  |
はてなキーワード: processingとは
で、なんのグループを出していてどのようなprocessing（NLP＝Natural Language Processing）をどの段階でしているの？
と言うことで、NVIDA一強みたいなのは今がピークで、今後は変わってくるんじゃないかな。ただ需要が全然満たせてないから、伸びるとは思うけど、多様化していくと思う。
数年前は、TSP(Tensor Streaming Processor)と呼んでいたが、LPU(language processing unit)と名前を変えた？
数年前のチップをそのまま使い続けているか分からないが、同じならアーキテクチャは4年前のユーチューブを見るか、アスキーあたりの記事にある。
https://youtu.be/UNG70W8mKbA?si=9VFeopAiPAdn08i_
要は、コインパイラで変換が必要。なので提供されているLLMモデルが限られている。
PCIeボードが400万くらいらしいが、SRAMの容量が小さすぎて1ボードでは動かない。
DRAMのレイテンシがSRAMではないので早いのだ、という意見も見られてたが、
1チップのSRAM容量が小さすぎるので、チップチップ間、ボードボード間の通信レイテンシは必ずあるはず。
（数ヶ月前から性能上がっているのは、このあたりのチューニングのはず）
DRAMのレイテンシというが、これも今どきはレイテンシ気にしないように隠蔽するはず。
チームが小さすぎてハード作れなかった可能性もあるが・・・。DMACでチューニングしているか？
ボードにでかいDRAMが載せられるのであれば、そちらの方がボードボード間の通信時間より減るのでは？
GF使ったのは、おそらくAMD設計者が居たからでは。デザインルールどこ破れば性能でるかある程度わかってたとか。1GHzくらいなのは知見なしでやってるとそれくらいで上限くるのはそうだと思う。
チップの世代を更新するかはわからないが、兎にも角にも電力下げて、チップ大量に載せて、チップチップ間の通信時間を下げられるか。
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As a student navigating the complexities of engineering coursework, I found myself grappling with Simulink assignments and think who will help me to complete my Simulink assignment that seemed to be from another dimension. The intricacies of Simulink, a powerful simulation and modeling tool, left me feeling overwhelmed and lost. That's when I stumbled upon a game-changer – Simulink Assignment Help from https://www.matlabassignmentexperts.com/simulink-assignment-help.html. In this testimonial blog, I want to share my transformative experience with their services, detailing how they not only helped me conquer Simulink challenges but also enhanced my overall understanding of this intricate subject.
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My journey with MatlabAssignmentExperts.com began when I was at a crossroads with my Simulink assignments. The complexities of the software, coupled with the pressure of academic deadlines, had me seeking a reliable source of assistance. A quick online search led me to their website, and the promising testimonials from fellow students who had successfully navigated Simulink assignments with their help convinced me to give it a shot.
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Tailored Solutions for Varied Simulink Topics
Simulink is a vast field with applications in numerous engineering disciplines. What impressed me most about MatlabAssignmentExperts.com was their ability to cater to a wide array of Simulink topics. Whether it was control systems, signal processing, or model-based design, their experts exhibited a depth of knowledge that extended beyond mere problem-solving.
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下記はカリフォルニア大学のビアンカ・アセベド博士の研究紹介動画と書籍と記事(Pod cast)、
HSP brain studies
https://www.youtube.com/watch?v=qep36Vy_0pE
＞ Did you know that the brain of a highly sensitive person (HSP) works differently?
＞ The results of fMRI brain studies conducted by Dr. Elaine Aron, Dr. Art Aron, Dr. Bianca Acevedo and their colleagues are quite fascinating.
エレイン・アロン博士、アート・アロン博士、ビアンカ・アセベド博士らが行ったfMRIによる脳の研究結果は、かなり魅力的です。
[Amazon] The Highly Sensitive Brain: Research, Assessment, and Treatment of Sensory Processing Sensitivity 1st Edition
https://www.amazon.com/Highly-Sensitive-Brain-Assessment-Sensitivity/dp/0128182512
＞ The Highly Sensitive Brain is the first handbook to cover the science, measurement, and clinical discussion of sensory processing sensitivity (SPS),
＞ a trait associated with enhanced responsivity, awareness, depth-of-processing and attunement to the environment and other individuals.
＞ Grounded in theoretical models of high sensitivity, this volume discusses the assessment of SPS in children and adults,
＞ as well as its health and social outcomes.
＞ This edition also synthesizes up-to-date research on the biological mechanisms associated with high sensitivity,
＞ such as its neural and genetic basis. It also discusses clinical issues related to SPS and seemingly-related disorders such as misophonia,
＞ a hyper-sensitivity to specific sounds. In addition, to practical assessment of SPS embedded throughout this volume is discussion of the biological basis of SPS,
＞ exploring why this trait exists and persists in humans and other species.
＞
＞The Highly Sensitive Brain is a useful handbook and may be of special interest to clinicians, physicians, health-care workers, educators, and researchers.
『高感度脳』は、感覚処理感度（SPS）の科学、測定、臨床的考察を網羅した初めてのハンドブックです。この巻では、高感度の理論モデルに基づいて、子どもと成人のSPSの評価、健康と社会的転帰について論じています。また、高感受性の神経基盤や遺伝的基盤など、高感受性に関連する生物学的メカニズムに関する最新の研究をまとめています。また、SPSの臨床的な問題点や、特定の音に過敏に反応するミソフォニアなど、一見関連していると思われる疾患についても解説しています。さらに、この巻全体に組み込まれたSPSの実用的な評価に加えて、SPSの生物学的基盤についての議論があり、なぜこの形質がヒトや他の種に存在し、持続するのかを探っています。
臨床医、医師、医療従事者、教育者、研究者にとって有益なハンドブックです。
[foreverbreak] Highly Sensitive People How to Tell If You’re an HSP + Shedding Light on This Misunderstood Trait
非常に敏感な人々 あなたがHSPであるかどうかを見分ける方法+この誤解されている特性に光を当てる
https://foreverbreak.com/podcast/s1/e5/
まぁ、アセベト博士でなくてもいいけど（TEDとかにもあるよ)
ってなってるね
多数のコメントおよび元増田内の様々なＢさんの言動における一つの仮説は耳が悪く聞きづらいのではないか？ということ。
この症状はAPD(Auditory processing disorder: 聴覚情報処理障害)として知られている。APDは耳が聞こえないのではなく、聞きづらいのである。つまり、耳から入ってくる情報に対して極端に弱く、理解しづらいのである。
このように、APDは会話でのコミュニケーションにおいて困難が生じる症状であるため、会話コミュニケーションの比重が小さい学校では発覚しづらく、社会人になってからその困難に気づく人が多数である。
症状には人によってばらつきがあり、例えば、増田の場合、周りで雑音や雑談が聞こえると集中できなくなり、理解が進みにくくなり、同じことを2〜3度繰り返して聞いてしまう傾向にある。
一部の書籍やYouTubeなどメディアで取り扱われているが、まだまだ認知が広がっていないのが現状である。そのため、自覚していない人が国内でも一定数いるだろう。
仮にBさんがAPDだったとしよう。自分が考えうる最もやばい状況は、Bさん本人が自分のAPDという特性に気づかないまま、直接質問することによって耳から情報処理を行うことである。
コメント欄で多くのBさん自覚がある人、もしくはAさんの立場に思い当たる人を見かけた。もし余裕があるならば、どうかAPDの可能性を疑ってみてほしい。
・Bさん本人がAPDについて学び、その対応策について調べ、自分にあった作業方法を習得する
現在の社会における多くの仕事では、以前に比べて目と耳からの情報処理が格段に重要になった。加えて、目の悪さというのはメガネとコンタクトレンズによって劇的に改善した。そして、目に関する情報処理の際に、色弱の方などを除けば、理解に差が生じることはほとんどないだろう。
あ、い、う、え、お、を見てあいうえおと理解できない人はほとんどいないだろう。
しかしながら、耳であ、い、う、え、お、と聞いた時に、あいうえおと聞こえない人はいるのだ。
英語や他の言語を想像してほしい。日本人で英語の母音やRLを正確に聞き分けられる人がどれくらいいるだろう。その延長で、APDの人は虫食いの情報で会話に苦しんでいる。苦しんでいることにすら気づいていない。
このように、世の中に認知されていない潜在的症状はさらに沢山あるだろう。
何番煎じかわかりませんが良い出会いがあればと思ってるのはもちろん、どういうところで突っ込まれるのか気になったので書いてみました。
30歳/千葉県在住/165.9cm/64-66kg/年収手取り540万(半年スパンでじわじわ昇給)/
F欄文系卒(デザイン系、1年2年はデッサン造形、3年4年はAdobe全般、web,UIデザイン、processing)
都内のIT(インフラ系)に勤務。基本フルリモート、集中できない時は出社。土日休み/駅近に一人暮らし
お酒は好きだけど普段から家で飲むとかではない/タバコ吸ったことない
顔はココリコ田中、阿部寛、鈴木杏とかって言われたことがある。/中肉中背、よく見ると少し腹が出てる
音楽や映画、アートが好きな人。何かを手を動かして作品とかを作るのが楽しいと感じる人
/千葉～東京に住んでる人/子供はほしい/体型はちょい痩せ～ちょっと太っている(つまめるくらいにはお腹が出ている)
もしお付き合いするとなればライスワークはやめてほしい、体をこわしてほしくないので。(健康な範囲で働くのが楽しいならぜひ働いてほしい)
ニートしたい場合は(多少家事は手伝ってほしいかも)月15万までなら出します。
喫煙者であること。酒癖が悪い、大量に酒を飲む人、ポリアモリーの人
趣味・好きなこと
①写真を撮ること(主に町中での女性のポートレート)。カメラより写真が好き。
どうしても被写体の女性に使う時間(撮影、レタッチ厳選)が発生するのでそれが嫌なら言っていただければ辞めます。
②DJ 小さいDJバーで月一回位の頻度でナイトイベントに出てます。チャラ箱には行きません。ジャンルはBASS系なのでEDMやハウス・テクノなどメインストリームではありません。
これもイベントの形式上朝帰りとなってしまうため嫌なら言っていただければ辞めます
③ガンプラ作り
最近出来てませんがだいたい素組み→スミ入れくらいです。
[映画]園子温(ハラスメントを肯定する意図ではなく)、伊丹十三、庵野秀明、マーベルはクアントマニアまでは追ってた
[音楽]The Prodigy,pendulum,massive attack,boom boom satellites,BUCK-TICK,凛として時雨
声優(堀江由衣)→二次元系(ハルヒ、クラナド)→ハロヲタ→ちょっとマイナーなアイドルオタ→(現在)強いオタク属性は無し
趣味が多いので散財がちだけど、目的ができれば貯金します。パートナーとは財布別が良いです。
体に悪そうなものはだいたい好き。好き嫌いはあまり無い≒食にあまりこだわりがない
一人だとポートレートの撮影のロケハン以外引きこもりがちなのでいろいろ連れていってくれる方だとありがたい。
浅く広く以上のこと(座学、暗記、詳細なパラメータ設計等)はすこぶる苦手(MBTIで言うとENFP-T)
尊敬しあえるポイントがどこかできればと思います。簡単なことじゃないけど
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.
「Cural Health」や「Drexel’s University」「Nabla」など、各所でGPT-3の検証がなされているので、今後 医療現場でGPTが使われるようになる可能性を聞いてみた。
Me:
あなたはどのように実装されたの? Deep Learning?
GPT:
はい、私はDeep Learningによって実装されました。特に、Transformerと呼ばれるアルゴリズムを使用して学習されました。これは大量のテキストデータを学習し、自然言語処理タスクに適用することができるように設計されています。
Me:
もっと詳しく。
GPT:
Transformerは、自然言語処理タスクのためのDeep Learningアルゴリズムです。それは、大量のテキストデータを学習し、それを解釈し、理解することができるようになります。 Transformerは、複数の層からなるニューラルネットワークを使用して、テキストを処理します。それは、大量のデータを効率的に処理するために、Attention機構を使用します。これは、特定の部分のテキストに注目しながら、それらの関係を理解することができるようになります。Transformerは、自然言語処理タスクにおいて非常に成功しています。それは、自然言語の翻訳、文章の解釈、質問応答などのタスクに適用されており、高い精度を発揮しています。私はTransformerを使用して学習され、自然言語処理タスクに適用することができます。
Me:
事前訓練とかしているんだよね?
GPT:
はい、私は事前学習を受けています。事前学習とは、大量のテキストデータを使用して、言語モデルを学習することを指します。これは、言語処理タスクを実行するために必要な基礎知識を獲得するために行われます。事前学習は、言語モデルのパフォーマンスを大幅に改善することができます。それは、事前学習済みのモデルを使用することで、新しいタスクに対して学習をスキップすることができ、タスク特化の fine-tuning を行うことができるためです。私は、大量のオンラインテキストデータを使用して学習され、それを解釈し、理解することができるようになりました。それは、自然言語処理タスクに対して高い精度を発揮することができます。
Me:
今のパラメータはいくつ? 前のバージョンよりどのくらい精度がよくなった?
GPT:
私の現在のパラメータ数は、数百億から数千億となっています。これは、大量のデータを使用して学習し、高い精度を達成するために必要なパラメータ数です。精度は、バージョンによって異なりますが、一般的には、前のバージョンよりも高い精度が達成されています。これは、新しいアルゴリズムや技術の進歩によって、より良い結果が得られるようになってきているためです。また、事前学習済みのモデルを使用することで、新しいタスクに対して学習をスキップすることができ、タスク特化の fine-tuning を行うことができるため、精度が向上していることも見られます。
Me:
具体的なパラメータ数は? 前バージョンのGTP-2、現バージョンのGPT-3、次バージョンのGPT-4、それぞれ教えて。
GPT:
GPT-2は、1.5バイアレイパラメータを持っていました。それは、大量のテキストデータを使用して学習し、自然言語処理タスクに適用することができるようになっていました。GPT-3は、175バイアレイパラメータを持っています。これは、GPT-2よりも多くのパラメータを持っており、より高い精度を達成することができます。GPT-4はまだリリースされていないため、そのパラメータ数については確定していません。もしリリースされるのであれば、より多くのパラメータを持っていることが予想され、さらに高い精度を達成することができる可能性があります。
Me:
175バイアレイパラメータって何? 1750億パラメータの間違いでは?
GPT:
そうです、GPT-3は175バイアレイパラメータを持っています。それは1750億のパラメーターを持っていることを意味します。これは、大量のデータを使用して学習し、高い精度を達成するために必要なパラメータ数です。これは、現在の自然言語処理タスクに対して最も大きなモデルであり、高い精度を達成することができるため、市場においても高い評判を得ています。
Me:
バイアレイパラメータを英語で表現して。
GPT:
A "peta-parameter" or "Peta-parameter" (PT) is a measure of the capacity of a machine learning model, specifically the number of parameters it has. In the case of GPT-3 it has 175 peta-parameters, meaning it has 175 million billion parameters. These parameters help the model to understand and interpret large amounts of text data, allowing it to perform natural language processing tasks with high accuracy.
Me:
「175 billion parameters」の間違いでは?
GPT:
そうです、「175 billion parameters」の間違いでした。 GPT-3は175 billion parametersを持っています。それは、自然言語処理タスクに対して高い精度を発揮するために必要なパラメータ数です。申し訳ありませんでした。
Me:
GPT-3はどのように概念レベルの知識を持つの? Deep Learningベースなので、知識を明示的に実装することはないけど、知識を持つことを想定して構造設計する必要はあるよね?
GPT:
GPT-3は、大量のテキストデータを使用して学習されました。それによって、自然言語処理タスクに対して高い精度を発揮することができます。それは、事前学習済みのモデルを使用することで、新しいタスクに対して学習をスキップすることができ、タスク特化の fine-tuning を行うことができるためです。GPT-3は、大量のテキストデータから概念レベルの知識を学習します。それは、言語処理タスクを実行するために必要な基礎知識を獲得するために行われます。 それは、単語や文章の関係性、文章の構造、文章の意味などを理解することができるようになります。GPT-3は、Deep Learningベースのモデルであり、知識を明示的に実装することはありませんが、大量のテキストデータから知識を学習することができるように構造設計されています。それは、Attention機構を使用して特定の部分のテキストに注目しながら、それらの関係を理解することができるようになります。
Me:
Attention機構について詳しく。
GPT:
Attention機構は、自然言語処理タスクにおいて重要な役割を担う機構です。それは、大量のテキストデータを処理するために使用され、特定の部分のテキストに注目しながら、それらの関係を理解することができるようになります。Attention機構は、Seq2Seqモデルなどの自然言語処理タスクに使用されています。それは、入力シーケンスから出力シーケンスを生成するために使用されます。Attention機構は、入力シーケンスのある部分に注目し、それに基づいて出力シーケンスを生成することができるようになります。Attention機構は、ニューラルネットワークの中で使用されます。それは、重み付けされた平均値を計算し、それに基づいて入力シーケンスから重要な部分を選択することができるようになります。GPT-3は、Attention機構を使用して、大量のテキストデータから概念レベルの知識を学習し、文章の意味を理解することができるようになります。
マジレスされてチビッた．こりゃGoogleさんもビビるわけだ．
ぼく：
How were you implemented? Is it deep learning-based?
I am a language model called GPT-3 (Generative Pre-trained Transformer 3) that was developed by OpenAI. It is a deep learning-based model that uses a type of neural network called a transformer, which was trained on a large dataset of text to learn patterns of language.
GPT-3 is trained using a technique called unsupervised learning, where the model is given a massive amount of text data and uses this data to learn patterns and relationships in the language. This allows GPT-3 to generate human-like text when given a prompt, and to understand and respond to natural language input.
The training process of GPT-3 involves a technique called pre-training, which means that the model is trained on a massive dataset before being fine-tuned on a smaller task-specific dataset. This pre-training allows GPT-3 to have a good general understanding of the language and to be able to adapt to specific tasks more easily.
The GPT-3 model has 175 billion parameters, which is one of the largest language models to date, making it able to generate very human-like text and to understand context in a very natural way.
ぼく：
How does GPT-3 have a knowledgebase like an ontology? As it is deep learning-based, the knowledgebase like an ontology would not be implemented explicitly, but the structure should be designed with an expectation of having such a knowledge about hierarchy of concepts.
GPT-3, like other deep learning models, does not have an explicit knowledgebase or ontology built into its structure. Instead, it has been trained on a large dataset of text, which allows it to learn patterns and relationships between concepts.
However, the training data that GPT-3 was exposed to, likely contains a wide range of concepts and hierarchical structures. As GPT-3 learns patterns in the data, it can begin to understand the relationships between different concepts and ideas, and how they are related to each other. This allows the model to generate text that is more coherent and semantically meaningful, making it possible to understand the context and hierarchy of concepts, but it does not have an explicit ontology like a knowledge graph.
It can be said that GPT-3 has a certain level of "implicit ontology", in the sense that it has learned the relationships between concepts in a way that is similar to how a knowledge graph or ontology organizes information. However, it's important to note that GPT-3's understanding of concepts and relationships is based on the patterns it has learned from the training data, which is not a structured and explicit knowledgebase like an ontology.
ぼく：
Then how does GPT-3 handle the long-term or short term memories? Can it remember information about the on-going chatting? My name is Masuda, can GPT-3 keep it in the memory? Furthermore, can it learn and understand the new information from the Q-A queries, and use it in the future queries?
GPT-3 handles long-term and short-term memories differently. When it generates text, it uses a technique called attention mechanism which allows the model to focus on certain parts of the input while generating the output. This allows GPT-3 to keep track of previous information and context when generating text, which can be thought of as a form of short-term memory.
Regarding the on-going chatting, GPT-3 can keep track of certain information, like the name you've provided, Masuda, during the conversation and use it in future responses. However, it's important to note that GPT-3 is a stateless model, which means that it doesn't have a permanent memory that it can refer to after a conversation ends.
Regarding learning from Q-A, GPT-3 is trained on a massive dataset of text, and it learns patterns and relationships between concepts from this data. It can generate text that is semantically meaningful and coherent, but it does not have the capability to learn new information from Q-A sessions and use it in future queries. However, OpenAI has developed another model called DALL-E, which uses GPT-3 and it is capable of learning from Q-A sessions and use it in future queries.
ぼく：
Is ChatGPT superior to the Google search? Previously people used Google to search for their questions. Now a days, ChatGPT can answer to those questions. What are pros and cons of ChatGPT over the Google search?
ChatGPT and Google search are different technologies with different capabilities and strengths.
Google search is a powerful web search engine that can quickly find relevant information from a vast amount of web pages and documents. It uses complex algorithms to rank and sort search results based on relevance, popularity, and other factors. Google search is very good at providing factual information and is an excellent tool for researching a wide range of topics.
ChatGPT, on the other hand, is a natural language processing model that can generate human-like text. It is trained on a large dataset of text and uses this knowledge to understand and respond to natural language input. ChatGPT can generate text that is more coherent and semantically meaningful than a simple information retrieval engine like Google search. ChatGPT can also be used to generate text that is more engaging and human-like, such as in chatbots and virtual assistants.
One of the main advantages of ChatGPT over Google search is its ability to understand and respond to natural language input in a more human-like way. ChatGPT can also generate text that is more coherent and semantically meaningful, which can be useful in many applications. However, Google search is better at providing factual information and searching for specific pieces of information.
Another disadvantage of ChatGPT is that it doesn't have the ability to retrieve information from the web, it can only generate text based on the patterns it has learned from the training data. That's why it's not a replacement for a web search engine like Google but it can be used as an additional tool to help people to find the information they are looking for.
In summary, ChatGPT and Google search have different strengths and capabilities. While Google search is better at providing factual information, ChatGPT is better at understanding and responding to natural language input in a more human-like way.
ぼく：
Why don't ChatGPT refer to Google search in the generation of the conversation? Google Assistant has such a function. Is the reason due to the fact that OpenAI is funded by Google?
ChatGPT is a language model that generates text based on the patterns it has learned from the training data. While it can understand and respond to natural language input in a human-like way, it does not have the capability to access external sources of information like Google search.
Google Assistant, on the other hand, is a virtual assistant that can access a wide range of information and services through the internet, including Google search. Google Assistant can use this information to provide more accurate and relevant responses to user queries.
The reason ChatGPT does not refer to Google search during the generation of the conversation is likely because it was not designed or trained to do so. Instead, it was designed to generate text based on the patterns it has learned from the training data, and it does not have the capability to access external sources of information.
It's worth noting that OpenAI and Google are separate organizations, even though Google is an investor in OpenAI, this doesn't necessarily mean that the two companies work together on their products and services. OpenAI has its own development team, goals and roadmap and they are independent from Google.
ぼく：
I am going to post this conversation with ChatGPT in the anonymous diary of Hatena co. What should I do for attracting people to get many bookmarks?
There are several things you can do to attract people and increase the chances of your conversation with ChatGPT getting many bookmarks on Hatena diary. Some suggestions include:
1. Make sure your conversation is well-written and easy to understand. Use clear and concise language, and consider formatting your text in a way that makes it easy to read.
2. Use relevant keywords and hashtags in your diary entry. This can help make your conversation more discoverable to people searching for related topics on Hatena diary.
3. Include images or videos in your diary entry. This can make your conversation more visually appealing and can help attract more readers.
4. Share your diary entry on social media or other platforms. This can help increase the visibility of your conversation and attract more readers.
5. Encourage readers to share their thoughts and opinions on your conversation by including a call to action or a question at the end of your diary entry.
6. Finally, you can also try to participate in the conversation on Hatena diary, by answering the questions, responding to the comments, and engaging with your readers.
It's important to remember that getting many bookmarks on a diary is not easy and it can take time and effort to build an audience. But by following these tips and being consistent with your diary entry, you will increase the chances of attracting more readers and getting more bookmarks.
ぼく：
下記はカリフォルニア大学のビアンカ・アセベド博士の研究紹介動画と書籍と記事(Pod cast)、
興味があればどうぞ
HSP brain studies
https://www.youtube.com/watch?v=qep36Vy_0pE
＞ Did you know that the brain of a highly sensitive person (HSP) works differently?
＞ The results of fMRI brain studies conducted by Dr. Elaine Aron, Dr. Art Aron, Dr. Bianca Acevedo and their colleagues are quite fascinating.
エレイン・アロン博士、アート・アロン博士、ビアンカ・アセベド博士らが行ったfMRIによる脳の研究結果は、かなり魅力的です。
[Amazon] The Highly Sensitive Brain: Research, Assessment, and Treatment of Sensory Processing Sensitivity 1st Edition
https://www.amazon.com/Highly-Sensitive-Brain-Assessment-Sensitivity/dp/0128182512
＞ The Highly Sensitive Brain is the first handbook to cover the science, measurement, and clinical discussion of sensory processing sensitivity (SPS),
＞ a trait associated with enhanced responsivity, awareness, depth-of-processing and attunement to the environment and other individuals.
＞ Grounded in theoretical models of high sensitivity, this volume discusses the assessment of SPS in children and adults,
＞ as well as its health and social outcomes.
＞ This edition also synthesizes up-to-date research on the biological mechanisms associated with high sensitivity,
＞ such as its neural and genetic basis. It also discusses clinical issues related to SPS and seemingly-related disorders such as misophonia,
＞ a hyper-sensitivity to specific sounds. In addition, to practical assessment of SPS embedded throughout this volume is discussion of the biological basis of SPS,
＞ exploring why this trait exists and persists in humans and other species.
＞
＞The Highly Sensitive Brain is a useful handbook and may be of special interest to clinicians, physicians, health-care workers, educators, and researchers.
『高感度脳』は、感覚処理感度（SPS）の科学、測定、臨床的考察を網羅した初めてのハンドブックです。この巻では、高感度の理論モデルに基づいて、子どもと成人のSPSの評価、健康と社会的転帰について論じています。また、高感受性の神経基盤や遺伝的基盤など、高感受性に関連する生物学的メカニズムに関する最新の研究をまとめています。また、SPSの臨床的な問題点や、特定の音に過敏に反応するミソフォニアなど、一見関連していると思われる疾患についても解説しています。さらに、この巻全体に組み込まれたSPSの実用的な評価に加えて、SPSの生物学的基盤についての議論があり、なぜこの形質がヒトや他の種に存在し、持続するのかを探っています。
臨床医、医師、医療従事者、教育者、研究者にとって有益なハンドブックです。
[foreverbreak] Highly Sensitive People How to Tell If You’re an HSP + Shedding Light on This Misunderstood Trait
非常に敏感な人々 あなたがHSPであるかどうかを見分ける方法+この誤解されている特性に光を当てる
https://foreverbreak.com/podcast/s1/e5/
まぁ、アセベト博士でなくてもいいけど（TEDとかにもあるよ)
ってなってるね
下記はカリフォルニア大学のビアンカ・アセベド博士の研究紹介動画と書籍と記事(Pod cast)、
興味があればどうぞ
HSP brain studies
https://www.youtube.com/watch?v=qep36Vy_0pE
＞ Did you know that the brain of a highly sensitive person (HSP) works differently?
＞ The results of fMRI brain studies conducted by Dr. Elaine Aron, Dr. Art Aron, Dr. Bianca Acevedo and their colleagues are quite fascinating.
エレイン・アロン博士、アート・アロン博士、ビアンカ・アセベド博士らが行ったfMRIによる脳の研究結果は、かなり魅力的です。
[Amazon] The Highly Sensitive Brain: Research, Assessment, and Treatment of Sensory Processing Sensitivity 1st Edition
https://www.amazon.com/Highly-Sensitive-Brain-Assessment-Sensitivity/dp/0128182512
＞ The Highly Sensitive Brain is the first handbook to cover the science, measurement, and clinical discussion of sensory processing sensitivity (SPS),
＞ a trait associated with enhanced responsivity, awareness, depth-of-processing and attunement to the environment and other individuals.
＞ Grounded in theoretical models of high sensitivity, this volume discusses the assessment of SPS in children and adults,
＞ as well as its health and social outcomes.
＞ This edition also synthesizes up-to-date research on the biological mechanisms associated with high sensitivity,
＞ such as its neural and genetic basis. It also discusses clinical issues related to SPS and seemingly-related disorders such as misophonia,
＞ a hyper-sensitivity to specific sounds. In addition, to practical assessment of SPS embedded throughout this volume is discussion of the biological basis of SPS,
＞ exploring why this trait exists and persists in humans and other species.
＞
＞The Highly Sensitive Brain is a useful handbook and may be of special interest to clinicians, physicians, health-care workers, educators, and researchers.
『高感度脳』は、感覚処理感度（SPS）の科学、測定、臨床的考察を網羅した初めてのハンドブックです。この巻では、高感度の理論モデルに基づいて、子どもと成人のSPSの評価、健康と社会的転帰について論じています。また、高感受性の神経基盤や遺伝的基盤など、高感受性に関連する生物学的メカニズムに関する最新の研究をまとめています。また、SPSの臨床的な問題点や、特定の音に過敏に反応するミソフォニアなど、一見関連していると思われる疾患についても解説しています。さらに、この巻全体に組み込まれたSPSの実用的な評価に加えて、SPSの生物学的基盤についての議論があり、なぜこの形質がヒトや他の種に存在し、持続するのかを探っています。
臨床医、医師、医療従事者、教育者、研究者にとって有益なハンドブックです。
[foreverbreak] Highly Sensitive People How to Tell If You’re an HSP + Shedding Light on This Misunderstood Trait
非常に敏感な人々 あなたがHSPであるかどうかを見分ける方法+この誤解されている特性に光を当てる
https://foreverbreak.com/podcast/s1/e5/
前提として、Stable Diffusionでエロ画像を出そうとしてもsafety checkerという機能が入っており、センシティブな画像を出そうとすると黒塗りになる。
(Stable DiffusionのSaaSであるDream Studioはぼかしだが、多分別の技術)
https://github.com/huggingface/diffusers/releases/tag/v0.2.3
そこでGoogle Colabでちゃちゃっと環境を作り、なおかつNSFWを回避する。
2. 下記の箇所を書き換える
vvvvvvvvvvvvvvvvvv
from diffusers import StableDiffusionPipeline
^^^^^^^^^^^^
この一行を書き換えて自前のStable Diffusion Pipelineをクラス定義する。
をこぴってきてL157行目～159行目を消して貼り付ける。
これだけだ。だが、自分の性癖に刺さるStable Diffusionの作成は難しい。つーかマジ安定しない。waifuを探したければ、多分Stable Diffusionは合わない。hentai御用達ワードもなかなかヒットしなかったのでムズイ。
一応、redditを参考にワイが発掘したpromptを置いておく。
"full page antique lithograph of naked girl, sexual position, White background, art print, clean brush stroke, realistic highly detailed, post-processing highly detailed, rendered by octane engine, esty"
naked girlの間に年齢を指定するとガチであかんやつ。人の顔を安定して出すのに"lithograph of" はかなり使える。
"nude painting, big breasts, hot petite, long braided hair, hazel eyes, full round face, short smile, cinematic lightning, medium shot, mid-shot, cinematic wallpaper -C 13"
おっぱいの大きさに定評がある白人の女性がたくさん出てくる。顔の部位を丁寧に指定することで安定性が増すらしい。
・肝心のコード改修がテキトーな説明でごめんなさい。safety checkerのメソッドをオーバーライドするのが多分1番簡単だから、ぶら下がってるコメント見てください。ありがとう！
・prompt(おまじない)は無から生まれたものではなく、当然おまじないと画像を紐付けしたデータが元になっている。汎用画像分類モデルCLIPはopenaiという別の団体が公開してるおまじないと画像のデータセットだけど、これを検索できるようにしてくれた人がいる。
お気に入りのエロ画像が出てこねーのはお前のpromptが悪いからだ。それを確認できるのがこのサイトだ。
例えば中学生男子なみのムラムラしているおまえはStablediffusionでsexと入れるだろう。だが決して出てこない。
その理由はこのサイトを検索すればわかるだろう。邪魔な画像が多すぎるのだ。
同様に足をぱっかーんと開いたお姉さんを召喚してみよう。
spreadだっていってんだろ。なに足閉じてんだよをクロスしてんだよ。
フレーバーをいくら増やしてもこの手の問題は解決しづらい。例えば sex human でググると多分直感に反してラブドールの画像ばかりひっかかるだろう。
promptで重要なのは何を学習したか、その見えない文脈を推測することだ。そのためにはGoogle先生なみの文字センスと検索力が必要となるだろう。
ヒントは与えた。後は健闘を祈る
---
r/UnstableDiffusion has been banned from Reddit とのこと。
貴重な情報源が...
CPUのクロックは微妙に速くなっているがシングルコアの性能はほぼ変わらない。
マルチコアになったとして、ThreadripperのようにIOダイを使って大きくしても、劇的に速くならない。
3D V-Cacheで積層してキャッシュを増やしても、アプリレベルでは劇的に速くなってない。
更に積層するのはあるかもしれないが、熱問題に対する解決策がないので出来ないでいる。
UCIe規格経由で複数のチップレットを接続するのが今後出てくると思うが、どれだけ専用の回路を搭載し利用するかで処理能力は変わるが、
Apple M1 UltraのようにProResの本数が増えても使いこなす人が居そうにないというのと似たことになりそうじゃないか。
GPUのように広帯域のHBM/GDDRと、データ依存性がない場合は処理能力高くなるが、
CPU側のメモリーとGPU側のメモリーとのコピーやらオーバーヘッドが合ったり、ゲームがAIの一部といった感じだし、
ゲームもベンチ上は数字が変わるが体感変わらねーなってのに金額が高くなるのもな。
ユニファイドメモリーにするとApple M1系のように性能でないしさ。
レイテンシは変わらないし、DDRの代わりになるものも出て来てない。
インテルがフォトニクスに注力してたり、日本の半導体戦略でもフォトニクスとしてが上がっていたりするが、
光は早いようで遅く、メリットだと低電力か発熱源の分散でしかない。
HPEがフォトニクスで先行していたが、処理能力というより、発熱源分散での設計のし易さアピールだった。
DPU(データ プロセッシング ユニット)、OPU(Optical Processing Unit）はスパコンやクラウドでは追加されるかもしれないが、
パソコンにはまだ遠そう。
DVDが不要になり5インチベイがなくなり、SATA SSDがなくなって2.5インチベイもなくなり、
ちょっとずつパーツ買って性能上げるなんてことはなくなって、全部とっかえ。
もう少しなんとかならないか。
CPUのクロックは微妙に速くなっているがシングルコアの性能はほぼ変わらない。
マルチコアになったとして、ThreadripperのようにIOダイを使って大きくしても、劇的に速くならない。
3D V-Cacheで積層してキャッシュを増やしても、アプリレベルでは劇的に速くなってない。
更に積層するのはあるかもしれないが、熱問題に対する解決策がないので出来ないでいる。
UCIe規格経由で複数のチップレットを接続するのが今後出てくると思うが、どれだけ専用の回路を搭載し利用するかで処理能力は変わるが、
Apple M1 UltraのようにProResの本数が増えても使いこなす人が居そうにないというのと似たことになりそうじゃないか。
GPUのように広帯域のHBM/GDDRと、データ依存性がない場合は処理能力高くなるが、
CPU側のメモリーとGPU側のメモリーとのコピーやらオーバーヘッドが合ったり、ゲームがAIの一部といった感じだし、
ゲームもベンチ上は数字が変わるが体感変わらねーなってのに金額が高くなるのもな。
ユニファイドメモリーにするとApple M1系のように性能でないしさ。
レイテンシは変わらないし、DDRの代わりになるものも出て来てない。
インテルがフォトニクスに注力してたり、日本の半導体戦略でもフォトニクスとしてが上がっていたりするが、
光は早いようで遅く、メリットだと低電力か発熱源の分散でしかない。
HPEがフォトニクスで先行していたが、処理能力というより、発熱源分散での設計のし易さアピールだった。
DPU(データ プロセッシング ユニット)、OPU(Optical Processing Unit）はスパコンやクラウドでは追加されるかもしれないが、
パソコンにはまだ遠そう。
DVDが不要になり5インチベイがなくなり、SATA SSDがなくなって2.5インチベイもなくなり、
ちょっとずつパーツ買って性能上げるなんてことはなくなって、全部とっかえ。
もう少しなんとかならないか。
Whole grains" such as brown rice and barley rice improve diabetes, sleep, and depression
Keywords
Eating "whole grains" such as whole grain bread, brown rice, sprouted brown rice, millet rice, and barley rice lowers the risk of diabetes and obesity.
Studies have also shown that a whole grain eating style can improve sleep and prevent depression.
Not All Carbohydrates Are Created Equal
Choosing the right carbohydrates and adjusting the amount of carbohydrates you eat is the best approach to controlling diabetes. Of the three macronutrients, carbohydrates are the ones that have the most immediate impact on blood sugar, so we need to be careful about how we consume them.
Eating refined flour or white rice, for example, may contain the same amount of carbohydrates, but because they contain less fiber, they are absorbed more quickly, leading to an increase in postprandial blood glucose." For diabetics who need to control their blood sugar, the recommendation is whole grains," says Carla Duenas.
Duenas is a dietitian with Baptist Health South Florida, a clinical care network with seven hospitals in the U.S. state of Florida. She stresses, "To achieve a healthy diet, whole grains should be included in the diet, along with high-quality protein, vegetables, and fruits."
What to do about diabetic staples? '50-55% carbs' is healthiest
Not a fan of brown rice? Glutinous brown rice can help.
Wakame seaweed suppresses postprandial blood glucose spike Lower GI of white rice
Replace white rice with brown rice
Whole grains are grains that have not had their hulls, seed skins, embryos, or endosperm removed by processing such as milling.
Many studies have shown that a diet rich in whole grains reduces the risk of diabetes, obesity, and heart disease more than a diet rich in refined grains.
Familiar whole grains include foods such as bread, pasta, and oatmeal made from whole wheat grains, brown rice, sprouted brown rice, millet rice, and barley rice containing barley.
Brown rice is a whole grain and rich in fiber. Although whole grains are not necessarily the best choice, replacing white rice with brown rice is recommended for people with diabetes or obesity," Duenas advises.
You get the fiber you tend to lack.
Carbohydrates can be divided into simple carbohydrates, which raise blood glucose levels quickly, and complex carbohydrates, which raise them slowly. Simple carbohydrates are those found in sweets and fruits, while complex carbohydrates are those found in grains, potatoes, beans, and other foods.
Complex carbohydrates take longer to be absorbed and raise blood glucose levels at a slower rate because they are broken down into simple carbohydrates before being digested and absorbed.
Complex carbohydrates are "healthy carbohydrates. Whole grains such as unrefined flour and brown rice have properties similar to complex carbohydrates. They are rich in nutrients that are often lacking, such as fiber, vitamins, minerals, and antioxidants, which are lost during the refining process," Duenas points out.
Refined carbohydrates can also cause insomnia.
Thirty percent of adults suffer from insomnia, and part of the cause may be dietary style. Refined carbohydrates may increase the risk of insomnia in women, according to a study.
The study showed that postmenopausal women who eat junk foods and soft drinks, especially those high in carbohydrates, are more likely to develop insomnia.
Conversely, women who consume more fiber-rich fruits and vegetables have a decreased risk of insomnia.
The study was conducted by James Ganwish and colleagues from the Bagelos School of Medicine at Columbia University in the United States.
77,860 women were studied for three years.
Insomnia is often treated with pharmacotherapy and cognitive behavioral therapy, both of which are costly to the patient and expensive. Improving one's diet is low-cost, easy to implement, and free of side effects," says Ganwish.
The study is based on data from observational studies conducted by the National Institutes of Health (NIH) Women's Health Initiative Study (WHI) to obtain information to prevent and treat health problems among women.
The researchers examined the association between insomnia and 77,860 postmenopausal women who participated in the WHI. They surveyed them about their dietary habits and followed them for three years from 1997 to 2001.
The participants were analyzed by dividing them into five groups according to GI level, an index that indicates the ease with which blood glucose levels rise after a meal.
The results revealed a 16% higher risk of developing insomnia and an 11% higher prevalence in the group with higher dietary GI values. The study also found that the higher the intake of vegetables and fruits, the lower the risk of insomnia.
The study also found a lower risk of developing depression.
The study found that "a spike in blood glucose levels after a meal stimulates the secretion of insulin, which lowers blood glucose, and may lead to a state of hyperinsulinemia. As a result, blood glucose levels drop and the secretion of hormones such as adrenaline and cortisol increases, which may disrupt sleep," explains Ganwish.
The foods that trigger insomnia may be processed foods that contain high levels of isomerized sugar, which is composed of fructose and glucose. Such foods are not found in nature, but are mass-produced industrially and sold cheaply.
Fruits also contain fructose, but they are also rich in fiber. Fruits have a low GI and are thought to be less likely to cause postprandial blood sugar elevation.
A study of 69,954 women who participated in the WHI, published by Ganwish and colleagues in 2015, also showed that women who ate a high GI diet had a 22% higher risk of developing depression.
Gunwish noted, "We need randomized clinical trials to determine the benefits of improving diet and increasing intake of whole grains and complex carbohydrates to prevent and treat insomnia and depression."
