Looking back on other normal but transformative technologies: steam power, electricity, nuclear physics, the transistor, etc you do actually see similarly stratified opinions. Most of those are surrounded by an initial burst of enthusiasm and pessimism and follow a hype cycle.
The reason this piece is compelling is because during the initial hype phase taking a nuanced middle of the road viewpoint is difficult. Maybe AI really is some “next step” but it is significantly more likely that belief is propped up by science fiction and it’s important to keep expectations inline historically.
If its a "middle" road what is it in the middle of (i.e. what "scale")? And how so?
I'm not trying to be pedantic. I think our tendency to call nuanced, principled positions as "middle" encourages an inherent "hierarchy of ideas" which often leads to applying some sort of...valence to opinions and discourse. And I worry that makes it easier for people to "take sides" on topics which leads to more superficial, myopic, and repetitive takes that are much more about those "sides" than they are about the pertinent evidence, facts, reality, whatever.
That's pretty clear. We already have two "sides": AI is the latest useless tech boondoggle that consumes vast quantities of money and energy while not actually doing anything of value vs. AI is the dawn of a new era of human existence that will fundamentally change all aspects of our world (possibly with the additional "and lead to the AGI Singularity").
Going to be a simultaneously wild and boring ride.
100%. It just happened with the advent of the internet and then smartphones.
After these technologies, certainly life is "normal" as in "life goes on" but the social impacts are most definitely new and transformative. Fast travel, instantaneous direct and mass communications, control over family formation all have had massive impact on how people live and interact and then transform again.
https://www.vatican.va/content/paul-vi/en/encyclicals/docume...
Consequences of Artificial Methods
17. Responsible men can ... first consider how easily this course of action could open wide the way for marital infidelity and a general lowering of moral standards. ... [A] man who grows accustomed to the use of contraceptive methods may forget the reverence due to a woman, and, disregarding her physical and emotional equilibrium, reduce her to being a mere instrument for the satisfaction of his own desires, no longer considering her as his partner whom he should surround with care and affection.
(Naturally, the excitement surrounding artificial intelligence has less to do with reality than with commerce. It is a product to be sold, and selling, as ever, relies less on the truth than on sentiment. It’s not new. That’s how it’s always been.)
Sound good common sense suffices - the ability to go "dude, that's <whatever it is>". Preferring a clear idea of reality to intoxication... That should not be hard to ask and obtain.
> it requires a certain moral maturity to
Same thing.
====
History suggests normal AI may introduce many kinds of systemic risks While the risks discussed above have the potential to be catastrophic or existential, there is a long list of AI risks that are below this level but which are nonetheless large-scale and systemic, transcending the immediate effects of any particular AI system. These include the systemic entrenchment of bias and discrimination, massive job losses in specific occupations, worsening labor conditions, increasing inequality, concentration of power, erosion of social trust, pollution of the information ecosystem, decline of the free press, democratic backsliding, mass surveillance, and enabling authoritarianism.
If AI is normal technology, these risks become far more important than the catastrophic ones discussed above. That is because these risks arise from people and organizations using AI to advance their own interests, with AI merely serving as an amplifier of existing instabilities in our society.
There is plenty of precedent for these kinds of socio-political disruption in the history of transformative technologies. Notably, the Industrial Revolution led to rapid mass urbanization that was characterized by harsh working conditions, exploitation, and inequality, catalyzing both industrial capitalism and the rise of socialism and Marxism in response.
The shift in focus that we recommend roughly maps onto Kasirzadeh’s distinction between decisive and accumulative x-risk. Decisive x-risk involves “overt AI takeover pathway, characterized by scenarios like uncontrollable superintelligence,” whereas accumulative x-risk refers to “a gradual accumulation of critical AI-induced threats such as severe vulnerabilities and systemic erosion of econopolitical structures.” ... But there are important differences: Kasirzadeh’s account of accumulative risk still relies on threat actors such as cyberattackers to a large extent, whereas our concern is simply about the current path of capitalism. And we think that such risks are unlikely to be existential, but are still extremely serious.
====
That tangentially relates to my sig: "The biggest challenge of the 21st century is the irony of technologies of abundance in the hands of those still thinking in terms of scarcity." Because as our technological capabilities continue to change, it becomes ever more essential to revisit our political and economic assumptions.
As I outline here: https://pdfernhout.net/recognizing-irony-is-a-key-to-transce... "There is a fundamental mismatch between 21st century reality and 20th century security [and economic] thinking. Those "security" agencies [and economic corporations] are using those tools of abundance, cooperation, and sharing mainly from a mindset of scarcity, competition, and secrecy. Given the power of 21st century technology as an amplifier (including as weapons of mass destruction), a scarcity-based approach to using such technology ultimately is just making us all insecure. Such powerful technologies of abundance, designed, organized, and used from a mindset of scarcity could well ironically doom us all whether through military robots, nukes, plagues, propaganda, or whatever else... Or alternatively, as Bucky Fuller and others have suggested, we could use such technologies to build a world that is abundant and secure for all. ... The big problem is that all these new war machines [and economic machines] and the surrounding infrastructure are created with the tools of abundance. The irony is that these tools of abundance are being wielded by people still obsessed with fighting over scarcity. So, the scarcity-based political [and economic] mindset driving the military uses the technologies of abundance to create artificial scarcity. That is a tremendously deep irony that remains so far unappreciated by the mainstream."
A couple Slashdot comments by me from Tuesday, linking to stuff I have posted on risks form AI and other advanced tech -- and ways to address those risks -- back to 1999:
https://slashdot.org/comments.pl?sid=23665937&cid=65308877
https://slashdot.org/comments.pl?sid=23665937&cid=65308923
So, AI just cranks up an existing trend of technology-as-an-amplifier to "11". And as I've written before, if it is possible our path out of any singularity may have a lot to do with our moral path going into the singularity, we really need to step up our moral game right now to make a society that works better for everyone in healthy joyful ways.
A question for the author(s), at least one of whom is participating in the discussion (thanks!): Why try to lump together description, prediction, and prescription under the "normal" adjective?
Discussing AI is fraught. My claim: conflating those three under the "normal" label seems likely to backfire and lead to unnecessary confusion. Why not instead keep these separate?
My main objection is this: it locks in a narrative that tries to neatly fuse description, prediction, and prescription. I recoil at this; it feels like an unnecessary coupling. Better to remain fluid and not lock in a narrative. The field is changing so fast, making description by itself very challenging. Predictions should update on new information, including how we frame the problem and our evolving values.
A little bit about my POV in case it gives useful context: I've found the authors (Narayanan and Kapoor) to be quite level-headed and sane w.r.t. AI discussions, unlike many others. I'll mention Gary Marcus as one counterexample; I find it hard to pin Marcus down on the actual form of his arguments or concrete predictions. His pieces often feel like rants without a clear underlying logical backbone (at least in the year or so I've read his work).
We do try to admit it when we get things wrong. One example is our past view (that we have since repudiated) that worrying about superintelligence distracts from more immediate harms.
I'll restate your comment in my language in the hopes of making it clearer. First, the mean is a descriptive statistic. Second, it is possible to build a very simple predictive model using the mean (over observed data).
Ok, but I don't see how this applies to my comment above. Are you disagreeing with some part of my comment?
You're using a metaphor "two sides of the same coin"... what is the coin here? How does it connect with my points above?
The healthiest thing for /actual/ AI to develop is for the current addiction to LLMs to die off. For the current bets by OpenAI, Gemini, DeepSeek, etc to lose steam. Prompts are a distraction, and every single company trying to commodify this are facing an impossible problem in /paying for the electricity/. Currently they're just insisting on building more power plants, more datacenters, which is like trying to do more compute with vacuum relays. They're digging in the wrong place for breakthroughs, and all the current ventures will go bust and be losses for investors. If they start doing computation with photons or something like that, then call me back.
[1] https://www.technologyreview.com/2024/09/26/1104516/three-mi...
We need new techniques and algorithms, not new datacenters.
If you read the EU AI act, you'll see it's not really about AI at all, but about quality assurance of business processes that are scaled. (Look at pharma, where GMP rules about QA apply equally to people pipetting and making single-patient doses as it does to mass production of ibuprofen - those rules are eerily similar to the quality system prescribed by the AI act.)
Will a think piece like this be used to argue that regulation is bad, no matter how benificial to the citizenry, because the regulation has 'AI' in the name, because the policy impedes someone who shouts 'AI' as a buzzword, or just because it was introduced in the present in which AI exists? Yes.
The "drastic" policy interventions that that sentence refers to are ideas like banning open-source or open-weight AI — those explicitly motivated by perceived superintelligence risks.
Or, put another way:
He discussed the structural and cultural changes, the weird and dangerous period when things moved fast and broke badly and drew the obvious parallels between "electricity is new" to "internet is new" as a core paradigm shift for humanity. AI certainly feels like another similar potential shift.
Important is an understatement. Recursively self-improving AI with military applications does not mesh with the claim that "Arms races are an old problem".
> Again, our message is that this is not a new problem. The tradeoff between innovation and regulation is a recurring dilemma for the regulatory state.
I take the point, but the above statement is scoped to a _state_, not an international dynamic. The AI arms race is international in nature. There are relatively few examples of similar international agreements. The classic examples are bans on chemical weapons and genetic engineering.
"The normal technology frame is about the relationship between technology and society. It rejects technological determinism, especially the notion of AI itself as an agent in determining its future. It is guided by lessons from past technological revolutions, such as the slow and uncertain nature of technology adoption and diffusion. It also emphasizes continuity between the past and the future trajectory of AI in terms of societal impact and the role of institutions in shaping this trajectory."
Why write it so overblown like this? You can say the same thing much more cleanly like, "AI doesn’t shape the future on its own. Society and institutions do, slowly, as with past technologies."
They note that they don’t expect their view to address challenges without additional material, but one challenge struck me.
Slow diffusion, which gets bottlenecked by human beings learning to adapt to significant new technologies, drops considerably if a technology juices startups in other areas than the tech itself.
I.e. existing organizations may not be the bottleneck for change, if widely available AI makes disruptive (cheaper initially, higher quality eventually) startups much easier in general to start and to scale.
And of course there is no viable path at this moment to make AIs actually smart so he, we use it and know the issues.
I think their stances and predictions will start to be held by more and more people as the illusion / frenzy / FUD from the current..."fog" created by all the AI hype and mystique subsides. It may take another year or two, but public discourse eventually adapts/tires of repeated notions of "the sky is falling" once enough time has piled up without convincing evidence.
LLM reasoning abilities are very fragile and often overfitted to training data. But if still you haven't figured out how to do anything useful with an LLM, warts and all, that says more about you than LLMs.
As new "thinking" techniques and agentic behavior takes off, I think LLMs will continue to incrementally improve and the real trick is finding ways to make them work with the known limitations they have. And they can do quite a bit
More seriously: software can drive hardware, and software can be endlessly replicated. The ramifications of these for those of us living in the physical world may be surprising.
Imagine for a moment what would happen if suddenly one company “buys” the Linux kernel, and suddenly you need to pay per the number of processes you run in your machine. Awful.
Spreadsheets for example became normal technology long before we had a good open source one. And arguably we still don't have an open source one that's more powerful than the closed source ones.
I don’t fully believe that either, but I see where the point would come from.
> Linux isn’t the “best” OS
kernel != OS.
kernel makes the OS possible. and the linux kernel makes it possible for a lot of linux OSes to exist.
also… android?
There is a huge differentiating factor for LLMs that makes it not normal: the blatant disregard for the ownership rights of everyone in the world. What other "normal" technology has so callously stolen everything it can without consequence?
The music industry? Artists getting inspired and too closely imitating other artists? I genuinely want to know. And if there is such a suitable example, how did society react? Is there relevant history we can learn from here?
Putting aside other the other problems (capital ownership class salivating at the prospect of using LLM bots instead of humans, reduced critical thinking and traditional learning, environmental impact, other societal changes), this is my main turn-off for LLMs.
Give me a model trained on a responsible dataset (not something our grandparents would scold us for doing) and that I can on consumer hardware then I can use LLMs guilt free.
The problem with logical AI is that it can in some sense be held accountable. There's right and wrong and an explainable algorithmic path from input to result. Fuzzy, probabilistic vector spaces remove that inconvenience and make it easier for people with power to shrug and say 'computer says no' when they deprive someone else of freedom or resources.
This is why it is so important to get technicians to accept and preferably get hooked on the newfangled AI. Without buy-in from them it'd be much harder to disseminate this regime in other parts of society since they're likely to be the ones doing the actual dissemination. It's not like there are enough of the people in power to do it themselves, and they also don't know enough about computer stuff to be able to.
There will be things you like that comes out of it, but it's likely incidental, much like dentistry and vaccines and food production in the wake of fossil fuel extraction.
