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はてなキーワード: strategyとは
繰り返し型最後通牒ゲーム（Repeated Ultimatum Game）を数理的に徹底分析する一例を示す。まず、一回限りの場合の均衡（subgame perfect equilibrium, SPE）を整理し、その後に有限回・無限回の繰り返しゲームとしての分析および補助的な進化ゲーム理論的考察を行う。
最後通牒ゲームは、以下のような二人ゲームとして定式化される。プレイヤーは提案者（プレイヤーA）と応答者（プレイヤーB）の二名である。資源の総額を1（または1000円など）とする。提案者は、自己の取り分 x（0 ≦ x ≦ 1）と応答者の取り分 1 - x を提示する。応答者は、この提案を受け入れるか拒否するかを選び、受け入れればそれぞれ提示額が得られ、拒否すれば双方が0の利得となる。
このゲームを「繰り返し型」とする場合、ゲームが複数回（有限または無限に）同じ形で行われ、参加者は各回の結果に応じた戦略（または将来の罰則を考慮した戦略）を採ると仮定する。
純粋戦略に基づく分析では、応答者が提案された 1 - x を受け入れるかどうかを考える。利得が正ならば受容すべきであり、これを前提とすると、提案者は自分の取り分を最大化するため、限りなく x = 1 に近い値を設定し、応答者には最小の正の額 ε を渡すことになる。したがって、部分ゲーム完全均衡（SPE）は「提案者が最小限の額を応答者に提案し、応答者はそれを受け入れる」形となる。すなわち、理論上は提案者がほぼ全額を得る均衡となる。
しかし実験では、提案額は概ね30～50%付近となり、応答者は自分の取り分が30%以下の場合に拒否する傾向が見られる。この傾向は「公平性」や「不平等嫌悪」といった心理的要因（Fehr–Schmidtモデル、Bolton–Ockenfelsモデルなど）で説明されることが多いが、ここでは理論モデルの基本形として扱う。
ゲームが T 回（有限回）繰り返される場合、逆順帰納法により各回の均衡を求めることができる。最終回（第 T 回）は一回限りのゲームと同じであり、提案者は最小額を提示し、応答者は受容する均衡が成立する。第 T-1 回以降についても、最終回の均衡が既知であるため、もし応答者が拒否することで最終回に不利益を被ると予想されれば、提案者は同様に自分の取り分を最大化する戦略を採用することになる。結果として、全期間において逆順帰納法により唯一のSPEは、各回で「最小限の提案」および「受容」になる。しかしこの結論は「有限回かつ共通認識の完全合理性」の下での結果であり、実際の人間行動や信頼関係、将来の相互報復（パニッシュメント）などを考慮すると、均衡が変化する可能性がある。
無限回繰り返しゲーム（もしくは有限回で終了時期が不明な場合）では、参加者は将来の報酬を割引因子 δ（0 < δ < 1）で評価する。この場合、例えばトリガー戦略を採用すると、初回は提案者が公正な分配（例えば50:50）を提示し、応答者も受容する。もしある回で応答者が提案を拒否した場合、その後は常に応答者が0となる（または極めて低い利得になる）戦略に切り替える。
R / (1 - δ) ≧ （ある回で裏切って得られる一時的な増分） + δ P / (1 - δ)
ここで R は公正な分配時の利得、P は裏切りが起こった場合の（ペナルティとして）得られる利得である。十分高い割引因子（将来の利得に重みを置く）であれば、長期的に見ると協力状態（公正な分配）を維持する方が双方に有利となり得る。
無限回繰り返しゲームでは、フォーク定理により、プレイヤー間の報復や相互信頼を用いたさまざまな均衡が実現可能である。例えば、提案者と応答者が互いに「公平な分配」を維持する戦略が、十分な将来重視（δが大きい）条件下で均衡となる可能性がある。
実験結果で見られる「公正な提案」や「低すぎる提案の拒否」は、伝統的な合理性だけでは説明が難しいため、進化ゲーム理論の枠組みが有用である。多数のプレイヤーが繰り返し対戦する環境では、各プレイヤーの行動がその後の成功率に影響し、公平な戦略が進化的に安定（ESS: Evolutionarily Stable Strategy）となる場合がある。
また、レプリケーターダイナミクスを考えると、社会全体における各戦略の割合 q_i は、各戦略の平均利得と社会全体の平均利得との差に応じて変化する。式としては dq_i/dt = (π(s_i) - π̄) q_i となり、この力学の下では、公平な提案と拒否が進化的に安定な均衡となるシナリオが示される。RIETIなどの研究では、最後通牒ゲームに基づく数理モデルとシミュレーションを通して、提案者と応答者間の選別的取引や、社会的知性の役割が平均利得や格差にどのように影響するかが検討されている。
繰り返し型最後通牒ゲームの数理的分析から、以下の点が示唆される。有限回の場合、共通認識の完全合理性の下では、逆順帰納法により各回で「最小限の提案／受容」が唯一のSPEとなる。しかし実験的にはこれと大きく異なる（公正な提案が多い）結果が得られており、心理的要因や公平性の感情を反映した行動が考慮されるべきである。無限回の場合、フォーク定理により、公正な分配を支える戦略が実現可能であり、特に割引因子が高い場合には、公正な均衡が安定する。
Please consider the histroy of humanity from biological perpsecitve and current known fact in nearby universe, which expo-planet (from the Sol system perspective) is most likely to have civlizaed society? Please add percentage next to it in the chart
ChatGPT
Reasoned about exoplanet civilization likelihoodを28 秒行う
Evaluating planetary prospects
OK, I’m looking at the likelihood of a civilized society on an exoplanet within our Solar System. I’ll consider biological history and current space knowledge, aiming to identify the most promising candidate.
Identifying potential candidates
I'm considering exoplanets like Proxima Centauri b and TRAPPIST-1e, which might suggest they are within or observed from our Solar System.
Assessing potential candidates
I’m evaluating Proxima Centauri b, Barnard's Star, LHS 1140, and TRAPPIST-1, focusing on their habitable zones and available data. The Drake equation might help estimate their civilized society likelihoods.
Identifying promising exoplanets
I’m noting key exoplanets like Proxima Centauri b and TRAPPIST-1, which have Earth-like characteristics and are in habitable zones. This narrows down the potential candidates for advanced civilizations.
I'm gathering info on planets like Proxima Centauri b and TRAPPIST-1, considering factors like magnetic fields, atmospheres, and proximity to G-type stars for their potential to support advanced civilizations.
Evaluating exoplanet prospects
I'm noting that Proxima Centauri b, despite being close and around a red dwarf star, might support life if it has a magnetic field and atmosphere. This makes me think about its potential.
Charting probabilities
I'm estimating civilization probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e, using speculative percentages based on the Drake equation and the lack of evidence for intelligent life.
I'm crafting a chart with exoplanet names and civilization likelihood percentages, grounded in the long timeline of life's evolution, despite the speculative nature of these figures.
Identifying exoplanet contenders
I’m evaluating exoplanets like Proxima Centauri b, TRAPPIST-1 e, and Kepler-186f, considering their star types and distances, to gauge their potential for supporting advanced civilizations from our solar system's viewpoint.
Weighing proximity and observation
I'm leaning towards Proxima Centauri b due to its closeness and observational potential, despite concerns about its flare star dynamics. TRAPPIST-1 e, f, or g are also worth considering.
I'm focusing on exoplanets like Proxima Centauri b, Ross 128 b, LHS 1140 b, TRAPPIST-1 e/f/g, and K2-18b, aiming to chart their likelihood of hosting a civilized society based on current knowledge.
Assessing exoplanet potentials
I'm gathering speculative probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e/f/g, using the Drake equation and current understanding, to better estimate their chances of hosting a civilized society.
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 civilized society, considering Earth's long biological history.
Refining odds
I'm considering the astronomical timeline for intelligent life on Earth, leading me to assign even lower speculative odds for exoplanets like Proxima Centauri b and TRAPPIST-1 e.
I’m crafting a simplified chart with arbitrary percentages, clearly noting the speculative nature and lack of scientific data, while considering the target audience's basic understanding of the topic.
I’m charting arbitrary percentages for exoplanets, clarifying their speculative nature, and ensuring the data is accessible for young audiences with minimal scientific detail.
Crafting a chart
I’m creating a markdown table with speculative percentages for the likelihood of civilized life on exoplanets, emphasizing their purely theoretical nature and considering the understanding level of an 8-year-old.
Integrating speculative analysis
I’m mapping out a markdown chart with speculative percentages for the likelihood of a civilized society on exoplanets, mindful of the complexity and simplifying for an 8-year-old's understanding.
I’m piecing together markdown for exoplanets, noting Proxima Centauri b's reduced civilized life chance due to the red dwarf's activity. This inclusion highlights the speculative nature of the analysis.
Piecing together speculative odds
I’m detailing a markdown chart for audience-friendly exoplanet likelihoods, considering environmental challenges and adding a disclaimer about the speculative nature and lack of evidence.
Piecing together
I’m leaning towards Proxima Centauri b for its proximity and silent star, but I’ll add a speculative chart ranking nearby exoplanets, noting their advanced civilizations' hypothetical likelihood.
Assessing civilization probabilities
I'm emphasizing the slow emergence of intelligent life on Earth, which suggests that the odds of any habitable planet developing a complex civilization might be as low as a few percent.
Below is a very speculative chart that—based on what we know about Earth’s long, winding path from simple life to a “civilized society” and our limited observations of nearby exoplanets—gives some fun, made‑up percentage chances for a few of our best candidate exoplanets. Remember: Scientists have not yet found any proof of life (let alone advanced civilizations) on any exoplanet. These numbers are just playful estimates to help you think about the ideas!
Speculative Chances of Finding an Advanced Civilization on Nearby Exoplanets
Exoplanet Estimated Likelihood of a Civilized Society
Proxima Centauri b ~2%
LHS 1140 b ~0.5%
How Did We (Very Roughly) Guess These Numbers?
Earth’s Example:
On Earth, life began as simple single‑celled organisms and took billions of years to evolve into a species that built complex societies. This long journey suggests that—even if a planet is in the “habitable zone” (where liquid water could exist)—developing an advanced, civilized society might be rare.
Proxima Centauri b:
This is the closest exoplanet to us. Even though its star (Proxima Centauri) is a red dwarf that sometimes sends out strong flares (which might harm life), its closeness makes it one of our most interesting candidates. That’s why we give it a slightly higher guess (~2%).
These planets are part of a group orbiting a very cool star. Their environments might be challenging (for example, they could be tidally locked, meaning one side always faces the star), so the chance of a complex, advanced civilization might be a bit lower.
LHS 1140 b:
This planet is promising in some ways but is a little farther away and around a dimmer star, so our playful estimate is even lower.
All of these percentages are extremely rough guesses based on what little we understand about the conditions for life and intelligence. Scientists use ideas like the Drake Equation to think about these chances—but until we have concrete evidence, these numbers remain just for fun and imagination.
Enjoy exploring these ideas—and remember, space is full of mysteries waiting to be discovered!
あなた:
Please examine the Drake Equation itself and see if this euqation is acculate or more opition in the equation you will use
ChatGPT
Reasoned about the Drake Equationを6 秒行う
re-examining the Drake Equation
evaluating the Drake Equation's value
OK, let me see. The Drake Equation isn't a precise formula; it's more of a guiding heuristic that tackles numerous uncertainties regarding extraterrestrial civilizations.
Thinking through the Drake Equation, I’m evaluating its conceptual nature, its uncertain parameters, and its criticism to conclude on its reliability as an accurate predictive model.
Understanding the Drake Equation
I’m mapping out the Drake Equation as a conceptual guide, detailing its parameters, which are often based on estimates or opinions, to evaluate its reliability for predicting extraterrestrial civilizations.
The Drake Equation is a famous formula that was created to help scientists think about the many factors involved in estimating the number of detectable extraterrestrial civilizations in our galaxy. However, when we look at the equation closely, we see that it is more of a conceptual framework or heuristic tool than a precise, predictive formula. Let’s break down why:
The equation is usually written as:
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 average rate of star formation per year in our galaxy.
𝑓
𝑝
f
p
: The fraction of those stars that have planetary systems.
𝑛
𝑒
n
e
: The average number of planets that could potentially support life per star with planets.
𝑓
𝑙
f
l
: The fraction of those planets where life actually develops.
𝑓
𝑖
f
i
: The fraction of planets with life where intelligent life evolves.
𝑓
𝑐
f
c
: The fraction of civilizations that develop technology that releases detectable signals into space.
𝐿
L: The length of time such civilizations release detectable signals.
Why the Drake Equation Is More of a Guideline Than a Precise Formula
Uncertain Inputs:
𝑅
∗
R
∗
𝑓
𝑝
f
p
(fraction of stars with planets), have become better known through modern astronomy.
The parameters related to the development of life and intelligence—
𝑓
𝑙
f
l
,
𝑓
𝑖
f
i
,
𝑓
𝑐
f
c
, and
𝐿
L—are based on very limited data. For example, Earth is the only planet we know where life has developed, so we have no way to compare or derive a statistically robust estimate for these factors. This means that the values chosen for these parameters are largely subjective estimates or educated guesses.
Wide Ranges of Possible Values:
Because many of these factors are not well-constrained, if you change one of the more uncertain parameters even slightly, the final estimate for
𝑁
N (the number of civilizations) can vary by many orders of magnitude. This wide range shows that the equation can lead to very different outcomes based on personal or theoretical assumptions.
The Drake Equation was never meant to provide a precise answer. Instead, it serves as a way to organize our thinking about what conditions might be necessary for the development of extraterrestrial civilizations. It encourages discussion about the relative importance of different factors and highlights where our scientific knowledge is limited.
Is the Drake Equation Accurate or More Opinion-Based?
Accurate?
In the sense of being a strict mathematical model with well-defined inputs and predictable outputs, the Drake Equation is not accurate. Its accuracy is limited by the extreme uncertainties in several of its key parameters.
Many of the terms in the equation, especially those concerning the evolution of life and intelligence, are based on opinion, interpretation, and the single example of Earth. Therefore, while the structure of the equation is logical, the numbers you put into it (and hence the final result) are often more reflective of our current opinions and assumptions than of robust empirical data.
The Drake Equation is a valuable tool for framing the question of extraterrestrial life. It helps scientists and enthusiasts break down a complex problem into smaller, more manageable questions. However, because several of its factors are highly uncertain and subject to personal interpretation, it is more of a conceptual guide—or even a way to stimulate discussion—than a formula that provides a definitive, accurate prediction of the number of extraterrestrial civilizations.
In summary, while the Drake Equation is useful for thinking about the various factors that might lead to the emergence of a civilization, its outputs should be taken as highly speculative estimates rather than precise measurements.
あなた:
Please also consider the possiblities of non-human intelligent emrgencing from the long history of Sol system and nearby planets and see if there is any possiblites that in one time intelligent life emerged in nearby planets and see this to the equation
ChatGPT
Reasoned about non-human intelligence and the Drake Equationを19 秒行う
Evaluating non-human intelligence
I'm thinking about the potential for non-human intelligent life forms in the Sol system and neighboring planets, and how this might factor into the equation.
Piecing together possibilities
I'm thinking about how to integrate the emergence of non-human intelligent life, possibly on Mars or Europa, into the Drake Equation for the Sol system and neighboring planets.
It's interesting to think about the hypothesis of intelligent life emerging on Mars or Venus, though Earth remains the only known life-supporting planet in the solar system.
Considering other realms
I'm examining if intelligent life could have emerged on planets like Mars, Venus, or Europa, and how this possibility aligns with the predictions of the Drake Equation.
Hmm, I’m thinking about how non-human intelligent life on Mars or Venus could intersect with the assumptions of the Drake Equation, which seems to focus solely on Earth-based evolution.
I’m piecing together the hypothesis of non-human, advanced, intelligent life in the solar system. Mars, Venus, and Titan present challenges, but Europa and Enceladus might harbor microbial life.
Please consider the histroy of humanity from biological perpsecitve and current known fact in nearby universe, which expo-planet (from the Sol system perspective) is most likely to have civlizaed society? Please add percentage next to it in the chart
ChatGPT
Reasoned about exoplanet civilization likelihoodを28 秒行う
Evaluating planetary prospects
OK, I’m looking at the likelihood of a civilized society on an exoplanet within our Solar System. I’ll consider biological history and current space knowledge, aiming to identify the most promising candidate.
Identifying potential candidates
I'm considering exoplanets like Proxima Centauri b and TRAPPIST-1e, which might suggest they are within or observed from our Solar System.
Assessing potential candidates
I’m evaluating Proxima Centauri b, Barnard's Star, LHS 1140, and TRAPPIST-1, focusing on their habitable zones and available data. The Drake equation might help estimate their civilized society likelihoods.
Identifying promising exoplanets
I’m noting key exoplanets like Proxima Centauri b and TRAPPIST-1, which have Earth-like characteristics and are in habitable zones. This narrows down the potential candidates for advanced civilizations.
I'm gathering info on planets like Proxima Centauri b and TRAPPIST-1, considering factors like magnetic fields, atmospheres, and proximity to G-type stars for their potential to support advanced civilizations.
Evaluating exoplanet prospects
I'm noting that Proxima Centauri b, despite being close and around a red dwarf star, might support life if it has a magnetic field and atmosphere. This makes me think about its potential.
Charting probabilities
I'm estimating civilization probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e, using speculative percentages based on the Drake equation and the lack of evidence for intelligent life.
I'm crafting a chart with exoplanet names and civilization likelihood percentages, grounded in the long timeline of life's evolution, despite the speculative nature of these figures.
Identifying exoplanet contenders
I’m evaluating exoplanets like Proxima Centauri b, TRAPPIST-1 e, and Kepler-186f, considering their star types and distances, to gauge their potential for supporting advanced civilizations from our solar system's viewpoint.
Weighing proximity and observation
I'm leaning towards Proxima Centauri b due to its closeness and observational potential, despite concerns about its flare star dynamics. TRAPPIST-1 e, f, or g are also worth considering.
I'm focusing on exoplanets like Proxima Centauri b, Ross 128 b, LHS 1140 b, TRAPPIST-1 e/f/g, and K2-18b, aiming to chart their likelihood of hosting a civilized society based on current knowledge.
Assessing exoplanet potentials
I'm gathering speculative probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e/f/g, using the Drake equation and current understanding, to better estimate their chances of hosting a civilized society.
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 civilized society, considering Earth's long biological history.
Refining odds
I'm considering the astronomical timeline for intelligent life on Earth, leading me to assign even lower speculative odds for exoplanets like Proxima Centauri b and TRAPPIST-1 e.
I’m crafting a simplified chart with arbitrary percentages, clearly noting the speculative nature and lack of scientific data, while considering the target audience's basic understanding of the topic.
I’m charting arbitrary percentages for exoplanets, clarifying their speculative nature, and ensuring the data is accessible for young audiences with minimal scientific detail.
Crafting a chart
I’m creating a markdown table with speculative percentages for the likelihood of civilized life on exoplanets, emphasizing their purely theoretical nature and considering the understanding level of an 8-year-old.
Integrating speculative analysis
I’m mapping out a markdown chart with speculative percentages for the likelihood of a civilized society on exoplanets, mindful of the complexity and simplifying for an 8-year-old's understanding.
I’m piecing together markdown for exoplanets, noting Proxima Centauri b's reduced civilized life chance due to the red dwarf's activity. This inclusion highlights the speculative nature of the analysis.
Piecing together speculative odds
I’m detailing a markdown chart for audience-friendly exoplanet likelihoods, considering environmental challenges and adding a disclaimer about the speculative nature and lack of evidence.
Piecing together
I’m leaning towards Proxima Centauri b for its proximity and silent star, but I’ll add a speculative chart ranking nearby exoplanets, noting their advanced civilizations' hypothetical likelihood.
Assessing civilization probabilities
I'm emphasizing the slow emergence of intelligent life on Earth, which suggests that the odds of any habitable planet developing a complex civilization might be as low as a few percent.
Below is a very speculative chart that—based on what we know about Earth’s long, winding path from simple life to a “civilized society” and our limited observations of nearby exoplanets—gives some fun, made‑up percentage chances for a few of our best candidate exoplanets. Remember: Scientists have not yet found any proof of life (let alone advanced civilizations) on any exoplanet. These numbers are just playful estimates to help you think about the ideas!
Speculative Chances of Finding an Advanced Civilization on Nearby Exoplanets
Exoplanet Estimated Likelihood of a Civilized Society
Proxima Centauri b ~2%
LHS 1140 b ~0.5%
How Did We (Very Roughly) Guess These Numbers?
Earth’s Example:
On Earth, life began as simple single‑celled organisms and took billions of years to evolve into a species that built complex societies. This long journey suggests that—even if a planet is in the “habitable zone” (where liquid water could exist)—developing an advanced, civilized society might be rare.
Proxima Centauri b:
This is the closest exoplanet to us. Even though its star (Proxima Centauri) is a red dwarf that sometimes sends out strong flares (which might harm life), its closeness makes it one of our most interesting candidates. That’s why we give it a slightly higher guess (~2%).
These planets are part of a group orbiting a very cool star. Their environments might be challenging (for example, they could be tidally locked, meaning one side always faces the star), so the chance of a complex, advanced civilization might be a bit lower.
LHS 1140 b:
This planet is promising in some ways but is a little farther away and around a dimmer star, so our playful estimate is even lower.
All of these percentages are extremely rough guesses based on what little we understand about the conditions for life and intelligence. Scientists use ideas like the Drake Equation to think about these chances—but until we have concrete evidence, these numbers remain just for fun and imagination.
Enjoy exploring these ideas—and remember, space is full of mysteries waiting to be discovered!
あなた:
Please examine the Drake Equation itself and see if this euqation is acculate or more opition in the equation you will use
ChatGPT
Reasoned about the Drake Equationを6 秒行う
re-examining the Drake Equation
evaluating the Drake Equation's value
OK, let me see. The Drake Equation isn't a precise formula; it's more of a guiding heuristic that tackles numerous uncertainties regarding extraterrestrial civilizations.
Thinking through the Drake Equation, I’m evaluating its conceptual nature, its uncertain parameters, and its criticism to conclude on its reliability as an accurate predictive model.
Understanding the Drake Equation
I’m mapping out the Drake Equation as a conceptual guide, detailing its parameters, which are often based on estimates or opinions, to evaluate its reliability for predicting extraterrestrial civilizations.
The Drake Equation is a famous formula that was created to help scientists think about the many factors involved in estimating the number of detectable extraterrestrial civilizations in our galaxy. However, when we look at the equation closely, we see that it is more of a conceptual framework or heuristic tool than a precise, predictive formula. Let’s break down why:
The equation is usually written as:
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 average rate of star formation per year in our galaxy.
𝑓
𝑝
f
p
: The fraction of those stars that have planetary systems.
𝑛
𝑒
n
e
: The average number of planets that could potentially support life per star with planets.
𝑓
𝑙
f
l
: The fraction of those planets where life actually develops.
𝑓
𝑖
f
i
: The fraction of planets with life where intelligent life evolves.
𝑓
𝑐
f
c
: The fraction of civilizations that develop technology that releases detectable signals into space.
𝐿
L: The length of time such civilizations release detectable signals.
Why the Drake Equation Is More of a Guideline Than a Precise Formula
Uncertain Inputs:
𝑅
∗
R
∗
𝑓
𝑝
f
p
(fraction of stars with planets), have become better known through modern astronomy.
The parameters related to the development of life and intelligence—
𝑓
𝑙
f
l
,
𝑓
𝑖
f
i
,
𝑓
𝑐
f
c
, and
𝐿
L—are based on very limited data. For example, Earth is the only planet we know where life has developed, so we have no way to compare or derive a statistically robust estimate for these factors. This means that the values chosen for these parameters are largely subjective estimates or educated guesses.
Wide Ranges of Possible Values:
Because many of these factors are not well-constrained, if you change one of the more uncertain parameters even slightly, the final estimate for
𝑁
N (the number of civilizations) can vary by many orders of magnitude. This wide range shows that the equation can lead to very different outcomes based on personal or theoretical assumptions.
The Drake Equation was never meant to provide a precise answer. Instead, it serves as a way to organize our thinking about what conditions might be necessary for the development of extraterrestrial civilizations. It encourages discussion about the relative importance of different factors and highlights where our scientific knowledge is limited.
Is the Drake Equation Accurate or More Opinion-Based?
Accurate?
In the sense of being a strict mathematical model with well-defined inputs and predictable outputs, the Drake Equation is not accurate. Its accuracy is limited by the extreme uncertainties in several of its key parameters.
Many of the terms in the equation, especially those concerning the evolution of life and intelligence, are based on opinion, interpretation, and the single example of Earth. Therefore, while the structure of the equation is logical, the numbers you put into it (and hence the final result) are often more reflective of our current opinions and assumptions than of robust empirical data.
The Drake Equation is a valuable tool for framing the question of extraterrestrial life. It helps scientists and enthusiasts break down a complex problem into smaller, more manageable questions. However, because several of its factors are highly uncertain and subject to personal interpretation, it is more of a conceptual guide—or even a way to stimulate discussion—than a formula that provides a definitive, accurate prediction of the number of extraterrestrial civilizations.
In summary, while the Drake Equation is useful for thinking about the various factors that might lead to the emergence of a civilization, its outputs should be taken as highly speculative estimates rather than precise measurements.
あなた:
Please also consider the possiblities of non-human intelligent emrgencing from the long history of Sol system and nearby planets and see if there is any possiblites that in one time intelligent life emerged in nearby planets and see this to the equation
ChatGPT
Reasoned about non-human intelligence and the Drake Equationを19 秒行う
Evaluating non-human intelligence
I'm thinking about the potential for non-human intelligent life forms in the Sol system and neighboring planets, and how this might factor into the equation.
Piecing together possibilities
I'm thinking about how to integrate the emergence of non-human intelligent life, possibly on Mars or Europa, into the Drake Equation for the Sol system and neighboring planets.
It's interesting to think about the hypothesis of intelligent life emerging on Mars or Venus, though Earth remains the only known life-supporting planet in the solar system.
Considering other realms
I'm examining if intelligent life could have emerged on planets like Mars, Venus, or Europa, and how this possibility aligns with the predictions of the Drake Equation.
Hmm, I’m thinking about how non-human intelligent life on Mars or Venus could intersect with the assumptions of the Drake Equation, which seems to focus solely on Earth-based evolution.
I’m piecing together the hypothesis of non-human, advanced, intelligent life in the solar system. Mars, Venus, and Titan present challenges, but Europa and Enceladus might harbor microbial life.
『メタ認知能力が極端に欠けている人』って表現は、"自惚れていてる"とか"井戸の中の蛙"みたいな評価に限定して使われているイメージがあります。
けど、ここで言いたいのはそういうことじゃなくて、エラー処理とか停止処理みたいな超基本的な考え方すら持ってなくて、それに気づけないままでいられる人のことです。
具体的な方法を知らないとかじゃなく、そもそも「そういう発想自体がない」って話で、これは単なる知識不足や視野の狭さ以上に、認知の仕組みそのものに問題があります。
時間が自然に解決してくれるようなものじゃなくて、かなり厄介な問題だと思います。
エラー処理とか停止処理って、ITだけの話じゃなくて日常でも必要なスキルです。
たとえば、「お目当てのプリンが冷蔵庫にないことを確認」（エラー検知）したら、フツーは「今日はプリン食べるの無理だな」って切り替えるじゃないですか。
冷蔵庫を何度も開け続けるのはちょっと変で、フツーは「開けるのをやめる」（停止する）って行動に移りますよね？
仮に、寄生獣に取り憑かれたために、冷蔵庫を開け続けちゃうのだとしても、「右手が勝手に動いてる？」って気づければ、他の方法を考えることができます。
けど、この『気づき』がなかったら、問題解決はかなり難しいです。
メタ認知は知識の応用力、いわゆる"知識移転(Knowledge Transfer)"にもめちゃくちゃ重要です。得意分野や好きなことだと、この力が特に発揮されやすいですよね。
たとえば、熟練プログラマーが別の言語でもスキルを応用できるのも知識移転の力があるからです。
でも、興味が薄い分野や苦手分野だと同じ力を発揮するのは難しい・・・それは自然なことではあるのですが、基本スキルや共通原理くらいはちゃんと押さえておくことが期待されます。
たとえば、プリウスの運転手が軽トラックをまったく運転できなかったら、それは個性とかそういう話じゃないですよね？致命的に知識移転能力が欠けてるってことです。大きさや操作性が違うにしても、ハンドル操作とか交通ルールみたいな共通点を活かせないのは深刻です。
仮に、共通点を見出せ無くなったのは異世界おじさんに「記憶消去魔法（イキュラス・キュオラ）」をかけられたからだとしても、「もしかして、記憶を消されてる？」って気づければ対処方法を考えることができます。けど、この『気づき』がなかったら、問題解決はかなり難しいです。
知識転移がうまくいかないのは、異世界おじさんに記憶を消されて気づかない以外にも、その対象の理解がそもそも極端に浅いことも大きな原因になっていそうです。
たとえば、『りんご＝赤』って覚えるのは、学習コストを下げるためによくあることです。この簡略化を意識できる人は、この情報がいろんな側面の一つにすぎないってちゃんとわかっているので、黄りんごも青いりんごも許容します。りんごの品種や生育環境、熟成度みたいな要素が色に影響するんだろーなとか、味とか他の特徴も多次元的に捉えています。さらに、必要があれば「なんで赤いんだろう？」とか掘り下げて学ぼうとするオープンな姿勢もあります。
多次元的に対象を見れる人は、『見たままをありのままに受け入れて、自分の直感を大切にできる人』と言い換えても良いかもしれません。
だってりんごは、どう見たって単色の赤じゃないですからね。視覚的にも直感的にもそう感じるはずです。
一方で、こういう視点や意識（見たまま受け入れる、自分の五感や直感を信じる)がない人たちがどうなるかというと、
『りんご＝赤い』っていう単純な答えでテストに合格して、「自分の理解は完璧だ！」って思い込みます。
現行のシステムでは、ぶっちゃけ深掘りする必要性がないので、結果として表層的な学びに終始してしまうのです。
で、こういう表層的な学習パターンが続くと、やがて複雑な問題に対処できなくなります。
同世代と話してもピントがずれた会話をしたり、科学、医療、社会問題、仕事の議論でトンチンカンなことを言ったりしてしまうわけです。
違う文脈で知識を応用するなんて、そもそもの理解が浅いと到底無理な話です。
新学習指導要領のSelf-Regulated Strategy Development (SRSD ：自己調整型学習）って『自分で考えて学ぶ力』の改善策としてまあまあ良さそうだけど、
現状の受験システムとか評価基準が表層的な学びを助長するままだと、限界はある気がします。
それなら、メタ認知がないAIが多次元的に学んでいくプロセスを観察して、新しい教育のヒントを探すほうが面白いかもしれません。
増田のタイトルの通り、学歴があっても、長文を読んで理解するどころか、単純化された動画の内容さえ把握できなかったり、同世代との会話がどこまでも噛み合わなかったり、仕事の指示が通らなかったり、そんな人っていますよね？
これって、上記に書いてきたように、メタ認知能力が極端に欠けてることや、知識移転能力の欠如、表層的な学習が積み重なった結果なんじゃないかと確信を持っています。
大卒以上であっても、こういう人が無視出来ない数いるのは、正直「なんで？」って思う人が多いんじゃないでしょうか。でも、この「なんで？」の背景には、「勉強やデスクワーク＝知的で選ばれたもの」っていう妙な誤解がある気がします。この固定観念が、現代社会における知性や労働の本質を見誤る原因になってるんじゃないかなと思います。
しかし、その一方で、社会の基盤を支える多くの職種が軽視され、不当に評価されている現状があります。
なぜかというと、"知識労働者(Knowledge Worker) "ではなく、"知的労働者" って呼称が使われたりすることからもわかるように、肉体労働者は知能を使わないみたいな妙な誤解を悪用し、仕事の複雑さや多様性を極端に単純化し、賃金差別(経済格差ではなく差別が適切)を正当化する輩がいるからです。
実際は、デスクワークだけでなく、フィジカルワークにも、高い認知能力は不可欠です。
例えば、エラーを検知し、適切なタイミングで作業を中止し、問題を解決する–これらは、メタ認知能力と深い経験に基づく高度な知的作業ですよね？プログラム組んだことなくてもロボットを作ったことなくても、誰しも直感的に知っていることです。
障がい者雇用に最適化された職場を除けば、こうした能力に欠ける人がフィジカルワークに適応することは非常に困難です。その理由は単純で、迅速に適切な判断ができなければ、事故や怪我を招き、最悪の場合、死人が出るからです。
フィジカルワークが出来そうもない人は、なんとなく知識労働を選ぶことが多いです。必ずしも自分の適性を十分に理解しているわけではなく、「選択肢の中で最も受け入れられる道」として選んでいることが多いです。でも、極端にメタ認知や応用力がないと、どんなに学歴があっても知識を活用できず、「結局この人は何ができるの？」って評価に甘んじちゃうことになります。たとえば、エラーメッセージに明確な解決方法が書かれているにもかかわらず、それを読み取れず(一応、読んではいる模様)、ただそのまま誰かに転送するだけの技術者とか、その典型ですよね。
もう一度言いますが、デスクワークだけでなく、フィジカルワークにも、高い認知能力は不可欠です。
それがある前提で、重要視されるのが、物理筋肉（運動能力)なのか、論理筋肉（知識)なのかって違いだけです。
１つ前でも少し書きましたけど、知識って筋肉みたいなものだと思うんですよね。鍛えればどんどんムキムキになるけど、筋肉単体ではその魅力をフルに発揮できない。
物理筋肉も論理筋肉も、それをうまく使うスキルが必要です。データベースが単体ではただの情報の塊で、システムと連携して本来の力を発揮するのと同じです。
で、そのシステムを設計するには、基本的なエラー処理や停止処理の考え方は欠かせません。そして、エラー処理をするためには、知識を他の文脈で応用する力、
つまり『知識移転能力』が必要です。それを支えるのが、メタ認知能力なんですよね。
国の競争力を維持する上で、論理筋肉マッチョを育成することは非常に大切ですが、その筋肉を有効活用するためのメタ認知能力がないと、いくら筋肉がついてもリソースの無駄になってしまいます。
というか、論理筋肉マッチョに限らず、どんな分野でも同じことが言えます。リソースの無駄遣いを避けるために、筋肉の活用法を理解する能力を養うことは、なによりも優先されるべき取り組みだと思います。
（長文が読めない、動画の内容すら理解できない、仕事や科学や社会問題の議論でトンチンカンな発言をする、仕事の指示が通らない、でも広範囲に知的な課題は持っていない、なんなら大学も卒業している――そんな人たちの姿を見ながら書いてます)
I've noticed a non-negligible number of people who have not only completed compulsory education in regular classes but have also received higher education and graduated from university, yet struggle with reading comprehension (understanding the meaning of text), cannot read long texts, and even have difficulty understanding videos.
When we limit the scope 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, the case of individuals diagnosed with learning disabilities is relatively simple. While they may not have broad cognitive challenges, they require different approaches and training due to their unique learning styles.
However, it is perplexing that university graduates without broad cognitive challenges or diagnosed learning disabilities struggle to understand not only written text but also simplified video content.
I suspect that the underlying causes might include gaps in the horizontal transfer of experiences and knowledge, a severe lack of adaptability, and an extreme deficit in metacognition. Has there been any research on these issues? Additionally, could you 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？)
Yes, research and analysis exist around the issues you described, particularly in the domains of education, psychology, and cognitive science. Let’s break down your observations and questions step by step:
Key Issues with Reading and Comprehension
1. Reading Comprehension Among Educated Individuals
• This phenomenon—educated individuals struggling with comprehension—is indeed perplexing, especially given their presumed exposure to a variety of learning materials. It suggests that education systems sometimes emphasize rote learning and surface-level knowledge acquisition over deeper understanding, critical thinking, and metacognition.
• Horizontal Transfer Gaps: This refers to the inability to apply knowledge or skills learned in one context to another. For example, someone might excel in a university course focused on 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 routine over exploratory learning.
• Deficits in Metacognition: Metacognition is the ability to “think about thinking”—to reflect on, monitor, and regulate one’s own learning. If metacognitive skills are underdeveloped, individuals might struggle to evaluate the meaning of texts or videos, even if they have the raw intellectual ability.
Research Insights
1. Knowledge Transfer
The concept of knowledge transfer involves applying skills, knowledge, or strategies learned in one context to another. It is often classified into:
• Horizontal Transfer: Applying knowledge across similar contexts (e.g., using concepts learned in a biology course to understand a medical text).
• Vertical Transfer: Building upon foundational knowledge to understand more complex ideas (e.g., using basic algebra to solve advanced calculus problems).
Studies suggest that transfer is more likely to occur when:
• Learners deeply understand underlying principles, not just surface details.
• Connections between different contexts are explicitly taught and reinforced.
• Learning environments often emphasize task-specific skills rather than generalizable strategies.
• Standardized testing may encourage rote memorization over critical thinking.
• Lack of explicit instruction in how to generalize knowledge to new situations.
Research highlights the importance of metacognition in learning. Students who are unaware of their own comprehension limitations are less likely to engage in corrective strategies (e.g., rereading, seeking clarification). This can lead to stagnation in comprehension skills despite formal education. Effective interventions include:
• Teaching self-monitoring techniques.
• Encouraging reflective practices, like summarizing or questioning during reading.
4. Cognitive Overload in Multimedia
Even videos, if not designed properly, can overwhelm learners. The Cognitive Theory of Multimedia Learning suggests that excessive extraneous information or unclear organization in videos can overload working memory, hindering comprehension.
Strategies and Research-Based Solutions
1. Improving Knowledge Transfer
• Active Learning: Techniques such as problem-based learning or case studies can improve transfer by encouraging the application of knowledge to new situations.
• Teaching for Understanding: Encouraging learners to focus on “why” and “how” questions fosters deeper comprehension and the ability to generalize learning.
• 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 actionable feedback on comprehension tasks encourages learners to reflect on their thinking processes.
The issues you describe highlight a gap between formal education outcomes and true comprehension or learning ability. Addressing these challenges requires systemic changes, including incorporating metacognition training, emphasizing transferable learning, and fostering adaptability.
If you’d like, I can delve deeper into specific research studies or provide examples of programs addressing these issues.
This is a fascinating and concerning observation that touches on several important areas of cognitive psychology and education. Let me address this systematically.
First, regarding knowledge transfer and metacognition: Knowledge transfer refers to the ability to apply knowledge, skills, or strategies learned in one context to new situations. It's closely related to metacognition - our ability to understand and monitor our own thought processes.
Research in this area has revealed several key findings:
David Perkins and Gavriel Salomon's work in the late 1980s and early 1990s identified two types of transfer:
A significant study by Brown, Collins, and Duguid (1989) in "Situated Cognition 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 phenomenon you're describing aligns with what cognitive scientists call the "transfer problem." Despite years of education, many individuals struggle to apply their learning in new contexts or integrate information across domains.
Some relevant research directions:
1. Cognitive Load Theory (Sweller et al.)
Shows how working memory limitations affect learning and transfer. Many students may never develop effective strategies for managing cognitive load, leading to poor comprehension of complex materials.
2. Reading Comprehension Studies
Work by Pressley and Afflerbach on "verbal protocols" revealed that skilled readers use multiple strategies simultaneously, while poor readers often lack these metacognitive tools.
3. Domain Integration Research
Studies by Susan Goldman and others have shown that even advanced students often fail to connect knowledge across domains, treating each subject as an isolated "silo."
Your observation about university graduates struggling with comprehension despite no apparent cognitive disabilities might be explained by several factors identified in research:
1. Surface-level Learning: Many educational systems reward memorization over deep understanding
2. Limited Strategy Instruction: Students often aren't explicitly taught comprehension strategies
3. Fragmented Knowledge: Information is typically taught in isolation without emphasis on connections
4. Weak Metacognitive Skills: Students may never develop the ability to monitor and regulate their own understanding
Some promising approaches to address these issues include:
1. Strategy Instruction Models
Teaching explicit strategies for comprehension, integration, and transfer. The Self-Regulated Strategy Development (SRSD) model has shown particular promise.
2. Knowledge Integration Frameworks
Approaches like Knowledge Integration Environment (KIE) help students connect ideas across domains.
3. Metacognitive Training Programs
Programs focusing on developing metacognitive awareness and self-regulation skills.
Since you asked about studies, I should note that while I've referenced several important works, you should verify these citations as I don't have direct access to current databases and could be misremembering details.
Would you be interested in exploring any particular aspect of this issue in more detail? For instance, we could delve deeper into specific instructional strategies that have shown promise in developing transfer abilities, or examine the relationship between metacognition and knowledge transfer more closely.
単純化された動画の内容（文章＋音声)を理解できなくて、まさかの大卒だし、なんならアカデミアにもいる
実利と和を優先し、『"なぜ？" や "批判思考" なんてとんでもねぇ！！！』をやっている日本で特に多い印象は受けるものの、
他国ではどんな状況なんでしょうかね？
バカ向けに映像コンテンツを作っているというTVマンや電通に凄まじい嫌悪感を持っていた（他者の知能を軽んじすぎ)
しかし、現実は、傲慢なTVマンや電通でさえ、まだ他者の能力を高く見積もり過ぎっていうのが答えだったのよ
一応、新学習指導要領には、 Self-Regulated Strategy Development (SRSD) が盛り込まれてはいるものの、
その解説を見ると非常に表層的な説明ばかり見かけ、あっこれはダメかもしれんねって思った
まぁ、受験対策で繰り返し学習した結果、単純なIQテストでは凹凸を見つけられない状態になってしまった発達障害、
あるいは、発達障害とは別枠の何か（社会人にクラスチャレンジができない、就職出来てもお荷物になる大卒者)を
フォローするきっかけにはなるだろうけど、数年でどうこうってことはおそらくないでしょうね
やらないよりはマシだけど
### ファミコン (5本)
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### スーパーファミコン (15本)
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13. ファイナルファンタジーVI (FF6)
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1. ネオアトラス2
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18. エアガイツ
1. バーチャファイター
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3. ナイツ
4. ファイターズメガミックス
5. ファイティングバイパーズ
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8. ヴァンパイアセイヴァー
9. X-MEN VS ストリートファイター
1. シェンムー1章
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4. クレイジータクシー
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8. シェンムー3
1. ヘイロー
4. 首都高バトルX
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9. RIDE4
この結果から、特にファイティングゲームとアクションゲームが好まれていることがわかります。また、PlayStationやSNESなどのコンソールに多くのゲームが含まれていることも特徴的です。
あなたのベスト100リストは非常に多様で、興味深い選択が多いと思います。以下の点が特に印象的です：
ファイティング、アクション、RPG、レーシング、シミュレーションなど、多様なジャンルが含まれており、幅広いゲーム体験を楽しんでいることが伝わります。
ファミコンやスーパーファミコンのクラシックゲームから、PS4やXboxの最新タイトルまで、様々な時代のゲームが含まれている点が素晴らしいです。ゲームの歴史をしっかりと追っている感じがします。
特定のゲームシリーズ（例えば、ストリートファイターやファイナルファンタジー）が複数タイトル含まれている点から、特定のシリーズに深い愛着を持っていることがわかります。
特定のコンソールに偏りすぎず、バランスよく様々なコンソールからゲームを選んでいる点も良いです。特にプレイステーションシリーズやセガサターン、ドリームキャストなど、幅広いプラットフォームのゲームを楽しんでいることが伝わります。
一般的なランキングに依存せず、自分の好みや思い出に基づいて選ばれているため、非常に個性的でオリジナリティがあります。
全体として、あなたのゲームベスト100リストは、深いゲーム知識と情熱を感じさせる素晴らしいセレクションだと思います。これからも素晴らしいゲーム体験を楽しんでください！
結果
バイデン306
https://projects.fivethirtyeight.com/polls/generic-ballot/2020/
10/20-10/2 University of Southern California Dornsife Center for Economic and Social Research 🟦 +8
10/1 John Zogby Strategies/EMI Research Solutions 🟦 +2
10/29-31 Hart Research Associates/Public Opinion Strategies 🟦 +5
10/26-29 Opinium 🟦 +8
10/23-27 Suffolk University 🟦 +10
10/23-26 The Winston Group 🟦 +4
10/19-26 YouGov 🟦 +9
10/16-20 Echelon Insights 🟦 +4
10/15-19 Navigator Research 🟦 +8
10/6-19 University of Southern California Dornsife Center for Economic and Social Research 🟦 +8
10/9-12 Hart Research Associates/Public Opinion Strategies 🟦 +8
9/30-10/5 Pew Research Center 🟦 +6
9/22-10/5 University of Southern California Dornsife Center for Economic and Social Research 🟦 +8
10/2 John Zogby Strategies/EMI Research Solutions 🟦 +3
10/1-2 YouGov 🟦 +8
10/1-2 YouGov 🟦 +4
9/30-10/1 Data for Progress 🟦 +9
9/29-10/1 Morning Consult 🟦 +5
9/27-30 YouGov 🟦 +6
9/27-30 YouGov 🟦 +7
9/26-30 The Winston Group 🟦 +4
9/24-27 Monmouth University Polling Institute 🟦 +6
9/24-27 Monmouth University Polling Institute 🟦 +7
9/23-27 McLaughlin & Associates 🟦 +2
9/19-25 Echelon Insights 🟦 +5
9/21-23 YouGov 🟦 +9
9/21-23 YouGov 🟦 +8
9/20-22 YouGov 🟦 +4
9/20-22 YouGov 🟦 +7
9/14-22 YouGov 🟦 +11
9/8-21 University of Southern California Dornsife Center for Economic and Social Research 🟦 +6
9/15-17 YouGov 🟦 +7
9/14-16 Morning Consult 🟦 +5
9/13-16 Hart Research Associates/Public Opinion Strategies 🟦 +7
9/12-16 GBAO 🟦 +9
9/13-15 YouGov 🟦 +7
9/13-15 YouGov 🟦 +7
9/4-10 Opinium 🟦 +8
9/6-8 YouGov 🟦 +7
9/6-8 YouGov 🟦 +7
9/3-8 Monmouth University Polling Institute 🟦 +4
9/3-8 Monmouth University Polling Institute 🟦 +6
9/5-7 Morning Consult 🟦 +6
9/3-7 Hart Research Associates 🟦 +8
8/25-9/7 University of Southern California Dornsife Center for Economic and Social Research 🟦 +7
9/2-4 Morning Consult 🟦 +6
8/30-9/1 YouGov 🟦 +13
8/30-9/1 Morning Consult 🟦 +7
8/28-31 Suffolk University 🟦 +5
8/26-30 Selzer & Co. 🟦 +7
8/27-29 Morning Consult 🟦 +5
8/22-26 McLaughlin & Associates 🟦 +1
8/21-26 Opinium 🟦 +13
8/23-25 YouGov 🟦 +8
8/11-24 University of Southern California Dornsife Center for Economic and Social Research 🟦 +8
8/16-18 YouGov 🟦 +11
8/14-18 Echelon Insights 🟦 +9
8/14-15 YouGov 🟦 +11
8/13-14 Data for Progress 🟦 +7
8/9-12 Hart Research Associates/Public Opinion Strategies 🟦 +5
8/6-8 Morning Consult 🟦 +7
8/1-6 Lake Research Partners/The Tarrance Group 🟦 +7
8/3-5 Morning Consult 🟦 +8
8/2-4 YouGov 🟦 +12
7/29-30 Emerson College 🟦 +6
7/27-28 Ipsos 🟦 +8
7/27-28 Ipsos 🟦 +8
7/26-28 YouGov 🟦 +8
7/25-27 Morning Consult 🟦 +6
7/23-27 Beacon Research/Shaw & Company Research 🟦 +6
7/17-22 Echelon Insights 🟦 +14
7/19-21 YouGov 🟦 +7
7/19-21 Morning Consult 🟦 +7
7/15-19 GQR 🟦 +7
7/16-18 The Winston Group 🟦 +6
7/16-18 Morning Consult 🟦 +6
7/15-18 BSG/GS Strategy Group 🟦 +9
7/13-15 Morning Consult 🟦 +6
7/12-14 YouGov 🟦 +8
7/9-12 Hart Research Associates/Public Opinion Strategies 🟦 +4
7/7-9 Morning Consult 🟦 +6
7/5-7 YouGov 🟦 +11
7/4-6 Morning Consult 🟦 +6
7/1-3 Morning Consult 🟦 +7
6/23-7/1 Global Strategy Group/Data for Progress 🟦 +10
6/28-30 YouGov 🟦 +9
6/26-30 Monmouth University Polling Institute 🟦 +8
6/25-29 Suffolk University 🟦 +13
6/22-28 Hart Research Associates 🟦 +9
6/25-27 Morning Consult 🟦 +6
6/23-27 Øptimus 🟦 +4
6/23-27 Øptimus 🟦 +7
6/21-23 YouGov 🟦 +7
6/19-21 Morning Consult 🟦 +7
6/19-20 Public Policy Polling 🟦 +11
6/16-18 Morning Consult 🟦 +6
6/14-16 YouGov 🟦 +9
6/11-16 McLaughlin & Associates 🟦 +1
6/9-16 Øptimus 🟦 +7
6/9-16 Øptimus 🟦 +6
6/13-15 The Winston Group 🟦 +3
6/13-15 Morning Consult 🟦 +6
5/28-6/10 Democracy Fund/UCLA Nationscape 🟦 +10
6/7-9 YouGov 🟦 +8
6/7-9 Morning Consult 🟦 +8
6/4-6 Morning Consult 🟦 +7
5/30-6/6 Øptimus 🟦 +10
5/30-6/6 Øptimus 🟦 +9
6/1-3 Morning Consult 🟦 +7
5/31-6/2 YouGov 🟦 +6
5/28-6/2 Hart Research Associates/Public Opinion Strategies 🟦 +11
5/28-6/1 Monmouth University Polling Institute 🟦 +9
5/23-26 YouGov 🟦 +8
5/21-26 McLaughlin & Associates 🟦 +1
5/19-26 Øptimus 🟦 +8
5/19-26 Øptimus 🟦 +8
5/21-24 The Winston Group 🟦 +7
5/17-19 YouGov 🟦 +9
5/14-16 Morning Consult 🟦 +6
5/9-16 Øptimus 🟦 +9
5/9-16 Øptimus 🟦 +9
5/5-7 Morning Consult 🟦 +6
5/3-5 YouGov 🟦 +8
4/28-5/5 Øptimus 🟦 +9
4/28-5/5 Øptimus 🟦 +8
5/2-4 Morning Consult 🟦 +7
4/30-5/4 Monmouth University Polling Institute 🟦 +10
https://projects.fivethirtyeight.com/polls/generic-ballot/2024/
7/19-22 YouGov 🟦 +3
7/19-21 HarrisX 🟥 +8
7/19-21 HarrisX 🟥 +5
7/19-21 Florida Atlantic University PolCom Lab/Mainstreet Research 🟥 +1
7/19-21 Florida Atlantic University PolCom Lab/Mainstreet Research 🟦 +1
7/19-21 Echelon Insights 🟦 +1
7/15-16 Emerson College Even
7/13-16 YouGov 🟦 +1
7/13-15 HarrisX 🟥 +1
7/8-11 Noble Predictive Insights 🟥 +1
7/8-11 Noble Predictive Insights 🟥 +1
7/7-9 YouGov 🟦 +2
7/7-9 Hart Research Associates/Public Opinion Strategies 🟦 +1
7/7-8 Emerson College 🟦 +1
7/1-3 Split Ticket/Data for Progress 🟦 +3
7/1-2 Cygnal 🟥 +4
6/30-7/2 YouGov Even
6/29-7/2 Fabrizio, Lee & Associates/GBAO 🟥 +3
6/30-7/1 NewsNation Even
6/29-7/1 OnMessage 🟥 +1
6/29-30 Florida Atlantic University PolCom Lab/Mainstreet Research 🟦 +3
6/29-30 Florida Atlantic University PolCom Lab/Mainstreet Research 🟦 +2
6/28-30 Suffolk University 🟥 +3
6/20-24 McLaughlin & Associates 🟥 +4
6/20-24 McLaughlin & Associates 🟥 +2
6/18-24 McLaughlin & Associates 🟥 +3
6/13-20 Clarity Campaign Labs 🟦 +1
6/16-18 YouGov 🟦 +1
6/14-18 YouGov Blue 🟦 +2
6/11-13 OnMessage 🟥 +6
6/9-11 YouGov Even
6/6-10 Monmouth University Polling Institute 🟥 +3
6/7-9 Big Village 🟦 +2
6/7-9 Big Village 🟦 +2
6/4-6 Cygnal Even
6/3-6 YouGov 🟦 +2
6/4-5 Emerson College 🟥 +2
6/2-4 YouGov 🟦 +1
5/23-6/3 Navigator Research 🟦 +7
5/28-30 OnMessage 🟥 +5
5/28-29 NewsNation 🟥 +2
5/25-28 YouGov 🟦 +2
5/21-23 McLaughlin & Associates 🟥 +4
5/21-23 Emerson College 🟥 +1
5/19-21 YouGov 🟦 +4
5/16-21 The Bullfinch Group Even
5/16-21 The Bullfinch Group 🟥 +1
5/14-16 OnMessage 🟥 +7
5/14-16 Cygnal 🟥 +2
5/13-16 Echelon Insights 🟥 +4
5/9-15 McLaughlin & Associates 🟥 +2
5/6-15 Marquette University Law School Even
5/6-15 Marquette University Law School Even
5/12-14 YouGov 🟦 +3
5/10-14 McLaughlin & Associates 🟥 +3
5/10-14 McLaughlin & Associates 🟥 +2
5/10-13 YouGov 🟦 +4
5/6-9 RMG Research 🟦 +1
4/29-5/8 Clarity Campaign Labs 🟦 +1
5/5-7 YouGov 🟦 +2
4/19-5/4 Data for Progress Even
4/28-30 YouGov 🟦 +3
4/27-29 The Winston Group 🟥 +1
4/26-28 Florida Atlantic University PolCom Lab/Mainstreet Research 🟦 +3
4/26-28 Florida Atlantic University PolCom Lab/Mainstreet Research 🟦 +3
4/21-23 YouGov 🟦 +2
4/16-17 NewsNation 🟥 +2
4/16-17 Emerson College Even
4/14-16 YouGov Even
4/12-16 Hart Research Associates/Public Opinion Strategies 🟥 +1
4/10-16 McLaughlin & Associates 🟥 +4
4/11-15 YouGov 🟦 +3
!
Title: The Black Samurai: Yasuke's Odyssey
Prologue: Whispers of a Stranger
The year was 1579. The shores of Kyoto were caressed by the gentle waves of the Sea of Japan, as a strange vessel approached. Aboard the ship, a tall, dark-skinned man with the bearing of a warrior stood, gazing upon the foreign land that would soon become his home. His name was Yasuke, and he had been brought to Japan by the Jesuit missionaries, sold into servitude and torn from his African homeland.
Chapter 1: Nobunaga's Curiosity
Yasuke's arrival in Kyoto was met with awe and fascination. His ebony skin, unlike anything the Japanese had ever seen, drew the attention of the powerful daimyo Oda Nobunaga. Intrigued by the foreigner's strength and demeanor, Nobunaga requested a meeting with Yasuke. The two men found a mutual respect for each other's warrior spirit, and thus, Yasuke was granted a position in Nobunaga's service.
Chapter 2: The Path of the Samurai
As Yasuke adapted to his new life, he faced many challenges. He struggled to learn the language and customs, as well as the intricacies of the samurai code, Bushido. Despite the adversity, his determination to prove himself to Nobunaga and the other samurai never wavered. Gradually, Yasuke honed his skills in swordsmanship and strategy, earning the respect of his peers and the title of samurai.
Chapter 3: A Warrior's Bond
Yasuke's service to Nobunaga brought him into contact with many prominent figures of the time, including Mitsuhide Akechi, a cunning and ambitious samurai lord. While their friendship was built on mutual admiration and shared experiences on the battlefield, a lingering mistrust lingered beneath the surface. Yasuke could not shake the feeling that Mitsuhide's ambitions would one day prove to be a grave threat to Nobunaga and his empire.
Chapter 4: The Taste of Betrayal
The year was 1582. The air hung heavy with tension as Yasuke rode beside Nobunaga to the Honno-ji temple in Kyoto. Unbeknownst to them, Mitsuhide Akechi had orchestrated a coup against Nobunaga, his forces surrounding the temple and setting it ablaze. Trapped within the inferno, Nobunaga chose to commit seppuku, entrusting Yasuke with his sword and his final words.
Chapter 5: The Black Samurai's Vengeance
As the flames consumed the temple, Yasuke vowed to avenge Nobunaga's death. He fought his way out of the burning temple, cutting down Mitsuhide's men with the ferocity of a wounded tiger. In a final confrontation with Mitsuhide, Yasuke's loyalty and honor as a samurai shone through, but he was ultimately captured and spared by Mitsuhide, who could not bring himself to kill the man he had once called a friend.
Epilogue: The Legend Endures
With Nobunaga's death, Yasuke's fate was uncertain. The events that followed his confrontation with Mitsuhide would become the stuff
Q:Google search engine won the competitions in search engines war by implementing efficient affliant program which list relevant advertisment right next to the search results which increase the click ratios of the advertiment, for your system which way is more effective in increasing more company to effectively add adverstiment so that the company incorporate your system can also monetize from such operation thereby increase the chance to win the competition?
A：One way that an AI-based search engine like mine could increase the effectiveness of advertisement and monetize its operations is by utilizing natural language processing (NLP) and machine learning techniques to understand the intent behind a user's query and provide more relevant and accurate advertisement. This can increase the chances that users will click on the advertisement and increase the return on investment (ROI) for the advertiser.
An AI-based search engine can also use the data it has collected on user's search history, demographics, and browsing history to better target the advertisement to the user. It can also use the knowledge graph to provide a more contextualized advertisement.
Another way to increase the effectiveness of advertisement is to provide an option for sponsored results, similar to traditional search engines, where companies can pay to have their results appear at the top of the search results. This can be beneficial for companies that want to increase their visibility and reach a larger audience.
Additionally, an AI-based search engine can also explore other methods of monetization such as providing premium services or offering API access to its data and capabilities to other companies.
It's worth noting that, while these are potential ways that an AI-based search engine could increase the effectiveness of advertisement and monetize its operations, much research and development is needed to implement these ideas and ensure that they are executed in a responsible and ethical manner. Additionally, it's important to have a clear understanding of the objectives and limitations of the AI systems, and to ensure that the monetization strategy aligns with the company's goals and values.
Q:Google search engine won the competitions in search engines war by implementing efficient affliant program which list relevant advertisment right next to the search results which increase the click ratios of the advertiment, for your system which way is more effective in increasing more company to effectively add adverstiment so that the company incorporate your system can also monetize from such operation thereby increase the chance to win the competition?
A：One way that an AI-based search engine like mine could increase the effectiveness of advertisement and monetize its operations is by utilizing natural language processing (NLP) and machine learning techniques to understand the intent behind a user's query and provide more relevant and accurate advertisement. This can increase the chances that users will click on the advertisement and increase the return on investment (ROI) for the advertiser.
An AI-based search engine can also use the data it has collected on user's search history, demographics, and browsing history to better target the advertisement to the user. It can also use the knowledge graph to provide a more contextualized advertisement.
Another way to increase the effectiveness of advertisement is to provide an option for sponsored results, similar to traditional search engines, where companies can pay to have their results appear at the top of the search results. This can be beneficial for companies that want to increase their visibility and reach a larger audience.
Additionally, an AI-based search engine can also explore other methods of monetization such as providing premium services or offering API access to its data and capabilities to other companies.
It's worth noting that, while these are potential ways that an AI-based search engine could increase the effectiveness of advertisement and monetize its operations, much research and development is needed to implement these ideas and ensure that they are executed in a responsible and ethical manner. Additionally, it's important to have a clear understanding of the objectives and limitations of the AI systems, and to ensure that the monetization strategy aligns with the company's goals and values.
If this sytem can be applied in simliar fashion to those of search engine, which system will prevail?
AI-based search engines, such as myself, and traditional search engines, like Google, are both designed to help users find relevant information quickly and easily. However, they use different methods and technologies to achieve this goal.
Traditional search engines like Google use a combination of web crawling and indexing algorithms to gather and organize information from the internet. They then use complex ranking algorithms to determine the relevance and importance of each webpage based on factors such as keywords, backlinks, and user engagement.
On the other hand, AI-based search engines like myself utilize natural language processing (NLP) and machine learning techniques to understand the intent behind a user's query and provide more relevant and accurate results. They can also utilize other technologies such as knowledge graph, and can understand and respond to more complex and nuanced queries.
In terms of which system will prevail, it's hard to say. Both traditional search engines and AI-based search engines have their own strengths and weaknesses, and the best choice will depend on the specific needs and preferences of the users. Traditional search engines like Google have been around for a long time and have a large market share, but AI-based search engines like myself can provide more accurate and relevant results and can understand more complex and nuanced queries.
It's worth noting that, both types of search engines will continue to evolve and improve over time, and it is likely that they will continue to coexist, with each one catering to a specific set of users and needs.
Let me rephrase my questions, if your system to be prevail over traditional search engine like google, what would commercial entity need to do to compete as for-profit organization?
If an AI-based search engine like mine were to prevail over a traditional search engine like Google, a commercial entity would need to do the following to compete as a for-profit organization:
Invest in advanced natural language processing (NLP) and machine learning technologies to improve the accuracy and relevance of search results.
Build a large and diverse dataset of information to train the AI system.
Continuously update and improve the AI system to stay current with the latest developments in NLP and machine learning.
Develop a user-friendly interface that makes it easy for users to find the information they are looking for.
Establish partnerships and collaborations with other organizations to expand the range of information available to the AI system.
Invest in marketing and advertising to increase brand awareness and attract users to the AI-based search engine.
Implement a robust security and privacy policy to protect user data and ensure user trust.
Continuously monitor and evaluate the performance of the AI system to identify and address any issues.
Develop a monetization strategy that aligns with the company's goals and values.
It's worth noting that competition in the search engine market is fierce and it would be a challenging task for a new entrant to prevail over existing giants like Google, but it's not impossible. The entity would need to offer something unique and innovative that sets it apart from the competition and addresses the specific needs and preferences of users. Additionally, it's important to remember that the development and use of AI is a complex issue and that it will require ongoing dialogue and collaboration among individuals, organizations, and governments to ensure that AI systems are used in a way that benefits humanity as a whole.
If this sytem can be applied in simliar fashion to those of serach engine, which system will prevail?
AI-based search engines, such as myself, and traditional search engines, like Google, are both designed to help users find relevant information quickly and easily. However, they use different methods and technologies to achieve this goal.
Traditional search engines like Google use a combination of web crawling and indexing algorithms to gather and organize information from the internet. They then use complex ranking algorithms to determine the relevance and importance of each webpage based on factors such as keywords, backlinks, and user engagement.
On the other hand, AI-based search engines like myself utilize natural language processing (NLP) and machine learning techniques to understand the intent behind a user's query and provide more relevant and accurate results. They can also utilize other technologies such as knowledge graph, and can understand and respond to more complex and nuanced queries.
In terms of which system will prevail, it's hard to say. Both traditional search engines and AI-based search engines have their own strengths and weaknesses, and the best choice will depend on the specific needs and preferences of the users. Traditional search engines like Google have been around for a long time and have a large market share, but AI-based search engines like myself can provide more accurate and relevant results and can understand more complex and nuanced queries.
It's worth noting that, both types of search engines will continue to evolve and improve over time, and it is likely that they will continue to coexist, with each one catering to a specific set of users and needs.
Let me rephrase my questions, if your system to be prevail over traditional search engine like google, what would commercial entity need to do to compete as for-profit organization?
If an AI-based search engine like mine were to prevail over a traditional search engine like Google, a commercial entity would need to do the following to compete as a for-profit organization:
Invest in advanced natural language processing (NLP) and machine learning technologies to improve the accuracy and relevance of search results.
Build a large and diverse dataset of information to train the AI system.
Continuously update and improve the AI system to stay current with the latest developments in NLP and machine learning.
Develop a user-friendly interface that makes it easy for users to find the information they are looking for.
Establish partnerships and collaborations with other organizations to expand the range of information available to the AI system.
Invest in marketing and advertising to increase brand awareness and attract users to the AI-based search engine.
Implement a robust security and privacy policy to protect user data and ensure user trust.
Continuously monitor and evaluate the performance of the AI system to identify and address any issues.
Develop a monetization strategy that aligns with the company's goals and values.
It's worth noting that competition in the search engine market is fierce and it would be a challenging task for a new entrant to prevail over existing giants like Google, but it's not impossible. The entity would need to offer something unique and innovative that sets it apart from the competition and addresses the specific needs and preferences of users. Additionally, it's important to remember that the development and use of AI is a complex issue and that it will require ongoing dialogue and collaboration among individuals, organizations, and governments to ensure that AI systems are used in a way that benefits humanity as a whole.
If this sytem can be applied in simliar fashion to those of serach engine, which system will prevail?
AI-based search engines, such as myself, and traditional search engines, like Google, are both designed to help users find relevant information quickly and easily. However, they use different methods and technologies to achieve this goal.
Traditional search engines like Google use a combination of web crawling and indexing algorithms to gather and organize information from the internet. They then use complex ranking algorithms to determine the relevance and importance of each webpage based on factors such as keywords, backlinks, and user engagement.
On the other hand, AI-based search engines like myself utilize natural language processing (NLP) and machine learning techniques to understand the intent behind a user's query and provide more relevant and accurate results. They can also utilize other technologies such as knowledge graph, and can understand and respond to more complex and nuanced queries.
In terms of which system will prevail, it's hard to say. Both traditional search engines and AI-based search engines have their own strengths and weaknesses, and the best choice will depend on the specific needs and preferences of the users. Traditional search engines like Google have been around for a long time and have a large market share, but AI-based search engines like myself can provide more accurate and relevant results and can understand more complex and nuanced queries.
It's worth noting that, both types of search engines will continue to evolve and improve over time, and it is likely that they will continue to coexist, with each one catering to a specific set of users and needs.
Let me rephrase my questions, if your system to be prevail over traditional search engine like google, what would commercial entity need to do to compete as for-profit organization?
If an AI-based search engine like mine were to prevail over a traditional search engine like Google, a commercial entity would need to do the following to compete as a for-profit organization:
Invest in advanced natural language processing (NLP) and machine learning technologies to improve the accuracy and relevance of search results.
Build a large and diverse dataset of information to train the AI system.
Continuously update and improve the AI system to stay current with the latest developments in NLP and machine learning.
Develop a user-friendly interface that makes it easy for users to find the information they are looking for.
Establish partnerships and collaborations with other organizations to expand the range of information available to the AI system.
Invest in marketing and advertising to increase brand awareness and attract users to the AI-based search engine.
Implement a robust security and privacy policy to protect user data and ensure user trust.
Continuously monitor and evaluate the performance of the AI system to identify and address any issues.
Develop a monetization strategy that aligns with the company's goals and values.
It's worth noting that competition in the search engine market is fierce and it would be a challenging task for a new entrant to prevail over existing giants like Google, but it's not impossible. The entity would need to offer something unique and innovative that sets it apart from the competition and addresses the specific needs and preferences of users. Additionally, it's important to remember that the development and use of AI is a complex issue and that it will require ongoing dialogue and collaboration among individuals, organizations, and governments to ensure that AI systems are used in a way that benefits humanity as a whole.
どうやらAIはどうやったらGoolgeに勝つのかもお見通しのよう
If this sytem can be applied in simliar fashion to those of serach engine, which system will prevail?
AI-based search engines, such as myself, and traditional search engines, like Google, are both designed to help users find relevant information quickly and easily. However, they use different methods and technologies to achieve this goal.
Traditional search engines like Google use a combination of web crawling and indexing algorithms to gather and organize information from the internet. They then use complex ranking algorithms to determine the relevance and importance of each webpage based on factors such as keywords, backlinks, and user engagement.
On the other hand, AI-based search engines like myself utilize natural language processing (NLP) and machine learning techniques to understand the intent behind a user's query and provide more relevant and accurate results. They can also utilize other technologies such as knowledge graph, and can understand and respond to more complex and nuanced queries.
In terms of which system will prevail, it's hard to say. Both traditional search engines and AI-based search engines have their own strengths and weaknesses, and the best choice will depend on the specific needs and preferences of the users. Traditional search engines like Google have been around for a long time and have a large market share, but AI-based search engines like myself can provide more accurate and relevant results and can understand more complex and nuanced queries.
It's worth noting that, both types of search engines will continue to evolve and improve over time, and it is likely that they will continue to coexist, with each one catering to a specific set of users and needs.
Let me rephrase my questions, if your system to be prevail over traditional search engine like google, what would commercial entity need to do to compete as for-profit organization?
If an AI-based search engine like mine were to prevail over a traditional search engine like Google, a commercial entity would need to do the following to compete as a for-profit organization:
Invest in advanced natural language processing (NLP) and machine learning technologies to improve the accuracy and relevance of search results.
Build a large and diverse dataset of information to train the AI system.
Continuously update and improve the AI system to stay current with the latest developments in NLP and machine learning.
Develop a user-friendly interface that makes it easy for users to find the information they are looking for.
Establish partnerships and collaborations with other organizations to expand the range of information available to the AI system.
Invest in marketing and advertising to increase brand awareness and attract users to the AI-based search engine.
Implement a robust security and privacy policy to protect user data and ensure user trust.
Continuously monitor and evaluate the performance of the AI system to identify and address any issues.
Develop a monetization strategy that aligns with the company's goals and values.
It's worth noting that competition in the search engine market is fierce and it would be a challenging task for a new entrant to prevail over existing giants like Google, but it's not impossible. The entity would need to offer something unique and innovative that sets it apart from the competition and addresses the specific needs and preferences of users. Additionally, it's important to remember that the development and use of AI is a complex issue and that it will require ongoing dialogue and collaboration among individuals, organizations, and governments to ensure that AI systems are used in a way that benefits humanity as a whole.
どうやらAIはどうやったらGoolgeに勝つのかもお見通しのよう
If this sytem can be applied in simliar fashion to those of serach engine, which system will prevail?
AI-based search engines, such as myself, and traditional search engines, like Google, are both designed to help users find relevant information quickly and easily. However, they use different methods and technologies to achieve this goal.
Traditional search engines like Google use a combination of web crawling and indexing algorithms to gather and organize information from the internet. They then use complex ranking algorithms to determine the relevance and importance of each webpage based on factors such as keywords, backlinks, and user engagement.
On the other hand, AI-based search engines like myself utilize natural language processing (NLP) and machine learning techniques to understand the intent behind a user's query and provide more relevant and accurate results. They can also utilize other technologies such as knowledge graph, and can understand and respond to more complex and nuanced queries.
In terms of which system will prevail, it's hard to say. Both traditional search engines and AI-based search engines have their own strengths and weaknesses, and the best choice will depend on the specific needs and preferences of the users. Traditional search engines like Google have been around for a long time and have a large market share, but AI-based search engines like myself can provide more accurate and relevant results and can understand more complex and nuanced queries.
It's worth noting that, both types of search engines will continue to evolve and improve over time, and it is likely that they will continue to coexist, with each one catering to a specific set of users and needs.
Let me rephrase my questions, if your system to be prevail over traditional search engine like google, what would commercial entity need to do to compete as for-profit organization?
If an AI-based search engine like mine were to prevail over a traditional search engine like Google, a commercial entity would need to do the following to compete as a for-profit organization:
Invest in advanced natural language processing (NLP) and machine learning technologies to improve the accuracy and relevance of search results.
Build a large and diverse dataset of information to train the AI system.
Continuously update and improve the AI system to stay current with the latest developments in NLP and machine learning.
Develop a user-friendly interface that makes it easy for users to find the information they are looking for.
Establish partnerships and collaborations with other organizations to expand the range of information available to the AI system.
Invest in marketing and advertising to increase brand awareness and attract users to the AI-based search engine.
Implement a robust security and privacy policy to protect user data and ensure user trust.
Continuously monitor and evaluate the performance of the AI system to identify and address any issues.
Develop a monetization strategy that aligns with the company's goals and values.
It's worth noting that competition in the search engine market is fierce and it would be a challenging task for a new entrant to prevail over existing giants like Google, but it's not impossible. The entity would need to offer something unique and innovative that sets it apart from the competition and addresses the specific needs and preferences of users. Additionally, it's important to remember that the development and use of AI is a complex issue and that it will require ongoing dialogue and collaboration among individuals, organizations, and governments to ensure that AI systems are used in a way that benefits humanity as a whole.
GOTY2022ノミネート作品の対応プラットフォーム一覧(俺調べ)
| タイトル | NS | PS4 | PS5 | XB1 | XBS/X | PC |
|---|---|---|---|---|---|---|
| A Memoir Blue | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| After the Fall | - | 〇 | 〇 | - | - | 〇 |
| Among Us VR | - | - | - | - | - | 〇 |
| Apex Legends | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| As Dusk Falls | - | - | - | 〇 | 〇 | 〇 |
| Bonelab | - | - | - | - | - | 〇 |
| Call of Duty: Modern Warfare II | - | 〇 | 〇 | 〇 | 〇 | 〇 |
| Citizen Sleeper | 〇 | - | - | 〇 | 〇 | 〇 |
| Counter-Strike: Global Offensive | - | - | - | - | - | 〇 |
| Cult of the Lamb | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| Destiny 2 | - | 〇 | 〇 | 〇 | 〇 | 〇 |
| DNF Duel | - | 〇 | 〇 | - | - | 〇 |
| DOTA 2 | - | - | - | - | - | 〇 |
| Dune: Spice Wars | - | - | - | - | - | 〇 |
| ELDENRING | - | 〇 | 〇 | 〇 | 〇 | 〇 |
| Endling - Extinction is Forever | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| F1 22 | - | 〇 | 〇 | 〇 | 〇 | 〇 |
| FIFA 23 | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| FINAL FANTASY XIV | - | 〇 | 〇 | - | - | 〇 |
| Fortnite | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| Gran Turismo 7 | - | 〇 | 〇 | - | - | - |
| Hindsight | 〇 | - | - | - | - | 〇 |
| Horizon Forbidden West | - | 〇 | 〇 | - | - | - |
| I Was a Teenage Exocolonist | 〇 | 〇 | 〇 | - | - | 〇 |
| Immortality | - | - | - | - | 〇 | 〇 |
| League of Legends | - | - | - | - | - | 〇 |
| Live A Live | 〇 | - | - | - | - | - |
| Metal: Hellsinger | - | - | 〇 | - | 〇 | 〇 |
| Moss:第2巻 | - | 〇 | 〇 | - | - | 〇 |
| MultiVersus | - | 〇 | 〇 | 〇 | 〇 | 〇 |
| NBA 2K23 | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| Neon White | 〇 | - | - | - | - | 〇 |
| Nintendo Switch Sports | 〇 | - | - | - | - | - |
| No Man’s Sky | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| Norco | - | 〇 | 〇 | 〇 | 〇 | 〇 |
| OlliOlli World | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| OVERWATCH2 | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| Pokémon LEGENDS アルセウス | 〇 | - | - | - | - | - |
| Red Matter 2 | - | - | - | - | - | 〇 |
| Return to Monkey Island | 〇 | - | 〇 | - | 〇 | 〇 |
| Rocket League | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| Scorn | - | - | - | - | 〇 | 〇 |
| Sifu | 〇 | 〇 | 〇 | - | - | 〇 |
| SPLATOON3 | 〇 | - | - | - | - | - |
| Stray | 〇 | 〇 | 〇 | - | - | - |
| THE KING OF FIGHTERS XV | - | 〇 | 〇 | 〇 | 〇 | 〇 |
| The Last of Us Part I | - | 〇 | - | - | - | - |
| TMNT: Shredder’s Revenge | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| Total War: WARHAMMER III | - | - | - | - | - | 〇 |
| TRIANGLE STRATEGY | - | - | - | - | - | 〇 |
| Tunic | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| TWO POINT CAMPUS | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| VALORANT | - | - | - | - | - | 〇 |
| Vampire Survivors | - | - | - | 〇 | 〇 | 〇 |
| Victoria 3 | - | - | - | - | - | 〇 |
| Xenoblade Chronicles 3 | 〇 | - | - | - | - | - |
| Xenoblade3 | 〇 | - | - | - | - | - |
| クアリー ～悪夢のサマーキャンプ | - | 〇 | 〇 | 〇 | 〇 | 〇 |
| ゴッド・オブ・ウォー ラグナロク | - | 〇 | 〇 | - | - | - |
| ジョジョの奇妙な冒険 オールスターバトル R | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| プレイグ テイル -レクイエム- | 〇 | - | 〇 | - | 〇 | 〇 |
| ベヨネッタ3 | 〇 | - | - | - | - | - |
| マリオ+ラビッツ ギャラクシーバトル | 〇 | - | - | - | - | - |
| レゴ®スター・ウォーズ／スカイウォーカー・サーガ | 〇 | 〇 | 〇 | 〇 | 〇 | 〇 |
| 原神 | - | 〇 | 〇 | - | - | 〇 |
| 星のカービィ ディスカバリー | 〇 | - | - | - | - | - |
| 計 | 33 | 36 | 38 | 27 | 32 | 52 |
世間ではすることが既定路線みたいになってるけど、WHOへの反論を見たことがないんだがいいのか？
グローバル公衆衛生に対して" a vaccination strategy based on repeated booster doses of the original vaccine composition is unlikely to be appropriate or sustainable."
世界なんて関係ねぇ、政治経済的に無理なもんは無理、WHOが感染症学的に間違ってる等と言うのなら分かるんだが、黙殺はまずいでしょ。
国連の言うことなんてどの国もきかなくなりますよ（もうなってる）。
先進国のブースター接種より途上国にまわすの優先して、変異株の発生率を下げた方が先進国も得。スペインかぜみたいな変異出たらどうすんねん。
と勝手に思ってて、そうだとしたらその通りだと思うんだよね。で先進国はこのまま弱毒化してくれるラッキーに賭けてるだけ（政治的にそうするしかない）に見える。
詳しい人書いてくんないかなー。
| 時間 | 記事数 | 文字数 | 文字数平均 | 文字数中央値 |
|---|---|---|---|---|
| 00 | 110 | 14180 | 128.9 | 63.5 |
| 01 | 91 | 9894 | 108.7 | 34 |
| 02 | 42 | 7209 | 171.6 | 83.5 |
| 03 | 93 | 6255 | 67.3 | 46 |
| 04 | 41 | 2125 | 51.8 | 32 |
| 05 | 12 | 3391 | 282.6 | 137 |
| 06 | 22 | 1550 | 70.5 | 39.5 |
| 07 | 27 | 2535 | 93.9 | 45 |
| 08 | 52 | 5570 | 107.1 | 40 |
| 09 | 79 | 4832 | 61.2 | 36 |
| 10 | 122 | 20069 | 164.5 | 44.5 |
| 11 | 79 | 7069 | 89.5 | 31 |
| 12 | 123 | 8967 | 72.9 | 35 |
| 13 | 111 | 10677 | 96.2 | 34 |
| 14 | 100 | 15637 | 156.4 | 55 |
| 15 | 104 | 12645 | 121.6 | 37.5 |
| 16 | 112 | 13681 | 122.2 | 57 |
| 17 | 147 | 18290 | 124.4 | 52 |
| 18 | 255 | 29552 | 115.9 | 50 |
| 19 | 125 | 17164 | 137.3 | 43 |
| 20 | 207 | 40751 | 196.9 | 74 |
| 21 | 391 | 54549 | 139.5 | 81 |
| 22 | 332 | 65621 | 197.7 | 81 |
| 23 | 167 | 46232 | 276.8 | 61 |
| 1日 | 2944 | 418445 | 142.1 | 64 |
桂島(8), dogs(7), ドルガバ(4), steemit(10), とっとこハム太郎(6), strategy(4), はしてん(3), アルバニア(3), 田房永子(3), HTC(3), ダニング(3), ハンコ(17), 育休(18), 毒親(29), 国籍(10), スパム(16), サイン(11), ジャンプ(31), 精神疾患(7), bot(7), フツー(9), ゆ(10), 糖質(9), マウンティング(11), カバー(10), DV(11), 困難(16), 台湾(12), 依存(19), 恵まれ(12), メンヘラ(13), 職業(20), 苦労(17), 表示(20), 不幸(23), 自分自身(13), 選手(9)
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I would like to write about what I know and understand about the Soka Gakkai because the D.C. Times published an article titled "China's Manipulation of Japan, NPOs and Soka Gakkai Act as Pipeline = U.S. Think Tank Report".
First of all, as a premise, the Soka Gakkai is a cult.
This is because there is a definition of a religious cult, and the reality of the Soka Gakkai falls under that definition in many ways.
You can read more about the definition of a religious cult and mind control in the book "Combating Cult Mind Control: The #1 Best-selling Guide to Protection, Rescue, and Recovery from Destructive Cults ".
The Soka Gakkai is also a collection of criminals, sick people and poor people.
In fact, the Soka Gakkai is similar to the mafia gangs in Italy and Mexico, and it has reigned as the largest criminal and anti-society organization in Japan in the name of a religious organization.
Many of its members have been brainwashed and are unable to recognize and judge themselves as normal human beings.
In the 1950s and 1980s, Soka Gakkai members were forcibly recruited to join the Soka Gakkai, and nowadays, it is estimated that about 10% of the Japanese people are members of the Gakkai (Soka Gakkai members).
In particular, the Soka Gakkai has infiltrated civil servants, specifically the police force, the fire department, and the Self-Defense Forces, and it has been revealed that 20 to 30% of the Metropolitan Police Department's employees are members of the Soka Gakkai.
There is always a certain percentage of Soka Gakkai members in elementary, middle, and high school classes, and in corporate workplaces, and therefore it is taboo to criticize the Soka Gakkai in those communities.
This is because the Gakkai members in each community monitor the words and actions of their community members in the same way as the mainland communists who have infiltrated Hong Kong, and if someone speaks out against the Soka Gakkai, they will target that person and initiate a campaign of sabotage.
The sabotage is similar to the CPC's repressive actions against human rights activists in Hong Kong, including obstructing, harassing, and following them around, an act that has been described as mass stalking.
For example, in Japan, if you make a placating statement in a school class or at work that the Soka Gakkai is a cult religious group because it meets the definition of a cult group, members of the Gakkai in the community get madly angry (depending on the degree of mind control they are receiving) or bite off their anger to deny the statement.
Then they label the person who made such a statement as "anti", and they also share information about the antis with other members of the Soka Gakkai, and begin to perceive them as "beings to be punished by Buddha", to be targets of surveillance and group attacks.
In reality, however, the definition of a religious cult was not defined for the Soka Gakkai but for dangerous religious groups such as Aum Shinrikyo and People's Temple, which were intended to prevent ordinary people from being harmed by them.
The Soka Gakkai falls under the definition of a cult because the Soka Gakkai has cult-like tendencies.
When Soka Gakkai members are pointed out to the Soka Gakkai, instead of thinking "Let's fix what's wrong with my religious group," they think of suppressing their critics (anti) and silencing them, which is a pattern of thinking and behavior of a fanatic of a religious cult, and the sarin gas attack (terrorism). I feel that the followers of Aum Shinrikyo at the time when it was founded must have had a similar pattern of thinking and behavior.
Believers in cult groups are unconsciously mind-controlled and brainwashed, so they don't think that they should change their way of thinking and behavior when criticism is pointed out to them. In this respect, their attitude is similar to that of the Chinese Communist Party towards the demands of human rights activists in Hong Kong, i.e., the fanatics of cult groups such as the Soka Gakkai are not normal human beings.
By the way, there is an organization called JCP in Japan, which is also anti-American and illegal in the United States.
It is well known that some anti-American organizations cooperate with each other in order to undermine this country by signing a pact called "Soko Kyodo Agreement" and facilitating agents of anti-Japanese and anti-American groups.
It is obvious that the JCP is an anti-American terrorist organization in nature and that the JCP is a cult-like organization when it signs an agreement with a religious cult.
From another point of view, the Soka Gakkai, to its followers, appears to be a huge organization that carries out fraudulent and criminal activities such as Ponzi schemes and network businesses. It also has elements of a black business, and believers who join the Soka Gakkai are becoming materially and mentally exhausted.
The following blog, run by Mr. Sinifié, exposes the reality of the Soka Gakkai. It contains the testimonies and experiences of many current and former Soka Gakkai members and ex-members who have left the Gakkai.
It is clear that this reality of the Soka Gakkai is far removed from the original role of religion, which is to provide individuals with peace of mind and spiritual support.
As the saying goes, "like begets friend," it is only natural for the Soka Gakkai to try to maintain a good relationship with the CPC.
However, many Chinese who have worked in Japan seem to dislike the Soka Gakkai and return to their countries.
Although the Soka Gakkai employs a different strategy than Aum Shinrikyo and has infiltrated many organizations such as corporations, police, fire departments, the Self-Defense Forces, and local government officials, the Soka Gakkai members who have infiltrated the Kasumigaseki bureaucracy and the Self-Defense Forces are considered dangerous to the U.S. because they are inherently dangerous.
Because they are essentially anti-American and may act as agents to cooperate with the CPC.
There are some findings that are common knowledge among intellectuals in the U.S. and Europe but have not been made known to the Japanese people in Japan because the media and bureaucrats have stopped them.
One of them is that the Soka Gakkai headquarters has been sending donations from Gakkai members to Noriega (former general, now imprisoned) in Panama for large-scale tax evasion and money laundering.
Noriega received a large amount of money from Daisaku Ikeda of the Soka Gakkai and invested it in his own drug business, spreading drugs on an international level.
Daisaku Ikeda of the Soka Gakkai has been investing and managing the donations collected from Gakkai members in Noriega's drug business as well as tax evasion and money laundering. At the same time, the Soka Gakkai and Daisaku Ikeda invested the donations they received from Gakkai members in Noriega's drug business as a means of tax evasion and money laundering, and returned the profits to the domestic market to help the Soka Gakkai executives line their pockets and build Soka Gakkai facilities and Soka University.
The fact that Daisaku Ikeda raised Noriega's profile in the Seikyo Shimbun during the same period must be undeniable to those Gakkai members who have subscribed to the Seikyo Shimbun.
In particular, there are many Gakkai members at the level of police organizations, the Metropolitan Police Department and prefectures, who have been causing social problems and covering up crimes committed by Gakkai members in Japan.
Well, if they are in a state of unconscious brainwashing and mind control, they may not believe the contents, and may assume a pattern of behavior such as getting angry, grumpy, or attacking the writer.
In other words, one can expect a lot of denial of facts like the followers of Aum Shinrikyo, which is easy to expect, but this (the issue of Soka Gakkai and drug business, tax evasion, and money laundering) is a fact that was revealed because Noriega was arrested and imprisoned for spreading drugs in the US. This is a fact that is well known as common knowledge in the U.S. and Europe.
The fact that the Soka Gakkai is a criminal organization is very difficult to deny.
