wandered around Amazon’s offices trying to find someone to hear his case. The receptionist gave him the same phone number for Seller Support he’d been trying for weeks.

And Amazon employees get turned into frustrated bots:

In reality, there were likely humans reading Harmon’s appeal, but they’re part of a highly automated bureaucracy, according to former Amazon employees. An algorithm flags sellers based on a range of metrics — customer complaints, number of returns, certain keywords used in reviews, and other, more mysterious variables — and passes them to Performance workers based in India, Costa Rica, and other locations. These workers choose between several prewritten blurbs to send to sellers. They may see what the actual problem is or the key item missing from an appeal, but they can’t be more specific than the forms allow, according to Rachel Greer, who worked as a fraud investigator at Amazon before becoming a seller consultant. “It feels like it’s a bot, but it’s actually a human who is very frustrated about the fact that they have to work like that,” she says.

H/t Tyler Cowen. 

A Note on Groundhog Day (the movie)

I saw it when it came out in theaters and I’ve seen it on a small screen once or twice before. And I watched it again yesterday evening.

I like it.

I could almost imagine doing a detailed analysis of it. And I’d be looking for ring composition, that is, for a center point around which the whole movie pivots. Whether I’d find it, that’s another matter.

But it almost seems made for that kind of a plot. As you may recall, Groundhog Day is set mostly on Groundhog Day. Bill Murray is a cynical weatherman from a local station in Pittsburgh. He’s got to journey to Punxsutawney, once again, to report on whether or not the groundhog sees his shadow. He covers the story and then heads back to Pittsburgh with his producer (Andie McDowell) and cameraman (Chris Elliott). They get caught in a blizzard and have to return to Punxsutawney. He wakes up at 6AM expecting to return to Pittsburgh. That doesn’t happen.

Instead, he discovers that Groundhog Day is going to repeat itself. He does the same things, meets the same people in the same order, over and over again. But with differences. At one point he starts killing himself. Day after day after day. And day by day he becomes less of a jerk, more of a mensch. He pursues Andie, more and more successfully. But this his a bit late in the series of repetitions. By this time he’s shown himself to be an excellent ice sculptor. And he’s been taking piano lessons and has gotten so good that he’s able to be the life of the party. Not long after than Andie sleeps with him. And when they wake up, guess what, it’s no longer Groundhog Day. It’s become February 3rd. End of film.

So, first question: where’s the midpoint, if there is one? Off hand I’d go for the first time he tries to kill himself, though that may be a bit late. But that’s the question – where’s the midpoint? – to ask. Just don’t press too hard on it.

On the other hand, what argues against the ring-composition notion is the general timing of the film. If I were to undertake an analysis of the film, one thing I’d do is list each and every Groundhog day along with the time in the firm when it starts. That’s easy enough to do because each time starts the same way, a shot of the clock turning to 6AM and “It Got You Babe” on the radio. When we’ve got, then, is this:

Before GH Day
GH Day 1
GH Day 2
...
GH Day N-1
GH Day N
February 3

How long is each of these segments? And what changes from one GH day to the next?

I’m guessing that the longest segment in the film is either before GH Day, or the first GH Day. Successive GH Days get shorter and shorter, up to a point. And then some of them are certainly longer, certainly those near the end of the series of repetitions. But the final segment of the film, February 3, is quite short. Temporally it may mirror the day before GH Day, but it is a much shorter segment. That suggests it may not be a structural mirror for the opening segment.

So, Groundhog Day may not be a ring composition. Have I talked myself into doing the analysis? Not yet.

We’ll see.

Friday Fotos: From Longwood Gardens December 2017

Come fly with me

Isochrony in Human Rhythm

Andrea Ravignani and Guy Madison, The Paradox of Isochrony in the Evolution of Human Rhythm, Front. Psychol., 06 November 2017 | https://doi.org/10.3389/fpsyg.2017.01820

Isochrony is crucial to the rhythm of human music. Some neural, behavioral and anatomical traits underlying rhythm perception and production are shared with a broad range of species. These may either have a common evolutionary origin, or have evolved into similar traits under different evolutionary pressures. Other traits underlying rhythm are rare across species, only found in humans and few other animals. Isochrony, or stable periodicity, is common to most human music, but isochronous behaviors are also found in many species. It appears paradoxical that humans are particularly good at producing and perceiving isochronous patterns, although this ability does not conceivably confer any evolutionary advantage to modern humans. This article will attempt to solve this conundrum. To this end, we define the concept of isochrony from the present functional perspective of physiology, cognitive neuroscience, signal processing, and interactive behavior, and review available evidence on isochrony in the signals of humans and other animals. We then attempt to resolve the paradox of isochrony by expanding an evolutionary hypothesis about the function that isochronous behavior may have had in early hominids. Finally, we propose avenues for empirical research to examine this hypothesis and to understand the evolutionary origin of isochrony in general.

Contents

This paper deals with isochronous temporal patterns. The emphasis is on the quantitative properties of isochronous patterns, and their perception and production in humans. The paper is organized in seven sections, namely:

(1) What is isochrony?, where we lay out crucial definitions and summarize basic relevant concepts;

(2) The relevance of isochrony to human music and speech, where we discuss how isochrony might partly underlie some behaviors in modern humans, such as music, speech and dance;

(3) Mathematics, physics and signal processing, where we discuss isochrony from the pure perspective of its physical and mathematical structure (as opposed, for instance, to its biological, behavioral or cognitive nature);

(4) Physiology and neuroscience, where we suggest how isochronous patterns have biological and psychological relevance for living organisms;

(5) Comparative cognition: Non-human animals, where we briefly summarize previous empirical attempts in finding, either directly or indirectly, isochronous behaviors in other species;

(6) Isochrony in interaction, where we move from isochronous behaviors in single individuals to group behaviors potentially involving isochrony;

(7) Evolutionary hypotheses and future empirical work, where we join all strands laid out in the previous six sections, and sketch an evolutionary account for the origin of isochrony in our species.

The aim of this paper is not to provide an exhaustive review of each of these areas. Rather, we attempt to establish a first connection between as many explanatory levels of isochrony as possible, across scientific disciplines and research traditions.

In the penultimate section:

There is a close match between the most precise levels of isochrony that humans are capable of producing and those they are capable of perceiving (Madison and Merker, 2002; Merker et al., 2009). This match also offers some support for the hypothesis that isochrony might have been shaped for communicative purposes. In other words, a communication system, and in particular one that takes advantage of, and evolves from, perceptual biases (Ryan, 1998), will show a match between features of the signal and the capacities to perceive those features. For example, the plumages of many bird species reflect ultraviolet light, which humans and other species cannot see, while conspecific birds can readily perceive and use to select a mate (Andersson and Amundsen, 1997; Vorobyev et al., 1998; Eaton, 2005). We hypothesize that an analogous process might have resulted from isochrony (expanding on Merker, 1999, 2000), if this were a communicative trait. In particular, a communication system employed to transmit information about deviations from an isochronous pulse would evolve toward levels of precision comparable between production and perception (Merker, 1999, 2000). This comparable precision is exactly what can be observed in human motoric and perceptual isochrony (Madison and Merker, 2002; Merker et al., 2009), offering some preliminary, indirect support for a possible communicative function of isochrony.

Isochrony does not appear to be used in the overt communication of modern humans, but might have played a role in some form of communication employed by our ancestors. In fact, isochrony is the optimal way to establish synchronized group signaling because it makes the duration of next interval perfectly predictable by another person or conspecific (Merker et al., 2009). This musical perspective on the evolution of isochrony connects to turn-taking, which is a crucial component of human language (Figure 8). Turn-taking allows speakers to effectively interact in conversation: it avoids that speakers’ utterances overlap, while still enabling utterances to occur within a reasonable amount of time from each other. Interestingly, turn-taking in language is both predictive and exogenous, but seems to lack isochrony, except maybe in a few special cases. Still, turn-taking exhibits a particular temporal structure (Stivers et al., 2009; Levinson and Torreira, 2015). This structure appears to arise by a constant 200 ms lag (Figure 9C) between the ends and starts of utterances across cultures (Stivers et al., 2009), rather than a lag between the starts of consecutive utterances. This fixed-interval delay contrasts with the slightly positive or negative lags found in animal synchronization experiments (Figures 9A,B), and the anticipatory reaction in human musical synchronization. So, in modern humans, turn-taking is far from isochrony (except for when it is a by product of utterances having the same duration within and between speakers), but it might promote isochrony (Schultz et al., 2016). This makes turn-taking in modern organisms a potential approach to understand the evolution of isochrony (see Figure 8).

Stagnation 1.3: On the structure of ideas [thinking out loud] and into philosophy

My initial impulses on the stagnation issue were different from the direction I ended up taking in my first post in response to the recent paper by Bloom, Jones, Van Reenen, and Webb [1]. Here’s what I said in a note to Tyler Cowen:

I’ve been working my way through the Bloom, Jones et al. article linked from this interview. Interesting stuff. And, yes, of course, measuring ideas is a puzzle. Memeticists have discussed the problem, but memetics has never managed intellectual seriousness very well. [...]

There seems to be an implicit assumption that unit ideas, whatever they may be, are more or less independent entities. Unit ideas aside, actual ideas aren’t at all independent of one another. There are asymmetric dependencies and codependencies, some of which can be guessed at by looking at citation patterns. And there surely is path dependence & the longer you go on in a specific line of investigation, the longer and longer the path leading to the latest useful idea. And that useful idea may well depend on long paths emanating from a half dozen different sources, so you’ve also got a coordination problem as no one person knows all the hinterlands feeding streams into this particular delta. You’ve got to have a team and they have to talk with one another, etc.

Why? Because that’s how the world is?

I ended up concentrating on “how the world is” without saying much about the structure of ideas. Of course, it is the way of the world to force proliferation and complexity in the structure of ideas.

Now that I’ve said a bit about how the world is, I’d like to say bit about ideas. First of all I simply want to reiterate what I’ve said above: ideas are not in fact atomic entities bouncing around. They’re intimately intertwined with one another. We all know this; it’s there in the citation structure of the article, books, and monographs we publish. And we know that some ideas, whatever they are, are more important that others. That too is obvious in the citation structure. BJV&W know it too, not simply as tacit knowledge about the academy, but as foreground knowledge in their research.

Let me cite two passage of moderate length, both from their methodological preliminaries. In this passage they’re discussing their choice of case studies (pp. 3-4):

Our selection of cases is driven primarily by the requirement that we are able to obtain data on both the “idea output” and the corresponding “research input.” We looked into a large number of possible cases to study, only a few of which have made it into this paper; indeed, we wanted to report as many cases as possible. For example, we also considered the internal combustion engine, the speed of air travel, the efficiency of solar panels, the Nordhaus (1997) “price of light” evidence, and the sequencing of the human genome. We would have loved to report results for these cases. In all of them, however, it was relatively easy to get an “idea output” measure. However, it proved impossible to get a series for the research input that we felt corresponded to the idea output. For example, the Nordhaus price of light series would make a great additional case. However, many different types of research contribute to the falling price of light, including the development of electric generators, the discovery of compact fluorescent bulbs, and the discovery of LEDs. We simply did not know how to construct a research series that would capture all the relevent R&D. The same problem applies to the other cases we considered but could not complete. For example, it is possible to get R&D spending by the government and by a few select companies on sequencing the human genome. But it turns out that Moore’s Law is itself an important contributor to the fall in the price of gene sequencing. How should we combine these research inputs? In the end, we report the cases in which we felt most confident.

There it is, right there in the middle in their discussion of Nordhaus (which sounds fascinating): “many different types of research contribute to the falling price of light”. Right. To some degree or another it’s all like that.

A bit later they bring up the problem of measurement (p. 5):

Even as simple a question as “What are the units of ideas?” is troublesome. We follow much of the literature — including Aghion and Howitt (1992), Grossman and Helpman (1991), and Kortum (1997) — and define ideas to be in units so that a constant flow of new ideas leads to constant exponential growth in A. For example, each new idea raises incomes by a constant percentage (on average), rather than by a certain number of dollars. This is the standard approach in the quality ladder literature on growth: ideas are proportional improvements in productivity. The patent statistics for most of the 20th century are consistent with this view; indeed, this was a key piece of evidence motivating Kortum (1997). This definition means that the left hand side of equation (1) corresponds to the flow of new ideas. However, this is clearly just a convenient definition, and in some ways a more accurate title for this paper would be “Is exponential growth getting harder to achieve?”

They aren’t discussing ideas in any psychologically, linguistically, or, for that matter, philosophically robust sense. They’re just a unit of measure and what they measure would seem to be something like research effort.

They’re right about their suggestion for a better title. It is more accurate. But I suppose they’re contributing to (contending with?) a research tradition that has its conventions, and talk of ideas is one of them.

And so I ask: What’s the point of that convention? And I ask that question as an outsider. I’m not an economist, so of course I’m an outsider. But that’s not what I mean. I am now playing the role of an outsider, an ethnographer of anthropology if you will, and I want to know what the natives are up to. I’ve not been in this role long so what I have to say is speculative, to say the least. But still...

Of course ideas, whatever they may be, ARE involved, so there’s (at least) that. But the thing about ideas are that they’re within the human realm. We have some measure of control over them. We even have various kinds of legal regulation of ideas, freedom of speech with all that entails, and intellectual property of various kinds. So maybe there’s something we can do that will get the ideas flowing again and make exponential growth more tractable.

Is that it? How would I know, I just thought it up. Moreover, as I’ve remarked in earlier posts in this series, I think that the problem that has been identified in this literature has to do with the structure of the world, and I mean that in the fullest sense. It’s not simply the world “out there” exclusive of us humans. It is THAT plus us. What these economists are staring at is the cost of gaining ever deeper knowledge of the world.

Here is a (crudely drawn) logistics curve:

The further you move up that curve, the more difficult it gets. That’s what’s being measure in this literature. The proliferation of papers, and the complex linkage between ideas, the sense of “learning more and more about less and less”, that’s what happens as we move up that curve.

And when we get to the top, what then? It’s a new world, with new challenges. Maybe we rest; maybe we just ramble about on the plateau. But sooner or later, it’s up the hill. But a different hill. Like so:

By my count, not just mine, but mine and Dave Hays’s (but really, many others as well), we’ve made it up three levels and we’re climbing the fourth [2]. The commonest term for that next plateau is The Singularity, which is generally taken to imply superintelligent computers programming themselves to exponentially increasing levels of “intelligence”, whatever that is. I think that’s, if not nonsense, something pretty close (not even wrong?). I expect amazing things from computers. But I also expect much deeper knowledge about them, and many other things, in us [3].

And that will be the subject of Stagnation 2.0: [some appropriate subtitle].

References

[1] Nicholas Bloom, Charles I. Jones, John Van Reenen, and Michael Webb, Are Ideas Getting Harder to Find? March 5, 2018, https://web.stanford.edu/~chadj/IdeaPF.pdf.

[2] William Benzon, Mind-Culture Coevolution: Major Transitions in the Development of Human Culture and Society, Version 2, November 29, 2018, 10 pp., https://www.academia.edu/37815917/Mind-Culture_Coevolution_Major_Transitions_in_the_Development_of_Human_Culture_and_Society.

[3] William Benzon, Redefining the Coming Singularity – It’s not what you think, Version 2, November 2015, pp. 14, https://www.academia.edu/8847096/Redefining_the_Coming_Singularity_It_s_not_what_you_think.

Graffiti in winter with sofa

Knowledge: Lore, practice, engineering, systemics [Tech Evol]

This is another segment from David Hays (1995) The Evolution of Technology Through Four Cognitive Ranks, New York, Metagram Press. It is section 2.3.1 from Chapter 2, “Ranks, Revolutions, and Paideias.” Hays provides a brief sketch of the different kinds of knowledge characteristic of each cognitive rank.

* * * * *

I love tools, as you would expect of a writer on technology. My computer monitor stopped working while I was writing the first draft of Chapter 1, and I replaced it instantly because I cannot write without it. But I love thought, and the cognitive tools of thought, even more. The changes in technology from rank to rank are wide and deep; they seem inexplicable without changes in thinking.

For the kinds of thought that produce the technologies of the four ranks, for what I called know-how, Benzon and I have these names:

Rank 1 Lore
Rank 2 Practice
Rank 3 Engineering
Rank 4 Systemics

(Fig 2.1 shows some characteristics of lore, practice, engineering, and systemics, rank by rank. The content of the figure is speculative and a little more technical than the main narrative.) 

Figure 2.1 – Characteristics of the Ranks

Rank 1
	LORE
	Thinking is analogous to action.    Child learns from parent by example.    Skills belong to families.    An innovation is an intuitive leap.
Rank 1.2
	Ideas from one craft applied in another.

Rank 2
	LORE	PRACTICE
	Apprenticeship opens crafts to recruits.	Thinking is at the level of first order relations over actions. Each culture makes its own choice of relations. Handbooks summarize as much as can be put into words. Some teaching of technology is from books. Contemplation of written descriptions leads to new methods.
Rank 2.5
		Knowledge from natural philosophy is acquired  by practitioners. Renaissance is the first time application is fruitful.

Rank 3
	LORE	PRACTICE	ENGINEERING
	Elementary education produces trainable workers, who acquire a little lore on the job. Craft skills are necessary in biological fields (both agriculture and medicine)	Supervisors in advanced industry keep records of operations. Innovations are reported in written media.	Thinking is at the level of second order relations: Causality, in the West. Arithmetic and experiment guide development of new devices and processes. Innovation is deliberate.
Rank 3.5
			Knowledge obtained by science is taught to engineers.

Rank 4
	LORE	PRACTICE	ENGINEERING	SYSTEMICS
			Computer models help operation and installation of systems.	Thinking is at the level of 3rd order relations. Theories of particular cases are created for control and innovation.

 
Let's look at the knowledge of each rank in turn.  

Rank 1 begins with speech and is transcended in classical antiquity. For the most part, rank 1 societies live by hunting and gathering. They make tools and weapons, houses and clothing, boats and ornaments. Almost nothing that they make has moving parts. For weaving, tie threads side by side along a stick and hang the stick from a tree. Pass another thread back and forth to make a strip of cloth. The potter's wheel appears late; it belongs on the growth curve toward rank 2. In the simplest societies, each man knows all of the men's skills and each woman knows all of the women's skills. In more complex societies of rank 1, some crafts require special skills. The number of specialties ranges up to 10 at most. Each specialty is passed from parent to child, although in some places a specialist may adopt and train someone else's child.  

Lore is the knowledge of an apprentice, acquired largely by imitation and practice with only a little talk about method. One theory has it that the date of the origin of language can be fixed by observing a change from

stone tools that the apprentice can learn to make by imitation alone

to

more elegant tools that can be learned only by listening to explanations along with imitation.

I won't vouch for the logic of this argument, but it is plausible. Still, it can not displace the fact that most of the know- how in rank 1 is acquired by imitation; the talk is only to polish the fine details of the skill. Lore is the oldest kind of knowledge, and we need it today. What art can be mastered without imitation? Kuhn argues that even science requires imitative learning.  

Being knowledge we don't know we know, lore is hard for some to recognize. Zuboff* discusses the implicit skills of workers in modern plants at great length; her point is the undercutting of their skills by the introduction of computers. Gellner* also writes of lore:

Modern society has many 'specialisms', but the few in agricultural society are more sharply distinguished. They are "fruits of lifelong, very prolonged and totally dedicated training ..." (p. 26)

Two examples:

1842: Buddle writes that coal miners must begin work in mines from before age 13. They have to get a feel for the coal. HBC2 340 1780s: Operation of the mule can be learned in a few months, but maintenance takes several years. And only for those who grew up in the mill. Estimates by Harold Catling. TIRv 30

Growing up in mine or mill, one becomes a miner or miller just as one becomes a native speaker of English by growing up among speakers of English. Possessing the lore of mill or mine, the adult is no better able to teach it than the ordinary sapient can teach a language. 

First the New Criticism, several decades later (two intellectual generations?), cultural studies

Eleanor Courtemanche, The Peculiar Success of Cultural Studies 2.0, Stanford Arcade, 9.19. 2016.

We’re seeing a moment—like the spread of the New Criticism in the ’50s and ’60s—in which a movement originally developed in the ivory tower has trickled down to the high schools—and in this case, has been actively embraced by teens outside school hours. It’s odd to think that these two movements, which seem to have nothing else in common, should have been the ones to spread the most widely outside the university, but they do share one basic precondition. The New Criticism started as a rejection of historical criticism in the name of close reading the ironies and paradoxes of important, complex Romantic and modern poems; it spread because “close reading” can be done in any classroom without library research, and was suitable for the vast expansion of college education after WWII. Cultural studies 1.0 started off with nuanced readings of Benjamin and Foucault, but in fact you can also do it without expensive research and training—all you really need is the Bechdel test. It’s obvious, once you think about it, that girls should be able to kill vampires and bust ghosts, that black teens deserve second chances from the police like white teens, that Asian-American comedians should be on TV more, that lovers should love who they please. The new cultural studies combines the cheapness and accessibility of the New Criticism with the enthusiasm of internet fan culture and the urgency of the fight against political injustice.

Skylight

Kahneman on AI, machine learning and common sense, and sunk costs

From a conversation with Tyler Cowen:

COWEN: Do you side with the analysts, such as Martin Ford, who see really a very large number of jobs being potentially automatable with artificial intelligence, machine learning? Or will we always need the human beings to work with the machines?

KAHNEMAN: That we will need human beings is, I think, an illusion. Take chess for example. Kasparov was beaten 20 years ago, and he went on for a while — and it was true for a while — saying the teams of chess players with grand masters — of programs with grand masters would be stronger than either. And it was true for a while. It is true no longer. The programs do not need the grand masters.

You know how it happened, and it’s likely to happen in many other fields. It’s happening in dermatology. The diagnosis is now better done by programs than by people, and they are not going to need the person very often. That is, to have a person intervene, with the right to intervene, they will sometimes correct mistakes. But they will more often, I think, introduce mistakes. So when you have a well-running program, leave it alone.

COWEN: So we as professors won’t need to grade exams anymore, and I don’t just mean multiple choice. You run machine learning on papers, you find what correlates with a good paper, you put the paper through the program.

KAHNEMAN: Look, the point is, there is so much noise in essay grading that it’s quite easy to imagine a program that would look at various indices and that would do better than hurried and tired professors.

COWEN: If you consider people working in psychology or maybe economics or just social sciences, do you think people persist with their professional and research projects too long or not long enough? Where’s the bias?

KAHNEMAN: My guess is too long, but it’s a personal bias.

COWEN: Because of sunk costs.

KAHNEMAN: Because of sunk costs. I think sunk cost is really the enemy when you’re doing research, innovative research. You’re to recognize that something isn’t working and just move on. And there are different views on that, but my sense is that this is the direction of the bias, yeah, sunk costs. [...] Sunk cost is a fairly specific thing. It is that you’re putting a different value on a move or an investment that you make because of investment that you have already made than you would if you were looking at that de novo.

Sunk costs, by and large, I think, are a negative.

Local optima and commonsense:

KAHNEMAN: But that AI is developing faster than anybody could have anticipated — no question. And if it continues to develop at that rate, meaning a lot faster than we expect, then things are going to happen relatively quickly.

COWEN: What do you think are the main obstacles? Some people in Silicon Valley will argue AI is stuck at a kind of local optimum. Driverless cars — although they’re ahead of the pace we thought 10 years ago, they may be behind the pace we thought 2 years ago. There’s always a problem with emergency situations, the policeman waving you on. The last 1 percent maybe is very, very difficult.

KAHNEMAN: Yeah. But I can’t evaluate that. That’s a technical problem — how long it will take to get the cleanup, the last 1 percent. The questions that are of interest as a psychologist is, when can you simulate common sense? There is the really serious question that people raise about computers, whether they know what they’re talking about, whether they understand what they’re talking about.

Without sense or whims, and without the perceptual apparatus that we have and the ability to cause things by acting on the world, they can’t be exactly like us. But that sense of understanding . . . nobody actually today would, I think, claim that even the most sophisticated programs have it.

COWEN: Do you think we’ve learned anything general about common sense by having some artificial intelligence?

KAHNEMAN: What we have learned is that our basic ideas about what’s difficult and what’s easy, what’s going to be simple and what’s going . . . have undergone a series of revolutionary changes.

We used to think that perception would be easy, and thinking would be difficult. It turns out that thinking was relatively easy and perception was difficult. Now, there are ways of handling perceptual problems, and so thinking is difficult again. And it’s a very interesting developing thing.

Irises: In the thick of things

Is that it, do humanists (really) want to speak with the dead?

Steven Klein, The New Science Wars, The Chronicle Review, 12.16.16:

Because it dwells on these historically specific phenomena, humanistic inquiry is equipped to understand the contours of human experience and activity in a way the sciences cannot. The stance of humanistic inquiry is one of dialogue with its subjects — an imaginary one, of course, but one full of chastisement and support. In Stephen Greenblatt’s well-known phrase, humanistic scholarship springs from "the desire to speak with the dead." Scholars are interested in how people understood themselves, how they interacted with their cultural worlds, how they negotiated their everyday lives. At the core of the humanities is the attempt to enter into distant worlds and to see their connection to us.

[Alas] I think that's how it is, certain with literary criticism. After WWII the discipline decided to ground itself in the activity of interpretation and that, in turn, is grounded in conversation. That allows a certain kind of access, access necessary and valuable. But it also closes certain things off. Klein continues:

Because it rests on this act of imaginative judgment, humanistic scholarship can never suspend or escape the particular perspective of the researcher. To understand someone’s reasons for doing something, we are, to some extent, always imagining how we would act under similar circumstances. As a result, humanistic approaches can never fully embrace the ideal of pure scientific objectivity. Nor would they want to. The humanities cannot help but view humans as morally responsible agents. We are interested not just in why people do something, but in the reasons they themselves give. Interpreting our actions through the lens of the justifications we provide, a humanistic account passes some judgment on those justifications. We want to know the reasons people do things so we can reflect on whether our current reasons are good reasons.

I don't know about scientific objectivity – as you may know, "I don't give a crap about science" – but, objectivity, yes. That's what I'm after in the study of literary form, its analysis and description.

And if I had to speculate about how that kind of inquiry is grounded in a basic human activity, I'd pick tracking. At some point the skilled animal tracker may well try to imagine what the animal is thinking at every step of the way; in fact, I'm sure of it. But that's always in service of interpreting the signs, which are out there in space. Those signs are objects, as are the animals whose presence/absence they betray (in the act of tracking). Tracking is not an entirely visual activity – one attends to sounds and smells – but it is grounded in what you see before you.

And that, in my view, is what describing literary form is about. You are visualizing the text and using visual means to describe it, whether simple tables,, or diagrams of various kinds. These visual aids are not mere aids, they're the map itself.

Do literary critics really want to abandon the study of literary form? Though "abandon" is a bit of a stretch, at least from my point of view. As far as I'm concerned, the discipline has never really engaged with literary form. Nor, in a sense, with the text.  But those are larger discussions, one's I've engaged here and there.

Addendum, an hour or so later: What I dislike about this kind of argument is that there is little or no sense of loss, that this humanist stance has real costs, that it closes off avenues to worthwhile understanding. It's all well and good to argue for "dialog with the dead". But to do so without realizing that that is a limited view, that's unfortunate. That is enslavement to the past. I want to see humanists who choose the dialogic stance while at the same time recognizing its limitations, its cost.

H/t 3QD.

Air vent: This is why you can breathe in the middle of the Holland Tunnel