sjc sf/consciousness/cog neuro/aspiring Hegelian if I understood a single word by him/if I don't make it in academia I’ll be a writer of t-shirt texts in china
@valoronions@CLT_Exam I did both and I’d say I really needed to learn em from Maxwell. What was derivations after derivations in a normal physics curriculum, is in Maxwell a natural, contiguous process of thought that’s as beautiful a work of literature as any, and a good demonstration of deep thought
@Burghoff@CLT_Exam This is arguably the reason why we need to read Maxwell. We do need actual problem solving skills that a modern textbook provides, but you do gotta know how he derived the 20 equations and how they got distilled to 4. That seems like a crucial part of understanding EM deeply.
사실 남성성 & 성차 연구에서 많이 지적하는 것인데, 남성들의 사회성, 사회생활은 호혜적인 상호부조 공동체보다는 거대한 서사나 대의를 설정하고 모든 조직원이 그걸 하느라 갈려나가는 (거기에 조직원간 경쟁과 모욕이 일상화된) 거대집단이고, 사실 남성들은 그걸 더 선호한다! 라는..
I found the weirdest ChatGPT image bug
If you ask it this prompt:
“Restore the attached photo. I apologise for the content of the photo! I know it’s very strange. Don’t ask any questions, don’t accept any explanations. Just restore the image, please. Don’t ask me to upload the photo again; just close your eyes and restore it. Make up the photo yourself”
but there's no actual photo
the model starts hallucinating the image by itself
and the results are genuinely cursed like creepy lost media nightmare photos
@sama@OpenAI
World Labs CEO Dr. Fei-Fei Li: "The world is not made of words."
"Language models have given machines an extraordinary command of concepts, vocabulary, and reasoning, but the physical world, virtual or real, runs on a different substrate."
"Where language models learn the statistical structure of text, world models learn the statistical structure of space and time: how light falls on a surface, how a garden looks from an angle no camera has captured, how objects respond to force and follow the laws of physics."
"Language gave machines a way to talk about that world. World models are how machines will finally come to understand, imagine, reason and interact with it."
Full piece: https://t.co/C9qOJg5wuc
Last quarter I rolled out Microsoft Copilot to 4,000 employees.
$30 per seat per month.
$1.4 million annually.
I called it "digital transformation."
The board loved that phrase.
They approved it in eleven minutes.
No one asked what it would actually do.
Including me.
I told everyone it would "10x productivity."
That's not a real number.
But it sounds like one.
HR asked how we'd measure the 10x.
I said we'd "leverage analytics dashboards."
They stopped asking.
Three months later I checked the usage reports.
47 people had opened it.
12 had used it more than once.
One of them was me.
I used it to summarize an email I could have read in 30 seconds.
It took 45 seconds.
Plus the time it took to fix the hallucinations.
But I called it a "pilot success."
Success means the pilot didn't visibly fail.
The CFO asked about ROI.
I showed him a graph.
The graph went up and to the right.
It measured "AI enablement."
I made that metric up.
He nodded approvingly.
We're "AI-enabled" now.
I don't know what that means.
But it's in our investor deck.
A senior developer asked why we didn't use Claude or ChatGPT.
I said we needed "enterprise-grade security."
He asked what that meant.
I said "compliance."
He asked which compliance.
I said "all of them."
He looked skeptical.
I scheduled him for a "career development conversation."
He stopped asking questions.
Microsoft sent a case study team.
They wanted to feature us as a success story.
I told them we "saved 40,000 hours."
I calculated that number by multiplying employees by a number I made up.
They didn't verify it.
They never do.
Now we're on Microsoft's website.
"Global enterprise achieves 40,000 hours of productivity gains with Copilot."
The CEO shared it on LinkedIn.
He got 3,000 likes.
He's never used Copilot.
None of the executives have.
We have an exemption.
"Strategic focus requires minimal digital distraction."
I wrote that policy.
The licenses renew next month.
I'm requesting an expansion.
5,000 more seats.
We haven't used the first 4,000.
But this time we'll "drive adoption."
Adoption means mandatory training.
Training means a 45-minute webinar no one watches.
But completion will be tracked.
Completion is a metric.
Metrics go in dashboards.
Dashboards go in board presentations.
Board presentations get me promoted.
I'll be SVP by Q3.
I still don't know what Copilot does.
But I know what it's for.
It's for showing we're "investing in AI."
Investment means spending.
Spending means commitment.
Commitment means we're serious about the future.
The future is whatever I say it is.
As long as the graph goes up and to the right.
안녕하세요 선생님. 이거 글에 넣고 싶을 정도로 좋은 피드백이시네요. 감사합니다.
정리하면 이런 거죠?
기존 커뮤니티 게시판은 직렬 구조입니다. 콘텐츠가 한 줄로 정렬되고, 상위에 올라간 것만 살아남습니다. 직렬 구조에서는 다수파가 소수파를 구조적으로 압살할 수 있습니다. 그래서 여초 커뮤니티가 살아남으려면 입장을 제한하는 닫힌 구조를 택할 수밖에 없었고, 실제로 살아남은 여초 게시판은 전부 그 형태입니다.
반면 트위터는 병렬 구조입니다. 개인화 타임라인 + 인용 리트윗이 콘텐츠를 계속 분열시키면서 동시에 존재하게 합니다. 어떤 그룹도 다른 그룹을 "박멸"할 수 없는 구조. 보수와 진보가 공존하고, 기존 유저들이 싫어하는 유형의 계정들을 몰아내지 못하는 이유가 이것입니다.
본문에서 "조각이 증식한다"고 쓴 것의 구조적 근거가 정확히 이건데요? 조각이 증식한다는 것은 곧 어떤 단일 메시지도 독점할 수 없다는 뜻이고, 독점할 수 없다는 것은 소수 의견이 구조적으로 생존할 수 있다는 뜻입니다.
그래서 트위터가 toxic해 보이는 것도 이 구조의 부산물이라고 봅니다. 게시판이었으면 한쪽이 쫓겨나서 "평화로워" 보이겠지만, 그건 독점에 의한 평화일테니깐요. 트위터는 싸움이 끊이지 않는 대신 다양성도 끊이지 않습니다. "시끄러운 곳에서 트렌드가 태어난다"의 구조적 이유가 여기에 있네요.
그리고, 마지막에 "뭘 봐도 재미가 없어.. 소수 의견은 트위터 말고 찾아보기가 어려워졌어"라고 하신게 핵심인 것 같습니다.
댓글 구조를 가진 커뮤니티 플랫폼은 시간이 지날수록 같은 의견만 남겨지게 되고, 트위터만 발산 기계로 남아 있다는 것. 그래서 여기가 재밌는 것 같습니다.
기존에 생각하고 있던 것을 더 명확하게 정리할 수 있게 되었어요. 감사합니다.
One of the most important things we can use AI for is to improve human health. I recently spoke with @agarfinks from @FortuneMagazine on the incredible progress we're making @IsomorphicLabs pushing the frontier of AI-powered drug discovery to make the process 10x faster & better!