What do people enjoy?

Fascinating graph showing how much people report enjoying different activities. Much of it backs up common sense but here's what jumped out to me:

1. 'Computer games' are among the most enjoyable, probably deserve more respect. Clearly beat 'watching TV'.

2. 'Play with child'… pic.twitter.com/CCQVpKpGhw

— Rob Wiblin (@robertwiblin) July 1, 2024

Bill Maher and William Shatner talk about deep fundamental things while also being silly

 

This clip is two years old. I believe that Maher was 66 at the time and Shatner was 90. Here's the description that came with the clip: "Bill and William Shatner comically riff on the last acceptable prejudice, the spectrum of human sexuality, bad crowds in comedy, the making of Religious, and the origin of the universe." What kind of conversation is this? It seems to me that it's both real and an act. These guys are performers and they're certainly performing here, but I don't think they've got a script. They're just making it up as they go along. 

At the time I'm posting this, almost 800,000 people have watched this clip. What's is say or imply about the world that 800,000 people have found this entertaining?

Paul Fry on Influence: Eliot and Bloom (Theory of Literature)

87,872 views Sep 1, 2009
Introduction to Theory of Literature (ENGL 300)

In this lecture on the psyche in literary theory, Professor Paul Fry explores the work of T. S. Eliot and Harold Bloom, specifically their studies of tradition and individualism. Related and divergent perspectives on tradition, innovation, conservatism, and self-effacement are traced throughout Eliot's "Tradition and the Individual Talent" and Bloom's "Meditation upon Priority." Particular emphasis is placed on the process by which poets struggle with the literary legacies of their precursors. The relationship of Bloom's thinking, in particular, to Freud's Oedipus complex is duly noted. The lecture draws heavily from the works of Pope, Borges, Joyce, Homer, Wordsworth, Longinus, and Milton.

00:00 - Chapter 1. Introduction to Harold Bloom
06:31 - Chapter 2. Mimesis and Imitatio
11:51 - Chapter 3. Bloom "Misreads" Eliot
29:34 - Chapter 4: Literary History: the Always Already Written "Strong Poem"
48:09 - Chapter 5. Lacan and Bloom on Tony the Tow Truck

Complete course materials are available at the Open Yale Courses website: http://open.yale.edu/courses

This course was recorded in Spring 2009.

Pop Tarts on a pier along the Hudson

Laughter on the campaign trail: Harris (yes) vs. Trump (no)

Jason Zinoman, Kamala Harris’s Laugh Is a Campaign Issue. Our Comedy Critic Weighs in. NYTimes, July 28, 2024.

Plato warned against a love of laughter, suggesting it indicates a loss of control. Ever alert to the theater of power, Trump rarely laughs, dating back to long before he was in politics. Recounting his season appearing on “The Apprentice,” the magician Penn Jillette marveled at how he would regularly spend hours watching Trump talk and never notice the slightest chuckle.

Watching interviews with both candidates, it’s clear that there’s a sizable disparity in laughter. Trump scoffs and occasionally smirks, which can be a crowd-pleaser. But chuckling isn’t his thing. On talk shows, Harris does it to deflect and connect, to establish intimacy, but also to underline the absurdity of something. At its most effusive, her laugh can draw attention to itself, and taken out of context, it can seem as if she’s the only one in on the joke.

Harris has said she got her guffaw from her mother. But that’s not her only laugh. In her debut campaign speech, she even got a big response from muffling a snicker after saying that in the past, “I have taken on perpetrators of all kinds.”

This hint of a laugh sets up her most successful stump line so far: “So hear me when I say, I know Donald Trump’s type.”

The case against laughing is that it makes a leader come off as less serious. This rests on a common misunderstanding that laughter is primarily a response to something funny. Research over the past few decades has backed up what philosophers have said for over a century, which is that laughter is inherently social, more about relationships and communication than jokes.

Later:

Bill Clinton connected with people by biting his lip and making eye contact. He famously felt your pain. With her biggest laughs, the ones that she does with her whole body, Harris projects something else: joy.

There's more at the link.

The art of misdirection – Giving shots to infants and toddlers

One day my YouTube feed presented me with this sort video. I was curious, so I watched it. It was quite remarkable.

This one is similar, but instead of giving a shot to a toddler, we see a doctor givine a shot to an infant.

Magicians and pickpockets also employ misdirection, through to achieve somewhat different ends.

Whisper Not - Benny Golson and McCoy Tyner

Whisper Not (Golson): Benny Golson (born 1929), tenor saxophone; McCoy Tyner (1938-2020), piano; Avery Sharpe, bass and Aaron Scott on drums. Jazz in Marciac 1997.

Hot pink petals

Communication stripped to the barest minimum?

Hays, D. G. (1973). "Language and Interpersonal Relationships." Daedalus 102(3): 203-216. The following passage is from pp. 204-205:

The experiment strips conversation down to its barest essentials by depriving the subject of all language except for two pushbuttons and two lights, and by suggesting to him that he is attempting to reach an accord with a mere machine. We brought two students into our building through different doors and led them separately to adjoining rooms. We told each that he was working with a machine, and showed him lights and pushbuttons. Over and over again, at a signal, he would press one or the other of the two buttons, and then one of two lights would come on. If the light that appeared corresponded to the button he pressed, he was right; otherwise, wrong. The students faced identical displays, but their feedback was reversed: if student A pressed the red button, then a moment later student B would see the red light go on, and if student B pressed the red button, then student A would see the red light. On any trial, therefore, if the two students pressed matching buttons they would both be correct, and if they chose opposite buttons they would both be wrong.

We used a few pairs of RAND mathematicians; but they would quickly settle on one color, say red, and choose it every time. Always correct, they soon grew bored. The students began with difficulty, but after enough experience they would generally hit on something. Some, like the mathematicians, chose one color and stuck with it. Some chose simple alternations (red-green-red-green). Some chose double alternations (red-red-green-green). Some adopted more complex patterns (four red, four green, four red, four green, sixteen mixed and mostly incorrect, then repeat). The students, although they were sometimes wrong, were rarely bored. They were busy figuring out the complex patterns of the machine.

But where did the patterns come from? Although neither student knew it, they arose out of the interaction of two students.

Double reflection of the sun

The risks of proliferating AI agents

Malcolm Murray, The Shifting Nature Of AI Risk, 3 Quarks Daily, July 22, 2024.

Second and more importantly, much of the focus in AI is currently on building AI agents. As can be seen in comments from Sam Altman and Andrew Ng, this is seen as the next frontier for AI. When intelligent AI agents are available at scale, this is likely to change the risk landscape dramatically. [...]

Introducing agentic intelligences could mean that we lose the ability to analyze AI risks and chart their path through the risk space. The analysis can become too complex since there are too many unknown variables. Note that this is not a question of “AGI” (Artificial General Intelligence), just agentic intelligences. We already have AI models that are much more capable than humans in many domains (playing games, producing images, optical recognition). However, the world is still recognizable and analyzable because these models are not agentic. The question of AGI and when we will “get it”, a common discussion topic, is a red herring and the wrong question. There is no such thing as one type of “general” intelligence. The question should be when will we have a plethora of agentic intelligences operating in the world trying to act according to preferences that may be largely unknown or incomprehensible to us.

For now, what this means is that we need to increase our efforts on AI risk assessment, to be as prepared as possible as AI risk continues to get more complex. However, it also means we should start planning for a world where we won’t be able to understand the risks. The focus in that world needs to instead be on resilience.

There's more at the link.

A touch of yellow

Nine-year old chess prodigy hopes to become the youngest grandmaster ever

Isabella Kwai, At 5, She Picked Up Chess as a Pandemic Hobby. At 9, She’s a Prodigy. NYTimes, July 21, 2024.

Since learning chess during a pandemic lockdown, Bodhana Sivanandan has won a European title in the game, qualified for this year’s prestigious Chess Olympiad tournament, and established herself as one of England’s best players.

She also turned 9 in March. That makes Bodhana, a prodigy from the London borough of Harrow, the youngest player to represent England at such an elite level in chess, and quite possibly the youngest in any international sporting competition.

“I was happy and I was ready to play,” Bodhana said in a phone interview, two days after she learned that she had been selected for this year’s Olympiad, an international competition considered to be the game’s version of the Olympics.

The fourth-grader, who learned chess four years ago when she stumbled across a board her father was planning to discard, knows exactly what she wants to accomplish next. “I’m trying to become the youngest grandmaster in the world,” she said, “and also one of the greatest players of all time.”

Chess is one of the arenas in which prodigies emerge. Music and math are others. Why? I assume it has something to do with their brains. What?

I note also that playing chess has been central to the development of AI and that it is the first arena in which computers and equaled and then surpassed the best human performance. What can we make of that? I don’t know, but surely there’s something to be discovered here.

* * * * *

Note the final paragraph of this post, On the significance of human language to the problem of intelligence (& superintelligence).

The question of machine superintelligence would then become:

Will there ever come a time when we have problem-solving networks where there exists at least one node that is assigned to a non-routine task, a creative task, if you will, that only a computer can perform?

That’s an interesting question. I specify non-routine task because we have all kinds of computing systems that are more effective at various tasks than humans are, from simple arithmetic calculations to such things solving the structure of a protein string. I fully expect the more and more systems will evolve that are capable of solving such sophisticated, but ultimately routine, problems. But it’s not at all obvious to me that computational systems will eventually usurp all problem-solving tasks.

Human Go players learn from superhuman AIs

🗡️ Last year, we found superhuman Go AIs are vulnerable to “cyclic attacks”. This adversarial strategy was discovered by AI but replicable by humans. Below @KellinPelrine (⚪) gives the superhuman AI KataGo (⚫) a 9-stone handicap but still wins. See https://t.co/m97yFtvz0F pic.twitter.com/4VfgsJUS73

— FAR AI (@farairesearch) June 19, 2024

There are more links in the thread.

* * * * *

So: "Last year, we found superhuman Go AIs are vulnerable to “cyclic attacks”. This adversarial strategy was discovered by AI but replicable by humans."

Superhuman Go AIs discover a new region of the Go search-space. That's one thing. The fact that, once discovered, humans are able to exploit this region against a superhuman Go AI. That is just as interesting. 

One question we can ask about superintelligence is whether or not so-called superintelligent AIs can do things that are inherently and forever beyond human capacity. In this particular case, we have humans learning things initially discovered by AIs.

Coffee and cream