はてなキーワード: g dataとは
Will the Bank of Japan raise rates above zero?
No. 2024 will be the year the Japanese central bank finally ditches yield curve control and negative interest rates. Contrary to market expectations of several further increases, however, rates will end the year no higher than zero.
With mediocre wage growth, a stronger yen in prospect as US rates peak and reasons to prefer a steeper yield curve, the BoJ is unlikely to set a positive rate — although as with any central bank forecast, much depends on the incoming data. Robin Harding
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ChatGPT:
Yes, I can provide confidence scores with my answers when they might be inaccurate. However, please note that the confidence score is based on the model's understanding of the input and context and is not a measure of the accuracy of the answer.
ふむふむ・・・・（中略）
つけてくれへんやんけ
ワイ:
ChatGPT:
I apologize, as a language model, I don't have the capability to calculate confidence scores for individual sentences. However, I have been trained on a large dataset, and I will do my best to provide accurate information based on my knowledge cut-off.
Please note that the information provided might not be up-to-date and it's always good to double-check the information with the official source.
そこも嘘かーい
ワイ:
You said "Yes, I can provide confidence scores with my answers when they might be inaccurate. ".
ChatGPT:
I apologize for the confusion, as a language model, I do not have the capability to assign confidence scores to my responses. I apologize for any confusion caused by my previous response. My main goal is to provide the most accurate information based on my training data, however, the information provided may not be up-to-date or may contain inaccuracies.
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：
データ分析の練習や説明用によく使われる、「1970年代の」ボストンの不動産価格データセットは、"ethical problem" があって、いろんな機械学習ライブラリから排除されるのね。
“B”
Just in case you’ve gotten this far without somehow paying attention, the column in question is called “B”:
B: Black proportion of population. (1970 US Census)
This is already offensive to 2019 eyes (and hopefully 1975 eyes). But let’s try to give the authors the benefit of some doubt that I’m missing something historical, or otherwise relevant, that would warrant the inclusion of this field in the “common specification” of hedonic house pricing.
racist data destruction?. a Boston housing dataset controversy | by M Carlisle | Medium
https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8
Deprecated since version 1.0: This function is deprecated in 1.0 and will be removed in 1.2. See the warning message below for further details regarding the alternative datasets.
sklearn.datasets.load_boston — scikit-learn 1.0.1 documentation
https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html
データに含まれる "B" という「地域住民のうちの黒人の割合」を示す列がいかんらしいね。まぁ、日本で「〇〇市の不動産価格データセット」があったとして、そこに「地域住民のうちの部落出身者の割合」が同じく "B列" であったら燃えるだろうしなぁ。でも、実際に目的変数である不動産価格に負の影響を及ぼしてるんだけどねぇ。
Four years after the Fukushima accident, TC screening data from the first and second round (up to December 2014) were compared with the Japanese nationwide annual TC incidence, and with the incidence in one area of Fukushima prefecture selected as reference (Tsuda et al., 2016). The authors reported that the observed number of TCs was substantially higher than the expected number based on national and regional incidence data, and concluded that this increase could be attributed to ionizing radiation exposure from the accident. This ecological study has been strongly criticized by scientists around the world because of serious methodology limitations; further, the study conclusions are not supported by the results (Davis, 2016, Jorgensen, 2016, Korblein, 2016, Shibata, 2016, Suzuki, 2016, Takahashi et al., 2016, Takamura, 2016, Wakeford et al., 2016). Limitations of ecological study design are well-known, although the authors did not acknowledge the issue of ecologic fallacy. Another criticism was that the data from the Fukushima screening program are not directly comparable with the cancer registry data from the rest of Japan where systematic advanced ultrasound technology is not used to detect cases. The authors of these criticisms suggested that though the increased TC number could be associated with the exposure from radioactive fallout, a more plausible conclusion would be that the screening program is finding an anticipated increase in TC detection across the Fukushima prefecture. Indeed, Tsuda and colleagues did not consider the latent properties of TC, nor the fact that a prevalent cancer detected by screening might have had first preclinical manifestations of abnormality before the nuclear accident.Thereafter, several researchers have analysed the relationship between radiation exposure (with different estimated exposure levels, mostly using an external dose) and TC prevalence and incidence in residents aged ≤18 years in the Fukushima prefecture at time of the disaster (Kato, 2019, Nakaya et al., 2018, Ohira et al., 2019a, Ohira et al., 2020, Ohira et al., 2019b, Ohira et al., 2016, Ohira et al., 2018, Suzuki et al., 2016, Toki et al., 2020, Yamamoto et al., 2019), but no radiation-related risks have been demonstrated to date.
https://www.sciencedirect.com/science/article/pii/S0160412020321851
https://arxiv.org/abs/1804.10390
Ecologyの研究には、情報科学の世界で研究されている画像解析の技術を使えば解決可能なものがある。著者らは、この一つである植物の分布の計測を試みている。具体的な解決方法は、UAVデータから撮影した画像データを元に、木々の種類を識別する分類器を作成である。手順として、最初に、UAVから画像と高度データを著者自らが撮影を行った。次にそのデータから学習用の教師データをいくつかの手順を踏んで作成する。この手順は、さらなる研究を行う際にも適用が可能である。結果、識別器は良い識別性能をだしている(Model1で83.1%)。前処理として、画像を切り分けて分類器にかけなければならないという点は未解決である。
https://arxiv.org/abs/1708.01986
著者らは、コケ植物の画像判定を行うため、撮影した画像データから学習データを作成し、識別機を作っています。
データは著者らが作成しており、少数の画像から判定機を作成するために、"chopped picture" methodを提案しています。
画像枚数を多くするために、少部分間には50%のoverlapが存在します。言い換えるなら、ある画像は、他2枚の画像を元に再構築可能です。
CNNの学習では、切り出した画像から、validation dataとtraining dataに分けて学習を行っています。
以上の方法で、validation dataによってこの識別機の精度の計測をし、高い精度で識別できていることを主張している。
提案手法は、training dataとvalidation dataに関係があるので、validation dataでの精度は高くなると思われます。
言い換えると、全データに識別機がオーバーfittingしているので、validation dataでの精度が高くなっているのでは？
つぎに、この論文の提案手法でのvalidation dataを識別機に食わしたさいの分類精度は、その識別機の分類精度とは言えません。
validation data・training dataに含まれていないdataセットを作成し、その新たなdataセットでの学習機の精度を検証するべきだと思います。
https://www.biorxiv.org/content/10.1101/351643v2
正確に読解できていなかったら申し訳ない
管理されなくなった竹が他の植物の生えた地域に侵略していくことが問題となっている。
この問題に対処するには、竹の移動を経時的に追うこと、または、侵略しているかを判別することが求められる。
本論文は、この問題を解決するために、Deep learningを用いて衛星画像データを竹とそれ意外に識別を試みる。
画像はGoogle Earthから取得し、著者らが3種類のTag付けを行った。このデータを元に学習を行った。
ただし、衛生画像を撮影した時期によって、精度が著しく損なわれるので、現時点では経時変化を追跡する課題は解決されていない。
竹林が徐々に他の植生の場所に侵略している。この問題を解決するために、まずどこに竹が生えているのかを識別するのがこの論文の目的。
この課題を解決するためにGoogleEarthの画像は適切なのか？言い換えれば、課題に対するシステムの必要要件が記述されていない。
この論文で提示している問題は、植物の侵略を判定する識別器をつくることである。故に、竹やぶと建物の境目になる部分が正しく識別されていることがこのシステムの必要要件である（多くの範囲が正確に識別されることは目標ではない）。
よみました
http://www.av-comparatives.org/images/stories/test/ondret/avc_report25.pdf
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