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?

.

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.

• for: AI - consciousness, zombies, question, question - AI - zombie
• question: AI
  • is AI a zombie?
  • It would seem that by interviewing AI, there would be no way to tell if it's a zombie or not
    • AI would say all the right things that would try to convince you that it's not a zombie

These regulations only apply to public services, not to internal uses of AI.

• reference
  • Biology, Buddhism, and AI: Care as the Driver of Intelligence
    • https://www.mdpi.com/1099-4300/24/5/710/htm

• for: Alan Turing, morphogenesis, AI - morphogenesis, self-organizing systems

This passage really speaks to me here. This is likely the chricton-esqe danger I could see. Apathy from elected officials and general disinterest could really cause the proliferation of un-fettered growth in AI research

I found this particularly interesting especially considering to the 'AI revolution' of sorts we are experiencing today. With tools such as ChatGPT, one may argue that our 'powers of the mind' will begin to decrease provided that we will become tempted to turn to this tool (and others) to do our work for us. Innovation continues to extend our physical rather than intellectual capabilities.

https://www.theguardian.com/technology/2023/sep/01/mushroom-pickers-urged-to-avoid-foraging-books-on-amazon-that-appear-to-be-written-by-ai

https://claude.ai/

https://chat.openai.com/auth/login

Mills, Anna, Maha Bali, and Lance Eaton. “How Do We Respond to Generative AI in Education? Open Educational Practices Give Us a Framework for an Ongoing Process.” Journal of Applied Learning and Teaching 6, no. 1 (June 11, 2023): 16–30. https://doi.org/10.37074/jalt.2023.6.1.34.

Annotation url: urn:x-pdf:bb16e6f65a326e4089ed46b15987c1e7

Search: https://jonudell.info/h/facet/?user=chrisaldrich&max=100&exactTagSearch=true&expanded=true&addQuoteContext=true&url=urn%3Ax-pdf%3Abb16e6f65a326e4089ed46b15987c1e7

Might generative AI worries follow the track of the MOOC scare? (Many felt that creating courseware was going to put educators out of business...)

How exactly? Give us an example? Otherwise not very clear.

T9 (text prediction):generative AI::handgun:machine gun

Link to: https://hypothes.is/a/n6wXvkeNEe6DOFexaCD-Qg

Some may not realize it yet, but the shift in technology represented by ChatGPT is just another small evolution in the chain of predictive text with the realms of information theory and corpus linguistics.

Claude Shannon's work along with Warren Weaver's introduction in The Mathematical Theory of Communication (1948), shows some of the predictive structure of written communication. This is potentially better underlined for the non-mathematician in John R. Pierce's book An Introduction to Information Theory: Symbols, Signals and Noise (1961) in which discusses how one can do a basic analysis of written English to discover that "e" is the most prolific letter or to predict which letters are more likely to come after other letters. The mathematical structures have interesting consequences like the fact that crossword puzzles are only possible because of the repetitive nature of the English language or that one can use the editor's notation "TK" (usually meaning facts or date To Come) in writing their papers to make it easy to find missing information prior to publication because the statistical existence of the letter combination T followed by K is exceptionally rare and the only appearances of it in long documents are almost assuredly areas which need to be double checked for data or accuracy.

Cell phone manufacturers took advantage of the lower levels of this mathematical predictability to create T9 predictive text in early mobile phone technology. This functionality is still used in current cell phones to help speed up our texting abilities. The difference between then and now is that almost everyone takes the predictive magic for granted.

As anyone with "fat fingers" can attest, your phone doesn't always type out exactly what you mean which can result in autocorrect mistakes (see: DYAC (Damn You AutoCorrect)) of varying levels of frustration or hilarity. This means that when texting, one needs to carefully double check their work before sending their text or social media posts or risk sending their messages to Grand Master Flash instead of Grandma.

The evolution in technology effected by larger amounts of storage, faster processing speeds, and more text to study means that we've gone beyond the level of predicting a single word or two ahead of what you intend to text, but now we're predicting whole sentences and even paragraphs which make sense within a context. ChatGPT means that one can generate whole sections of text which will likely make some sense.

Sadly, as we know from our T9 experience, this massive jump in predictability doesn't mean that ChatGPT or other predictive artificial intelligence tools are "magically" correct! In fact, quite often they're wrong or will predict nonsense, a phenomenon known as AI hallucination. Just as with T9, we need to take even more time and effort to not only spell check the outputs from the machine, but now we may need to check for the appropriateness of style as well as factual substance!

The bigger near-term problem is one of human understanding and human communication. While the machine may appear to magically communicate (often on our behalf if we're publishing it's words under our names), is it relaying actual meaning? Is the other person reading these words understanding what was meant to have been communicated? Do the words create knowledge? Insight?

We need to recall that Claude Shannon specifically carved semantics and meaning out of the picture in the second paragraph of his seminal paper:

Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem.

So far ChatGPT seems to be accomplishing magic by solving a small part of an engineering problem by being able to explore the adjacent possible. It is far from solving the human semantic problem much less the un-adjacent possibilities (potentially representing wisdom or insight), and we need to take care to be aware of that portion of the unsolved problem. Generative AIs are also just choosing weighted probabilities and spitting out something which is prone to seem possible, but they're not optimizing for which of many potential probabilities is the "best" or the "correct" one. For that, we still need our humanity and faculties for decision making.

Shannon, Claude E. A Mathematical Theory of Communication. Bell System Technical Journal, 1948.

Shannon, Claude E., and Warren Weaver. The Mathematical Theory of Communication. University of Illinois Press, 1949.

Pierce, John Robinson. An Introduction to Information Theory: Symbols, Signals and Noise. Second, Revised. Dover Books on Mathematics. 1961. Reprint, Mineola, N.Y: Dover Publications, Inc., 1980. https://www.amazon.com/Introduction-Information-Theory-Symbols-Mathematics/dp/0486240614.

Shannon, Claude Elwood. “The Bandwagon.” IEEE Transactions on Information Theory 2, no. 1 (March 1956): 3. https://doi.org/10.1109/TIT.1956.1056774.

We may also need to explore The Bandwagon, an early effect which Shannon noticed and commented upon. Everyone seems to be piling on the AI bandwagon right now...

这个越来越可怕了，以后越来越有很多的东西能够直接有写代码的能力了

A Generative AI Primer on 2023-08-15 by Brian Basgen

ᔥGeoff Corb in LinkedIn update (accessed:: 2023-08-26 01:34:45)

Roland Barthes (1915-1980, France, literary critic/theorist) declared the death of the author (in English in 1967 and in French a year later). An author's intentions and biography are not the means to explain definitively what the meaning of a (fictional I think) text is. [[Observator geeft betekenis 20210417124703]] dwz de lezer bepaalt.

Barthes reduceert auteur to de scribent, die niet verder bestaat dan m.b.t. de voortbrenging van de tekst. Het werk staat geheel los van de maker. Kwam het tegen in [[Information edited by Ann Blair]] in lemma over de Reader.

Don't disagree with the notion that readers glean meaning in layers from a text that the author not intended. But thinking about the author's intent is one of those layers. Separating the author from their work entirely is cutting yourself of from one source of potential meaning.

In [[Generative AI detectie doe je met context 20230407085245]] I posit that seeing the author through the text is a neccesity as proof of human creation, not #algogen My point there is that there's only a scriptor and no author who's own meaning, intention and existence becomes visible in a text.

https://www.agconnect.nl/tech-en-toekomst/artificial-intelligence/liquid-neural-networks-in-ai-is-groter-niet-altijd-beter Liquid Neural Networks (liquid i.e. the nodes in a neuronal network remain flexible and adaptable after training (different from deep learning and LL models). They are also smaller. This improves explainability of its working. This reduces energy consumption (#openvraag is the energy consumption of usage a concern or rather the training? here it reduces the usage energy)

Number of nodes reduction can be orders of magnitude. Autonomous steering example talks about 4 orders of magnitude (19 versus 100k nodes)

Mainly useful for data streams like audio/video, real time data from meteo / mobility sensors. Applications in areas with limited energy (battery usage) and real time data inputs.

• for: population decline, economic growth vs population decline
• comment
  • John makes a good point
  • how will humans negotiate a growth economy when population is shrinking?
  • it may be that AI automation may lessen the need for human capacity, but the future is unknown how these forces will balance out

holy shit....bad!!!!!

What are all the automated decisions currently be made by AI systems globally? How to get a database/list of these?

Computational != objective! Common idea rests on lots of assumptions

"Zoom terms of service now require you to allow AI to train on ALL your data—audio, facial recognition, private conversations—unconditionally and irrevocably, with no opt out.

Don’t try to negotiate with our new overlords." https://twitter.com/tedgioia/status/1688221240790528000?s=20

information literacy

the engineering would require a sophisticated understanding of the subject, if it is to be done effectively. This serves as an example of the benefits of OEP over OER, and how the creator gains the most through the process.

Perhaps it would be better to use language that does not give ChatGPT agency.

meaning - is not capable of insight?

But if it requires extensive human intervention, does it remain ownership-free?

Wikipedia experimented w AI generated text and found it needed extensive editing. While that may not save time for Wikipedia editors, that type of mental labor may benefit students engaged in OEP.

Really? I thought insight was beyond the scope of AI.

Traditional artforms (direct manipulation) versus AI (tools have a built-in aesthetic)

Some authors speak of having to wrestle control of the AI output from its trained style, making it challenging to create unique aesthetic styles. The artist indirectly influences the output by selecting training data and manipulating prompts.

As use of the technology becomes more diverse—as consumer photography did over the last century, the authors point out—how will biases and decisions by the owners of the AI tools influence what creators are able to make?

To a limited extent, this is already happening in photography. The smartphones are running algorithms on image sensor data to construct the picture. This is the source of controversy; see Why Dark and Light is Complicated in Photographs | Aaron Hertzmann’s blog and Putting Google Pixel's Real Tone to the test against other phone cameras - The Washington Post.

• Machine learning
  • will learn all our bad habits
  • and become supercharged, amplified versions of them
• The antidote to apocalyptic machine learning
  • is human happiness and wisdom

• Title
  • One Billion Happy

• Author

  • Mo Gawdat

• Description

  • Mo Gawdat was former chief business officer at Google X, Google's innovation center.
  • Mo left Google after seeing the rapid pace of AI development was going to lead to a progress trap in which
    • the risk of AI destroying human civilization is becoming real because AI will be learning from too many unhappy people whose trauma AI will learn and incorporate into its algorithms
  • Hence, human happiness becomes paramount to prevent this catastrophe from happening
• See Ronald Wright's prescient quote
  • https://hyp.is/go?url=https%3A%2F%2Fwww.goodreads.com%2Fwork%2Fquotes%2F321797-a-short-history-of-progress&group=Q2X5j1pR

• quote
  • 2029 - AI will surpass human intelligence
  • 2049 - AI will be one billion X more intelligent than us

• Singularity
  • will happen within the next few decades

• It is the immoral human being that is the real problem
• they will teach AI to be immoral and with its power, can end up destroying humanity

• bad actor will teach AI to be immoral
• this also creates an arms race as "good" actors are forced to develop AI to counter the AI of bad actors

• AI Arms race is premised on
  • whoever controls AI has enormous powers over everyone else
  • All the world's competing super powers are developing it but with the aim of weaponizing it against its enemies
  • It will be difficult to regulate when so many actors are antagonistic towards each other

• Alphago
  • first version took months of Google UK software developers to program. It won the world Go championship.
  • Alphago Master played itself without ever watching a human player. It beat the first Alphago version after 3 days of playing itself.
  • In 21 days, it beat Alphago version one a thousand to zero.

• three boundaries that industry should have abided by but have been violated:
  • don't put them on the open internet until you solve the control problem
  • don't teach them to code because that enables them to learn and develop on their own
  • Don't allow other AI's prompting them, other AI agents working with them

• Title
  • Mo Gawdat Warns the Dangers of AI Are "Happening As We Speak"
• Author
  • Piers Morgan Uncensored

AI as subject matter expert?

What does that look like though?

Needs citation

AI for teachers as creating efficiencies around how they use their time. Providing feedback to students as opposed to creating or even leading activities.

Midjourney & Stable Diffusion > Dall-E and Adobe Firefly

• AI accelerates the left hemisphere view and impacts in the world

Have in mind the genres tested.

Looking from a perspective of "how might we use such tools in UX" we're better served by looking at documents that UX generates through the lens of identifying parallels to the study's findings for business documents.

To use AI to generate drafts, we'll want to look at AI tools built into design tools UXers use to create drafts. Those tools are under development but still developing.

The numbers for how users divided their time may not be reliable as they're self-reported.

Still leaves me curious about the accuracy of reported brainstorming time.

This points to AI providing the best time savings in draft generation, which fits with the idea of having the AI generate the drafts based on the professional's queries.

For UX designers, this points to AI in a design tool being most useful when it generates drafts (sketches) that the designer then revises. Where UX deliverables don't compare easily to written deliverables is the contextual factors that influence the design, like style guides or design systems. Design too AI assistants don't yet factor those in, though it seems likely it will, if provided style guides and design systems in a format it can read.

Given a draft of sufficient quality that it doesn't require longer to revise than a draft the designer would create on their own, getting additional time to refine sounds great.

I'm not sure what to make of the reduced time to brainstorm when using AI. Without additional information, it's hard not to assume that the AI tool may be influencing the direction of brainstorming as professionals think through the queries they'll use to get the AI to generate the most useful draft possible.

Mistaken human assumptions that AI will generate what a human would given the same prompt are reinforced by claims by those selling AI tools that such tools "understand human language." We don't actually know that AI understands, just that it provides a result that we can interpret as understanding (with the help of our cognitive biases).

This claim to understanding is especially misleading for neural network-based AI. We don't know how neural networks think. With older Lisp based AI we could at least trace through the code to see how the AI thinks.

By providing interfaces to AI tools that help us duplicate the aligning, clarifying, and iterating behaviors that we perform with human collaborators we can increase the sense that users can predict what results the AI will provide in subsequent iterations. This will remove the frustration of working with a collaborator that doesn't understand you.

Collaborating with humans beats collaborating with AI because we can sync up our mental models, clarify ambiguity, and iterate.

Current AI tools are limited in the methods they make available to perform these tasks.

Natural language alone is a poor interface for creating an effective prompt. So bad that communities and businesses are surfacing to help people create effective prompts.