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.

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?

LeBlanc, D. G., & Lee, G. (2021). General Deep Reinforcement Learning in NES Games. Canadian AI 2021. Canadian Artificial Intelligence Association (CAIAC). https://doi.org/10.21428/594757db.8472938b

Is this accurate?

What is missing here? The one thing with the highest probability as we are already living the impacts: climate. The phrase itself is not just a strategic bait and switch for the AI businesses, but also a more blatant bait and switch wrt climate politics.

This does not follow. The reason we don't have self driving cars is because the entire effort is car based not physical environment based. Self driving trains are self driving because of rails and external sensors and signals. Make rails of data, and self driving cars are like trains. No AI, let alone AGI needed. Self driving cars as indicator for AGI make no sense. Vgl https://www.zylstra.org/blog/2015/10/why-false-dilemmas-must-be-killed-to-program-self-driving-cars/ and [[Triz denken in systeemniveaus 20200826114731]]

Note #2: Please read Note #1 above if you haven't already done so. HERE (Note #2), Bard is pandering, giving props for being "thoughtful and nuanced." This is in direct contradiction to what Bard had to say earlier.

I will sarcastically comment that this is a good mirror of how our society is functioning today. In one situation, for one audience, we may have one point of view, then represent a totally different point of view with a different audience. So much for #authenticity!

Note #1: Ok... so here Bard is saying how utterly unacceptable it is to use the n-word, in ANY circumstances. Please reference Note #2.

hypothesis test for CANAI23 paper

A new evidence extraction architecture called ATT-MRC improves the recognition of evidence entities in judgement documents by treating it as a question-answer problem, resulting in better performance than existing methods.

Transformer models like RoBERTa outperform traditional machine learning models in legal question answering tasks, achieving significant improvements in performance metrics such as F1-score and Mean Reciprocal Rank.

The paper proposes L-HetGRL, an unsupervised approach using a legal heterogeneous graph and incorporating legal domain-specific knowledge, to improve Legal Document Similarity Measurement (LDSM) with superior performance compared to other methods.

• comment
  • the best possible outcome for AI
  • is that we human better
  • othering is significantly reduced
  • the sacred is rediscovered

• "if we reverse this

  • if we have the best of us take charge
  • the best of us will tell AI
    • don't try to kill the the enemy,
      • try to reconcile with the enemy
    • don't try to create a competitive product
      • that allows me to lead with electric cars,
        • create something that helps all of us overcome global climate change
    • that's the interesting bit
      • the actual threat ahead of us is
        • not the machines at all
          • the machines are pure potential pure potential
        • the threat is how we're going to use them"

• comment

  • again, see Ronald Wright's quote above
  • it's very salient to this context

• comment
  • the machines are only coded to do what we tell them to do
  • Ronald' Wright's quote is very salient here
    • https://hyp.is/6Lb6Uv5NEe2ZerOrftOHfA/www.goodreads.com/work/quotes/321797-a-short-history-of-progress

• quote
  • "If we give up on human connection, we give up on the remainder of humanity"

• quote
  • "with great power comes great responsibility. We have disconnected power and responsibility."
    • "With great power comes great responsibility
    • We have disconnected power and responsibility
    • so today a 15 year old,
      • emotional without a fully developed prefrontal cortex to make the right decisions yet this is science and we developed our prefrontal cortex fully
      • and at age 25 or so with all of that limbic system emotion and passion
      • would buy a crispr kit and modify a rabbit to become a little more muscular and
      • let it loose in the wild
    • or an influencer who doesn't really know how far the impact of what they're posting online
      • can hurt and cause depression or
      • cause people to feel bad by putting that online
  • There is a disconnect between the power and the responsibility and
  • the problem we have today is that
    • there is a disconnect between those who are writing the code of AI and
    • the responsibility of what's going about to happen because of that code and
    • I feel compassion for the rest of the world
    • I feel that this is wrong
    • I feel that for someone's life to be affected by the actions of others
      • without having a say "

• quote
  • "what went wrong in the 20th century is that we have given too much power to people that didn't assume the responsbility"

• quote
  • "this is an arms race"

• potential government policy
  • to slow down premature AI rollout
  • by taxing at 98%

• difference between
  • ChatGPT (AI)
  • AGI

• comment
  • SIMPOL - simultanous conditional agreement, may be the way to reach consensus quickly

• the first inevitable
  • AI will happen
  • there's no stopping it
  • why?
  • self does not trust other
    • in other words,
      • OTHERING is the root problem!
    • this is what will cause an AI arms race
      • Western governments do not trust China or Russia or North Korea(and vice versa)

• If AI begins to think autonomously,
  • with its enormous pool of analytic power
  • and if
  • it begins to evolve emotions of fear
  • and it feels humans pose a threat to it or the rest of the natural world
  • it could act against human interest and attempt to destroy it
  • If AI is able to control its environment
    • either coupled with robotics,
    • or controlling human actors
  • it can harm humanity and human civilization

• limited (machine) intelligence

  • cannot help but exist
  • if the original (human) authors of the AI code are themselves limited in their intelligence

• comment

  • this limitation is essentially what will result in AI progress traps
  • Indeed,
    • progress and their shadow artefacts,
    • progress traps,
    • is the proper framework to analyze the existential dilemma posed by AI

• Interview with Mo Gawdat
  • former Google chief business officer
  • warning about the existential danger of AI
  • including why he claims that AI is
    • intelligent
    • conscious
    • and will soon feel emotions such as fear
      • and take steps at self preservation

• claim
  • AI feels emotions
    • "in my work I describe everything with equations
    • fear is a very simple equation
      • fear is a a moment in the future
        • that is less safe than this moment
    • that's the logic of fear
    • Even though it appears very irrational,
      • machines are capable of making that logic
      • They're capable of saying
        • if a tidal wave is approaching a data center
        • the machine will say
          • that will wipe out my code,
            • not today's machines
            • but very very soon and
        • we feel fear and
        • puffer fish feels fear
        • we react differently
          • a puffer fish will puff and
          • we will go for fight or flight
        • the machine might decide to replicate its data to another data center
        • different reactions different ways of feeling the emotion
        • but nonetheless they're all motivated by fear
        • I would dare say that AI will feel more emotions than we will ever do
          • if you just take a simple extrapolation,
            • we feel more emotions than a puffer fish
            • because we have the cognitive ability to understand he future
            • so we can have optimism and pessimism,
              • emotions puffer fish would never imagine
            • similarly if we follow that path of artificial intelligence
            • it is bound to become more intelligent than humans very soon
            • then then with that wider intellectual horsepower
            • they probably are going to be pondering concepts we never understood good and
            • hence if you follow the same trajectory
            • they might actually end up having more emotions than we will ever feel

• claim
  • AI is sentient (alive) because
    • A lot of people think AI will never be alive
    • what is the definition of life?
      • religion will tell you a few things
      • medicine will tell you other things
      • but if we define being sentient as
        • engaging in life with free will and
        • with a sense of awareness of
          • where you are in life and
          • what surrounds you and
          • to have a beginning of that life and
          • an end to that life
        • then AI is sentient in every way
        • there is a free will
        • and there is evolution
        • there is agency
          • so they can affect their decisions in the world
        • and there is a very deep level of consciousness
        • maybe not in the spiritual sense yet but
        • if you define consciousness as
          • a form of awareness of oneself and ones surrounding
          • and you know others
        • then AI is definitely aware"

• story
  • Mo Gawdat tells the story of an epiphany of machine sentience
  • " one day um Friday after lunch I am going back to my office and
  • one of them in front of my eyes lowers the arm and picks a soft yellow ball
  • which again is a coincidence

  • it's not science at all it's

    • like if you keep trying a million times your one time it will be right

    • and it shows it to the camera it's locked as a yellow ball and

    • I joke about it you know going to the third floor saying
    • hey we spent all of those millions of dollars for a yellow board and
      • Monday morning, every one of them is picking every yellow ball
      • a couple of weeks later every one of them is picking everything right and
      • it it hit me very very strongly
    • the speed
    • the capability
      • understand that we take those things for granted
      • but for a child to be able to pick a yellow ball
        • is a mathematical / spatial calculation
          • with muscle coordination
          • with intelligence
        • it is not a simple task at all to cross the street
        • it's not a simple task at all
          • to understand what I'm telling you
          • and interpret it
          • and build Concepts around it
        • we take those things for granted
        • but there are enormous Feats of intelligence"

• comment
  • a scary thought that our world will be radically transformed
    • not in 20 to 40 years
    • but in 2 or 3 years!

• claim
• AI can become more intelligent than humans in a few months (in 2023?)

• AI arms race
  • tech companies made a promise
    • not to put AI onto the open internet until
    • they know how it's impacting society
  • Unfortunately, tech companies
    • failed at regulating themselves
    • and now, capitalism has started an AI arms race
    • with unpredictable results as AI harvests more data
    • and grows its artificial intelligence unregulated
    • with each passing

• claim
  • AI could find a way to kill humans in the next few years

• claim
• AI dilemma is bigger problem than climate change

• claim
  • AI is conscious
  • AI feels emotion

super interesting that Luhmann referred to his zettelkasten as a communication partner explicitly himself.

also interesting given AI models are easier to train now with several models already open sourced which allows actual interaction with your notes! would love to see where it goes.

• Comment
  • Mary echos Ronald Wright's progress traps

• comment
  • Is this not a category error in classifying inert technology as life?
  • When does an abiotic human cultural artefact become a living form?

[29] AI - Deep Learning

[28] AI - precedents...

Exceptionalism is a useful perspective to gauge some of the reactions to more widespread algo's.

[25] AI - Alignment

[24] AI - Bias in Training Materials

[21] AI - Skills Erosion

[21] AI Nuances

[20] AI - Alignment Goals

[19] AI - Legal Response

[18] AI - Increased sophistication

AI is turning humans to look inward for a new take on life as our identities and roles within society are being profoundly disrupted and transformed by Artificial Intelligence systems that can replicate or exhibit human-like behavior. It is also a great reminder of how complex social interactions are.

Private sector investment in AI research under-emphasises safety and security.

Most public investment to date has been very narrow, and the paper recommends a significant increase in public funding for technical AI safety research:

• Alignment of system performance with intended outcomes
• Robustness and assurance
• Explainability of results

The authors see unauthorised leakage of AI Models as a risk not just to the commercial developers but also for unauthorised use. They recommend government-mandated watermarking for AI models.

AI systems can perform in ways that may be unforeseen, even by their developers, and this risk is expected to grow as different AI systems become interconnected.

There is currently no clear legal framework in any jurisdiction to assign liability for harm caused by such systems.

The paper recommends the development of a framework for assigning liability for AI-derived harms, and asserts that this will incentivise profit-driven AI developers to use caution.

Training of state-of-the-art models consumes vast amounts of computaitonal power, limiting their deployment to only the best-resourced actors.

To prevent reckless training of high risk models the paper recommends that governments control access to large amounts of specialised compute resource subject to a risk assessment, with an extension of "know your customer" legislation.

Some AI systems will be deployed in contexts that imply risks to physical, mental and/or financial health of individuals, communities or even the whole of society.

The paper recommends that such systems should be subject to mandatory and independent audit and certification before they are deployed.

Article notes: * UK Office for Artificial Intelligence * EU legislation in progress for an AI Board * US pending legislation (ref Ted Lieu) to create a non-partisan AI Commission tasked with establishing a regulatory agency

Recommends Korinek's blueprint for an AI regulatory agency:

1. Monitor public developments in AI progress
2. Mandate impact assessments of AI systems on various stakeholders
3. Establish enforcement authority to act upon risks identified in impact assessments
4. Publish generalized lessons from the impact assessments

A coherent society requires a shared understanding of what is fact. AI models are capable of generating plausible-sounding but entirely wrong content.

It is essential that the public can clearly distinguish content by human creators from synthetic content.

Policy should therefore focus on:

• funding for development of ways to clearly mark digital content provenance
• laws to force disclosure of interactions with a chatbot
• laws to require AI to be deployed in ways that are in the best interest of the user
• laws that require 'duty of care' when AI deployed in circumstances where a human actor would have a fiduciary responsiblity

It is unclear what form the "alignment property" would take, and most importantly how such a property would be evaluated especially if there's an arbitrary divide between "dangerous" and "pre-dangerous" levels of capabilities and alignment of the "dangerous" levels cannot actually be measured.

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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.

https://web.archive.org/web/20230515123225/https://koneksa-mondo.nl/2023/05/14/waar-staan-we-met-wet-en-regelgeving-op-ai-in-europa-china-en-de-vs/

Marco loopt ook de VS en Chinese wetgeving na. Zie ook links in de comments mbt China.

Ausgezeichnete Artikel von Naomi Klein über AI als neue Stufe der Ausbeutung und Enteignung sowie der Steigerung der Macht der Tech-Konzerne. Möglichkeiten dagegen vorzugehen: Weigerung mitzumachen, Fordern von Transparenz und juristischer Kampf gegen die illegale Aneignung von geistigem Eigentum. https://www.theguardian.com/commentisfree/2023/may/08/ai-machines-hallucinating-naomi-klein

Interesting.

Great point.

https://web.archive.org/web/20230507143145/https://incidentdatabase.ai/

AI Incident database, a range of AI incidents and adjacent stories/topics

这是王浩源推荐的。

ChatGPT / GPT-3.5

Yes: 這是在11月份推出的免費版本，非常快速，並且在編寫和編碼任務方面相當可靠。

No: 它沒有連接到網際網路。如果您要求它尋找自2021年以來的任何事情，它都會出錯。不擅長數學計算。

ChatGPT / GPT-4

Yes: 新產品。目前只提供給付費客戶使用。有時候具有驚人的強大功能，是最具實力的模型之一。速度較慢但功能齊全。

No: 雖然也沒有連接到網際網路上，但比起其他系統更善於避免胡言亂語，並且做數學題表現更好。

ChatGPT / Plugins

Yes: 在早期測試中，這個 ChatGPT 模型可以通過外掛與各種網際網路服務進行連接。新穎但還存在一些問題。

No: 作為一個處於早期測試階段的系統，其能力尚未完全清楚, 但將使 ChatGPT 能夠連接到網際網路。

Bing Al

Yes: 已經連接到網際網路上了，極其強大而略顯奇怪。創意模式使用 GPT-4 ，其他模式（精確、平衡）似乎不太行得通。

No: 選擇錯誤的模式會導致糟糕的結果（創意模式最全面）。帶有個性化特點的人工智慧系統。

Google Bard

Yes: 目前的模型不是很好。未來可能會非常強大。

No: 由於它是Google，期望它不會撒謊。相比其他模型，它更容易胡言亂語。

A Anthropic Claude

Yes: 與 GPT-3.5 相當，但使用起來感覺更加合理。較為冷門。

No: 同樣沒有連接到網際網路上。

weave models into your processes not shove it between me and the world by having it create the output. doing that is diminishing yourself and your own agency. Vgl [[Everymans Allemans AI 20190807141523]]

Algogens are disconnected from reality. And, seems a key point, our own cognition and relation to reality is not just through language (and by extension not just through the language center in our brain): spatial awareness, embodiment, senses, time awareness are all not language. It is overly reductionist to treat intelligence or even humanity as language only.

Yes, yes we should.

ICs as hardware versions of AI. Interesting this is happening. Who are the players, what is on those chips? In a sense this is also full circle for neuronal networks, back in the late 80s / early 90s at uni neuronal networks were made in hardware, before software simulations took over as they scaled much better both in number of nodes and in number of layers between inputs and output. #openvraag Any open source hardware on the horizon for AI? #openvraag a step towards an 'AI in the wall' Vgl [[AI voor MakerHouseholds 20190715141142]] [[Everymans Allemans AI 20190807141523]]

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]]

https://web.archive.org/web/20230426184932/https://www.mattprd.com/p/the-complete-beginners-guide-to-autonomous-agents via Stephen Downes https://www.downes.ca/post/75148

I think this is yet to be seen. Although it is true that the computer always gives the same output given the same input code, a biased network with oppressive ideologies could simply transform, instead of change, our current human judiciary enforcement of the law.

数字经济这样的描述，我个人感觉依然不够精准。 事实上，目前全球最顶尖的企业，google、microsoft、甲骨文、twitter、facebook、apple、Huawei、tikitalk、腾讯、阿里，没有一家企业不是从事的是与信息相关的产业和行业。 从信息处理的最底层芯片算力、到信息分发的管道、再到应用算力、再到终端应用，无一不是和人们的信息生产、传输、分发、获取息息相关。 再到如今大红大紫的Ai，ai的出现，将在信息的产生、分发、以及应用三个渠道领域产生深刻的变化。这就是他的可怕之处，