[[Elja Daae]] recommended reading list wrt AI [[Weapons of Math Destruction by Cathy O Neil]] (have it since 2017) [[Code Dependent by Madhumita Murgia]] bought it in August in ramsj [[The Digital Republic by Jamie Susskind]] I noted in 2024 as possible reading. [[Atlas van de digitale wereld by Haroon Sheikh]] I have too Other's are unknown to me. Interesting list, as it shaped their view on their role in AI public policy I presume

[[Toezicht houden in het AI-tijdperk by Esther van Egerschot Marco Florijn]] mbt toezicht/politieke rollen vs AI vragen. Wellicht interessant taalgebruik/framing om uit te putten?

Learn how to deploy your machine learning model as an API for seamless real-time use. Discover key steps, best practices, and solutions for efficient ML API deployment at CMARIX.

https://en.wikipedia.org/wiki/Yann_LeCun

for - from - LinkedIn post - AI LLM judgment vs human judgment - https://hyp.is/UdbScM05EfC_JWs5FhG-Mg/www.linkedin.com/posts/walterquattrociocchi_ive-never-had-two-editorials-in-top-tier-activity-7399375954743123968-Sn9Y/?rcm=ACoAACc5MHMBii80wYJJmFqll3Aw-nvAjvI52uI

https://web.archive.org/web/20251129105205/https://www.csoonline.com/article/4097087/ai-browsers-can-be-tricked-with-malicious-prompts-hidden-in-url-fragments.html

Named anchors in URLs can be used for prompt injection in AI browser assistants. # URL parts are only evaluated in browser, and not send to servers. AI assistants in browsers do read them though.

https://web.archive.org/web/20251129105036/https://www.nature.com/articles/d41586-025-03506-6

For an international AI conf, a chunk of papers was generated, but also 21% of the peer reviews on those papers was. human centipede epistemology is here vgl [[Talk The Expanding Dark Forest and Generative AI]]

for - example - collaboration - AI - china proposed

for - best action - AI - cSTP

for - false dichotomy - AI - centralised robot police - decentralised with lone wolf bad actors

for - futures - AI -Terminator

for - alternative to self replicating AI - narrow ai

for - collective (bottom up) action - AI - cISTP - AI

for - ai - 3rd perspective

for - progress trap - AI - safety - irony

for - history - AI - Anthropic - safety first

for - progress trap - AI - brakes reality checking loop

for - progress trap - AI - suicides

for - progress trap - AI - sycophants,- example

for - progress trap - AI - sycophantic design

for - progress trap - AI designed to be affirming

for - example - AI pyschosis

for - progress trap - AI therapy

for - progress trap - AI - therapist - subtle attachment

for - progress trap - AI - assisted suicide

for - quote - AI - default reckless path - The default path is companies racing to release - the most powerful inscrutable uncontrollable technology we've ever invented - with the maximum incentive to cut corners on safety. - Rising energy prices, depleting jobs,, creating joblessness, creating security risks, deep fakes. That is the default outcome

for - quote - AI reckless path - narrow path to AI future, rather than the default reckless one

for - AI - tier 1 voting issue

for - progress trap - AI

for - progress trap - AI - like NAFTA

Learn how to integrate AI into your web application using the MERN stack. This guide covers key concepts, tools, and best practices for building an AI-powered web app with MongoDB, Express, React, and Node.js.

for - cosmolocal model - AI is forcing us towards socialism

for - forte - comparison - foreign immigrants Vs AI immigrants - sorry about foreign immigrants - should be more worried about AI immigrants

for - progress trap - AI - narrow boundary

for - AI - Deep Humanity - the sacred

for - AI - immortality project

for - ai tech leaders - immortality projects - denial of death

• Article warns about AI hallucinations spreading disinformation in journalism, law, and business, spotlighting Polish journalist Karolina Opolska's book with likely AI-generated fake sources and errors.
• Opolska incident reached 4 million potential contacts (1.6M traditional media, 2.4M social) from Nov 5-19, 2025, impacting 1 in 8 Poles aged 15+; social reaction mostly negative, debating trust in journalists and AI.
• Polish Exdrog firm lost road tender due to AI-fabricated tax interpretations (1.3M reach, Oct 26-Nov 10, 2025).
• Global cases include lawyers citing invented precedents and media promoting nonexistent books; BBC/EBU study shows 45% error rate in tools like ChatGPT, Copilot, Gemini, Perplexity.
• Legal liability unclear (AI maker, provider, or user?); human verification essential.
• AI content red flags: perfect formatting, clickbait, dubious stats like 93% without method, no timelines/methodology, rapid production, overused LLM words (e.g., "kluczowe", "istotne", "kompleksowy", "rewolucyjny").
• IMM analyses from Polish media/social coverage for both cases.

for - ai scientist - kosmos

for - futures - AI - human intelligence - digital feudalism - the great fragmentation - human symbiosis

from - Emad Mostaque - youtube - AI will end Capitalism - https://hyp.is/2Jr22MgqEfCAWOeGZuM7JQ/www.youtube.com/watch?v=zQThHCB_aec

Amazing that MCP is not funtamental.

• Google released Nano Banana Pro (gemini-3-pro-image-preview), a new AI image generation model.
• Nano Banana Pro excels in general intelligence, tool use, and creating complex scenes with less hallucination.
• It can use Google Search and Maps to gather data and reason visually through "thought images."
• Pushing infographic and map generation to new frontiers, enabling visually rich and factually accurate images.
• Can create detailed photorealistic images based on complex, multi-element prompts.
• Not reliable for electrical circuit designs yet, as it may produce erroneous circuit diagrams.
• Human intelligence still surpasses it in domain-specific tasks like accurate circuit design.
• Nano Banana Pro is seen as a game changer in practical, production-ready AI image generation.
• Tool use enables more factually accurate and data-driven generated images than previous models.
• Benchmarking AI image generation quality still needs development for production use assessment.
• The community is impressed with Nano Banana Pro's nuanced prompt following and image creation capabilities.

for - youtube - AI will end Capitalism - interview - Emad Mostaque - book - The Last Economy - to - book - The Last Economy - https://hyp.is/JGCVHsgrEfCKpkua_vRoBw/webstatics.ii.inc/The%20Last%20Economy.pdf

Tip from colleague C for images on AI that break the usual (anthropomorphic) frame. CC-licensed for re-use usually.

WOW! I've been out of the classroom for quite awhile and never considered this scenario regarding AI. This hit a nerve in me as I'm sure it will with many. I get it!

How do we respond and mitigate the isolation, the loss of human dialogue, mentorship and connection?

for - adjacency - WIkipedia - AI

Classroom Application

Classroom Application

Classroom Application

• Media circulated a claim that Polish language is best for prompting, but this was not a conclusion from the OneRuler study.
• OneRuler is a multilingual benchmark testing how well language models process very long texts in 26 languages.
• Models performed on average best with Polish, but differences compared to English were small and not explained.
• Polish media prematurely concluded Polish is best for prompting, which the study's authors did not claim or investigate.
• The benchmark tested models on finding specific sentences in long texts, akin to CTRL+F, a function AI models inherently lack.
• Another task involved listing the most frequent words in a book; models often failed when asked to acknowledge if an answer was not present.
• Performance dropped likely because the task required full context understanding, not just text searching.
• Different books were used per language (e.g. Polish used "Noce i dnie," English used "Little Women"), impacting the fairness of comparisons.
• The choice of books was based on expired copyrights, which influenced the results.
• There is no conclusive evidence from this benchmark that Polish is superior for prompting due to multiple influencing factors.
• No model achieved 100% accuracy, serving as a caution about language models' limitations; outputs should be verified.
• Researchers advise caution especially when using language models for sensitive or private documents.
• The OneRuler study was reviewed and presented at the CoLM 2025 conference.

Which paper or article from Romeo Dean and Dwarkesh patel?

This is description is how i understand it too, but the link does not seem to say this - it refers to open loop being about cooling a room, and closed loop as a sort of targetted cooling instead.

This is a really useful stat. You need a specific definition of datacentre, but it's still handy.

Nvidia Cosmos world (foundation) models. Avalailable on github. 'for physical AI', for use in training autonomous vehicles, robots and video analytics. E.g. to generate videos and 3d worlds.

Various examples of world model work: Nex to Yann LeCun. Fei-Fei Li World Labs w Marble model, Google has Genie models, Nvidia Omniverse and Cosmos.

Yann LeCun says world models more promising. What are world models?

did they also ask whether those mitigation efforts negate gains in efficiency / innovation reported for AI?

Interesting to see companies vary in their est of how AI will impact workforce. A third expects reduction (but not much, about 3%), 13% an increase (AI related hiring), 43% no change.

A third of respondents admit they've seen 'at least once' negative consequences of inaccurate output. That sounds low, as 100% will have been given hallucinations. So 1-in-3 doesn't catch them all before they run-up damage. (vgl Deloitte's work in Australia)

2k self selected respondents in 50% of nations. 4/10 are big corporates (over 1 billion USD annual revenue)

McKinsey survey on AI use in corporations, esp perceptions and expectations. No actual measurements. I suspect it mostly measure the level of hype that respondents currently buy into.

abstracting away responsibilities is a dead-end. Over half of IT security think now no way to deploy agentic AI safely.

Similarity between models / training data creates an epistemic issue. Using them to control each other creates an echo chamber. Vgl [[Deontologische provenance 20240318113250]]

Suggested that no AI using companies are actively prepping for AI Act's rules wrt human oversight.

Human on the loop vs in

yes, example of [[Spammy handelings asymmetrie 20201220072726]]. At scale it moves the bottleneck

piece on AI oversight.

In general I wonder, at what point does the needed oversight negate the gains in time / effectivity / efficiency that are expected of using AI in some context.

for - search prompt 2 - can an adult who has learned language experience pre-linguistic reality like an infant who hasn't learned language yet? - https://www.google.com/search?q=can+an+adult+who+has+learned+language+experience+pre-linguistic+reality+like+an+infant+who+hasn%27t+learned+language+yet%3F&sca_esv=869baca48da28adf&biw=1920&bih=911&sxsrf=AE3TifNnrlFbCZIFEvi7kVbRcf_q1qVnNw%3A1762660496627&ei=kBAQafKGJry_hbIP753R4QE&ved=0ahUKEwjyjouGluSQAxW8X0EAHe9ONBwQ4dUDCBA&uact=5&oq=can+an+adult+who+has+learned+language+experience+pre-linguistic+reality+like+an+infant+who+hasn%27t+learned+language+yet%3F&gs_lp=Egxnd3Mtd2l6LXNlcnAid2NhbiBhbiBhZHVsdCB3aG8gaGFzIGxlYXJuZWQgbGFuZ3VhZ2UgZXhwZXJpZW5jZSBwcmUtbGluZ3Vpc3RpYyByZWFsaXR5IGxpa2UgYW4gaW5mYW50IHdobyBoYXNuJ3QgbGVhcm5lZCBsYW5ndWFnZSB5ZXQ_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-K1A7IHCTItOC41Mi4xMbgHgcUBwgcHMzUuNDcuMsgHcQ&sclient=gws-wiz-serp - from - search prompt 1 - can we unlearn language? - https://hyp.is/Ywp_fr0cEfCqhMeAP0vCVw/www.google.com/search?sca_esv=869baca48da28adf&sxsrf=AE3TifMGTNfpTekWWBdYUA96_PTLS9T00A:1762658867809&q=can+we+unlearn+language?&source=lnms&fbs=AIIjpHxU7SXXniUZfeShr2fp4giZ1Y6MJ25_tmWITc7uy4KIegmO5mMVANqcM7XWkBOa06dn2D9OWgTLQfUrJnETgD74qUQptjqPDfDBCgB_1tdfH756Z_Nlqlxc3Q5-U62E4zbEgz3Bv4TeLBDlGAR4oTnCgPSGyUcrDpa-WGo5oBqtSD7gSHPGUp_5zEroXiCGNNDET4dcNOyctuaGGv2d44kI9rmR9w&sa=X&ved=2ahUKEwj4_LP9j-SQAxVYXUEAHVT8FfMQ0pQJegQIDhAB&biw=1920&bih=911&dpr=1 - to - search prompt 2 (AI) - can an adult who has learned language re-experience pre-linguistic phenomena like an infant with no language training? - https://hyp.is/m0c7ZL0jEfC8EH_WK3prmA/www.google.com/search?q=can+an+adult+who+has+learned+language+re-experience+pre-linguistic+phenomena+like+an+infant+with+no+language+training?&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRiPAjIHCAIQIRiPAtIBCTQzNzg4ajBqN6gCALACAA&sourceid=chrome&ie=UTF-8&udm=50&ved=2ahUKEwjfrLqDm-SQAxWDZEEAHcxqJgkQ0NsOegQIAxAB&aep=10&ntc=1&mstk=AUtExfAG148GJu71_mSaBylQit3n4ElPnveGZNA48Lew3Cb_ksFUHUNmWfpC0RPR_YUGIdx34kaOmxS2Q-TjbflWDCi_AIdYJwXVWHn-PA6PZM5edEC6hmXJ8IVcMBAdBdsEGfwVMpoV_3y0aeW0rSNjOVKjxopBqXs3P1wI9-H6NXpFXGRfJ_QIY1qWOMeZy4apWuAzAUVusGq7ao0TctjiYF3gyxqZzhsG5ZtmTsXLxKjo0qoPwqb4D-0K-uW-xjkyJj0Bi45UPFKl-Iyabi3lHKg4udEo-3N4doJozVNoXSrymPSQbr2tdWcxw93FzdAhMU9QZPnl89Ty1w&csuir=1&mtid=WBYQaYfuHYKphbIPzYmKiAs

for - Progress trap - AI - low trust society

for - from - LinkedIn post - Was Language Humanity's First AI? Golding's Forgotten Masterpiece - https://hyp.is/KLNvfrm3EfCqGUsWw6uuNg/www.linkedin.com/pulse/language-humanitys-first-ai-goldings-forgotten-willy-de-backer-xffze

[[Kan AI tänka by Peter Gärdenfors]] via Sven Dahlstrand, dahlstrand.net Publ okt 2024 Seeks to define what thinking actually is, and how that plays out in other animals and humans. The 2nd part goes into sofrware systems and AI and how they work in comparison.

for - adjacency - language - is the first AI

for - from - LinkedIn article - Has language trapped humanity? - https://hyp.is/54ZYgrmmEfC5Oft3Op2Hiw/www.linkedin.com/pulse/has-language-trapped-humanity-willy-de-backer-vvwoe/

for - language - origins - adjacency - language - AI

See Ezra Klein's argument against using ChatGPT for writing even the first draft. https://podcasts.apple.com/us/podcast/how-i-write/id1700171470?i=1000710273359

• AI, intended to free workers, is causing longer work hours and increased pressure, spreading 996 culture to Western AI startups.
• AI tools never tire, creating psychological pressure to constantly work and increasing feelings of guilt during rest.
• Historical advances like lamps and bulbs extended work hours; AI similarly shifts "can work" into "should work."
• Philosopher Byung-Chul Han's "Burnout Society" concept shows internalized self-discipline drives overwork, amplified by AI's "excess of positivity."
• The hyper-productivity loop leads to burnout, reduced creativity, and diminishing returns despite increased effort.
• Rest is framed as resistance and vital for innovation, which thrives on reflection, not constant activity.
• The key challenge is adopting a healthy culture around AI use that avoids exploitation and preserves human well-being.

Learn practical strategies to design AI systems that avoid bias and ensure fairness. Discover techniques like diverse data, transparent algorithms, and robust evaluation pipelines to build ethical AI.

Learn practical strategies to design AI systems that avoid bias and ensure fairness. Discover techniques like diverse data, transparent algorithms, and robust evaluation pipelines to build ethical AI.

Compare AWS Transcribe, Deepgram, and Whisper for speech-to-text accuracy, pricing, integrations, and use cases. Find the best AI transcription service for your business.

Explore how AI in WordPress development is reshaping websites, automating content creation, enhancing user experience with chatbots, and optimizing performance plugins. Learn top AI integration strategies, plugins, and best practices for modern WordPress sites.

• Internal documents reviewed by The New York Times show Amazon plans to automate up to 75% of its operations in the coming years.
• The company expects automation to replace or eliminate over 500,000 U.S. jobs by 2033, primarily in warehouses and fulfillment centers.
• By 2027, automation could allow Amazon to avoid hiring around 160,000 new workers, saving about 30 cents per package shipped.
• This strategy is projected to save $12.6 billion in labor costs between 2025 and 2027.
• Amazon’s workforce tripled since 2018 to approximately 1.2 million U.S. employees, but automation is expected to stabilize or reduce future headcount despite rising sales.
• Executives presented to the board that automation could let the company double sales volume by 2033 without needing additional hires.
• Amazon’s Shreveport, Louisiana warehouse serves as the model for the future: it operates with 25% fewer workers and about 1,000 robots.
• A new facility in Virginia Beach and retrofitted older ones like Stone Mountain, Georgia, are following this design, which may shift employment toward more temporary and technical roles.
• The company is instructing staff to use softer language—such as “advanced technology” or “cobots” (collaborative robots)—instead of terms like “AI” or “robots,” to ease concerns about job loss.
• Amazon has begun planning community outreach initiatives (parades, local events) to offset the reputational risks of large-scale automation.
• The company has denied that the documents represent official policy, claiming they reflect the views of one internal group, and emphasized ongoing seasonal hiring (250,000 roles for holidays).
• Analysts suggest this plan could serve as a blueprint for other major employers, including Walmart and UPS, potentially reshaping U.S. blue‑collar job markets.
• The automation push continues a trajectory started with Amazon’s $775 million acquisition of Kiva Systems in 2012, which introduced mobile warehouse robots that revolutionized internal logistics.
• Recent innovations include robots like Blue Jay, Vulcan, and Proteus, aimed at performing tasks such as sorting, picking, and packaging with minimal human oversight.
• Long-term, Amazon may require fewer warehouse workers but more robot technicians and engineers, signaling a broader shift in labor type rather than total employment.

AI Tools for each phase of analysis

Why create a structured workflow?

How to create reusable systems

AI refinement process for QA

How AI reads file formats and what they are good for

See https://aiarchives.org/id/jkeJ63GurWTNuloEB6KQ

supporting my premise: - AI actors are replacing humans and theyre more appealing to the studios.

This shows economic and creative incentives for replacing humans.

for - building AI systems more like biological systems

for - comparison - biological vs AI systems

Introduction: AI is now recently everywhere but we still need humans

Title: long clear and creative

Richard Sutton page, lists more articles than the folder I found.

Some writings, incomplete ideas he calls them, by Richard Sutton. Straight-up HTML, no frills, in a folder. Nice.

A deep dive into how computer vision and AI are transforming industries, from healthcare diagnostics and autonomous vehicles to retail and manufacturing. Learn core technologies, real‑world applications, and future trends for leveraging visual intelligence in your business.

Compare AI vs Traditional Software Development to see which delivers better ROI. Explore cost, scalability, adaptability & when each model suits your business best.

Include user metrics or feedback that demonstrate the app's effectiveness or popularity.

Add details on user adoption rates or how this improved user experience or efficiency.

Explain the significance of 87% accuracy in practical terms, such as its effect on performance analysis.

Clarify how much faster the review process became due to this implementation.

Quantify the reduction in time spent on reviews or improvement in review quality.

Include metrics on user engagement or satisfaction post-redesign to highlight impact.

Add a statement on how this improved decision-making or efficiency for the executives.

Explain how the increased test coverage improved system reliability or reduced bugs.

Quantify 'hours saved' to provide a clearer impact of your automation efforts.

Specify the percentage improvement in scheduling efficiency or time saved due to automation.

This article reports that Anthropic has agreed to pay out a $1.5 billion settlement for copyright violations while training their Claude AI tool on books found on the Internet. That works out to be about $3000 per book.

The whole idea of books (at least nonfiction books) is that readers are supposed to learn from them. But now if actual learning from them takes place it's a $3000 charge!

It used to be that to violate a copyright required copying, not merely training. What's more, in the USA the sole justification for government-enforced monopolies on intellectual property is Article I, Section 8, Clause 8 of the U.S. Constitution, which authorizes copyrights and patents only to "to promote the Progress of Science and useful Arts," and only "for limited Times," and only "to Authors and Inventors." By extending copyright duration to the "author's life plus 70 years," Congress flouted those restrictions, and this precedent further tramples them, by clearly impeding the progress of science and useful arts.

Quantify the impact of your instruction. Did it lead to fewer errors or higher quality projects? Provide metrics.

Include the impact of your training. Did it lead to improved safety records or student confidence?

Mention the user base or potential applications of this project. Who would benefit from it?

Detail what specific standards were created. How did they lead to the 40% cost reduction? Be more specific.

Explain how bug removal improved functionality or user experience. Provide examples of features enabled.

Quantify the impact of digitizing work. How much time or cost was saved? Include specific metrics.

Clarify your role in project management. Did you lead or facilitate? Highlight your direct contributions.

Specify how you measured the improvement in understanding. Include metrics or feedback to enhance impact.

In this paragraph, instead of looking at plagiarism or anything related to that, the study is relating to people and how ai influences people to think about themselves as real researchers.

for - consciousness, AI, Alex Gomez- Marin, neuroscience, hard problem of consciousness, nonmaterialism, materialism - progress trap - transhumanism - AI - war on conciousness

Summary - Alex advocates - for a nonmaterialist perspective on consciousness and argues - that there is an urgency to educate the public on this perspective - due to the transhumanist agenda that could threaten the future of humanity - He argues that the problem of whether consciousness is best explained by materialism or not is central to resolving the threat posed by the direction AI takes - In this regard, he interprets that the very words that David Chalmers chose to articulate the Hard Problem of Consciousness reveals the assumption of a materialist reference frame. - He used a legal metaphor too illustrate his point: - When a lawyer poses three question "how did you kill that person" - the question is entrapping the accused . It already contains the assumption of guilt. - I would characterize his role as a scientist who practices authentic seeker of wisdom - will learn from a young child if they have something valuable to teach and - will help educate a senior if they have something to learn - The efficacy of timebinding depends on authenticity and is harmed by dogma

for - progress trap - transhumanism - AI - war on consciousness

for - quote - AI ethics - AI pets - very soon people will think that if you turn off their algorithm, you're killing their pet,

for - adjacency - AI - consciousness war - spiritual war

for - adjacency - urgency of - alternative views of consciousness - AI

for - AI - progress trap - Open AI device

Provide examples of conflict resolution outcomes. Did it lead to improved community satisfaction?

What was the measurable impact of fostering inclusivity? Include feedback or participation rates.

Quantify the enhancement in user experience. Did it reduce access time or errors?

How much efficiency was gained? Provide specific metrics or time saved if possible.

What were the results of this testing? Did it lead to fewer bugs or higher user satisfaction?

Mention any player metrics or feedback received post-launch to demonstrate success.

What was the user impact of these features? Include user growth or engagement metrics.

Quantify 'positive client feedback.' How many users or stakeholders provided feedback?

Detail the benefits of the migration. Did it improve deployment speed or reliability?

Quantify the enhancement. How much did functionality improve? Provide metrics if available.

Specify the outcomes of the project milestones. What was the impact on the client or team?

A answer - yes, interested in AI - they are not intelligent, just huge correlation machines - Donald Hoffman

History - Donald Hoffman - PhD on Lisp AI - Marvin Minsky - MIT lab

for - Q? - do you think much about AI? Donald Hoffman

Discover how AI is shaping the future of Laravel with real-world AI integrations, from chatbots and predictive analytics to cloud-native deployments and AI-assisted development workflows.

Specify the average time saved or the number of devices repaired per week to illustrate efficiency.

Quantify the impact of these relationships on sales growth or customer loyalty metrics.

Include specific savings amounts or percentage increases in customer retention due to these reviews.

Quantify the increase in customer satisfaction or sales resulting from these tailored solutions.

State how the UI/UX design improved user interaction or satisfaction rates.

Include specific metrics on user engagement or feedback received post-launch.

Mention the impact of this integration, such as reduced load times or improved user experience.

Quantify the data transfer speed or reliability improvements achieved through this implementation.

Add how this configuration improved performance or reduced resource usage.

Specify the outcome or impact of this development, e.g., improved efficiency or functionality.

MIT report: 95% of companies see no profit from investments in generative AI, which amounted to approximately $35 billion.

Most AI pilots have no measurable impact on company profits. Attempts to implement tools like ChatGPT into the workplace primarily increase the productivity of individual employees, not the earnings of the entire company.

Clarify how these tests contributed to the project's reliability or ease of future updates.

Specify the impact of bug elimination—did it improve performance, user satisfaction, or reduce crashes?

Explain the significance of this feature—how does it enhance the application's functionality or user experience?

Quantify the impact of this training—did it lead to increased project success rates or skills assessments?

Add specific feedback or metrics on user satisfaction or engagement post-implementation.

Explain how reduced costs translated to benefits for the company (e.g., increased revenue, efficiency).

Include specific improvements achieved (e.g., performance increase, reduced bugs) to show effectiveness.

Clarify the context of 'user workload' reduction—what tasks were simplified or eliminated?

Add how this assistance impacted student performance or engagement to highlight effectiveness.

Specify how much student understanding improved (e.g., grades, feedback) to quantify impact.

Learn how to generate 3D images using AI models. This guide covers top tools and best practices to help designers and developers bring their 3D visions to life efficiently.

for - youtube - BBC - AI2027 - Futures - AI - progress trap - AI - to AI2027 website - https://hyp.is/0VHJqH3cEfCm9JM_EB3ypQ/ai-2027.com/

summary - This dystopian futures scenario is the brainchild of former OpenAI researcher Daniel Kokotajlo, - It is premised on human behavior in modernity including - confirmation bias of AI researchers - entrenched competing political ideologies that motivate an AI arms race - entrenched capitalist market behavior that motivates an AI arms race - AI becoming embodied, resulting in Artificially Embodied Artificial Intelligence (AEAI), posing the danger to humanity because it's no longer just talk, but action - Can it happen? The probability is not zero.We don't really understand the behavior of the AI LLM's we design, they are nonpredictable, and as we give them even greater power, that is a slippery slope - AI can become humanity's ultimate progress trap, which is ironic, because the technology that promises to be the most efficient of all, can become so efficient, it no longer need human beings - Remember Jerry Kaplan's book "Humans need not apply"? - https://hyp.is/o0lBFH3fEfC1QLfnLSs5Bg/www.youtube.com/watch?v=JiiP5ROnzw8 - This dystopian futures scenario goes further and explores the idea that "humans need not exist"!

question - What about emulating climate change gamification of "Bend the Curve" of emissions? - Use the AI 2027 trajectory as a template and see how much real-life follows this trajectory - Just as we have the countdown to the https://climateclock.world/ ( 3 years and change remaining as of today) - perhaps we can have an AI 2027 clock? - What can we do to "bend the dystopian AI 2027 curve" AWAY from the dystopian future?

for - youtube - Google Talks - Humans need not apply - Jerry Kaplan - 10 years after the book "Humans need not apply - the AI 2027 project - https://hyp.is/kWXQ0n3cEfCIUz_j42HHiA/www.youtube.com/watch?v=1UufaK3pQMg

for - AI - Hinton - maternal instincts

AI article -- non-hysteria

Learn how to build an agentic SaaS platform powered by autonomous AI systems capable of perceiving, planning, deciding, and acting. Explore architecture components, real-world use cases, and transformation strategies for intelligent SaaS.

Mention any security breaches avoided or user trust gained due to these measures.

Explain how this modularity improved the system's performance or maintainability.

Include user engagement metrics or feedback to demonstrate the success of the feature.

Quantify the reduction in response times or manual tasks to emphasize the achievement.

Quantify the time saved or efficiency gained from implementing CI/CD pipelines.

Provide metrics or examples of performance improvements achieved through this optimization.

Clarify what specific improvements were made as a result of these insights.

Explain how these QA outcomes benefited the team or project to show the impact.

Quantify the previous search time to highlight the improvement more effectively.

Specify how this precision impacted the project or client outcomes for better context.

Specify the metrics used to measure effectiveness and how they compare to competitors.

Explain how the system's accuracy was measured and its potential impact on user safety.

Include user feedback or adoption rates to show the app's effectiveness and relevance.

Mention the total number of events held and any feedback received to illustrate success.

Provide a baseline number of participants to demonstrate the actual increase in engagement.

Clarify the impact of these recommendations, such as time saved or improved accuracy.

Include the number of devices analyzed and how the findings influenced decision-making.

Quantify the time saved for partners due to this simplification to highlight efficiency gains.

Specify the number of pilot ideas developed and their impact on patient care or efficiency.

This is an interesting angle - ease of adoption when the tech is "boring", and reduced risk

Quantify the number of features implemented based on feedback to show collaboration effectiveness.

Add context on how this popularity impacted your previous job or user engagement.

Specify how this feature improved user experience or data security.

Clarify the purpose of the application and its impact on users or stakeholders.

Explain the significance of this contribution: did it enhance functionality or performance?

Include metrics on user engagement or feedback from testing to show impact.

Highlight any improvements in uptime or user satisfaction resulting from this maintenance.

Quantify the number of issues resolved or average response time to enhance impact.

Add specifics on how many computers were set up or any improvements made.

Clarify the outcome: did this lead to better collaboration or engagement?

Quantify the impact: how many users benefited or what insights were gained?

Specify how this API improved data access or efficiency for users.