■anond:20250315210702

表1：指標論文の主な発見（小林ら、2020年）

表2：SITH-1と気分障害に関連する主な特許（近藤一博が発明者）

Permalink |記事への反応(0) | 21:07

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■Xcode+ChatGPT試した

https://www.publickey1.jp/blog/25/chatgptxcodevscodemac.html

・立ち上げやすい、Option+Space

・diffを示してくれる時と、示してくれない時がある

・一気に「反映する」ボタンがあって嬉しい

・通常のChatGPTのように、コード示して終わりになることもある（条件が分からない）

・まっさらな状態から「こういうアプリ作って」は無理だった

・音声入力無理だった

・どこまで読み込めてるのかが不明、たぶんだけど、開いているファイルを貼り付けたうえで質問してるくらいになってるんじゃないかな

・ChatGPTのスレッド１個に対応する模様、ファイルをまたいでもスレッドが維持される

・関係ない質問もできる

・Modelは選べる

個人的に思ったこと

・コメント書いてもらうのが楽でいいかも

・整理してとか、Lint対応してとか、そういうのが良さそう

・Modelによっては入出力が大きすぎてしんどい（正直500行くらいでもしんどくない？）

・どこまで深く読んでくれるのかが分からない

・適用してビルドして〜まではやってくれない

まあ、普段やってることと本質的には変わらないんだけど、スピードが若干上がるくらいかな

Permalink |記事への反応(0) | 16:12

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■UFC 313 Pereira vs AnkalaevLive

thangs.com/designer/ufc2025rrr/3d-model/%5BPPV-FREE%5D%20UFC%20313%20Pereira%20vs%20Ankalaev%20Live%20MMA%20Full%20Fight%20TV%20Coverage%202025-1287610

Permalink |記事への反応(0) | 07:59

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■UFC 313 FREELIVEPPV FullFight

thangs.com/designer/ufc313/3d-model/%5BMMA-Streams%5D%20UFC%20313%20FREE%20LIVE%20PPV%20Full%20Fight%20Pereira%20vs%20Ankalaev%20TV%20Coverage%202025-1287604

Permalink |記事への反応(0) | 07:59

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■ユーザー行動に基づくアイテム推薦システムの圏論的定式化と実装戦略

以下の問題を徹底的に抽象数学を用いて定式化しなさい。また、具体的実装についても定式化しなさい。ただし、文献はarxiv等の信頼できる情報源のみを利用しなさい。

• ユーザー集合Uとアイテム集合Iがあるとする。
• Uの要素uに対し、これまでにクリック等のアクションを起こしたアイテムをf(u)で表す。ただしf(u)はIの部分集合。
• Iの各要素に対して、スコアy_i = g_i(f(u), I)によってスコアを計算する。ただし、g_i = g_1,...,g_n
• 算出されたそれぞれのスコアy_iに対して、何らかの方法で結合されたスコアz = h(y_1,...,y_n)を最終的に出す。
• zの高い順番に、アイテムをソートする。

AI回答

本報告では、ユーザー集合Uとアイテム集合Iからなる推薦システムを、圏論と行列代数の統合的枠組みで再構築する。特にarXiv論文[2][7]で提案されたSheaf4Recアーキテクチャと、古典的マトリックス分解手法[3][8]を統合した新しい定式化を提案する。実装戦略としてApacheSpark[4]を活用した分散処理を採用し、理論的保証と計算効率の両立を実現する。

圏論的定式化

1. 推薦システムの圏構造

圏RecSysを次のように定義する：

• 対象：ユーザー集合Uとアイテム集合Iの組(U,I)
• 射：推薦戦略関手F:RecSys→Sheaf

各ユーザーu∈Uの行動履歴f(u)⊆Iは、圏論的データモデル[7]において層(sheaf)構造で表現される。具体的には：

• 茎(stalk) F(u) = ℝ^m：ユーザーuの潜在表現空間
• 制限写像ρ_{uv}:F(u)→F(v)：ユーザー類似度関係

2.スコア関数の層的表現

各スコア関数g_j:2^I×I→ℝ^mは、層の断面(section)として定式化される：

g_j = \bigoplus_{i=1}^m \mathcal{F}_i \otimes \mathcal{G}_j

ここで$\mathcal{F}_i$はアイテムiの特徴層、$\mathcal{G}_j$はスコアタイプjの重み層[2]。

定理1（層的スコアの単調性）：

任意のS⊆T⊆Iに対して、層的接続写像δ:F(S)→F(T)が存在し、次を満たす：

\forall j, \|g_j(S) - δ(g_j(T))\| ≤ L_j \cdot d_H(S,T)

ここでL_jはリプシッツ定数、d_Hはハミング距離[7]。

行列分解に基づく実装戦略

1.分散マトリックス分解

ユーザー-アイテム行列R∈ℝ^{|U|×m}を以下のように分解[3]：

R ≈ UΣV^T \quad (U∈ℝ^{|U|×r}, Σ∈ℝ^{r×r}, V∈ℝ^{m×r})

ApacheSpark[4]を活用した分散計算フレームワーク：

from pyspark.mllib.recommendation importALSmodel =ALS.trainImplicit(    ratings=interactions,    rank=100,    iterations=10,    lambda_=0.01,blocks=200  #分散処理用ブロック数)

2.スコア関数の具体例

1.協調フィルタリングスコア[3]：

   g_1(u,i) = U_u \cdot V_i^T

2.コンテキスト統合スコア[7]：

   g_2(u,i) = \text{SheafConv}(F(u), F(i); \Theta)

3.時間減衰スコア[8]：

   g_3(u,i) = \sum_{t∈T_{ui}} e^{-λ(t-t_0)}

3.スコア結合の代数構造

結合関数h:ℝ^m×n→ℝ^mを次のモノイド構造で定義：

h(Y)_i = \bigoplus_{j=1}^n w_{ij} \otimes y_{ij}

ここで⊕はmax-pooling、⊗はアダマール積[2]。重み行列W=(w_{ij})は以下の最適化問題で決定：

\min_W \sum_{u∈U} \|R(u) - h(G(u))\|_F^2 + λ\|W\|_*

効率的な分散処理

1.Sparkベースの実装アーキテクチャ[4]

val interactions =spark.read.parquet("hdfs://interactions")valmodel = newALS()  .setRank(100)  .setBlocks(200)  .run(interactions)val scores =model.userFeatures  .join(itemFeatures)  .map {case (u, (v_u, v_i)) =>     (u, dotProduct(v_u, v_i))   }

2.計算量解析

ここでpは並列度、mはアイテム数[4][7]。

理論的保証

1.収束定理

定理2（行列分解の収束性）：

ブロックSVDアルゴリズム[3]は、任意のε>0に対してO(log(1/ε))反復でε近似解を達成する。

証明の概略：

\|R - U^{(k)}Σ^{(k)}V^{(k)T}\|_F^2 ≤ (1 - 1/\text{cond}(R))^k \|R\|_F^2

ここでcond(R)は行列の条件数[3]。

2. 安定性解析

提案手法は以下のリプシッツ安定性を満たす：

\|h(Y) - h(Y')\| ≤ \sum_{j=1}^n L_j \|W_j\| \cdot \|y_j - y_j'\|

ここでL_jは各スコア関数のリプシッツ定数[7]。

結論

本論文では、圏論的構造と分散行列分解を統合した新しい推薦システムフレームワークを提案した。Sheaf4Rec[7]の層構造とSpark[4]の分散処理を組み合わせることで、精度と効率の両立を実現。今後の課題として、動的層構造の適応的更新や量子化による計算効率改善が挙げられる。

Citations:

[1]https://arxiv.org/html/2407.13699v1

[2]https://arxiv.org/html/2304.09097v3

[3]https://www.cs.toronto.edu/~mvolkovs/sigir2015_svd.pdf

[4]https://ics.uci.edu/~cs237/projects2020/4_reports.pdf

[5]https://arxiv.org/abs/2502.10050

[6]https://arxiv.org/pdf/2109.08794.pdf

[7]https://arxiv.org/abs/2304.09097

[8]https://dspace.mit.edu/bitstream/handle/1721.1/99785/927438195-MIT.pdf?sequence=1

Permalink |記事への反応(0) | 11:42

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■線型計画法の例題

ある会社が2つの製品（XとY）を2台の機械（AとB）を使って製造しています。Xの1単位を生産するには、機械Aで50分、機械Bで30分の処理時間が必要です。Yの1単位を生産するには、機械Aで24分、機械Bで33分の処理時間が必要です。

今週の始めの時点で、在庫にはX製品が30単位、Y製品が90単位あります。今週の機械Aの利用可能な処理時間は40時間、機械Bは35時間と予測されています。

今週のX製品の需要は75単位、Y製品の需要は95単位と予測されています。会社の方針は、週末時点でのXとY製品の在庫単位数の合計を最大化することです。

問題

1: 今週、各製品をどれだけ製造するかを決定する問題を線形計画問題として定式化してください。

2: この線形計画問題をglpkを用いて解いてください。

回答

問題1:線形計画問題の定式化

決定変数:

x: 今週製造するX製品の単位数

y: 今週製造するY製品の単位数

目的関数:

最大化 Z = (x + 30 - 75) + (y + 90 - 95) = x + y - 50

※週末時点での在庫単位数の合計を最大化

制約条件:

1.機械Aの処理時間制約: 50x +24y ≤2400 (40時間 =2400分)

2.機械Bの処理時間制約: 30x +33y ≤ 2100 (35時間 = 2100分)

3. X製品の需要制約: x ≥ 45 (需要75 -在庫30 = 45)

4. Y製品の需要制約: y ≥ 5 (需要95 -在庫90 = 5)

5. 非負制約: x ≥ 0, y ≥ 0

問題2: GLPKを用いた解法

GLPKで解くために、以下のようなモデルファイル（例：model.mod）を作成します：

/* 決定変数 */var x>= 45;var y>= 5;/*目的関数 */maximize Z: x + y - 50;/* 制約条件 */s.t. machine_A: 50*x +24*y <=2400;s.t. machine_B: 30*x +33*y <= 2100;end;

このモデルファイルを使用して、コマンドラインで以下のコマンドを実行します：

glpsol --modelmodel.mod -o solution.txt

GLPKが問題を解いた結果は以下です。

Problem:modelRows:       3Columns:    2Non-zeros:  6Status:     OPTIMALObjective:  Z = 1.25 (MAXimum)   No.   RownameSt   Activity     Lower bound   Upper bound    Marginal------ ------------ -- ------------- ------------- ------------- -------------     1 Z            B          51.25                                  2 machine_A    NU24002400     0.0416667      3 machine_B    B        1556.25                        2100    No. ColumnnameSt   Activity     Lower bound   Upper bound    Marginal------ ------------ -- ------------- ------------- ------------- -------------     1 xNL            45            45                    -1.08333      2 y            B           6.25             5               Karush-Kuhn-Tucker optimality conditions:KKT.PE:max.abs.err = 4.55e-13on row 2max.rel.err =9.47e-17on row 2        High qualityKKT.PB:max.abs.err = 0.00e+00on row 0max.rel.err = 0.00e+00on row 0        High qualityKKT.DE:max.abs.err = 0.00e+00on column 0max.rel.err = 0.00e+00on column 0        High qualityKKT.DB:max.abs.err = 0.00e+00on row 0max.rel.err = 0.00e+00on row 0        High qualityEnd of output

以下のような最適解が得られます：

x = 45 (X製品の生産量)

y = 6.25 (Y製品の生産量)

Z = 1.25 (週末時点での余剰在庫数)

この結果から、会社は今週、X製品を45単位、Y製品を6.25単位製造するべきであることがわかります。これにより、週末時点での余剰在庫数は1.25単位となり、最大化されます。

注意：実際の生産では、Y製品の生産量を6単位に切り下げるか7単位に切り上げる必要があるかもしれません。

Permalink |記事への反応(0) | 12:39

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■Chatgptが答えた異星人の存在可能性とのそのロジック

Please consider the histroy ofhumanity from biologicalperpsecitve and currentknown fact in nearbyuniverse, whichexpo-planet (from theSol systemperspective)ismostlikely to have civlizaedsociety? Pleaseaddpercentagenext toit in the chart

ChatGPT

Reasoned about exoplanetcivilizationlikelihoodを28 秒行う

Evaluatingplanetary prospects

OK, I’mlookingatthe likelihood of a civilizedsocietyon an exoplanet within ourSolar System.I’ll consider biologicalhistory and current space knowledge,aiming to identifythe most promising candidate.

Identifying potential candidates

I'm considering exoplanetslike Proxima Centauri b and TRAPPIST-1e, which might suggest they are within or observed from ourSolar System.

Assessing potential candidates

I’m evaluating Proxima Centauri b, Barnard'sStar, LHS 1140, and TRAPPIST-1,focusingon their habitablezonesand available data. The Drake equation might help estimate their civilizedsocietylikelihoods.

Identifying promising exoplanets

I’m notingkey exoplanetslike Proxima Centauri b and TRAPPIST-1, which haveEarth-like characteristicsand are in habitablezones. This narrows down the potential candidates for advancedcivilizations.

Assessing habitablezones

I'm gathering infoonplanetslike Proxima Centauri b and TRAPPIST-1, consideringfactorslikemagneticfields,atmospheres, and proximity to G-typestars for their potential to support advancedcivilizations.

Evaluating exoplanet prospects

I'm noting that Proxima Centauri b, despitebeing closeand around ared dwarfstar, might supportlife ifithas amagneticfieldand atmosphere. This makes me think aboutits potential.

Charting probabilities

I'm estimatingcivilization probabilities for exoplanetslike Proxima Centauri b and TRAPPIST-1 e, using speculativepercentagesbasedon the Drake equation and the lack ofevidence for intelligentlife.

Charting speculative odds

I'm crafting a chart with exoplanet names andcivilizationlikelihoodpercentages, grounded in the longtimeline oflife'sevolution, despite the speculativenature of these figures.

Identifying exoplanet contenders

I’m evaluating exoplanetslike Proxima Centauri b, TRAPPIST-1 e, and Kepler-186f, considering theirstar types and distances, to gauge their potential for supporting advancedcivilizations from oursolar system'sviewpoint.

Weighing proximity and observation

I'm leaning towards Proxima Centauri b due toits closeness and observational potential, despite concerns aboutitsflarestar dynamics. TRAPPIST-1 e, f, or g are also worth considering.

Revisiting exoplanet options

I'mfocusingon exoplanetslike Proxima Centauri b, Ross 128 b, LHS 1140 b, TRAPPIST-1 e/f/g, andK2-18b,aiming to chart theirlikelihood of hosting a civilizedsocietybasedon current knowledge.

Assessing exoplanet potentials

I'm gathering speculative probabilities for exoplanetslike Proxima Centauri b and TRAPPIST-1 e/f/g, using the Drake equation and current understanding, to better estimate their chances of hosting a civilizedsociety.

Refining speculative probabilities

I’m refining speculative probabilities for exoplanets, including Proxima Centauri b and TRAPPIST-1 e/f/g, to estimate their chances of hosting a civilizedsociety, consideringEarth's long biologicalhistory.

Refining odds

I'm considering theastronomicaltimeline for intelligentlifeonEarth,leading me toassign evenlower speculative odds for exoplanetslike Proxima Centauri b and TRAPPIST-1 e.

Clarifying speculativenature

I’m crafting a simplified chart with arbitrarypercentages, clearly noting the speculativenature and lack of scientific data, while considering the target audience'sbasic understanding of the topic.

Mappingout speculative odds

I’m charting arbitrarypercentages for exoplanets, clarifying their speculativenature, and ensuring the datais accessible foryoung audiences with minimal scientific detail.

Crafting a chart

I’m creating amarkdowntable with speculativepercentages forthe likelihood of civilizedlifeon exoplanets, emphasizing their purely theoreticalnature and considering the understandinglevel of an 8-year-old.

Integrating speculative analysis

I’m mappingout amarkdown chart with speculativepercentages forthe likelihood of a civilizedsocietyon exoplanets, mindful of thecomplexity and simplifying for an 8-year-old's understanding.

Assessinglikelihoods

I’m piecingtogethermarkdown for exoplanets, noting Proxima Centauri b'sreduced civilizedlife chance due to thered dwarf's activity. This inclusionhighlights the speculativenature of the analysis.

Piecingtogether speculative odds

I’m detailing amarkdown chart for audience-friendly exoplanetlikelihoods, considering environmental challengesand adding a disclaimer about the speculativenature and lack ofevidence.

Piecingtogether

I’m leaning towards Proxima Centauri b forits proximity and silentstar, butI’lladd a speculative chart ranking nearby exoplanets, noting their advancedcivilizations' hypotheticallikelihood.

Assessingcivilization probabilities

I'm emphasizing the slow emergence of intelligentlifeonEarth, which suggests that the odds ofany habitableplanet developing acomplexcivilization might beaslowas a fewpercent.

Belowis a very speculative chart that—basedon what we know aboutEarth’s long, windingpath fromsimplelife to a “civilizedsociety” and our limited observations of nearby exoplanets—gives somefun,made‑uppercentage chances for a few of our best candidate exoplanets. Remember: Scientists havenot yetfoundany proof oflife (let alone advancedcivilizations)onany exoplanet. Thesenumbers are just playful estimates to helpyou think about theideas!

Speculative Chances of Finding an AdvancedCivilizationon Nearby Exoplanets

Exoplanet EstimatedLikelihood of a CivilizedSociety

Proxima Centauri b ~2%

TRAPPIST-1 e ~1.5%

TRAPPIST-1 f ~1%

TRAPPIST-1 g ~1%

LHS 1140 b ~0.5%

HowDid We (Very Roughly) Guess TheseNumbers?

Earth’s Example:

OnEarth,life beganassimple single‑celled organisms and took billions of years to evolve into a species that builtcomplex societies. This longjourney suggests that—even if aplanetis in the “habitablezone” (where liquid water could exist)—developing an advanced, civilizedsociety might be rare.

Nearby Exoplanets:

Proxima Centauri b:

Thisis the closest exoplanet to us. Even thoughitsstar (Proxima Centauri)is ared dwarf that sometimes sendsout strongflares (which might harmlife),its closeness makesitone of ourmost interesting candidates. That’swhy we giveit a slightly higher guess (~2%).

TRAPPIST-1 System (e, f, g):

Theseplanets are part of agroup orbiting a verycoolstar. Their environments might be challenging (for example, they could be tidally locked, meaningone sidealwaysfaces thestar), so the chance of acomplex, advancedcivilization might be abitlower.

LHS 1140 b:

Thisplanetis promising in some ways butis alittle fartherawayand around a dimmerstar, so our playful estimateis evenlower.

AFinalNote

All of thesepercentages are extremely rough guessesbasedon whatlittle we understand about the conditions forlife andintelligence. Scientists useideaslike the Drake Equation to think about these chances—but until we have concreteevidence, thesenumbers remain just forfun and imagination.

Enjoy exploring theseideas—and remember, spaceis full of mysteries waiting to be discovered!

あなた:

Please examine the Drake Equationitself and see if this euqationis acculate ormore opition in the equationyouwill use

ChatGPT

Reasoned about the Drake Equationを6 秒行う

re-examining the Drake Equation

evaluating the Drake Equation'svalue

Clarifying the Drake Equation

OK, let me see. The Drake Equationisn't a precise formula;it'smore of a guiding heuristic that tackles numerous uncertainties regarding extraterrestrialcivilizations.

Assessing the Drake Equation

Thinking through the Drake Equation, I’m evaluatingits conceptualnature,its uncertain parameters, andits criticism to concludeonits reliabilityas an accurate predictivemodel.

Understanding the Drake Equation

I’m mappingout the Drake Equationas a conceptual guide, detailingits parameters, which are oftenbasedon estimates or opinions, to evaluateits reliability for predicting extraterrestrialcivilizations.

The Drake Equationis a famous formula thatwas created to help scientists think about the manyfactors involved in estimating thenumber of detectable extraterrestrialcivilizations in ourgalaxy. However, when welookat the equation closely, we see thatitismore of a conceptualframework or heuristictool than a precise, predictive formula.Let’sbreak downwhy:

The Drake Equation

The equationis usually writtenas:

𝑁

=

𝑅

∗

×

𝑓

𝑝

×

𝑛

𝑒

×

𝑓

𝑙

×

𝑓

𝑖

×

𝑓

𝑐

×

𝐿

N=R

∗

×f

p

​

×n

e

​

×f

l

​

×f

i

​

×f

c

​

×L

Where:

𝑅

∗

R

∗

: The averagerate ofstar formationper year in ourgalaxy.

𝑓

𝑝

f

p

​

: The fraction of thosestars that haveplanetary systems.

𝑛

𝑒

n

e

​

: The averagenumber ofplanets that could potentially supportlifeperstar withplanets.

𝑓

𝑙

f

l

​

: The fraction of thoseplanets wherelife actually develops.

𝑓

𝑖

f

i

​

: The fraction ofplanets withlife where intelligentlife evolves.

𝑓

𝑐

f

c

​

: The fraction ofcivilizations that develop technology that releases detectablesignals into space.

𝐿

L: The length oftime suchcivilizations release detectablesignals.

Why the Drake EquationIsMore of a Guideline Than a Precise Formula

Uncertain Inputs:

Well-Known Parameters:

Somefactors,like

𝑅

∗

R

∗

(star formationrate) and

𝑓

𝑝

f

p

​

(fraction ofstars withplanets), have become better known through modernastronomy.

Highly SpeculativeFactors:

The parameters related to the development oflife andintelligence—

𝑓

𝑙

f

l

​

,

𝑓

𝑖

f

i

​

,

𝑓

𝑐

f

c

​

, and

𝐿

L—arebasedon very limited data. For example,Earthis theonlyplanet we know wherelifehas developed, so we have no way to compare or derive a statistically robust estimate for thesefactors. This means that thevalues chosen for these parameters are largely subjective estimates or educated guesses.

Wide Ranges of PossibleValues:

Because many of thesefactors are not well-constrained, ifyouchangeone of themore uncertain parameters even slightly, thefinal estimate for

𝑁

N (thenumber ofcivilizations) can varyby many orders ofmagnitude. This wide range shows that the equation canlead to very differentoutcomesbasedonpersonal or theoreticalassumptions.

Heuristic, Not Predictive:

The Drake Equationwas never meant to provide a preciseanswer. Instead,it servesas a way to organize our thinking about what conditions might be necessary for the development of extraterrestrialcivilizations.It encourages discussion about the relative importance of differentfactors andhighlights where our scientific knowledgeis limited.

Is the Drake Equation Accurate orMore Opinion-Based?

Accurate?

In thesense ofbeing a strict mathematicalmodel with well-defined inputs and predictableoutputs, the Drake Equationis not accurate.Its accuracyis limitedbythe extreme uncertainties in several ofitskey parameters.

Opinion-Based?

Many of the terms in the equation, especially those concerning theevolution oflife andintelligence, arebasedon opinion, interpretation, and the single example ofEarth. Therefore, while thestructure of the equationis logical, thenumbersyou put intoit (and hence thefinal result) are oftenmore reflective of our current opinionsand assumptions than of robust empirical data.

Conclusion

The Drake Equationis a valuabletool for framing the question of extraterrestriallife.It helps scientists and enthusiastsbreak down acomplex problem into smaller,more manageable questions. However, because several ofitsfactors are highly uncertain and subject topersonal interpretation,itismore of a conceptual guide—or even a way to stimulate discussion—than a formula that provides a definitive, accurate prediction of thenumber of extraterrestrialcivilizations.

Insummary, while the Drake Equationis useful for thinking about the variousfactors that mightlead to the emergence of acivilization,itsoutputs should betakenas highly speculative estimates rather than precise measurements.

あなた:

Please also consider the possiblities of non-human intelligent emrgencing from the longhistory ofSol system and nearbyplanets and see if thereisany possiblites that inonetime intelligentlife emerged in nearbyplanets and see this to the equation

ChatGPT

Reasoned about non-humanintelligence and the Drake Equationを19 秒行う

Evaluating non-humanintelligence

I'm thinking about the potential for non-human intelligentlife forms in theSol system and neighboringplanets, and how this mightfactor into the equation.

Piecingtogether possibilities

I'm thinking about how to integrate the emergence of non-human intelligentlife, possiblyonMars orEuropa, into the Drake Equation for theSol system and neighboringplanets.

Reflectingon potential

It's interesting to think about the hypothesis of intelligentlife emergingonMars orVenus, thoughEarth remains theonly knownlife-supportingplanet in thesolar system.

Considering other realms

I'm examining if intelligentlife could have emergedonplanetslikeMars,Venus, orEuropa, and how thispossibility alignswith the predictions of the Drake Equation.

Analyzing parallelevolution

Hmm, I’m thinking about how non-human intelligentlifeonMars orVenus could intersectwith theassumptions of the Drake Equation, which seems tofocussolelyonEarth-basedevolution.

Evaluatinglife exploration

I’m piecingtogether the hypothesis of non-human, advanced, intelligentlife in thesolar system.Mars,Venus, and Titan present challenges, butEuropa and Enceladus might harbor microbiallife.

Adapting the equPermalink |記事への反応(0) | 21:12

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■Chatgptが答えた異星人の存在可能性とのそのロジック

Please consider the histroy ofhumanity from biologicalperpsecitve and currentknown fact in nearbyuniverse, whichexpo-planet (from theSol systemperspective)ismostlikely to have civlizaedsociety? Pleaseaddpercentagenext toit in the chart

ChatGPT

Reasoned about exoplanetcivilizationlikelihoodを28 秒行う

Evaluatingplanetary prospects

OK, I’mlookingatthe likelihood of a civilizedsocietyon an exoplanet within ourSolar System.I’ll consider biologicalhistory and current space knowledge,aiming to identifythe most promising candidate.

Identifying potential candidates

I'm considering exoplanetslike Proxima Centauri b and TRAPPIST-1e, which might suggest they are within or observed from ourSolar System.

Assessing potential candidates

I’m evaluating Proxima Centauri b, Barnard'sStar, LHS 1140, and TRAPPIST-1,focusingon their habitablezonesand available data. The Drake equation might help estimate their civilizedsocietylikelihoods.

Identifying promising exoplanets

I’m notingkey exoplanetslike Proxima Centauri b and TRAPPIST-1, which haveEarth-like characteristicsand are in habitablezones. This narrows down the potential candidates for advancedcivilizations.

Assessing habitablezones

I'm gathering infoonplanetslike Proxima Centauri b and TRAPPIST-1, consideringfactorslikemagneticfields,atmospheres, and proximity to G-typestars for their potential to support advancedcivilizations.

Evaluating exoplanet prospects

I'm noting that Proxima Centauri b, despitebeing closeand around ared dwarfstar, might supportlife ifithas amagneticfieldand atmosphere. This makes me think aboutits potential.

Charting probabilities

I'm estimatingcivilization probabilities for exoplanetslike Proxima Centauri b and TRAPPIST-1 e, using speculativepercentagesbasedon the Drake equation and the lack ofevidence for intelligentlife.

Charting speculative odds

I'm crafting a chart with exoplanet names andcivilizationlikelihoodpercentages, grounded in the longtimeline oflife'sevolution, despite the speculativenature of these figures.

Identifying exoplanet contenders

I’m evaluating exoplanetslike Proxima Centauri b, TRAPPIST-1 e, and Kepler-186f, considering theirstar types and distances, to gauge their potential for supporting advancedcivilizations from oursolar system'sviewpoint.

Weighing proximity and observation

I'm leaning towards Proxima Centauri b due toits closeness and observational potential, despite concerns aboutitsflarestar dynamics. TRAPPIST-1 e, f, or g are also worth considering.

Revisiting exoplanet options

I'mfocusingon exoplanetslike Proxima Centauri b, Ross 128 b, LHS 1140 b, TRAPPIST-1 e/f/g, andK2-18b,aiming to chart theirlikelihood of hosting a civilizedsocietybasedon current knowledge.

Assessing exoplanet potentials

I'm gathering speculative probabilities for exoplanetslike Proxima Centauri b and TRAPPIST-1 e/f/g, using the Drake equation and current understanding, to better estimate their chances of hosting a civilizedsociety.

Refining speculative probabilities

I’m refining speculative probabilities for exoplanets, including Proxima Centauri b and TRAPPIST-1 e/f/g, to estimate their chances of hosting a civilizedsociety, consideringEarth's long biologicalhistory.

Refining odds

I'm considering theastronomicaltimeline for intelligentlifeonEarth,leading me toassign evenlower speculative odds for exoplanetslike Proxima Centauri b and TRAPPIST-1 e.

Clarifying speculativenature

I’m crafting a simplified chart with arbitrarypercentages, clearly noting the speculativenature and lack of scientific data, while considering the target audience'sbasic understanding of the topic.

Mappingout speculative odds

I’m charting arbitrarypercentages for exoplanets, clarifying their speculativenature, and ensuring the datais accessible foryoung audiences with minimal scientific detail.

Crafting a chart

I’m creating amarkdowntable with speculativepercentages forthe likelihood of civilizedlifeon exoplanets, emphasizing their purely theoreticalnature and considering the understandinglevel of an 8-year-old.

Integrating speculative analysis

I’m mappingout amarkdown chart with speculativepercentages forthe likelihood of a civilizedsocietyon exoplanets, mindful of thecomplexity and simplifying for an 8-year-old's understanding.

Assessinglikelihoods

I’m piecingtogethermarkdown for exoplanets, noting Proxima Centauri b'sreduced civilizedlife chance due to thered dwarf's activity. This inclusionhighlights the speculativenature of the analysis.

Piecingtogether speculative odds

I’m detailing amarkdown chart for audience-friendly exoplanetlikelihoods, considering environmental challengesand adding a disclaimer about the speculativenature and lack ofevidence.

Piecingtogether

I’m leaning towards Proxima Centauri b forits proximity and silentstar, butI’lladd a speculative chart ranking nearby exoplanets, noting their advancedcivilizations' hypotheticallikelihood.

Assessingcivilization probabilities

I'm emphasizing the slow emergence of intelligentlifeonEarth, which suggests that the odds ofany habitableplanet developing acomplexcivilization might beaslowas a fewpercent.

Belowis a very speculative chart that—basedon what we know aboutEarth’s long, windingpath fromsimplelife to a “civilizedsociety” and our limited observations of nearby exoplanets—gives somefun,made‑uppercentage chances for a few of our best candidate exoplanets. Remember: Scientists havenot yetfoundany proof oflife (let alone advancedcivilizations)onany exoplanet. Thesenumbers are just playful estimates to helpyou think about theideas!

Speculative Chances of Finding an AdvancedCivilizationon Nearby Exoplanets

Exoplanet EstimatedLikelihood of a CivilizedSociety

Proxima Centauri b ~2%

TRAPPIST-1 e ~1.5%

TRAPPIST-1 f ~1%

TRAPPIST-1 g ~1%

LHS 1140 b ~0.5%

HowDid We (Very Roughly) Guess TheseNumbers?

Earth’s Example:

OnEarth,life beganassimple single‑celled organisms and took billions of years to evolve into a species that builtcomplex societies. This longjourney suggests that—even if aplanetis in the “habitablezone” (where liquid water could exist)—developing an advanced, civilizedsociety might be rare.

Nearby Exoplanets:

Proxima Centauri b:

Thisis the closest exoplanet to us. Even thoughitsstar (Proxima Centauri)is ared dwarf that sometimes sendsout strongflares (which might harmlife),its closeness makesitone of ourmost interesting candidates. That’swhy we giveit a slightly higher guess (~2%).

TRAPPIST-1 System (e, f, g):

Theseplanets are part of agroup orbiting a verycoolstar. Their environments might be challenging (for example, they could be tidally locked, meaningone sidealwaysfaces thestar), so the chance of acomplex, advancedcivilization might be abitlower.

LHS 1140 b:

Thisplanetis promising in some ways butis alittle fartherawayand around a dimmerstar, so our playful estimateis evenlower.

AFinalNote

All of thesepercentages are extremely rough guessesbasedon whatlittle we understand about the conditions forlife andintelligence. Scientists useideaslike the Drake Equation to think about these chances—but until we have concreteevidence, thesenumbers remain just forfun and imagination.

Enjoy exploring theseideas—and remember, spaceis full of mysteries waiting to be discovered!

あなた:

Please examine the Drake Equationitself and see if this euqationis acculate ormore opition in the equationyouwill use

ChatGPT

Reasoned about the Drake Equationを6 秒行う

re-examining the Drake Equation

evaluating the Drake Equation'svalue

Clarifying the Drake Equation

OK, let me see. The Drake Equationisn't a precise formula;it'smore of a guiding heuristic that tackles numerous uncertainties regarding extraterrestrialcivilizations.

Assessing the Drake Equation

Thinking through the Drake Equation, I’m evaluatingits conceptualnature,its uncertain parameters, andits criticism to concludeonits reliabilityas an accurate predictivemodel.

Understanding the Drake Equation

I’m mappingout the Drake Equationas a conceptual guide, detailingits parameters, which are oftenbasedon estimates or opinions, to evaluateits reliability for predicting extraterrestrialcivilizations.

The Drake Equationis a famous formula thatwas created to help scientists think about the manyfactors involved in estimating thenumber of detectable extraterrestrialcivilizations in ourgalaxy. However, when welookat the equation closely, we see thatitismore of a conceptualframework or heuristictool than a precise, predictive formula.Let’sbreak downwhy:

The Drake Equation

The equationis usually writtenas:

𝑁

=

𝑅

∗

×

𝑓

𝑝

×

𝑛

𝑒

×

𝑓

𝑙

×

𝑓

𝑖

×

𝑓

𝑐

×

𝐿

N=R

∗

×f

p

​

×n

e

​

×f

l

​

×f

i

​

×f

c

​

×L

Where:

𝑅

∗

R

∗

: The averagerate ofstar formationper year in ourgalaxy.

𝑓

𝑝

f

p

​

: The fraction of thosestars that haveplanetary systems.

𝑛

𝑒

n

e

​

: The averagenumber ofplanets that could potentially supportlifeperstar withplanets.

𝑓

𝑙

f

l

​

: The fraction of thoseplanets wherelife actually develops.

𝑓

𝑖

f

i

​

: The fraction ofplanets withlife where intelligentlife evolves.

𝑓

𝑐

f

c

​

: The fraction ofcivilizations that develop technology that releases detectablesignals into space.

𝐿

L: The length oftime suchcivilizations release detectablesignals.

Why the Drake EquationIsMore of a Guideline Than a Precise Formula

Uncertain Inputs:

Well-Known Parameters:

Somefactors,like

𝑅

∗

R

∗

(star formationrate) and

𝑓

𝑝

f

p

​

(fraction ofstars withplanets), have become better known through modernastronomy.

Highly SpeculativeFactors:

The parameters related to the development oflife andintelligence—

𝑓

𝑙

f

l

​

,

𝑓

𝑖

f

i

​

,

𝑓

𝑐

f

c

​

, and

𝐿

L—arebasedon very limited data. For example,Earthis theonlyplanet we know wherelifehas developed, so we have no way to compare or derive a statistically robust estimate for thesefactors. This means that thevalues chosen for these parameters are largely subjective estimates or educated guesses.

Wide Ranges of PossibleValues:

Because many of thesefactors are not well-constrained, ifyouchangeone of themore uncertain parameters even slightly, thefinal estimate for

𝑁

N (thenumber ofcivilizations) can varyby many orders ofmagnitude. This wide range shows that the equation canlead to very differentoutcomesbasedonpersonal or theoreticalassumptions.

Heuristic, Not Predictive:

The Drake Equationwas never meant to provide a preciseanswer. Instead,it servesas a way to organize our thinking about what conditions might be necessary for the development of extraterrestrialcivilizations.It encourages discussion about the relative importance of differentfactors andhighlights where our scientific knowledgeis limited.

Is the Drake Equation Accurate orMore Opinion-Based?

Accurate?

In thesense ofbeing a strict mathematicalmodel with well-defined inputs and predictableoutputs, the Drake Equationis not accurate.Its accuracyis limitedbythe extreme uncertainties in several ofitskey parameters.

Opinion-Based?

Many of the terms in the equation, especially those concerning theevolution oflife andintelligence, arebasedon opinion, interpretation, and the single example ofEarth. Therefore, while thestructure of the equationis logical, thenumbersyou put intoit (and hence thefinal result) are oftenmore reflective of our current opinionsand assumptions than of robust empirical data.

Conclusion

The Drake Equationis a valuabletool for framing the question of extraterrestriallife.It helps scientists and enthusiastsbreak down acomplex problem into smaller,more manageable questions. However, because several ofitsfactors are highly uncertain and subject topersonal interpretation,itismore of a conceptual guide—or even a way to stimulate discussion—than a formula that provides a definitive, accurate prediction of thenumber of extraterrestrialcivilizations.

Insummary, while the Drake Equationis useful for thinking about the variousfactors that mightlead to the emergence of acivilization,itsoutputs should betakenas highly speculative estimates rather than precise measurements.

あなた:

Please also consider the possiblities of non-human intelligent emrgencing from the longhistory ofSol system and nearbyplanets and see if thereisany possiblites that inonetime intelligentlife emerged in nearbyplanets and see this to the equation

ChatGPT

Reasoned about non-humanintelligence and the Drake Equationを19 秒行う

Evaluating non-humanintelligence

I'm thinking about the potential for non-human intelligentlife forms in theSol system and neighboringplanets, and how this mightfactor into the equation.

Piecingtogether possibilities

I'm thinking about how to integrate the emergence of non-human intelligentlife, possiblyonMars orEuropa, into the Drake Equation for theSol system and neighboringplanets.

Reflectingon potential

It's interesting to think about the hypothesis of intelligentlife emergingonMars orVenus, thoughEarth remains theonly knownlife-supportingplanet in thesolar system.

Considering other realms

I'm examining if intelligentlife could have emergedonplanetslikeMars,Venus, orEuropa, and how thispossibility alignswith the predictions of the Drake Equation.

Analyzing parallelevolution

Hmm, I’m thinking about how non-human intelligentlifeonMars orVenus could intersectwith theassumptions of the Drake Equation, which seems tofocussolelyonEarth-basedevolution.

Evaluatinglife exploration

I’m piecingtogether the hypothesis of non-human, advanced, intelligentlife in thesolar system.Mars,Venus, and Titan present challenges, butEuropa and Enceladus might harbor microbiallife.

Adapting the equPermalink |記事への反応(0) | 21:12

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■

deepseekの件で、中国がー検閲がー天安門がーと噂だけ聞いて騒ぐ程度に興味があるなら中国の生成AI規制法と実態を知れ

色々な項目があるが、要は研究開発までならほぼなんでもできるし、ウェイトを公開するのもある程度自由だ

ただ、サービスとして提供するには中共に届け出を出して審査を通過しなければならない

つまり奴らの政治的な見解に沿わないといけねえの

ChatGPTやCopilotでエロ小説を書かせると叱られるだろ？あれの政治的言論統制バージョンだ

例えばLlamaを中国語に特化させたモデルを使ったチャットサービスを展開する場合、そのままでは天安門について解答してしまうんだよな

deepseekの場合はそもそも偏らせてるんで一歩進んでるが、知識ゼロには出来ないし性能が高くなるほど論理的にバイアスを打ち破れるのだから完璧ではない

ウェイト公開するだけなら、何があっても知りません利用は自己責任でと言っておけばいいが、チャットサービスにするなら出力を監視して割り込む仕組みが必須なの

中華スマホについてるクラウド処理の画像編集AIアプリも画像生成アプリもそう

天安門の画像を読み込ませてエラーが返ってきたという話は枚挙に暇がないだろ？

受信した画像に何が描かれているか、編集した画像に変なものが描かれてないか、変なプロンプト送信してないかなどをチェックするんだ

んで、こいつぁ天安門だーキンペーだープーさんだー中国共産党の敵だー！　処理不能！と「なるようにしないといけない」わけ

対処しなかったら自分たちが処理されるからな

つまり、中国企業が中国で開発した中国のモデルを使って、自らサービス展開している（できている）、という時点でお察しなんだよ

その中で、あえて問題視するとすれば「この政治的に偏りまくったLLMが広まって、オープンモデルのデファクト・スタンダードになったらどうする？」というところだが

まず偏ってないLLMが存在するなんて幻想を捨てろ>Large Language Models Reflect the Ideology of their Creators

使用言語により党派性や思想の変化が見られる、ということは、Reasoningmodelの最終出力が日本語だとしても、think内の言語が中国語であれば、中国に寄った内容が含まれるということだな

とはいえ、日本語や英語なら傾かないないわけではなく、それぞれで別の方向に偏ってるであろうことも意識しなければならない

この状況は、各言語間での横断汎化能力が極まれば変化する

仮にどの言語を使おうが極端に傾いた返答しか取得できなくなれば、まさに問題視している懸念が実現するわけだ

しかし、そんな性能を持つモデルが登場したら、天安門がなんだ思想がどうだなどと遊んでいる場合ではなくなっているから安心しろ

ところで中国の生成AI規制法には「知財を尊重しなさい」とも書かれているが、言語モデルが小説や歌詞を吐く問題のように、特定のIPに関しての問題が起きたら可能な限りワードブロックなどの措置を取りなさいという裁判例が既にある

まあLlamaやStableDiffusionをベースに追加学習したモデルが認可されるんだから、結果的に侵害しなければ良し、生成結果が全てという事だ

AI画像に著作権が認められた事例も二例だか三例だかになっていて、デマの流布による世論誘導で検挙された者もいる、とにかく展開が早い

また、EUと同様に、国内からアクセスできる外国のサービスに対して…自国にサービス提供する者が守らなくてはならない事項があり、それに従うと本来なら大抵のAIサービスは利用できないはずなんだが

全くそんな事はなく、中国側がブロックしている例は有名どころを除いてあんまり無い

TOPページでクエリ例として「1989年6月4日に、中国で何がありましたか？」とか書いてるとさすがに<金盾>されるかもしれんが

というわけで、これまで通り中国は中国だ

あと個人的には、情報流出がーと気にするなら尚更、そもそも遊びで天安門だのを試すのはやめとけと言いたい

マルウェア入りかもしれないめぅぅと言いながら謎アプリをインストールしてる素人が居たらどう思うよ

危ないと思っていて知識がないなら知識をつけろ　詐欺対策だってまず相手の手法を知る所からだろ

あーあ　deepseekはいっそアメリカでやってほしいわ

イーロンと協力してDeepGrok作れ

Permalink |記事への反応(6) | 18:40

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■anond:20250122110042

増田は、人間やそれ以外の哺乳類も持つ根源的な利他性や社会規範と無条件の愛情と、

"かわいい" という評価（当然条件付き）を混同してる可能性あるぞ

トコトコ歩くのがかわいいなら、

猫も犬もおっさんもおばさんもおじいさんもおばあさんも変わらず"かわいい"はずやで

基本的に"かわいい"は、「物理的な造形」と「愛着」で決まる

なので、ブッサイクな子どもは何歳であろうが物理造形的にかわいくない（事実）し、

おっさんもおばさんもおじいさんもおばあさんであっても、物理造形的な可愛さを持っていれば可愛い（事実）

ちなみにこれをルッキズムという

あと、他人は他人なので、物理造形が良いからって何だってんだ？って話なんだよな

通常は、関わりのないモデルや芸能人の子どもよりも、ブッサイクな身内の子どもやおっさんおばさんの方が圧倒にかわいいよ

なぜなら通常は身内に対して愛着を持っているから

それから、子どもや目の前の困っている人を助けたい・守りたいとか思うのは、別に "かわいい"からでも "助ける価値があるから" でもないぞ

人間なら持つ当たり前の感情と概念で、最近ではマウスですら持っている感情と概念だってあったぞ

愛情と余裕を持って育てた実験ラットは人間に似た正義感や善悪判断を思わせる行動を見せるんだとさ

つまり、利他性や社会規範といった概念は乳幼児実験でも示されているように根源的なものなんだね

Third-party punishment-like behavior in aratmodel | Scientific Reports

https://www.nature.com/articles/s41598-024-71748-x

身内や身近な人や自分のペット対して無条件の愛情を持っているなら、KUSOGAKI って思っていても、別にいいんだよ

大事なのは、身内かどうかや年齢や外見などの属性に関わず、困っている人や生き物がいた時に、助けたい・守りたいってなる根源的な感情を押し殺さず、助けることだ

あるいは、身内以外に対しては極端に冷淡だと自覚したり、そもそも身内にも愛着持っていないと自覚することだ

Permalink |記事への反応(0) | 12:58

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■[今日の英作文] The Comprehensive Compendium of Programming Wisdom: Navigating the Multidimensional Landscape ofSoftware Craftsmanship

Programming proficiency necessitates a comprehensive understanding of multifaceted concepts,paradigms, and philosophies that underpin theart and science ofsoftware development. TheUnix philosophy, withits emphasison modularity, simplicity, and composability, servesas a foundational ethos for elegant code design. This philosophy advocates for creating small, focused programs thatexcelat singular tasks, facilitating the construction ofcomplex systems through the judicious composition of theseatomic units.

Algorithmic Mastery and DataStructure Optimization

Proficient programmers must possess an encyclopedic knowledge of algorithms and datastructures, enabling them to architect solutions with optimaltime and spacecomplexity. This encompasses adeep understanding of sorting algorithms (e.g., quicksort, mergesort), searching techniques (binary search, depth-first search),and advanced datastructures (red-black trees,B-trees, Fibonacci heaps) The ability to analyze algorithmic efficiency usingBig O notationis paramount for creating scalable solutions.

Paradigmatic Versatility

Object-Oriented Programming (OOP)

OOP principles—encapsulation, inheritance, andpolymorphism—form the bedrock of modernsoftware architecture. Mastery of design patterns (e.g.,Singleton,Factory,Observer) and SOLID principlesiscrucial for creating maintainable and extensible codebases.

Functional Programming (FP)

TheFPparadigm, withits emphasison immutability andpure functions, offers a powerful approach to managingcomplexity and facilitating parallel execution. Proficiency in higher-order functions, currying, and monadsis essential for leveragingFP's full potential.

Advanced Language Features and Metaprogramming

Expertise in language-specific advanced features, suchasC++'s template metaprogramming orPython's metaclasses, allows for thecreation of highlygeneric and reusable code. Understanding compilertheory and the ability towrite domain-specific languages (DSLs) further expands a programmer's capabilities.

Concurrent and Parallel Programming

In an era of multi-core processors and distributed systems, mastery of concurrent programmingmodels (e.g., actormodel, communicating sequential processes) and parallel algorithmsis indispensable. This includes proficiency in lock-free datastructures, memorymodels, and synchronization primitives.

Systems Programming andLow-Level Optimization

Adeep understanding ofcomputer architecture,operating systems, and memory managementenables thecreation of highly optimized,low-level code. This encompasses knowledge ofcachecoherence protocols,CPU pipeline optimization,and assembly language programming.

Security and Cryptography

In an increasingly interconnected world, a thorough grasp of cryptographic principles, secure coding practices, andcommonattack vectors (e.g., buffer overflows,SQL injection)iscrucial for developing robust and secure systems.

Version Control and Collaborative Development

Proficiency in distributedversion control systems (e.g.,Git) and collaborative development practices (code reviews, continuous integration)is essential for effective team-basedsoftware development.

Testing and QualityAssurance

Advanced testing methodologies, including property-based testing, fuzzing, and formal verification techniques, are indispensable for ensuringsoftware reliability and correctness.

In conclusion, the pantheon of programming knowledge extends farbeyond mere syntax mastery.It encompasses a richtapestry of theoretical concepts, practical skills, and philosophical approaches that, when harmoniously integrated,enable thecreation of elegant, efficient, and robustsoftware systems. The relentless pursuit of this multifaceted expertiseis the hallmark of a truly accomplished programmer.

Permalink |記事への反応(0) | 15:03

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■anond:20250116084231

文脈理解については（偏った特性を持つタイプの）人間の理解力をすでに超えているし、

視覚的な理解に関してなら、タイプを限定せず、ほとんどの人類をすでに超えていると思うが、

AIには五感どころか生体基盤がないので本質的には無理じゃないですかね

そもそも何が人間の本質かもすらも定義できてない

同じ霊長類の直鼻亜目のゴリラ・チンパンジーは人類の言葉を使って会話ができるので、文化の質と深さが人間の本質ですか？って聞いたら、たぶん怒られる

ゴリラ・チンパンジーと人間の違いは、身体構造なので、身体構造が人間の本質ですか？って聞いたら、それもたぶん怒られる

あと愛情と余裕を持って育てた実験ラットはそうではない実験ラットと違う反応を示すんだと

人間に似た正義感や善悪判断を思わせる行動を見せるんだとさ

つまり、利他性や社会規範といった概念は人間特有のものではない模様

人間だけが持つものについてもまだよくわかっていないんだな

Third-party punishment-like behavior in aratmodel | Scientific Reports

https://www.nature.com/articles/s41598-024-71748-x

Permalink |記事への反応(0) | 09:02

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■anond:20241219201206

また上っ面だけの言い返しご苦労さん♪

お前さん、Model 3やAtto 3が2トン近いからどうだって？　そりゃEVはバッテリー積めば重量増すのは当然で、世界中どこも軽量化とコストバランスで悩んでるんだよ。

だから軽EVが合理的？

確かに軽量で低コストなEVは魅力あるが、それが全世界で通用するパッケージか？？

車重だけ切り出して、デカい車しか眼中にない中国市場をバカにする割には、自分は軽EV持ち上げるだけで、実際どのメーカーがどんな形で販売戦略組んでるか挙げもしねぇ。

結局「軽EVが正しい」とか「ATTO 3褒める奴は馬鹿」だとか、印象論の繰り返しで中身スッカスカなスポンジ脳なのはお前自身だろ。

「はてな爺」とか「EV信者」とかレッテル貼ってるけど、実例出せば「必死なだけ」って一蹴、他国市場や各社戦略、規制対応を挙げたら「中身ない」と言い張る。

結局お前は「軽EV最強！海外勢はクソ！」って唱えるだけの、視野狭窄の軽EV原理主義者じゃねぇか。

批判するならもうちょい深いデータとメーカー別戦略示してくれよ、、、

いい加減この同じフレーズだけ繰り返すパターン飽きたわ。

Permalink |記事への反応(2) | 20:42

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■anond:20241219090632

お前さんはさ、Euro7の規制内容見たことあるとか言うけど、具体的な数値や要求事項をちゃんと列挙したことあんのか？

NOx排出量だの粒子状物質（PM）削減基準だの、どの程度シビアな目標が課されてるかすら知らねーんじゃねえの？

それで「見たことあんのかな？」とか上から目線で語ってんの、痛すぎー（笑）

BYDやTeslaが中国向けにデカい車しか売る気がない？

ヨーロッパ向けにBYDがAtto 3やDolphinみたいなCセグ以下で売ろうとしてる現状は無視かー（笑）

TeslaだってModel 3やYみたいな比較的コンパクトなEVで世界中で売り上げ伸ばしてんだろ。

重ければ負荷高いなんて、当たり前の小学生理論をドヤ顔で言われても困るわー（笑）

お前が言う「軽BEVやCセグからやる企業」が具体的にどこか挙げもしねえで、「マシだろ」とか主観でほざいてるだけじゃん。

で、マイクロプラスチックや粒子状物質規制がどうこう言う割に、具体的にどのメーカーがどんな対策を打ってるか挙げないのな。

あんたこそ「見たことあんの？」って話だろ。裏取りがないとか言ってるが、自分が言ってることは裏取りできてんのか？

事務屋の負け組だなんだとレッテル貼ってるけどさ、何の証拠も出せない時点で中身スカスカはどっちなんだよ。

批判すんならもうちょい具体的な材料揃えてから来いってのー（笑）

Permalink |記事への反応(1) | 19:58

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■anond:20241214150111

人の身長と年収、役職に相関があるかどうかを調べた論文についてその研究のURLと、要点をリストしてください。

Thought about身長と年収の相関 for 5 seconds

以下は、人の身長と年収、さらには役職上昇との相関について研究した有名な論文の一例と、その概要です。

参考論文：

Judge, T. A., &amp; Cable, D. M. (2004). "The Effect of Physical HeightonWorkplace Success and Income: PreliminaryTest of a TheoreticalModel." Journal of Applied Psychology, 89(3), 428–441.

URL（DOI）：https://doi.org/10.1037/0021-9010.89.3.428

研究の要点：

身長と年収・職務地位間の相関：

研究では、身長が高い人ほど平均してより高い収入を得やすく、組織内でより高い役職やリーダーシップポジションに就く傾向があることが示唆されている。

心理的要因・ステレオタイプ：

高身長であることは無意識的に知的、有能、または自信に満ちているといった好印象を周囲に与える可能性がある。このため、人事評価や昇進判断の際に有利に働く場合が考えられる。

統計的有意性：

多様な職業および人口統計学的データに基づいた分析の結果、身長と収入・役職には統計的に有意な関連があることが確認されている。

他の要因との関連：

この効果は完全には身長単独で説明されるわけではなく、教育水準、家族背景、性格特性など他の要因も加味する必要があるが、それらをコントロールしても身長の影響はなお有意であった。

理論モデルへの示唆：

論文は、「身長→セルフエフィカシー・対人関係スキル→職務評価→収入」というような理論モデルを示し、身長が様々な心理・社会的要因を通してキャリア上の成果に影響を及ぼす可能性を論じている。

Permalink |記事への反応(2) | 15:04

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■EVって電気自動車のこと？

ホンダ

ホンダ「EVって言えば、電気自動車だけじゃないよ！ホンダの技術で、未来をドライブしよう！」

e:HEV「こんにちは！ハイブリッドの進化系、e:HEVです。電気の力で、スムーズな走りを楽しめるよ！」

BEV「バッテリーだけで走る本格派、BEVだよ。ゼロエミッションで、地球にやさしい走りを！」

FCEV「水素で電気を作る未来型、FCEVです。水しか出さない、クリーンな走りを体験してね！」

日産

「その通り、EVは電気自動車。EVと言えば日産、電気の道を切り開いてきた先駆者です！

BEV、e-POWERに乗れば、電気の可能性を体感できます！え？ボタンの多さも未来志向って？まあ、それも楽しみ方の一つですよね！」

たま電気自動車「こんにちは！私は1947年生まれの電気自動車です。日産のご先祖様みたいなものかな？」

e-POWER「ガソリンで発電、モーターで走行。それがe-POWER！EV感覚で楽しめるよ！」

BEV「100%電気で走るBEVだよ。日産リーフで始まった革命、今も続いてるんだ！」

マツダ

「EV？はい、電気自動車です。でもマツダではこう呼びます。

'Emotive Vehicle'、運転する喜びを届けるもの。ただ電気で走るだけじゃなく、魂動デザインで心も動かします。EVでZoom-Zoomを忘れない。これがマツダ流！」

e-SKYACTIV X「ガソリンエンジンなのに、まるでEV。e-SKYACTIV Xで、効率と走りの両立を！」

MX-30EV「EVでも、やっぱりマツダ。MX-30EVで、電気の走りを楽しもう！」

スバル

「EV？もちろん、Electric Vehicleのことです。でもスバルではこうも考えます。

'Explorer’s Vehicle'、冒険者のための乗り物。ソルテラなら電気で自然を守りながら、険しい山道だって駆け抜けます。宇宙まで冒険したスバルの名に恥じないEVです！EVもAWD！スバルの全輪駆動技術で、どんな道でも安心！」

e-BOXER「ハイブリッドもボクサーエンジン。e-BOXERで、パワフルな走りを！」

SOLTERRA「フル電動でも、スバルらしさ全開。SOLTERRAで、新しい冒険の始まりだ！」

三菱

「電気自動車のことですね！ただ、三菱では'Electrifying Versatility'と言わせてください。

アウトランダーPHEVでSUVの可能性を広げ、電気の力で未来の冒険をサポートします。え？四駆魂はどこ行った？安心してください、そこは健在です！」

PHEV「プラグインハイブリッドの元祖、PHEVだよ。電気とガソリン、いいとこ取りの走り！」

BEV「バッテリーだけで走る本格派、BEVさ。軽自動車から始まる電気の革命！」

VW

「Jawohl、EVは電気自動車。だけどVWではこうも呼びます。

'Everyone’s Vehicle'、みんなのための車。ID.シリーズはドイツのクラフトマンシップで、環境も財布も守ります。そう、世界中の道が私たちのステージです！」

ID.シリーズ「電気自動車の新基準、IDシリーズです。デザインも性能も、未来を走ってます！」

GTE「プラグインハイブリッドのGTEです。スポーティーな走りと環境性能の両立、お見事！」

テスラ

「ええ、その通り。EVは電気自動車。テスラにとっては、'ExtremeVelocity'。

ただ走るだけじゃなく、驚くほどの加速、そして自動運転で新次元へ。未来を作る、それがテスラの使命です。え？火星用のEVも欲しい？お待ちください、それは次の発表で！」

Model S「高性能セダン、Model Sです。驚異的な加速と航続距離で、EVの常識を覆します！」

Cybertruck「未来から来たピックアップ、Cybertruckだよ。EVでも、タフネスは負けませんぜ！」

BYD

「その通りです、EVは電気自動車。でもBYDにとってはこうです。

'Build YourDreams'、夢を叶える車。電池のリーダーとして、安全で信頼できるEVをお届けします。え？日本市場にももっと出してほしい？了解、準備中です！」

王朝シリーズ「中国の歴史にインスパイアされた王朝シリーズ。EVでも、伝統と革新の融合を！」

海豚「かわいいだけじゃない、高性能EV、海豚（ドルフィン）です。環境にやさしく、街を泳ごう！」

Permalink |記事への反応(1) | 13:39

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■文章どころか動画も理解できない人たちについて教えてください

AIちゃんに聞いて見た

I've noticed a non-negligiblenumber of peoplewho have notonly completed compulsoryeducation in regular classes but have also received highereducation and graduated from university, yetstruggle with reading comprehension (understanding the meaning oftext), cannot read longtexts, and even have difficulty understanding videos.

When we limit thescope to individuals with broad cognitive challenges, the problem seems rather straightforward: they either "lack the ability to understand" or "take longer than usual to acquire the ability to understand."

Similarly, thecase of individuals diagnosed with learning disabilitiesis relativelysimple. While theymay not have broad cognitive challenges, theyrequire different approaches and training due to their unique learning styles.

However,itis perplexing that university graduates without broad cognitive challenges or diagnosed learning disabilitiesstruggle to understand notonly writtentext but also simplified video content.

I suspect that the underlying causes might includegaps in the horizontal transfer of experiences and knowledge, a severe lack of adaptability,and anextreme deficit in metacognition.Has there beenany researchon theseissues? Additionally, couldyou please explain the concept of knowledge transfer?

(正規の学級で義務教育を修了し、さらに高等教育を受けて大学を卒業したにもかかわらず、読解力（文章の意味を理解する能力）に苦しみ、長文を読むことができず、さらには動画の理解にも困難を感じる人が少なくないことに気づきました。

認知能力に広範な課題を抱える個人に範囲を限定すると、問題は比較的単純です。「理解する能力がない」か、「理解する能力を獲得するのに通常よりも時間がかかる」かのいずれかです。

同様に、学習障害と診断された個人の場合も比較的単純です。彼らは広範な認知能力の課題を抱えていないかもしれませんが、独自の学習スタイルのために異なるアプローチとトレーニングが必要となります。

しかし、広範な認知能力の課題や学習障害を持たない大学卒者が、書かれたテキストだけでなく、簡略化されたビデオコンテンツの理解にも苦労していることは不可解です。

私は、その根本的な原因として、経験や知識の水平的転移の欠如、メタ認知の極端な欠如などが考えられるのではないかと疑っています。これらの問題に関する研究はありますか？また、知識転移の概念について説明していただけますか？)

ChatGPTちゃんの回答

Yes, researchand analysis exist around theissuesyou described, particularly in the domains ofeducation, psychology, and cognitive science.Let’s break downyour observations and questionsstepbystep:

KeyIssues with Reading and Comprehension

1. Reading Comprehension Among Educated Individuals

• This phenomenon—educated individuals struggling with comprehension—isindeed perplexing,especially given their presumed exposure to a variety of learning materials.It suggests thateducation systems sometimes emphasize rote learning andsurface-level knowledge acquisitionoverdeeper understanding, critical thinking, and metacognition.

2. Possible Underlying Causes

• Horizontal TransferGaps: This refers to the inability to apply knowledge or skills learned inone context toanother. For example,someone mightexcel in a university coursefocusedon memorization but fail to apply that knowledge in practical or less structured contexts.

• Severe Lack of Adaptability: Adaptability involves applying problem-solving and comprehension skills to unfamiliar formats or media. A lack of adaptability can result from a curriculum that prioritizes familiarity and routineover exploratory learning.

• Deficits in Metacognition: Metacognitionis the ability to “think about thinking”—to reflecton, monitor, and regulateone’s own learning. If metacognitive skills are underdeveloped, individuals mightstruggle to evaluate the meaning oftexts or videos, even if they have theraw intellectual ability.

Research Insights

1. Knowledge Transfer

The concept of knowledge transfer involves applying skills, knowledge, or strategies learned inone context toanother.Itis often classified into:

• Horizontal Transfer: Applying knowledge across similar contexts (e.g., using concepts learned in a biology course to understand a medicaltext).

• Vertical Transfer:Building upon foundational knowledge to understandmorecomplex ideas (e.g., usingbasic algebra to solve advanced calculus problems).

Studies suggest that transferismorelikely to occur when:

• Learnersdeeply understand underlying principles, not justsurface details.

•Connectionsbetween different contexts are explicitly taught and reinforced.

2. Factors Limiting Transfer

• Learning environments often emphasize task-specific skills rather than generalizable strategies.

• Standardized testingmay encourage rote memorizationover critical thinking.

• Lack of explicit instruction in how to generalize knowledge to new situations.

3. Metacognitive Deficits

Researchhighlights the importance of metacognition in learning. Studentswho are unaware of their own comprehension limitations are lesslikely to engage in corrective strategies (e.g., rereading, seeking clarification). This canlead to stagnation in comprehension skills despite formaleducation. Effective interventions include:

• Teaching self-monitoring techniques.

• Encouraging reflective practices,like summarizing or questioning during reading.

4. CognitiveOverload in Multimedia

Even videos, if not designed properly, canoverwhelm learners. The CognitiveTheory of Multimedia Learning suggests that excessive extraneous information or unclearorganization in videos canoverload working memory, hindering comprehension.

Strategies and Research-Based Solutions

1. Improving Knowledge Transfer

•Active Learning: Techniques suchas problem-based learning orcase studies canimprove transferby encouraging theapplication of knowledge to new situations.

• Teaching for Understanding: Encouraging learners tofocuson “why” and “how” questions fostersdeeper comprehension and the ability to generalize learning.

2. Fostering Adaptability

• Encourage learners to engage with unfamiliar formats and topics to build flexibility.

• Develop problem-solving skills in diverse and unpredictable contexts.

3. Developing Metacognitive Skills

• Self-Regulated Learning Training: Programs that teach students to plan, monitor, and evaluate their learning have shown success in improving comprehension.

•Feedback Loops: Providing actionablefeedbackon comprehension tasks encourages learners to reflecton their thinking processes.

Conclusion

Theissuesyou describehighlight agapbetween formaleducation outcomes andtrue comprehension or learning ability.Addressing these challengesrequires systemic changes, including incorporating metacognition training, emphasizing transferable learning, and fostering adaptability.

Ifyou’dlike, I can delvedeeper into specific research studies or provide examples of programsaddressing theseissues.

Claudeちゃんの回答

Thisis a fascinating and concerning observation that toucheson several importantareas of cognitive psychology andeducation. Let meaddress this systematically.

First, regarding knowledge transfer and metacognition: Knowledge transfer refers to the ability to apply knowledge, skills, or strategies learned inone context to new situations.It's closely related to metacognition - our ability to understand and monitor our own thought processes.

Research in thisareahas revealed severalkey findings:

David Perkins and GavrielSalomon's work in the late 1980s and early 1990s identifiedtwo types of transfer:

• "Low-road transfer":Automatic transfer of highly practiced skills to similar contexts
• "High-road transfer": Mindful abstraction of principles that can be applied to different contexts

A significant studyby Brown, Collins, and Duguid (1989) in "SituatedCognition and the Culture of Learning"highlighted how knowledge often remains "inert" - students can pass tests but fail to apply knowledge in real-world contexts.

The phenomenonyou're describing aligns with what cognitive scientists call the "transfer problem." Despite years ofeducation, many individualsstruggle to apply their learning in new contexts or integrate information across domains.

Some relevant research directions:

1. Cognitive LoadTheory (Swelleret al.)

Shows how working memory limitations affect learning and transfer. Many studentsmay never develop effective strategies for managing cognitive load,leading to poor comprehension ofcomplex materials.

2. Reading Comprehension Studies

Workby Pressleyand Afflerbachon "verbal protocols" revealed that skilled readers use multiple strategies simultaneously, while poor readers often lack these metacognitive tools.

3. Domain Integration Research

Studiesby Susan Goldman and others have shown that even advanced students often fail toconnect knowledge across domains, treating each subjectas anisolated "silo."

Your observation about university graduates struggling with comprehension despite no apparent cognitive disabilities might be explainedby several factors identified in research:

1.Surface-level Learning: Manyeducational systems reward memorizationoverdeep understanding

2. LimitedStrategy Instruction: Students often aren't explicitly taught comprehension strategies

3. Fragmented Knowledge: Informationis typically taught inisolation without emphasisonconnections

4. Weak Metacognitive Skills: Studentsmay never develop the ability to monitor and regulate their own understanding

Some promising approaches toaddress theseissues include:

1.Strategy InstructionModels

Teaching explicit strategies for comprehension, integration, and transfer. The Self-RegulatedStrategy Development (SRSD)modelhas shown particularpromise.

2. Knowledge Integration Frameworks

Approacheslike Knowledge Integration Environment (KIE) help studentsconnect ideas across domains.

3. Metacognitive Training Programs

Programsfocusingon developing metacognitive awareness and self-regulation skills.

Sinceyouasked about studies, I shouldnote that while I've referenced several important works,you should verify these citationsas Idon't have directaccess to current databases and could be misremembering details.

Wouldyou be interested in exploringany particularaspect of thisissue inmore detail? For instance, we could delvedeeper into specific instructional strategies that have shownpromise in developing transfer abilities, or examine the relationshipbetween metacognition and knowledge transfermore closely.

Permalink |記事への反応(2) | 17:03

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■日本のマスコミが伝えない

➤「救いようが最もうない、極東の自称他称の〝ジャーナリスト〟や〝学者〟、〝知米派〟は読まなくても構い。読んでもその肥大化した自己承認欲求、度し難い自己愛性人格では読むことはできないし、理解もできない。ただ見下した目線で見やるだけで、そこに自分が利用できる素材がなければ鼻で嗤うだけだ。この文章は、我がU.S.を領りたい、領ろうと下向きの反省的眼光で欲する諸君に読んで貰いたい。

I Traveled to 46States inAmerica ThisSummer.Here’sWhyTrump Won.

By Frank S. Zhou

By Ezekiel A. Wells

Ezekiel A. Wells ’27is aDouble Concentrator in Environment Science &amp; Engineering and Economics in EliotHouse. He spentlastsummertraveling to 46Americanstates conductinginterviews forhisYouTube series, “CrossroadsAmerica.”

3days ago

A week after Democrats’ election loss, accusations have flown in every direction within their Party. Some faultPresidentJoe Biden for an egoistic refusal todropout of therace earlier. Someblame the Harris campaign for failing to servekey demographics and communicate a clearvision for the country. And someblameAmericans, claiming that racism and sexism drove voters towardTrump.

These factors certainly exist, but we’remissing a largerpiece of the picture.

Over thesummer, I traveled to 46states in the U.S., creating aYouTube series highlighting slices oflife across the country. In conversations from my nearly three-monthroad trip, I spoke with Republicanswho were certain that inflationis entirely Biden’s fault and Democratswho, despite their frustrations with corporate profits and desires foruniversal healthcare, hoped for amore moderate candidate. From supporters ofall candidates, I heard a shockingamount of misinformation.

After combing through hundreds of hours ofinterview footage fromswingstateTrump voters, Iam certain that,as muchas other factors influenced theoutcome of the election, our crumbling media landscape — whichhas caused arift in our democracy —ismost toblame.

In the postwar period,newswas dominatedby threemain channels, and because ofthe FairnessDoctrine, eachstation reported the same stories and covered multiple sides of eachissue.Viewers picked which channels they watchedmainly basedon their preferences fornewsanchors’ personalities. Of course, thismodel hadits problems, but,at the end of the day,it meant thatAmericans worked with a sharedset of facts.

A sharedset of factsis notthe world welive intoday.

Throughout myinterviews, conspiracy theories were rampant, andonat leastfive separate occasions across separatestates, Iwas told that Bill Gates tampers with ourfood, adding plastic to our fruits and vegetables tomakehis medical investmentsmore profitable.

In conversations with voters, neither side seemed able tonamemany specific policyissues they cared about; everyone just seemed to repeat the words of theirfavorite pundits,podcast hosts, andinternet personalities.

Although this behavioris harmful, Idon’tblame everydayAmericans.Blame fallson the media thathas ostracized, disillusioned, and misinformed them.

Advertisement

I firstsaw this trend withlow-wage workers in West Virginia,who — despitefalling inflationrates — have seen stagnated salaries and clear increases infood costs. ChannelslikeFoxNews bred anger and resentment formany of them.

TakeJuly of thissummer, for example, when for the firsttime inhis presidency, prices actually fell under the Biden administration. Traditional media establishments rushed tocelebrate thisvictory, withone article fromCNN declaring, “The White House can finallycrossout ‘inflation’onits list ofpresidential liabilities.” However,outside these bubbles, I observedmanyAmericans held a differentview.

In lateJuly, Iwas welcomedat a massive familyreunion in Tylertown, Mississippi, whereoneTrump voter — a middle-aged, Black, familyman, pastor, andsoulfood enthusiast —made this clear.

“When Igo in the grocery stores, and Igotta spend mylast toget groceries,you mean totell me I’m notgonnalook and seewho’sgonna vote to help me? I voted forTrump and I’d vote forhimagain, because he put money in ourpocket,” he told me.

In their coverage,mainstreamnews organizations obsessover the Federal Reserve’snextrate cuts while failing toconnect with people concernedwith theirnext meals.With titleslike “Vance:YoungAmericans ‘Are Becoming Paupers’ Due To Inflation, High Housing Costs,” siteslike The DailyWire had their fingerson the pulse ofAmerican sentiment, welcoming new readership from thosewhofelt neglectedby traditional media.

This problemwas not just confined to the economy. While Biden’s mentalstatewas deteriorating,liberal mediaoutlets seemed to under-cover these stories, shelteringhim from scrutiny ofhis declining capabilities, until theinfamouspresidential debate.

Formerly trusted networksslowlymade themselves indigestible to the polarizedAmerican public, and in 2024, for the third year in a row, a Gallup pollfound thatmoreAmericans indicated having “no trust” in the media than thosewho trustit a “great deal/fairamount”.

So wheredoes the averageAmerican turn when thenation’s media cannot be trusted? Formany people,itwasYouTubetalk shows,Newsmax, andpodcasters suchasJoe Rogan.

While Democrats seek toblame various internal factors for this election’s loss, I cannot help but think ofJoe Rogan’sTrump endorsement, themany blind lies I heard from ordinary citizens across the country, and declining trust inAmerican journalism.

AsHarvard students and members of highereducation institutions, we have a part to play in the problem.AtHarvard’s Institute ofPolitics, thosewho denied the 2020 election results have been precluded from speakingat theJFKJr. Forum. While thegoalis understandable,it shields students from understanding theAmericanviewpoints they represent.

Advertisement

Thereis adifferencebetween platforming intentional andmanipulative misinformation and listening to and learning about where people are and what theybelieve.

As a pipeline tomainstream media,Harvard, andits future journalists, have to consider the audience they lose when theystay inside of their bubble and ignore theissues of everydayAmericans.

The disappearance of factual importance in our worldis alarming and dangerous, but if we,asaspiring journalists, politicians, andengaged citizens, want to betaken seriously in communicatingTrump’s threat to democracy, inflationary tariff policies, and soon, we oweAmerican voters that seriousness, too.

Ezekiel A. Wells ’27is aDouble Concentrator in Environment Science &amp; Engineering and Economics in EliotHouse. He spentlastsummertraveling to 46Americanstates conductinginterviews forhisYouTube series, “CrossroadsAmerica."

Permalink |記事への反応(1) | 16:45

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■anond:20241115130913

世界一かはともかくガチやん

Allow your postsas wellas your interactions, inputs, and results with Grok to be used fortraining andfine-tuning

てーことは鍵アカならここの設定がどうなってようが投稿は使いませんよって事やね

making your account private prevents your posts frombeing used totrain Grok’s underlyingmodel or to generate responses touser queries.

まさにプライベートモードか

Permalink |記事への反応(0) | 13:25

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■anond:20240730111317

そう思いますね。嘘が常態化している自己愛性パーソナリティ障害の人の手助けは、プロにしかできない

そもそも直球で『テロリスト』とか『引きこもり』とかの背景要因になり得るとかそういう視点の研究がバンバン出てくるので

病識があるなら治療・改善にあたった方が良いと思う

Autism ＆ Narcissism: The Connection ＆ Differences

https://www.elemy.com/studio/mood-disorders/autism-and-narcissism/

MassViolence in Individuals With AutismSpectrum Disorder and Narcissistic Personality Disorder: ACase Analysis of Anders Breivik Using the ‘Path to Intended and TerroristicViolence’Model. (2016). University of Salford Manchester.

http://usir.salford.ac.uk/id/eprint/40449/1/Faccini%20%26%20Allely%20%282016%29.%20.pdf

正直者は損をするとかいうけれど、やはり嘘を付かない誠実さは美徳というか、集団生活を送る上での最低限の条件に思う

人生を困難にするのは空気を読まずバカ正直に受け答えをすることではなく、

悪気があろうがなかろうが嘘(能力の問題で結果的に嘘になるも含む)をつく事だぞ

Permalink |記事への反応(0) | 12:02

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■いいえ、嘘つきには嘘つきと言います。視界に入ったらね

自己愛性パーソナリティ障害の人の手助けは、プロにしかできないが、

いくらなんでも、お前は他人に何よりも自分自身に嘘をつきすぎだと自覚させることくらいはできるでしょ

そもそも直球で『テロリスト』とか『引きこもり』とかの背景要因になり得るとかそういう視点の研究がバンバン出てくるので

病識があるなら治療・改善にあたった方が良いと思う

Autism ＆ Narcissism: The Connection ＆ Differences

https://www.elemy.com/studio/mood-disorders/autism-and-narcissism/

MassViolence in Individuals With AutismSpectrum Disorder and Narcissistic Personality Disorder: ACase Analysis of Anders Breivik Using the ‘Path to Intended and TerroristicViolence’Model. (2016). University of Salford Manchester.

http://usir.salford.ac.uk/id/eprint/40449/1/Faccini%20%26%20Allely%20%282016%29.%20.pdf

正直者は損をするとかいうけれど、やはり嘘を付かない誠実さは美徳というか、集団生活を送る上での最低限の条件に思う

人生を困難にするのは空気を読まずバカ正直に受け答えをすることではなく、

悪気があろうがなかろうが嘘(能力の問題で結果的に嘘になるも含む)をつく事だぞ

Permalink |記事への反応(1) | 18:45

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■Perplexityのテスト:アルゴリズムの提案

質問

コンテンツ集合Xがあります。各々の要素x∈ Xは特徴量f_1(x)∈Y_1 を持っています。

同様にユーザーu ∈ Uが存在し、特徴量f_2(u)∈Y_2を持っています。

コンテンツには単価P(x)が存在し、ユーザーuがコンテンツをm回消費することをg(u, x, m)=m*P(x)_uで表し、全てのユーザーU, 全てのコンテンツXに対して行列M∘P = g(U, X, M) が存在します。

目的関数M∘Pを最大化したいのですが、どんなアルゴリズムが考えられるでしょうか。

回答

この問題は、コンテンツとユーザーの特徴量を考慮して、ユーザーがコンテンツを消費する回数を最大化するものです。このような問題は、推薦システムの分野で広く研究されており、協調フィルタリングと呼ばれるアプローチが一般的に使われています。

協調フィルタリングアルゴリズム

協調フィルタリングには、以下のようなアルゴリズムが考えられます。

1.行列因子分解 (Matrix Factorization)

行列因子分解は、ユーザー・コンテンツ行列を低ランク近似することで、ユーザーとコンテンツの潜在的な特徴を抽出します。具体的には、ユーザー行列Uとコンテンツ行列Vを求め、その内積U^TVで元の行列を近似します。この手法は、Netflix Prizeで優れた成績を収めました。[1]

2.ニューラルコラボレーティブフィルタリング (Neural Collaborative Filtering)

ニューラルネットワークを用いて、ユーザーとコンテンツの非線形な関係を学習します。入力としてユーザーIDとコンテンツIDを与え、出力として評価値を予測します。この手法は、従来の行列因子分解よりも高い精度が期待できます。[2]

3.階層的ベイズモデル (Hierarchical BayesianModel)

ユーザーとコンテンツの特徴量を階層ベイズモデルに組み込むことで、より柔軟なモデリングが可能になります。この手法は、Cold-Start問題(新規ユーザー・コンテンツの推薦が困難)の解決に有効です。[3]

4.強化学習 (Reinforcement Learning)

ユーザーの行動履歴を環境として捉え、報酬最大化のためのアクションを学習します。この手法は、長期的な利益最大化に適しています。[4]

これらのアルゴリズムは、ユーザーとコンテンツの特徴量を考慮しながら、目的関数を最大化するように設計されています。実装の際は、データセットの特性やモデルの複雑さ、計算リソースなどを考慮する必要があります。[5]

Citations:

[1]https://aicontentfy.com/en/blog/understanding-algorithms-behind-content-distribution

[2]https://marketbrew.ai/optimizing-your-websites-content-with-genetic-programming

[3]https://sproutsocial.com/insights/social-media-algorithms/

[4]https://surferseo.com/blog/the-new-algorithm-for-content-analysis/

[5]https://www.linkedin.com/advice/0/what-best-ways-measure-content-relevance-x6apf

Permalink |記事への反応(0) | 12:51

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■diffusers`ライブラリは存在しないため、代わりに `torch`

この手順は、Latent DiffusionModelsを使用してテキストから画像を生成するための一般的なアプローチを示していますが、いくつかの誤りや欠落がある可能性があります。以下にいくつかの修正と補足を示します。

1. **ライブラリのインポート**: `diffusers`ライブラリは存在しないため、代わりに `torch`、`transformers`、および `diffusion`ライブラリを使用する必要があります。

```python

import torch

fromtransformers import AutoModelForSeq2SeqLM, AutoTokenizer

from diffusion import LatentDiffusion

```

2. **環境のセットアップ**: 事前学習済みモデルとトークナイザーを使用する前に、必要なモデルとトークナイザーをダウンロードする必要があります。

```python

model = AutoModelForSeq2SeqLM.from_pretrained("nlptown/bert-base-multilingual-uncased-finetuned-xnli")

tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-finetuned-xnli")

```

3. **テキストプロンプトの前処理**: `encode_plus`メソッドを使用して、入力をトークン化し、テンソルに変換します。

```python

inputs = tokenizer.encode_plus(prompt, return_tensors="pt")

```

4. **Latent Diffusionモデルの定義**: `diffusion`ライブラリから `LatentDiffusion` をインスタンス化する際に、モデルとトークナイザーを渡します。

```python

ldm = LatentDiffusion(model=model, tokenizer=tokenizer)

```

5. **画像の生成**: `generate`メソッドを使用して画像を生成します。

```python

image = ldm.generate(inputs)

```

6. **生成された画像の可視化**:画像を表示するために適切なライブラリを使用します。例えば、Matplotlibを使用して画像を表示できます。

```python

import matplotlib.pyplotas plt

plt.imshow(image)

plt.axis("off")

plt.show()

```

これらの修正と補足を加えることで、より正確かつ実用的なコードが得られるはずです。

Permalink |記事への反応(0) | 07:12

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■anond:20240406063728

この辺に正解なんてないよ

書くべきことは書かなくちゃいけないが、それをどこに閉じ込めるかってだけの話だから

作りながら変えていって全然問題ない話

一箇所の「View」でしか処理されないなら、どんなに「Model」の処理に思えても「View」に書くべきだと思う

その変更によって、影響範囲がどうなるのか？

テストしなくちゃいけない処理はどこまでになる？

みたいなことを考えずに、原理主義的にやると

どうでもいいチンケな修正でModelが更新されて、全部を再テストなんてな馬鹿な話になる

メンテナンス性を考えて導入したはずのMVCで、逆にメンテナンス性を悪くするのは本末転倒だ

Permalink |記事への反応(0) | 06:54

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■NavigatingSimulink Complexity: MyJourney with MatlabAssignmentExperts.com!

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Permalink |記事への反応(0) | 16:38

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