【謝晨彥分析師Line官方帳號】 https://lin.ee/cdWWQ9a 2025.02.19【馬斯克助攻AI機器人將噴發! 停戰商機來了航運還有戲!】台股怪談謝晨彥分析師☆ ...本基金為直接投資中華民國境內之指數股票型基金，屬於單一國家指數股票型基金，本基金主要投資於臺灣證券集中交易市場之上市上櫃股票及指數股票型基金受益憑證，可能面臨之 ...【00878國泰永續高股息/ 存股領息賺錢】更新開頭影片，月月買零股第14個月，本月加碼買進=1,000股 ...在開場時，如果需要特別指出缺席的人和原因，則可用下面簡單的表達方式。（someone） has sent her apologies. She's under the weather. （某某人深感抱歉，因 ...三、課程大綱 Course Outline (本課程大綱教師得依實際教學進度及學生學習情況 ... 面授In-person classes, 國泰金控– 氣候、健康與培力，打造3000億規模00878. 4 ...你手中持有永續股嗎? 截至2024年7月，臺灣共有9檔以ESG、永續、低碳為投資策略的ETF，深受投資人喜愛。但在這股浪潮中，我們是否已經全面了解了ESG評價標準、政府監管 ...... 00878或0056，比較容易累積張數，今年的股息會優先流向這三檔中的其中一檔，原因是它們相對安全。既然個人的資產配置會有60-70%轉向ETF，何不早點進行 ...... outline for a mystery podcast on X調查。" "整合, 請寫出500字的pocast腳本給第 ... 00878 國泰ESG 永續高股息、00900 富邦特選高股息、00713 元大台灣高息低波 ...1、Introduction：包括自我介紹、破冰開頭（Ice breaker）、大綱介紹（ Outline ... 00878才剛填息「就要除息」！填息率百分百、23元能上車？多次股災僅 ...居家衛教組在功能性平衡、下肢肌力沒有達到顯著差異。結論：個別化太極組與全套太極組經過八週訓練後，顯著提升高齡者平衡功能及下肢肌力的表現。而成效以個別化太極組優於 ...Word Embedding 圖片來源：slidestalk.com/u3805/Unsupervised_Learning_Word_Embedding Word apple banana orange strawberry grape cherry watermelon peach pear lemon vanilla chocolate mango durian blueberry pip install 是用來使用 Pip 套件管理器安裝 Python 套件的指令。 正在安裝 LangChain 套件以及 langchain-openai，這是由 LangChain 團隊提供的最新更新。 !pip install langchain==0.1.4 !pip install langchain-openai==0.0.5 正在安裝 OpenAI 套件，其中包含我們可以用來與 OpenAI 服務通訊的類別。 !pip install openai==1.10.0 Let's use OpenAI 匯入了 Python 內建的 "os" 模組。 這個模組提供了一種與作業系統互動的方式，如訪問環境變數、處理檔案和目錄、執行 shell 命令等等。 "environ" 屬性是一個類似字典的對象，其中包含當前作業系統會話的環境變數。 通過訪問 "os.environ"，您可以在您的 Python 程序中檢索和操作環境變數。例如，您可以使用以下語法檢索特定環境變數的值：os.environ['VARIABLE_NAME']，其中 "VARIABLE_NAME" 是您想要訪問的環境變數的名稱。 import os os.environ["OPENAI_API_KEY"] = "sk-AxSHoRskdajfhlksadfhg4bO7MWSig4ZsdfY9AT" LangChain 為 OpenAI API 建立了一個封裝器，我們可以通過它來訪問 OpenAI 提供的所有服務。 以下的程式碼片段從 'langchain' 庫的 'embeddings' 模組中匯入了一個特定的類別，名為 'OpenAIEmbeddings'（封裝了 OpenAI 大型語言模型的類別）。 #As Langchain team has been working aggresively on improving the tool, we can see a lot of changes happening every weeek, #As a part of it, the below import has been depreciated #from langchain.embeddings import OpenAIEmbeddings #New import from langchain, which replaces the above from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() our_Text = "Hey buddy” text_embedding = embeddings.embed_query(our_Text) print (f"Our embedding is {text_embedding}") ICCV23 workshop “Quo Vadis, Computer Vision?” answers this query. The field of computer vision has seen incredible progress, but some believe there are signs it is stalling. At the International Conference on Computer Vision 2023 workshop “Quo Vadis, Computer Vision?”, researchers discussed what’s next for Computer Vision. In this post we bring you the main takeaways from some of the best minds in the Computer Vision landscape that gathered for this workshop during ICCV23 in Paris. Table of Contents 目錄 1.Quo Vadis, Computer Vision? 2.The Anti Foundation Models 3.Data over Algorithms 4.Video can describe the world better than Text 5.After Data-Centric, the User will be the core 6.Bring back the fundamentals 7.So, is Computer Vision dead? Disclaimer: We went under cover into the workshop to bring you the most secret CAMRiP quality insights! 1. Quo Vadis, Computer Vision? Computer vision has reached a critical juncture with the emergence of large generative models. This development is having a dual impact. On one hand, it is opening new research avenues and attracting academics and businesses eager to capitalize on these innovations. However, the swift pace of advancement is also causing uncertainty among computer vision researchers about where to focus next. Many feel conflicted wondering if they can match the progress in generative models compared to more established computer vision problems. This ICCV 2023 workshop (see Figure 1) brought together experts like David Forsyth, Bill Freeman, and Jitendra Malik to discuss this pivotal moment. In the following sections we provide some highlights of the lively discussions followed on how computer vision should adapt and leverage generative models while still tackling core challenges in areas like video and embodied perception. There was consensus that combining strengths of computer vision and generative models thoughtfully is key, rather than seeing them as competing approaches. 2.The Anti Foundation Models MIT’s professor Bill Freeman, provided three reasons why he doesn’t like foundation models: Reason 1: They don’t tell us how vision works In short, Bill Freeman argues that foundation models are capable of solving vision tasks but despite this achievement, nobody is able to explain how vision works (i.e. they are still a black-box). Reason 2. They aren’t fundamental (and therefore not stable) As shown in Figure 2, professor’s Freeman hints that foundation models are simply just a trend. Reason 3. They separate academia from industry Finally, professor Freeman argues that foundation models create a boundary between those in academia (i.e. creative teams but no resources) versus those in industry (i.e. unimaginative teams but well-organized resources). 3. Data over Algorithms 3. 數據勝於演算法 Berkeley’s professor, Alexei (Alyosha) Efros, shared the two ingredients for achieving true AI: Focus on data over algorithms: GigaGAN [1] showed that large datasets enable old archiectures such as GAN to scale. Bottom-up emergence: data per-se is mostly noise, what is crucial is the right kind of (high-quality) data. Also, he argues that LLMs are winning because they are being trained on all the available data with just 1 single epoch! (see Figure 3). 4.Video can describe the world better than Text An audacious take was made by Berkeley’s professor Jitendra Malik, where he suggested that video is a more efficient (and perhaps effective) way to describe the world. His views are supported by arguing that any book (see Figure 4 for some examples) can be represented in a more compact way using video (i.e. frames) than text (i.e. tokens): the same information can be conveyed way more efficiently using video than text.Professor Malik believes video will help put Computer Vision again on the map in the next few years. 5. After Data-Centric, the User will be the core Princeton’s professor, Olga Russakovsky, provided fascinating insights on what is next after the data-centric approach to machine learning. She elegantly explained, Figure 5, how the field has evolved from a pure focus on models (i.e. year 2000) to the current moat of “data is king”, and argues that a time where the human (i.e. user) is the center is next. For instance, she makes the case for the need of gathering truly representative data from all over the world rather than simply focusing on web data, see Figure 6. 6. Bring back the fundamentals Finally, MIT’s professor, Antonio Torralba gave a lightweight talk where he candidly shared his views on why curiosity is more important than performance (see Figure 8), especially in today’s LLMs driven world. Professor’s Torralba argues that the field of Computer Vision has been already in a position where (mostly) outsiders confidently argue that the field has stalled, yet time has proven that someone comes up with some clever idea by. 7. So, is Computer Vision dead? The ICCV23 workshop makes clear that rather than being dead, computer vision is evolving. As leading experts argued, promising directions lie in the interplay between vision and language models. However, other frontiers also hold potential, like exploring when large vision models are needed or providing granular control over frozen generative architectures, as described by one of the papers awarded with the Marr Prize [2] in ICCV23. While progress may require integrating strengths of vision and language, key computer vision challenges remain in areas like texture perception or peripheral vision where the question of how to throw away information is still a challenge. With an influx of new researchers and industry interest, the field is poised to take on some of these questions. References 參考 [1] Scaling up GANs for Text-to-Image Synthesis [2] Adding Conditional Control to Text-to-Image Diffusion Models """請幫我總結此長篇文章，然後幫我總結成30條bullet points給我"""cover這個字有很多不同的意思如「報導」、「涵蓋」等，但這裡當動詞，表示「要討論」。 I think we’ve covered most of the items on the agenda. （我想我們應該討論完議程上大部分的項目了。） 2.開場與歡迎 會議開場時通常會有幾個步驟，首先時歡迎與會者（welcome）和介紹一些特殊的與會者、講者，或者是開場者的自我介紹，以下就介紹幾個簡單的開場方式： First of all, I’d like to welcome you all to today’s meeting. Well, since everyone is here, I think we should get started. We have a lot to cover today, so I think we should begin. I’d like to take a moment to introduce（someone）. 例如： Our writing teacher tried to be objective and impartial. （我們寫作老師試著公正客觀。） The managers pointed out the main objective for the project. （經理指出這專案的主要目的為何。） 參與者可用attendees或participants，其中主席可在名字後面加上（chair或chairperson）的字樣來表示。另外，apologies在這裡agenda中不是道歉的意思，通常是列出缺席的人名。而objective這個字的意思為「目的」，代替了名詞purpose。此外，objective也可當作形容詞，作為「公正的，客觀的」解釋，相反則是subjective、biased。 “When you go to meetings or auditions and you fail to prepare, prepare to fail. It is simple but true.” ——Paula Abdul 「當你毫無準備的去參加會議或試鏡，你只好準備接受失敗吧。事實就是如此簡單明瞭。」——寶拉阿巴杜（歌手） 許多職場人士經常都需要開會，但要如何在會議中展現專業的一面呢？就讓我們來看看會議流程中有哪些常見的英文字，學起來不但日後在會議可以派上用場，還可以多掌握幾個 多益 關鍵字！ 1.流程與計畫 一個有效率的會議都經過安排和計畫，如果沒有計畫的會議則會演變成漫無目的的討論，很可能最後只是浪費時間而且毫無收穫。因此一般來說會議通常會有事先排定的流程與計畫（agenda），而agenda或許有些不同，但大致上都會有基本資訊如日期（date／time）、地點（venue）等。AOB指的是Any Other Business的意思，也就是「其他事項」，通常會列於所有討論項目的最後。 在開場時，如果需要特別指出缺席的人和原因，則可用下面簡單的表達方式。（someone） has sent her apologies. She’s under the weather . （某某人深感抱歉，因病無法出席。）under the weather是表示身體微恙，不舒服的意思，和unwell或out of sorts相同意思。資料來源： Bloomberg 、國泰投信整理， 2013/05/31~2023/9/29 。註： (1)00878 追蹤指數為 MSCI 台灣 ESG 高股息指數，該指數發布日為 2013/05/31 、 00687B 追蹤指數為彭博 20 年期(以上)美國公債指數。 (2) 每單位報酬風險=年化報酬率/年化波動度，用來衡量投資人承擔相同風險下所獲得的報酬。 (3)以上指數皆為含息報酬指數，僅供參考用途，各指數之歷史績效不應被視為現在或未來表現及績效的保證，亦不代表基金現在或未來之報酬率。 注意：上述指數皆為含息報酬指數，僅供參考用途，各指數之歷史績效不應被視為現在或未來表現及績效的保證，亦不代表基金現在或未來之報酬率。 資料來源： Bloomberg 、國泰投信整理， 2013/05/31~2023/6/30 。 MSCI 臺灣 ESG 永續高股息精選