Levels of understanding genres: - 0) No understanding Like the song, never heard anything like it before, but no idea about anything. - 1) Basic Understanding Knowing a bit about the name of the genre and subgenres, but you can be wrong. - 2) Immersion Really dive into subgenres and flavors of the main genre... Also a bit of history about the genre. Research. - 3) Structure Breaking down the structure of the tracks in the genre. For example through DAW. Basically first-principles thinking.

To level 1: Song Analyzer tools (for example musicstax or AI). The author recommends everynoise.com too to gain a basic understanding of genres.

To level 2: Find similar songs and artists for your playlists with that genre. Perhaps playlists. Important to understand the origin of the genre.

Pouvons-nous dire que sa logique de jugement (ou modèle de jugement) est fondée sur la statistique et la théorie des probabilités ?

Pregunta a ChatGPT y te da foros interesantes para aprender

1. The Future of Autonomous Vehicles

   How autonomous cars will reshape urban transportation. Ethical implications of self-driving vehicles. The potential for autonomous public transport systems.

2. Electric Vehicles (EVs) and Sustainability

   The role of electric vehicles in reducing carbon emissions. The challenges and opportunities in EV infrastructure development. Comparison between EVs and traditional fuel-powered vehicles: environmental and economic perspectives.

3. Hyperloop Technology and Its Impact on Long-Distance Travel

   How Hyperloop could revolutionize intercity transportation. Technological and financial challenges in implementing Hyperloop systems. Environmental benefits of ultra-high-speed transport.

4. Urban Mobility Innovations

   How e-scooters, bikes, and ride-sharing apps are changing urban commuting. The future of smart cities and the role of connected transportation systems. Policy challenges surrounding micromobility in congested cities.

5. AI and Machine Learning in Transportation

   How AI is optimizing traffic flow and reducing congestion. Predictive maintenance in transportation through machine learning. AI-driven logistics: revolutionizing freight and delivery systems.

6. Drones and Aerial Transportation

   The use of drones for delivery services: possibilities and challenges. The future of passenger drones and urban air mobility. Regulatory and safety concerns surrounding the widespread use of drones.

7. Sustainable Transport Solutions

   Innovations in public transport to make it more sustainable. The role of hydrogen fuel cells in the future of transportation. The importance of integrating renewable energy sources into transportation infrastructure.

8. The Role of Big Data in Transportation

   How data analytics can improve traffic management and reduce congestion. The impact of big data on the logistics and freight industry. Privacy concerns and the use of personal data in smart transportation systems.

( ~ 6:25-end )

Steps for designing a reading plan/list: 1. Pick a topic/goal (or question you want to answer) & how long you want to take to achieve this. 2. Do research into the books necessary to achieve this goal. Meta-learning, scope out the subject. The number of books is relative to the goal and length of the goal. 3. Find the books using different tools such as Google & GoodReads & YouTube Recommendations (ChatGPT & Gemini are also useful). 4. Refine the book list (go through reviews, etc., in Adlerian steps, do an Inspectional Read of everything... Find out if it's truly useful). Also order them into a useful sequence for the syntopical reading project. Highlight the topics covered, how difficult they are, relevancy, etc. 5. Order the books (or download them)

Reminds me a bit of Scott Young's Metalearning step, and doing a skill decomposition in van Merriënboer et al.'s 10 Steps to Complex Learning

University of Mich

Upload a photo into ChatGPT and request it to transcribe the photo into text. Better than OCR? It creates meaning out of surrounding context; even though words may be wrong.

https://chat.openai.com/g/g-z5XcnT7cQ-zettel-critique-assistant

Zettel Critique Assistant<br /> By Florian Lengyel<br /> Critique Zettels following three rules: Zettels should have a single focus, WikiLinks indicate a shift in focus, Zettels should be written for your future self. The GPT will suggest how to split multi-focused notes into separate notes. Create structure note from a list of note titles and abstracts.

ᔥ[[ZettelDistraction]] in Share with us what is happening in your ZK this week. February 20, 2024

https://web.archive.org/web/20240215084925/https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-the-wrong-information-now-the-airline-has-to-pay-for-the-mistake-1.6769454

Air Canada in a dispute about misleading information from an AI chatbot suggested the chatbot was its own entity and responsible for its own actions, therefore Air Canada not liable for anything it generated. AI Act specifically introduces responsibilities throughout an AI tool's lifecycles, as well as the AI consumer liabilities act. Vgl [[AI Act final annotaties]]

https://web.archive.org/web/20240208185222/https://www.nature.com/articles/d41586-024-00349-5

Paper by author Lizzie Wolkovich refused because of inaccurate suspicion of ChatGPT usage. Another cut to the peer review system? She had her GitHub writing receipts. Intriguing. Makes me think about blogging in Obs while having a private blogging repo that tracks changes. n:: use github while writing for [[Reverse Turing menszijn bewijs vaker nodig 20230505100459]] purposes.

READ

emacs chatgpt

Here's the template to copy (prompt in ALT): pic.twitter.com/77OdtmfQhf

— Moritz Kremb (@moritzkremb) December 14, 2023

其實這裡ChatGPT的直譯翻譯意思才對。

人工的翻譯版犯了一個錯，「超過三十天不去找用戶」，這個意思原文完全沒有。難道，只要29天趕快去找用戶溝通一天，就沒事？

原文是說，他們必須連續30天在外跟用戶溝通，才能成功。

As an ex-Viv (w/ Siri team) eng, let me help ease everyone's future trauma as well with the Fundamentals of Assisted Intelligence.<br><br>Make no mistake, OpenAI is building a new kind of computer, beyond just an LLM for a middleware / frontend. Key parts they'll need to pull it off:… https://t.co/uIbMChqRF9

— Rob Phillips 🤖🦾 (@iwasrobbed) October 29, 2023

Wu, Prabhumoye, Yeon Min, Bisk, Salakhutdinov, Azaria, Mitchell and Li. "SPRING: GPT-4 Out-performs RL Algorithms byStudying Papers and Reasoning". Arxiv preprint arXiv:2305.15486v2, May, 2023.

"The Age of AI has begun : Artificial intelligence is as revolutionary as mobile phones and the Internet." Bill Gates, March 21, 2023. GatesNotes

Minda Zetlin. "Bill Gates Says We're Witnessing a 'Stunning' New Technology Age. 5 Ways You Must Prepare Now". Inc.com, March 2023.

This is true of any of these LLM models actually for any task.

Feng, 2022. "Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis"

Shared and found via: Gowthami Somepalli @gowthami@sigmoid.social Mastodon > Gowthami Somepalli @gowthami StructureDiffusion: Improve the compositional generation capabilities of text-to-image #diffusion models by modifying the text guidance by using a constituency tree or a scene graph.

Original Paper for discussion of the Reinforcement Learning with Human Feedback algorithm.

Three AI Chatbots, Two Books, and One Weird Annotation Experiment by Remi Kalir on September 29, 2023 https://remikalir.com/blog/three-ai-chatbots-two-books-and-one-weird-annotation-experiment/

An interesting divergent take on writing in library books...

Of course this is the basis of keeping a zettelkasten for writing as well. When you can pull up prior ideas as a bank of information to work from, you're never starting from scratch, which is difficult not only for ChatGPT, but for people in general.

Cross reference research on naming "white" things versus naming with a more specific prompt like "white" things in your refrigerator.

https://typeshare.co/go-go-golems/posts/under-the-hood-how-to-use-chatgpts-attention-mechanism-for-better-prompts

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.

https://math.stackexchange.com/questions/4767325/does-the-entropy-of-a-chatgpt-sentence-match-that-of-the-output

英语无疑是当前使用 Chatgpt效果最好的一种沟通方式。因此要想提高我们与chat gpt沟通的效率和效果。最好使用英语，进行沟通交流。而使用chat gpt本身就能够帮助我们。消除。英语使用上的障碍。对于非母语英语的。学生效果非常好。

在西班牙的一些大学里面。学校开始鼓励老师们使用Chatgpt去辅助老师进行课程测试设计以及效果评估检查。通过chat gpt的应用，能够极大地缓解老师的工作压力。但是还能够。对每一名学生的。学习状况进行。评估。并根据课程的进度实时的。进行。提问。通过这种方式。让学生。对所学内容。进行。调取应用。并。从而加深印象，实现对理论知识的理解与吸收。

现在国外的大学已经开始使用chart gpt。来充当助教的身份和角色，甚至于更加专业的一种身份。例如它可以去审视所有的辩论者，谁的发言更加全面、谁的发言更加薄弱。并且将这种发现的问题，及时的反馈回去。这就好比有一个非常资深的老师，随时在观察你的学习进展情况，并将你的学习的存在的问题及时的进行指点。

问题的关键在于——他是如何？加速学生的学习过程。学习的过程，他本身应该有哪些环节？他到底是在哪一个环节实现了这种。速度和质量上的提升。要把这个事情弄清楚。意义，非凡非常关键。

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

https://openai.com/

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...

What does open education tell us about AI in academia?

This is one of the things that doesn't seem to be being explored enough presently, or at least I'm not seeing it outside of the SAG and WGA strikes where it seems to be a side issue rather than a primary issue.

Educause

Example 20 from Univ of Del - 4 syllabus statements

citation

class policy

CTL and ChatGPT

Seems like this judge has a strong bias against the use of AI. I think this broad ban is too broad and unfair. Maybe they should ban spell check and every other tool that could make mistakes too? Ultimately, the humans using the tool shoudl be the ones responsible for checking the generaetd draft for accuracy and the ones to hold responsible for any mistakes; they shouldn't simply be forbidden from using the tool.

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

Excellent summary of what ChatGPT does and how to de-mystify the "black box" feelings about it.

An article recommended to me by Dalton V. that he thought I'd enjoy and appreciate. Looks like AlignmentForum is one of those "online Rationalist communities" (like LessWrong, SlateStarCodex, etc.).

The blog post "The Waluigi Effect" by Cleo Nardo touches on a variety of interesting topics:

• the Waluigi effect
• Simulator Theory
• Derrida's "there is no outside text"
• RLHF (Reinforcement Learning from Human Feedback) and potential limits

The future of blogging in the AI ​​era, how can we unleash the SEO potential? https://en.itpedia.nl/2023/06/11/de-toekomst-van-bloggen-in-het-ai-tijdperk-hoe-kunnen-we-het-seo-potentieel-ontketenen/ Let's take a look at the future of #blogging in the #AI_era. Does a blogging website still have a future now that visitors can find the answer directly in the browser? Or should we use #AI to improve our #weblog. Can AI help us improve our blog's #SEO?

Is this accurate?

• interesting fact
  • ChatGPT is only written with 2,000 lines of code
  • It's not very intelligent, but a very large external memory
  • and repeats the best of what humans have said

• claim
  • ChatGPT already knows 1000x more facts than any single human being alive

GPT models are prone to "hallucinations", producing false "facts" and committing error5s of reasoning. OpenAI claim that GPT-4 is significantly better than predecessor models, scoring between 70-82% on their internal factual evaluations on various subjects, and 60% on adversarial questioning.

Interesting.

Amen!

Students as experts. Experts not based on what they've read and can summarize but based on where they come from.

I love this as a response to the rise of ChatGPT.

Yes, yes we should.

Very [[Stealing Worlds by Karl Schroeder]] chatgpt wired into NPCs in Skyrim.

https://web.archive.org/web/20230502113317/https://wattenberger.com/thoughts/boo-chatbots

This seem like a number of useful observations wrt interacting with LLM based tools, and how to prompt them. E.g. I've seen mention of prompt marketplaces where you can buy better prompts for your queries last week. Which reinforces some of the points here. Vgl [[Prompting skill in conversation and AI chat 20230301120740]] and [[Prompting valkuil instrumentaliseren conversatiepartner 20230301120937]]

請chatgpt交代演算法是第一步 有趣

Maybe too dismissive.

Love this idea!

Fair point!

https://www.goodreads.com/review/show/5438280716

Manny's description of writing introductory language books using ChatGPT.

集體不是個體的總和

https://web.archive.org/web/20230411095546/https://www.reuters.com/technology/germany-principle-could-block-chat-gpt-if-needed-data-protection-chief-2023-04-03/

On the temporary ban of ChatGPT in Italy on the basis of GDPR concerns.

Italian DPA temporarily bans ChatGPT until adequate answers are received from OpenAI. Issues to address: 1. Absence of age check (older than 13) of ChatGPT users 2. Missing justification for the presence of personal data in trainingsdata of ChatGPT. 3. OpenAI has no EU based offices and as such there's no immediate counterparts for DPAs to interact with them. The temp ban is to ensure a conversation with OpenAI will be started.

The trigger was a 9 hour cybersecurity breach where user's financial information and content of their prompts/generated texts leaked over into other accounts.

This skill is useful for drafting a manuscript. Because I can give the idea and ChatGPT can boost my writing speed.

• Promt: Vertical farming provides a novel solution for producing food locally, reducing transportation costs and

Vertical farming provides a novel solution for producing food locally, reducing transportation costs and emissions, and improving food security.

Vertical farming is a type of agriculture where crops are grown in vertically stacked layers in a controlled environment. This type of farming is done indoors, in buildings or warehouses, and uses less land and water than traditional farming methods.

Vertical farming has many benefits. It

-u/Pambaiden at https://www.reddit.com/r/notebooks/comments/12go4ft/my_notebook_gets_stain_on_it_when_i_leave_it/

Example of someone who queried ChatGPT as a general search engine to solve a problem and mentioned it in a public Reddit when asking for general advice about a problem with their notebook.

More ELIZA. The opposite of searching on the internet for your symptoms and ending up with selfdiagnosing yourself with 'everything' as all outliers are there too (availability bias), doing so through prompting generative AI will result in never suggesting outliers because it will stick to dominant scripted situations (see the vignettes quote earlier) and it won't deviate from your prompts.

Things missing in a prompt will not result from a prompt. This may reinforce one's own blind spots / omissions, lowering the probability of an intuitive leap to other possibilities. The machine helps you search under the light you switched on with your prompt. Regardless of whether you're searching in the right place.

I am coming to see this as a pitfall of generative AI texts. It seduces us to anthromorphise the machine, to read intent and comprehension in the generated text. Removing the noise in generating text, meaning the machine would give the same rote answers to the same prompts would reduce this human projection. It would make the texts much 'flatter' and blander than they currently already are. Our fascination with these machines is that they sometimes sound like us, and it makes us easily overlook the actual value of the content produced. In human conversation we would give these responses a pass as they are plausible, but we'd also not treat conversation as likely fully true.

Machines will do well in scripted situations (in itself a form of automation / codification). This was a factor in Hzap 08 / 09 in Rotterdam, where in programming courses the problems were simplified and highly scripted to enable the teacher to be able to grade the results, but at the cost of removing students from actual real life programming challenges they might encounter. It's a form of greedy reductionism of complexity. Whereas the proof of the pudding is performing well within complexity.

https://web.archive.org/web/20230406083125/https://inflecthealth.medium.com/im-an-er-doctor-here-s-what-i-found-when-i-asked-chatgpt-to-diagnose-my-patients-7829c375a9da

A comparison of ChatGPT responses to actual ER case descriptions. Interesting experiment by the author, though there shouldn't be an expectation for better results than it gave.