삶의 재미

사진 일기

노루~ — Sun, 24 May 2026 01:29:11 +0900

Ketring Park (위, 아래 2장)

Writers Vista Park (위, 아래)

Heritage Village Park (위, 아래)

Pablo's Coffee 근처의 어느 집

Pablo's Coffee

Washington Park (오른쪽으로 잔디밭이 끝나는 곳에 Tennis Courts)

Congress Park (위, 아래). 위 사진의 수영장 오른쪽에는 테니스 코트(모두 8면)가 있는대 Gary네 그룹의 홈코트다. 아침 8시부터 친, 이 그룹과의 테니스는 이날이 처음이었다. 앞으로 가끔이나 오게 될 것 같다.

Holly St

University Blvd (위, 아래): 사진을 보니 두 사진 사이에 20분이 경과, 평소의 두 배 이상 걸렸다. 덴버대 졸업식 날인 때문도 컸다.

Ian Buruma, "Year Zero: A History of 1945" (2013)

우선, 한국 이야기가 많이 나오는 Chapter 7 을 재밌게 읽었다. 내가 생각하는 명치유신에 더해서, 일본의 만주국 개발 모델이 박정희 대통령에게 영향을 주었다는 걸 읽는 것도 흥미로웠다.

Sweet Water Park (위, 아래 5장)

Washington Park, 옆 코트 뒤편의 두 나무와 다소 비슷해 보이는 두 나무가 사진의 오른편 코트 밖에도 서 있다.

Washington Park 테니스 코트의 남쪽 길 건너편 집, 유리를 교체하고 있는 것 같은 창문들이 눈에 들어온다.

Near Greenwood Elementay

John Gribbin, "The Scientists" (2002)

Ian McEwan, "What We Can Know" (2025)

Thomas Hardy, "Jude the Obscure

노루~ — Thu, 21 May 2026 00:49:55 +0900

Thomas Hardy, "Jude the Obscure" (1895)

개개의 뇌의 진화는 대체로, 그것들의 유기적인 총체랄 수도 있는, 문화의 진화 속에서, 그에 적응 또는 대응해서, 이루어진다. 이 소설은 결혼에 대해 시대의 통념에서 많이 벗어난 생각을 하는 Jude와 Sue가 겪는 이야기다.

Rumi의 시구가 생각난다:

"Lovers don't finally meet somewhere.

They're in each other all along."

나는 mystic 풍의 시는 대체로 안 좋아한다. 이 시도, 나는 내가 좋아할 만한 사람을 좋아한다, 쯤으로 읽는다.

(한때 George Eliot의 소설을 처음 읽은 Silas Marner에 매료되어 대여섯 권을 이어서 읽은 적이 있다. 마지막으로 Daniel Deronda를 앞에 조금 읽다 만 게 80년대 후반이거나 90년대 초일 텐데, 최근에 다시 읽을 생각을 하면서도, 무엇보다도, 갖고 있는 책이 포켓북 작은 글씨체여서 일단 미루고 있었다. Eliot 이후였나 이전이었나, Thomas Hardy 소설도, Tess of the d’Urbervilles (1891)은 그 이전에 읽었고, The Mayor of Casterbridge (1886)로 시작해서 The Return of the Native (1878)와 Far from the Madding Crowd (1874를 읽고는 마지막으로, 역시 포켓북으로, 아마 몇 쪽이나, 읽다 만 책이 Jude the Obscure (1895)다. 마침 세 주 전 도서관 라운지에서 이 책 hardcover를 발견했다 (포켓북은 Little Free Library' 박스에 넣어주었다). 이 참에 Daniel Deronda도 글씨체가 아주 작지는 않은 Penguin Classics로 읽어야겠다.)

본문에서:

"My likng for you is not as some woman's perhaps. But it is a delight in being with you, of supremely delicate kind, and I don't want to go further and risk it by -- an attempt to intensify it! I quite realize that, as woman with man, it was a risk to come. But, as me with you, I resolved to trust you to set my wishes above your gratification. Don't discuss it further, dear Jude!" (p. 249)

"I said it was Nature's intention, Nature's law and raison d'être that wew should be joyful in what instincts she afforded us -- instincs which civilization had taken upon itself to thwart. What dreadful things I said! And now fate has given us this stab in the back for being such fools as to tak Nature at her word!" (p. 353)

Sandra Lim의 시 "The Great-Grandmothers"

노루~ — Mon, 18 May 2026 03:47:36 +0900

도서관에 들렀다가, 종종 그러듯, 생각이 나서 펼쳐본 잡지 New Yorker (April 6, 2026)에서, 서울에서 태어난 미국 시인, Sandra Llim의 이 시를 읽었다. 일단 사진에 담아와 집에서 한 번 더 읽어보고 삭제했었는데 한 주 지나서도 이 시의 두 분 증조할머니가 생각나는 거였다. 이빨 한 줄을 팔아 집세 내신 분, 유리반지가 탐나 성경책처럼 새까만 머리채를 싹둑 자르신 분. 그러나 나를 끄는 것은 시의 이런 앞 부분보다 뒷 부분이다. 온 라인 New Yorker에서는 비구독자라 읽을 수 없어서 결국 다른 사이트에서 시를 찾아냈다: "The_Great_Grandmothers"

매력적인 시다. 번역해 보려다 만다. 한 가지는, 원문은 한두 번 읽으면 저절로 외워질 정도인데 우리말로 번역해서는 그리 될 것 같지 않아서다. 간명성(산뜻함)과 리듬의 문제겠다. 내 기억에 나는 시를 외우려고 해 본 적이 없는데, 이런 시는 한 번 암송해 보고 넘어가는 것도 나쁘지 않을 것 같다.

The Great-Grandmothers / Sandra Lim

One paid the rent with a row of her teeth.

The other hacked off her Bible-black hair

for a glass ring. Then just as quickly she

shattered it in a fit of temper. She was always

on the side of men. They both know so much

about the world, being out of it now.

One says, God can always see you

with his unfeeling precision. A hiss under all speech.

The other says that sky is black,

then blue, and always empty. Or as clear as a window,

if you prefer. You can do anything you like.

이 시에 대해, 나로선 뜬금없이, 시 외우기 관점에서 언급하게 된 건, 최근 어느 분의 블로그에서 시 외우기를 다시 시작하셨다는 걸 읽은 데다가, 이번 주 뉴욕타임즈 'By the Book' 칼럼에서 Oscar 수상 배우이자 저자인 Ellen Burstyn이 평생 시를 외워 왔다는 걸 또 읽은 때문이다. 93세인 Burstyn은, 마지막으로 외운 시를 묻는 질문에, 현재 외우고 있는 시가 “Shadows,” by D.H. Lawrence 라면서 이렇게 말한다: " I am trying to get that securely in my memory. Of course, at 93 I am not as good at memorizing as I used to be, or at holding on to what I have already memorized. But it is good exercise for the memory to use it."

A Conversation with Sandra Lim

위 대화에서:

GD: Sandra, often your poems begin with a provocative statement. They’ll start with something that almost sounds like a philosophical statement or even a challenge. ... So, I’m interested in is how you move from that one perspective, that one strategy of communication, and then, almost always, you’ll zoom in on an image as if that resolves into that

SL: It wasn’t as if I thought, “I want to say something about the subject matter ‘X’ clearly.” It was just that I felt frustrated and wanted to be able to say something with some kind of confidence or boldness. I think the form came first, in a way, and within that form, I was able to somewhat confidently make a statement. And then the move into the image—I felt like the image could then be another form of inquiry, so sort of backing up, in a way, these sometimes maybe audacious statements or things that I was testing out in the poem. But, of course, I wish I had known that sometimes when sitting down to write some of these. It was a lot of trial and error.

SL: But in some ways, I never felt as if I was that good at writing images, actually, writing description. I would look at Elizabeth Bishop and be like, “How did she do that?” It didn’t come very easily to me.

대화 중에 Sandra Lim의 시 “Unfleur.”의 한 부분이 언급된다:

What is death
but reason
in flawless submission
to itself.

Sandra Llim

다 잘 된 건 아니다 하지만

노루~ — Tue, 12 May 2026 07:24:39 +0900

불을

발견한다

진화된 뇌는

신을 상상해 내고

어디서는 야웨의 명령을 받들어

여리고성 백성들을 몰살시킨다

보이지 않게 개개의 뇌는

저마다 다르게 진화한다

수많은 힘없는 이들의 살과 뼈와 기름으로

왕과 시민은 궁전을 짓고 신전을 세운다

이윽고 여기저기서 "자비!"를 외친다

축의 시대 Axial Age가 온 거다

그러고도 이 천 년도 더 지나서

홀로코스트

"홀로코스트를 잊어선 안 된다"

도망자 숨겨준 시민도

적어도, 그걸 가장 안 잊을 이들이

다른 집단을 집단학살할 수는 없을 거다

그런데 백 년도 안 돼서 또

Gaza Catastrophe

세상에 괴물뇌는 얼마나 많은 걸까?

다 잘 된 건 아니다

하지만 곧 다 잘 될지도 몰라

어린 세대들이 열광하는 저 BTS를 들어봐:

"반짝이는 별빛들

우린 빛나고 있네"*

* 교아님 블로그 포스트에서 처음 들어본 BTS 노래 "소우주"에서/

Gilbert Achcar, "Gaza Catastrophe" (2025)

사진 일기

노루~ — Sat, 9 May 2026 02:14:09 +0900

한강의 시 "A Very Small Snowflake"

노루~ — Thu, 7 May 2026 09:25:09 +0900

ㄱ

New Yorker Feb. 16 & 23 호에 실린 한강의 시 "A Very Small Snowflake" (Mary West 번역)을 읽는다. 눈송이처럼 아름다운 시다.

A Very Small Snowflake

A very small snowflake, you

As if dancing

As if slowly dancing, approach

My face

Instead of falling straightdown like all the other snowflakes

Somehow, you spread your wings toward my face

But where did you get to, after that?

I never saw you again.

세 주 전쯤에 역시 New Yorker에서 읽은, 한국에서 태어난 미국 시인 Sandra Lim의, "The Great-Grandmothers"를 다시 읽어보려고 도서관에 간 김에 잡지 서가의 뉴요커 댓 권을 뒤지다가 (Lim의 시는 못 찾고) 발견한 시다. 이럴 땐 자주 Seamus Heaney의 책 제목 "Finders Keepers"를 떠올린다.

영역본도 아름답지만 원문이 궁금해서 찾아보니 한강의 산문집 "빛과 실"(2024)에 실린 "아주 작은 눈송이"다. 아름다운 시다.

아주 작은 눈송이

아주 작은 눈송이, 너는

춤추듯

느리게 춤추듯이 왔지

내 얼굴로

다른 모든 눈송이들처럼 수직으로 떨어지지 않고

어쩐 일인지 내 얼굴을 향해 날개를 폈지

그리곤 어디로 가버렸니?

널 다시 보지 못했어.

한강

눈 내리는 날은 우리들 세상

노루~ — Thu, 7 May 2026 01:53:19 +0900

Cade Metz, "Genius Makers"

노루~ — Mon, 4 May 2026 02:41:01 +0900

Cade Metz, "Genius Makers: The Maverics Who Brought AI

to Go던ogle, Facebook, and the World" (2021)

AI의 가능성에 대해서 별로 기대하지 않다가 최근의 성과에 대해 보고 들으면서는 그것을 가져온 것이 무엇인지 궁금했었다. 그런데 이 책을 읽으면서, 아, 그렇겠구나, 했다. Neural networks! 전에, 80년대 중반에, 교과서에서 읽은 backpropagation neural network 또한 computing/network/data 전반에 상응해서 그간, 예상을 뛰어 넘는, 놀라운 성능 향상이 있었을 것을 막연히 잊고 있었던 거다.

우리 뇌가 디자인으로 만들어진 것이 아니고 우리의 논리적 사고력이나 intelligence가 수백만 년에 걸친 뇌의 진화에 의해 얻어진 것이라면, trial and error 방식의 'training'으로 그런 과정을 모방하는, neural network이 AI의 바른, 그리고 아마도 유일하게 효과적인, 접근 방향일 수밖에 없겠다.

본문에서

When [Geoff] Hinton gave a lecture at the annual NIPS[Neural Information Processing Systems] conference [in 2007], ... on his sixtieth birthday, the phrase "deep learning" appeared in the title for the first time. It was a cunning piece of rebranding. Referring to mulptiple layers of neural networks, there was nothing new about "deep learning." But it was an evocative term designed to galvanize research in an area that had once again fallen from favor. (p. 64)

At Microsoft [around 2010?], using those $10,000 cards [Nvidia's GPUs(Graphics Processing Units)] to train a neural network on spoken words collected by the company's Being voice search service, [George] Dahl pushed Hinton's speech prototype beyond the performance of anything else under development at the company. What he and [Abdel-rahman] Mohamed and Hinton showed was that a neural network could shit through a sea of very noisy speech and somehow find the stuff that mattered, the patterns that no human engineer could ever pinpoint on their own, the telltale signs that distinguished one subtle sound from another, one word from another. It was an inflection point in the long history of artificial intelligence. (p. 74)

The power of Google's GPU cluster was that it allowed the company to experiment with myriad technoloies on a massive scale. Building a neural network was a task of trial and error, and with tens of thousands of GPU chips at their disposal, researchers could explore more possibilities in less time.... Spurred by the $130 million in graphics chips it sold to Google, Nvidia recognized itself around the deep learning idea, and soon it was not merely selling chips for AI research, it was doing its own research, exploring the boundaries image recognition and self-driving cars .... (p. 139)

After Hinton, [Yann] LeCun, and [Yoshua] Bengio published a paper on the rise of deep learning in Nature, he[Jürgen Schmidhuber] wrote a critique arguing that "the Canadians" weren't as influential as they seem to be -- that they'd built their work on the ideas of others working in Europe and Japan. And around the same time, when Ian Goodfellow presented his paper on GANs[generative adversarial networks] -- a technology whose influence would soon reverberate across the industry -- Schmidhuber stood up in the audience and chastised him for not citing similar work in Switzerland from the 1990s. (p. 141)

After reading about the match, Jordi Ensign, a forty-five-year-old computer programmer from Florida, went out and got two tatoos. AlphaGo's move 37 was tatooed on the inside of her right arm -- and Lee Sedol's Move 78 was on the left. (p. 178)

When Ilya Sutskever published the paper that remade machine translation -- known as the Sequence to Sequence paper -- he said it was not really about translation. When Jeff Dean and Greg Corrado read it they agreed. They decided it was an ideal way of analyzing healthcare records. (p. 193)

As they did, they cast GANs and related technologies in a new light. These technogies, it seemed, were a way of generating fake news. (p. 209)

A neural network learned from such a wide array of examples that small and unexpected flaws could creep into its training without anyone ever knowing. (p. 212)

BERT was what researchers call a "universal language model." ... Universal language models are giant neural networks that learn the vagaries of language by analyzing millions of sentences written by humans.... BERT analyzed ... vast library of books as well as every article on Wikipedia, spending days poring over all this text with help from hundreds of GPU chips. (p. 273)

If they could build a large enough simulation of what humans encounter in their daily lives, these labs [OpemAI, DeepMind, ...] believed they could build AGI[artificial general intelligence]. (p. 297)

The hope was that researchers could change the equation with new kinds of computer chips -- chips that could drive this research to levels beyond both Nvidia's GPUs and Google's TPUs[tensor processing units]. Dozens of companies, including Google, Nvidia, and Intel, as well as a long line of start-ups, were building new chips just for training neural networks, so that systems built by labs like DeepMind and OpenAI could learn far more in far less time. (p. 298)

More than ever DeepMind was focused on the future. And though it operated with considerable independence, it could still draw on Google's vast resources. Since acquiring DeepMind, Google had invested $1.2 billion in its research. By 2020, in addition to the hundreds of computer scientists at the London lab, [Demis] Hassabis had hired a team of more than fhfty neuroscientists to investigate the inner workings of the brain. (p. 301)

During his Turing lecture [in 2019] ... Hinton explained the rise of machine learning and explored where it might be going.... Hinton did not believe in reinforcement learning, the method Demis Hassabis and DeepMind saw as the path to AGI. It required too much data and too much processing power to succeed with practical tasks in the real world.... That same year ... when Hinton saw what reinforcement learning could do for [Pieter] Abeel's robots, he changed his mind about the future of AI research. As Covariant's system [and Abeel's robots] moved into the warehouse in Berlin, he called it "the AlphaGo moment" for robotics. "I have always been skeptical of reinforcement learning because it required an extraordinary amount of computation. But we've now got that," he said. Still, he didn't believe in building AGI. (p. 310)

DeepSeek

Meaghan Tobin and Cade Metz,

DeepSeek’s Sequel Set to Extend China’s Reach in Open-Source A.I.

(April 24, 2026, New York Times):

When the Chinese start-up DeepSeek published details about one of its artificial intelligence models last year, it sent shock waves through the tech industry.

The company said it had built its system by spending far less on computer chips than American rivals like OpenAI and Anthropic. It marked the start of what became known as China’s “DeepSeek moment,” shorthand for the belief that Chinese A.I. companies were ready to showcase their technical capabilities to the world.

The DeepSeek moment reflected a shift in the global A.I. landscape. The change was about not only lower costs but also openness in how the technology is shared.

DeepSeek released its models as open source, which means others can freely use and modify them. By contrast, OpenAI and Anthropic kept their leading models proprietary. The episode demonstrated that an open-source system could perform almost as well as closed versions. In the months that followed, Chinese firms released dozens of other open-source models. By the end of 2025, these models made up a significant share of global A.I. use.

DeepSeek-R1

Slate: The OpenAI Case

사진 일기

노루~ — Fri, 1 May 2026 06:17:25 +0900

Big Dry Creek Trai에서 (위, 아래)

Orchard Hills Park (위, 아래) -- 위에 보이는 터키들은 온 동네를 쏘다니는 것 같았다.

Washington Park (위, 아래)

Lisa Randal, "Dark Matter and the Dinosaurs: The Astounding

Interconnectedness of the Universe" (2015)

Stephen Jay Gould, "Eight Little Piggies: Reflections

in Natural History" (1993)

망아지나 사람이나 태어날 때 'blank slate'는 아니라고 나는 생각한다.

그런 점에서는 Stephen Jay Gould가 아니라 Steven Pinker 쪽이지만,

어쨌거나 이 책은, 벌써 한 편 에세이 읽어본 대로, 재밌게 읽힐 것 같다.

Writers Vista Park

Terry Eagleton, "Culture" (2016)

2000년 무렵 Raymond Williams의 "Culture & Society: 1780-1950"을

즐기며 알게 됐던 영국의 영문학자 Terry Eagleton의 책을

Writers Vista Park에 앞서 들른 Tattered Cover 서점에서 발견했다.

Theodor Adorno, "Minima Moralia" (영역본 1974; 독일어 원본 1951).

도서관 책을 반납하고 아마존에 주문해서 샀다. 이런 책은 최근

신판이라도 -- 이 책은 2020년판 -- 미국 일반 서점에서는 보통

따로 주문해야 살 수 있다. 이런 점은 한국이 낫다.

Westland Park (위, 아래)

이제는 일반 공중은 누가 문을 열어주어야만 덴버대 건물에 들어갈 수 있어서

잘 안 가게 되는 Book Stack에 오랜만에 들렀다가 두 권의 책을 샀다.

John Leonard, "Reading for My Life: Writings 1958-2008" (2012)

"[E]very reader rewrites the text, and the rewrite is a measure of the reader's mind.... But it is not only his capacious mind that distinguishes him, it is the wisdom of his critical decency."

"In the realm of cultural journalism there was no one quite like him. He gave everything he was, each time out. That is why in this volume we have his mind still thinking, his voice still alive."

--- Introduction by E. L. Doctorow

Orhan Pamuk, "Other Colors Essays and A Story" (영역본 2007; Turkish 원본 1999)

큰딸과 Otero Tennis Courts에서

Eudora Welty, "The Optimist's Daughter"

노루~ — Sun, 26 Apr 2026 07:30:52 +0900

Eudora Welty, "The Optimist's Daughter" (1972)

Book Review (from Milam's Musings)

본문에서:

A screen of florist's ferns was being built up brfore her eyes brhind the coffin. The a man stepped out from behind the green and presented a full, square face with its small features pulled to the center -- what Laurel's mother had called "a Baptist face."

"Miss Laurel, I'm Mr. Pitts again. I recall your dear moher so clearly," he said. "And I believe you're going to be just as pleased now, with ypir father." He put out his hand and raised the lid.

Judge McKelva lay inside in his winter suit. All around him was draped the bright satin of a jewler's box, and its color was the same warm, foolish pink that had smothered the indows and spilled over the bed upstairs. His large face reflected the pink, so that his large, heavy cheek had the cast of a seashell, or a perl. The dark patches underneath his eyes had been erased like traces of human error.... The lid had been raised only by half-section, to show him propped on the pillow; below the waisthe lay cut off from any eyes. He was still not to be mistaken for any other man.

"You must close it," said Laurel quietly to Mr. Pitts.

"You're not pleased?" But he had never displeased anyone, his face said.

"Oh, look," said Miss Tennyson, arriving at Laurel's side. "Oh, look."

"I don't want it open, please," said Laurel to Mr. Pitts. She touched Miss Tennyson's hand. "But father would never allow -- when Mother died, he protected her from --"

"Your mother was different," said Miss Tennyson firmly.

"He was respecting her wishes," Laurel said. "Not to make her lie here in front of people's eyes --"

"And I've never forgiven him for that. Nobody ever really got to tell Becky goodbye," Miss Tennyson was saying at the same time. "But honey, your father's a Mount Salus man. He's a McKelva. A public figure. You can't deprive the public, can you? Oh, he's lovely."

"I would like him away from their eyes," said Laurel.

"It is Mrs. McKelva's desire that the coffin be open," said Mr. Pitts.

:See there? You can't deprive Fay," said Miss Tennyson. "That settles that." She held out her arms, inviting the room.

Laurel took up her place in front of the coffin, near the head, and stood to meet them as they came. (pp. 62-3)

위 인용은, 71세로 세상을 뜬, Laurel의 아버지, McKelva의 장례 이야기다. Miss Tennyson은 Laurel 어머니 Becky의 친구이고 Fay는 Laurel의 계모다. Beck가 세상을 뜨고 McKelva가, Laurel보다도 젊은, Fay와 재혼한 것은 다 몇 년 전 일이다. 장례에 대해서 Beck나 그 딸 Laurel처럼 생각하는 것을 소설에서 읽는 것만으로도 반갑다.