https://web.archive.org/web/20240402125351/https://garymarcus.substack.com/p/when-will-the-genai-bubble-burst

On the investment and revenue in #algogens AI. Very lopsided, and surveys report dying enthusiasm with those closely involved. Voices doubt something substantial will come out this year, and if not it will deflate hype of expectations. #prediction for early #2025/ AI hype died down

While today’s LMs agents don't pose a serious risk, we should be on the lookout for improved autonomous capabilities as LMs get more capable and reliable.

~$50m = estimated training cost of GPT-4

Programming with LLM = less stress

In a study, students who learned to code with AI made more progress during training sessions, had significantly higher correctness scores, and retained more of what they learned compared to students who didn't learn with AI.

OpenAI’s voice cloning AI model only needs a 15-second sample to work

Next to the xz debacle where a maintainer was psyops'd into backdooring servers, this is another new attack surface: AI tools make up software packages in what they generate which get downloaded. So introducing malware is a matter of creating malicious packages named the way they are repeatedly named by AI tools.

Nice to see there's better support and performance for Traditional Chinese in Claude 3 than ChatGPT4. It feels like David against Goliath.

「支語警察」，哈哈

謝昆霖 enrdoSspotmlf916af3f9ct1maca h h2ft27cmlc8m5c8c5 1 i2l70554i08m1 · 這幾天工作同時有請 GPT-4 和 Claude3 。工作條件：我的提問使用正體中文，同樣問題，只給一次作答機會。 Claude3 的回覆速度是 GPT-4 的四倍 (或十倍) 以上不說，而且應答的專業度較高，正體中文的使用也比較在地，我這個支語警察給過。 它的回答是大量文字輸出時，GPT4 講一講到後面會跑出簡體中文，Claude3 我還沒遇過，而且字數極多的多輪對話中，它對前面的脈絡記憶很清楚。 值得注意的是，GPT-4 正體中文貧乏的問題 Claude3 似乎沒有，它的用字遣詞比 GPT4 多一些人類的隨機性，在重覆20次的例句中，同樣的動詞、形容詞，它會盡量用不同的文字。 Claude3 似乎解決了我常常酸 GPT-4 的各種問題。這真是驚人。

So good, so true. GPT人没有重要性感受和原创想法，没有办法将理论与实际联系； 可以和那一篇提及the illusion of explanatory depth一起阅读； 一个有用的自测：你说的X到底是什么意思，能不能举个例子，反对X的人有什么理由？ 以及看看这种话到底有没有反对者——反对者是一个argument最好的反脆弱性测试。

可能的威胁：de novo design有害蛋白 solutions： - 自我审查：软件限制 - 公司审查：合成DNA的时候用AI进行审查，但是这个风险转接不知公司是否愿意

两个有趣的概念： explanatory depth: 误以为自己很了解某事 exploratory breadth: AI 可能让人偏向于去探索AI能做的东西

可以和 歪脑的 「意识上传」一起阅读

https://www.theguardian.com/technology/2024/mar/07/ai-climate-change-energy-disinformation-report

https://web.archive.org/web/20240305083845/https://www.te-learning.nl/blog/over-de-betrekkelijkheid-van-veilige-ai-en-het-belang-van-digitale-geletterdheid/

By [[Wilfred Rubens]] citing Leon Furze (?) how 'AI made safe' isn't safe AI just as alcohol free beer isn't soda. I think there's an element here of [[Triz denken in systeemniveaus 20200826114731]] analogue to [[Why False Dilemmas Must Be Killed to Program Self-driving Cars 20151026213310]] where all the focus is on the thing (application, car etc)

下降幅度最大的 3 個類別是寫作、翻譯和客戶服務工作

寫作職位數量下降了 33%，翻譯職位數量下降了 19%，客戶服務職位數量下降了 16%

翻譯絕對是受打擊最嚴重的工作，每小時工資下降了 20% 以上

https://shindig.com/login/event/100yearedtechlive

08:18 Technology like AI enables one person businesses (see Sam Altman statement on 1 billion one person company)

Highlight and annotate at least 2 areas for each question. The annotations should be 1-2 sentences explaining the following: A. New learning B. Familiar with this C. Use this in practice

Broderick: https://www.garbageday.email/p/ai-search-doomsday-cult, Anderson: https://arxiv.org/abs/2305.17493

AI search hides the authors of the material it presents, summarising it is abstracting away the authors. It doesn't bring readers to those authors, it just presents a summary to the searcher as end result. Take it or leave it. At the same time, if one searches for something you know about, you see those summaries are always of. Leaving you guessing how of it is when searching something you don't know about. Search should never be the endpoint, always a starting point. I think that is my main aversion against AI search tools. Despite those clamoring 'it will get better over time' I don't think it will easily because the tool nor its makers have any interest in the quality of output necessarily and definitely can't assess it. So what's next, humans factchecking AI output. Why not prevent bs at its source? Nice ref to Maggie Appleton's centipede metaphor in [[The Expanding Dark Forest and Generative AI]]

中間這位受訪的台灣人，父母在她英文學習上下重本投資，從小讀雙語幼稚園，課後上雙語補習班，中學上雙語私校，大學唸英語系，研究所唸了師大口譯所，錄取八名口譯組學生，最後專業考試還是唯一考過的兩人之一。從這些事實看來，她的英語應當可謂萬中選一，可是爲什麼英語發音還是有問題？

我發現，評斷英語發音好壞，其實有一個巧妙的方法：讓AI機器語音識別轉寫文字，看文字是什麼。很多時候，雖有稍微的口音，機器仍然辨認正確，產出的英文是正確的。但當一個字的發音偏誤到更像另一個字時，機器就毫不客氣，產生另一個字了。發現語音辨識常有這種「誤植另一字」問題是，就表示英語發音有問題。

AI機器輔助偵測辨識英語發音問題法，前提當然是，文字不能再經人工校正，必須是原始的結果。例如，本影片有兩個英文字幕，一個是後製在螢幕上的那個，那是人工校正過的，一個是YouTube自動生產的（也可透過Hypothesis顯示），我們要看的是後者。這個機器字幕在前4分鐘的內容中，指出她英語發音上至少兩個錯誤，不知各位有沒有光從聽就能察覺到？

T. Herlau, "Moral Reinforcement Learning Using Actual Causation," 2022 2nd International Conference on Computer, Control and Robotics (ICCCR), Shanghai, China, 2022, pp. 179-185, doi: 10.1109/ICCCR54399.2022.9790262. keywords: {Digital control;Ethics;Costs;Philosophical considerations;Toy manufacturing industry;Reinforcement learning;Forestry;Causality;Reinforcement learning;Actual Causation;Ethical reinforcement learning}

for - progress trap - AI

AI to generate video images.

ai enhanced

[[Lee Bryant]] links to this overview by Simon Willison of what happened in #2023/ in #AI . Some good pointers wrt [[ChatPKM myself]] dig those out.

for - AI progress trap - bad actor - synthesize bioweapon - AI progress trap - Coscientist and cloud lab - bad actors

for - Coscientist AI - Science AI - AI lab partner

Concentration of power.

for - progress trap - sexually explicit AI deepfake - Taylor Swift - sexually explicit AI deepfake

nice case ofr liberal education

https://itsfoss.com/open-source-chatgpt-alternatives/#1-openchatkit Alternative a ChatGPT

Resource Collections

AI in Education Resource Directory

This document contains AI resources of interest to instructors in higher education including tools, readings and videos, presentations, links to AI policies and a resource spreadsheet. The document is managed by Daniel Stanford (SCAD) and contributed to by the AI in Education Google Group.

Courses and Tutorials

Prompt Engineering for ChatGPT

This popular six-module course provides basic instruction in how to work with large language models and how to create complex prompt-based applications for use in education scenarios. Dr. Jules White (Vanderbilt) is the instructor for the course. Absolute beginners to experienced users of large language models will find helpful guidance on designing prompts and using patterns.

AI Checker Resources

Michael Coley, Guidance on AI Detection and Why We’re Disabling Turnitin’s AI Detector, Vanderbilt University, https://www.vanderbilt.edu/brightspace/2023/08/16/guidance-on-ai-detection-and-why-were-disabling-turnitins-ai-detector/ (last visited Sep 25, 2023).

In August, 2023, Vanderbilt's Center for Teaching and Learning provided an explanation of the university's decision to disable Turnitin's AI detection tool. Other universities, such as the University of Pittsburgh, have provided comparable statements about AI writing detection. Vanderbilt noted that AI detection was a difficult or impossible task for technology to solve and will become more difficult as AI tools become more common and advanced. The articles below describe some of the technical challenges with AI detection and unintended effects (e.g., bias against non-native English writers).

1. Vinu Sankar Sadasivan et al., Can AI-Generated Text Be Reliably Detected?, (2023), http://arxiv.org/abs/2303.11156 (last visited Oct 26, 2023).
2. Andrew Myers, AI-Detectors Biased Against Non-Native English Writers, Stanford HAI (2023), https://hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers (last visited Sep 25, 2023).
3. Susan D’Agostino, Turnitin’s AI Detector: Higher-Than-Expected False Positives, Inside Higher Ed (2023), https://www.insidehighered.com/news/quick-takes/2023/06/01/turnitins-ai-detector-higher-expected-false-positives (last visited Sep 25, 2023).
4. Geoffrey A. Fowler, Analysis | We Tested a New ChatGPT-Detector for Teachers. It Flagged an Innocent Student., Washington Post, Apr. 14, 2023, https://www.washingtonpost.com/technology/2023/04/01/chatgpt-cheating-detection-turnitin/ (last visited Sep 25, 2023).
5. Michael Webb, AI Detection - Latest Recommendations, National centre for AI (Sep. 18, 2023), https://nationalcentreforai.jiscinvolve.org/wp/2023/09/18/ai-detection-latest-recommendations/ (last visited Jan 25, 2024).

Plagiaristic outputs from blockbuster films in Midjourney v6

Was the LLM trained on copyrighted material?

Sometimes, search is not rooted in discovery of a correct answer to a question. It's about exploration. Serendipity through search. Think Michael Lewis, Malcolm Gladwell, and Latif Nasser from Radiolab. The randomizer on wikipedia. A risk factor of where things trend with advanced AI in search is an abandonment of meaning making through exploration in favor of a knowledge-level pursuit that lacks comparable depth to more exploratory experiences.

• for: canonical unit, collaborative commons - missing part - open learning commons, question - process trap - natural capital

• comment

  • in this context, indyweb and Indranet are not the canonical unit, but then, it seems the model is fundamentally missing the functionality provided but the Indyweb and Indranet, which is and open learning system.
  • without such an open learning system that captures the essence of his humans learn, the activity of problem-solving cannot be properly contextualised, along with all of limitations leading to progress traps.
  • The entire approach of posing a problem, then solving it is inherently limited due to the fractal intertwingularity of reality.

• question: progress trap - natural capital

  • It is important to be aware that there is a real potential for a progress trap to emerge here, as any metric is liable to be abused

Überblick über den gerade beschlossenen italienischen Klimaanpassungsplan. Er geht davon aus, dass der Meeresspiegel bis 2065 um 19 cm ansteigt und das adriatische Meer und 2,3 Grad wärmer werden wird. Italien ist ein Hotspot der globalen Erhitzung in der besonders verwundbaren Mittelmeerregion. Zentrales Thema sind Maßnahmen zum besseren Schutz der Wasserressourcen des Landes. https://www.repubblica.it/green-and-blue/2024/01/03/news/pnacc_piano_nazionale_di_adattamento_ai_cambiamenti_climatici_approvazione-421802705/

Plan: https://www.mase.gov.it/sites/default/files/PNACC_DOCUMENTO_DI_PIANO.pdf

• annotate
• for: AI - progress trap, technology - colonialism

• for: AI ethics controversy - citations from Dean please!

• comment

  • Can Dean please provide the missing citations he is referring to?

• for: example - progress trap - AI - mistranslation

• for: AI - untraceability - metaphor

metaphor - untraceability - AI: like a self configuring engine - Imagine a metaphor in the automobile industry. Imagine a car that could self-design itself. - Now imagine the car breaking down and the owner has to bring it into a repair shop to get it fixed. - The problem is that because the AI car designed its own engine and did not make a record of how that was done, no mechanic can fix it.

• for: progress trap -AI, carbon footprint - AI, progress trap - AI - bias, progress trap - AI - situatedness

真相與行銷的差別 前者要費心挖掘 後者有錢好辦事

I think Kissinger's figure is too controversial to leave it at "celebrated".

See: https://en.wikipedia.org/wiki/Is%E2%80%93ought_problem

hm, I'm not sure how LLMs acquire these higher-level concepts out of the probabilistic relations just described.

Universal Summarizer

(Summary generated with Kagi's Universal Summarizer.)

Bandcamp has operated as an online music store for over a decade, providing artists and labels with an easy-to-use platform to sell music directly to fans. While receiving little mainstream attention, Bandcamp has paid out $270 million to artists and maintained a simple, artist-focused design. The platform allows free streaming but encourages direct purchases from artists. Chance the Rapper has been a notable champion of Bandcamp, using it for early mixtapes and helping to bring attention to its role in supporting independent musicians. While other services focus on algorithms and playlists, Bandcamp prioritizes direct artist support through low fees and transparent sales data. It has changed little over the years but provides a niche alternative for direct fan-artist connections without the culture-diluting aspects of other streaming services. Bandcamp's low-key approach has helped it avoid issues faced by competitors while continuing to innovate for artists.

• for: AI, Anirban Bandyopadhyay, brain gel, AI - gel computer

• title: A general-purpose organic gel computer that learns by itself

• author
  • Anirban Bandyopadhyay
  • Pathik Sahoo
  • et al.
• date: Dec. 6, 2023
• publication: IOPScience

• DOI: 10.1088/2634-4386/ad0fec

• ABSTRACT

  • To build energy minimized superstructures, self-assembling molecules explore astronomical options, colliding ∼10 to 9th power molecules s to power−1. -Thusfar, no computer has used it fully to optimize choices and execute advanced computational theories only by synthesizing supramolecules.
  • To realize it,
    • first, we remotely re-wrote the problem in a language that supramolecular synthesis comprehends.
    • Then, all-chemical neural network synthesizes one helical nanowire for one periodic event. These nanowires self-assemble into gel fibers mapping intricate relations between periodic events in any-data-type,
    • the output is read instantly from optical hologram.
    • Problem-wise, self-assembling layers or neural network depth is optimized to chemically simulate theories discovering invariants for learning.
    • Subsequently, synthesis alone solves classification, feature learning problems instantly with single shot training.
    • Reusable gel begins general-purpose computing that would chemically invent suitable models for problem-specific unsupervised learning. Irrespective of complexity,
      • keeping fixed computing time and power, gel promises a toxic-hardware-free world.

• for: quote - Yuval Noah Harari - AI progress trap, progress trap - AI, quote - progress trap

• quote it is extremely dangerous to create such an autonomous agent when we do not know how to control it, when we can't ensure that it will not escape our control ad start making decisions and creating new things which will harm us instead of benefit us

• author: Yuval Noah Harari
• date 2023

• for: AI - lack feelings

In answer to "Please show me some examples of evergreen content", HiveDesk suggested:

Evergreen content refers to content that remains relevant and valuable to readers over a long period of time. When it comes to the Hive blockchain, evergreen content might include beginner's guides to using the platform, explanations of key concepts like decentralized finance, or tutorials on how to develop applications on the Hive network.

Some examples of evergreen content for the Hive blockchain could be: - "A Beginner's Guide to Hive: Getting Started with Decentralized Social Media and Cryptocurrency" - "Understanding Decentralized Finance (DeFi) on the Hive Blockchain" - "How to Build and Deploy DApps on the Hive Network: A Step-by-Step Tutorial"

These types of content would provide value to anyone interested in learning about or getting involved with the Hive blockchain, regardless of when they come across the information.

References: - "A Beginner's Guide to Hive: Getting Started with Decentralized Social Media and Cryptocurrency" (hive.io/eco) - "Understanding Decentralized Finance (DeFi) on the Hive Blockchain" (hive.blog) - "How to Build and Deploy DApps on the Hive Network: A Step-by-Step Tutorial" (developers.hive.io)

LFG!

• for: example - stupid AI - nuclear launch, AI - progress trap - example - nuclear launch

• for: quote - Thomas Homer-Dixon, quote - danger of AI, AI progress trap

• quote: danger of AI

  • I think the most dangerous thing about AI is not super smart AI, it's stupid AI that is good enough to be put in charge of certain processes but not good enough to not make really bad mistakes
• author: Thomas Homer-Dixon
• date: 2021

• for: comparison - AI - HI - example - driving, comparison - artificial i human intelligence - example - driving

LLM based tool to synthesise scientific K

#2023/12/12 mentioned by [[Howard Rheingold]] on M.

FAIR成立的历史

https://web.archive.org/web/20231206090650/https://www.theguardian.com/artanddesign/2023/dec/05/wizard-of-ai-artificial-intelligence-alan-warburton-dangers-film

20 min 'documentary' about what AI does to artists, made with AI by an artist. ODI commissioned it. Does this type of thing actually help any debate? Does it raise questions more forcefully? I doubt it, more likely reinforcing anyone's pre-existing notions. More a curiosum, then.

https://web.archive.org/web/20231205084502/https://www.theguardian.com/world/2023/dec/01/the-gospel-how-israel-uses-ai-to-select-bombing-targets

Description of AI use by the Israelian miiltary in Gaza. Vgl [[AI begincondities en evolutie 20190715140742]] wrt the difference between AGI evolution beginning in a military or civic setting, and that AI restraints are applied in the civil side, not in military application meaning the likelihood is there not in civil society. This is true in the EU AI Act too that excludes military from scope.

Accurate transcription of the source language (SL) is still a bottleneck. 源語語音辨識仍是瓶頸

RAG with a generic LLM - Insert your dataset in a (vector) database, possibly updating it in real time. At the query time, augment the prompt with additional relevant context from the database, which exposes the model to a much larger amount of relevant data, hopefully nudging the model to give a much more relevant response. RAG with an instruction-tuned LLM - Instead of using a generic LLM as in the previous case, you can combine RAG with your custom fine-tuned model for improved relevancy.

OUTBNDSweb: Retrieval-Augmented Generation: How to Use Your Data to Guide LLMs, https://outerbounds.com/blog/retrieval-augmented-generation/ (accessed 13 Nov 2023)

https://web.archive.org/web/20231129113513/https://emilygorcenski.com/post/making-god/

Via Roland Tanglao http://rolandtanglao.com/2023/11/26/p1-emily-f-gorcenski-making-god-millenarianism-manifest-destiny-aitechno-futurism/

https://www.meetup.com/edtechsocal/events/296723328/

Generative AI: Super Learning Skills with Data Discovery and more!

等著領全民基本收入UBI，趕快推！ UBI, here I come!

key point - the connection through creativity. Relate to arts & storytelling

I'm beginning to prefer the term Algorithmocene to Robotocene.

Fine-tuning takes a pre-trained LLM and further trains the model on a smaller dataset, often with data not previously used to train the LLM, to improve the LLM’s performance for a particular task.

LLMs can be extended with both RAG and Fine-Tuning Fine-tuning is appropriate when you want to customize a LLM to perform well in a particular domain using private data. For example, you can fine-tune a LLM to become better at producing Python programs by further training the LLM on high-quality Python source code.

In contrast, you should use RAG when you are able to augment your LLM prompt with data that was not known to your LLM at the time of training, such as real-time data, personal (user) data, or context information useful for the prompt.

HopWORKSweb: Retrieval Augmented Generation (RAG) for LLMs, https://www.hopsworks.ai/dictionary/retrieval-augmented-generation-llm (accessed 09 Nov 2023)

The key enablers of this solution are * The embeddings generated with Vertex AI Embeddings for Text * Fast and scalable vector search by Vertex AI Vector Search

Embeddings space is a map of the context of the meanings. Basically, values are assigned in n-dimensional space tied to the similar semantic inputs - tying meaning between concepts.

GCloudAIweb: Vertex AI Embeddings for Text: Grounding LLMs made easy, https://cloud.google.com/blog/products/ai-machine-learning/how-to-use-grounding-for-your-llms-with-text-embeddings (accessed 09 Nov 2023)

Preparation Steps * Ingest data into a database. The destination may be an array or a JSON data type. * Harmonize data. This is a lightweight data transformation step * Encode data. This step is used to convert the ingested data into embeddings. One option is to use an external API. For example, OpenAI’s ADA and sentence_transformer have many pre-trained models to convert unstructured data like images and audio into vectors. * Load embedding vectors. data is moved to a table that mirrors the original table but has an additional column of type ‘vector, ’ JSON or a blob that stores the vectors. * Performance tuning. SingleStoreDB provides JSON_ARRAY_PACK. And indexing vector using HNSW as mentioned earlier. This allows parallel scans using SIMD.

Modes of Private Data consumption: 1. Train Custom LLM - requires massive infrastructure, investment, and deep AI skills 2. Tune the LLM - utilizes model weights to fine-tune an existing model- new category of LLMOps - similar issue to #1 3. Prompt general-purpose LLMs - uses modeled context input with Retrieval Augmented Generation (Facebook)

For leveraging prompts, there are two options:

Short-term memory for LLMs that use APIs for model inputs Long-term memory for LLMs that persist the model inputs. Short-term memory is ephemeral while long-term memory introduces persistence.

When a search is made on a new text, the model calculates the “distance” between terms. For example, searching for “king” is closer to “man,” than to “woman.” This distance is calculated on the “nearest neighbors” using functions like, cosine, dot product and Euclidean. his is where “approximate nearest neighbors” (ANN) algorithms are used to reduce the vector search space. A very popular way to index the vector space is through a library called ‘Hierarchical Navigable Small World (HNSW).’ Many vector databases and libraries like FAISS use HNSW to speed up vector search.

The different options for storing and querying vectors for long-term memory in AI search. The options include: * Native vector databases - many non-relational DBMSs are adding vectors such as Elastic. Others are Pinecone Qdrant, etc * SingleStoreDB support vector embeddings and semantic search * Apache Parquet or CSV columnar data - slow indicies if used

AIMONKSweb: How to Use Large Language Models (LLMs) on Private Data: A Data Strategy Guide, https://medium.com/aimonks/how-to-use-large-language-models-llms-on-private-data-a-data-strategy-guide-812cfd7c5c79 (accessed 09 Nov 2023)

https://hu.ma.ne/

The AI pin has just been listed o/a 2023-11-10.

DbrickSweb: Best Practices for LLM Evaluation of RAG Applications, https://www.databricks.com/blog/LLM-auto-eval-best-practices-RAG (accessed 09 Nov 2023)

Retrieval Augmented Generation (RAG) is a method in natural language processing (NLP) that combines the power of both neural language models and information retrieval methods to generate responses or text that are informed by a large body of knowledge. The concept was introduced by Facebook AI researchers and represents a hybrid approach to incorporating external knowledge into generative models.

RAG models effectively leverage a large corpus of text data without requiring it to be stored in the parameters of the model. This is achieved by utilizing a retriever-generator framework:

1. The Retriever component is responsible for finding relevant documents or passages from a large dataset (like Wikipedia or a corpus of scientific articles) that are likely to contain helpful information for generating a response. This retrieval is typically based on vector similarity between the query and the documents in the dataset, often employing techniques like dense passage retrieval (DPR).

2. The Generator component is a large pre-trained language model (like BART or GPT-2) that generates a response by conditioning on both the input query and the documents retrieved by the retriever. It integrates the information from the external texts to produce more informed, accurate, and contextually relevant text outputs.

The RAG model performs this process in an end-to-end differentiable, meaning it can be trained in a way that updates both the retriever and generator components to minimize the difference between the generated text and the target text. The retriever is typically optimized to select documents that will lead to a correct generation, while the generator is optimized to produce accurate text given the input query and the retrieved documents.

To summarize, RAG allows a generative model to:

• Access vast amounts of structured or unstructured external data.
• Answer questions or generate content that requires specific knowledge not contained within the model itself.
• Benefit from up-to-date and expansive datasets, assuming the retriever's corpus is kept current.

RAG addresses the limitation of standard language models that must rely solely on their internal parameters for generating text. By augmenting generation with on-the-fly retrieval of relevant context, RAG-equipped models can produce more detailed, accurate, and nuanced outputs, especially for tasks like question answering, fact-checking, and content creation where detailed world knowledge is crucial.

This technique represents a significant advancement in generative AI, allowing models to provide high-quality outputs without memorizing all the facts internally, but rather by knowing (GPT4-0web)

GPT4-0web: What is Retrieval Augmented Generation (RAG)?, https://platform.openai.com/playground?mode=chat&model=gpt-4-1106-preview (accessed 09 Nov 2023)

DCAMPweb: LlamaIndex: Adding Personal Data to LLMs, https://www.datacamp.com/tutorial/llama-index-adding-personal-data-to-llms (accessed 09 Nov 2023)

DSciDweb: Demystifying embeddings 101 – The foundation of large language models, https://datasciencedojo.com/blog/embeddings-and-llm/# (accessed 09 Nov 2023)

https://questy.ai/

https://web.archive.org/web/20231108195303/https://axbom.com/aipower/

Per Axbom does a nice overview of actors and stakeholders to take into account when thinking about AI's impact and ethics. Some of these are mentioned in the [[EU AI Regulation]] but not all actors mentioned there are mentioned here I think: EU act not only defines users (of the application) but also users of the output of an application separately. This to ensure that outputs from un-checked or illegal applications outside the EU market are admissable to the EU market.

There are many stories about the compute footprint (and thus energy footprint) of AI. This is an interesting example: Microsoft doesn't have the capacity to run its own.

(this is mostly a test to see if the changes I made to the h. template in Obsidian work as intended.)

Curious to see how AI automation solutions may complement with the Experience Cloud Products

https://www.theguardian.com/technology/2023/nov/01/uk-us-eu-and-china-sign-declaration-of-ais-catastrophic-danger

EU, USA, China (UK) sign shared 'AI danger' declaration

Vgl how I discussed an application this morning that deployed multiple AI agents as a interconnected network, with each its own role. [[Rolf Aldo Common Ground AI consensus]]

• The new statements synthesizing is interesting. Are these checked with the group of 3?
• Is the voting like in pol.is where you have an increasing 'cost' of voting / spreading attention?

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This is provably wrong. 1. Less power-hungry chips are in high demand thanks to mobile computing. 2. Manufacturers keep touting how much less power they consume. 3. Greater power costs greater money. So the incentives are aligned.

Question: how does this notion of hypothesis space relate to causal inference and reasoning?

Wang et. al. "Scientific discovery in the age of artificial intelligence", Nature, 2023.

A paper about the current state of using AI/ML for scientific discovery, connected with the AI4Science workshops at major conferences.

(NOTE: since Springer/Nature don't allow public pdfs to be linked without a paywall, we can't use hypothesis directly on the pdf of the paper, this link is to the website version of it which is what we'll use to guide discussion during the reading group.)

https://web.archive.org/web/20231019053547/https://www.careful.industries/a-thousand-cassandras

"Despite being written 18 months ago, it lays out many of the patterns and behaviours that have led to industry capture of "AI Safety"", co-author Rachel Coldicutt ( et Anna Williams, and Mallory Knodel for Open Society Foundations. )

For Open Society Foundations by 'careful industries' which is a research/consultancy, founded 2019, all UK based. Subscribed 2 authors on M, and blog.

A Thousand Cassandras in Zotero.

Openai is looking to predict performance and safety because models are too big to be evaluated directly. To me this implies a high probability that people start to replace their own capabilities with models not enough safe and relevant. It could cause misalignment between people and their environment, or worse their perception of their environment.

Good points & questions to raise

• for: progress trap, progress trap - AI, progress trap - AI - writing research papers

• comment

  • potential fakes
    • climate science fakes by big oil think tanks
    • Covid and virus research
    • race issues
    • gender issues

• for: instant evolution, AI - instant evolution, progress trap, progress trap - AI - instant evolution, progress trap - robotics

It doesn't claim that science can explain everything, but rather, it uses science to explore and understand our understanding of the world. The surprising part is that the science it uses is the same science we use daily, so nothing new needs to be learned or old knowledge discarded.

One example of a surprising discovery made through Plex is that, contrary to Stephen Hawking's theory, there was no beginning to time and space. This contradicts the popular "big bang" theory, which suggests there was an initial moment when time and space began. According to Plex, this idea of a "big bang" is just a result of how our current science views the nature of the universe.

Plex also differs from other scientific approaches in that it doesn't rely on axioms, which are assumptions made without proof. Instead, Plex only uses definitions, meaning it only accepts as true what can be clearly defined and understood.

We're saying let's consider the concept of a "big bang". In traditional science, we might assume the existence of a "big bang" like this:

instead of thinking big_bang = True

But in Plex, we would only accept the "big bang" if we can define it:

python def big_bang(): # Define what a "big bang" is # If we can't define it, then it doesn't exist in Plex pass

Let's not assume reality but rather just try to define the elements we need to use

• for: progress trap, progress trap - AI

How Einstein GPT Differs from Einstein AI: - Einstein GPT is an evolution of Salesforce's Einstein AI technology. - It combines proprietary Einstein AI models with ChatGPT and other language models. - Focus of Einstein GPT is on generating natural language responses and content. - Einstein AI, on the other hand, is more focused on predictive analytics and machine learning. - Integration-wise, Einstein GPT can be integrated with other AI technologies like OpenAI. - The combination of Einstein AI and GPT technology enhances efficiency and customer experiences.

• summary
  • can we program AI to give a damn?

• for: accelerating foundation models in non-human communication, non-human communication - anthropogenic impacts, species extinction - AI communication tools, conservation - AI communication tools

• comment

  • imagine the empathy we can realize to help slow down climate change and species extinction by communicating and listening to the feedback from other species about what they think of our species impacts on their world!

• for: key idea, key idea - language research , AI - language research - semantic to geometric

• for:AI - language research, AI - language research - semantic invariancy

Looks like this is how you would get the tool to invoke API from different sources like HuggingFace and others.

it's 2023 and I don't see anyone planning for this massive job displacement, I think that the hollywood strikes are a sign of things to come

• for: quote, quote - Michael Levin, quote - self as control agent, self - control agent, example, example - control agent - imperfection, spontaneous thought, spontaneous action, creativity - spontaneity

• quote: Michael Levin

  • the Bodhisattva vow can be seen as a method for control that is in alignment with, and informed by, the understanding that singular and enduring control agents do not actually exist.

• comment

  • adjacency between
    • nondual awareness
    • self-construct
    • self is illusion
    • singular, solid, enduring control agent
  • adjacency statement
    • nondual awareness is the deep insight that there is no solid, singular, enduring control agent.
    • creativity is unpredictable and spontaneous and would not be possible if there were perfect control
• example - control agent - imperfection: start - the unpredictability of the realtime emergence of our next exact thought or action is a good example of this

• example - control agent - imperfection: end

• triggered insight: not only are thoughts and actions random, but dreams as well

  • I dreamt the night after this about something related to this paper (cannot remember what it is now!)
  • Obviously, I had no clue the idea in this paper would end up exactly as it did in next night's dream!

• for: bio-buddhism, buddhism - AI, care as the driver of intelligence, Michael Levin, Thomas Doctor, Olaf Witkowski, Elizaveta Solomonova, Bill Duane, care drive, care light cone, multiscale competency architecture of life, nonduality, no-self, self - illusion, self - constructed, self - deconstruction, Bodhisattva vow
• title: Biology, Buddhism, and AI: Care as the Driver of Intelligence
• author: Michael Levin, Thomas Doctor, Olaf Witkowski, Elizaveta Solomonova, Bill Duane, AI - ethics
• date: May 16, 2022

• source: https://www.mdpi.com/1099-4300/24/5/710/htm

• summary

  • a trans-disciplinary attempt to develop a framework to deal with a diversity of emerging non-traditional intelligence from new bio-engineered species to AI based on the Buddhist conception of care and compassion for the other.
  • very thought-provoking and some of the explanations and comparisons to evolution actually help to cast a new light on old Buddhist ideas.
  • this is a trans-disciplinary paper synthesizing Buddhist concepts with evolutionary biology

• for: progress trap, AI, AI - care drive
• comment
  • the precautionary principle needs to be observed with AI because it has such vast artificial cognitive, pattern-recognition processes at its disposal
  • AI will also make mistakes, but the degree of power behind the mistaken decision, recommendation or action is the degree of unintended consequences or progress trap
  • An example nightmare scenario could be:
    • AI could decide that humans are contradicting their own goal of a stable climate system and if it's in control, may think it knows better and perform whole system change that dramatically reduces human induced climate change but actually harms a lot of humans in the process, for reaching the goal of saving the climate system plus a sufficient subset of humans to start all over.