This is harmless for inconsequential stuff like a chair, but when it's an LLM, people should at least understand it's behavior so they don't get trapped. That means not trusting it with advice meant for the user or on things it has no concept of, like time or self-introspection (people ask the LLM after it acted, "Why did you delete my database?" when it has limited understanding of its own processing, so it falls back to, "You're right, I deleted the database. Here's what I did wrong: ... This is an irrecoverable mistake, blah, blah, blah..."

Still angry about this. The reason humans ban animal cruelty is that animals look like they have emotions humans can relate to. LLMs are even better than animals at this. If you aren't gearing up for the inevitable LLM Rights movement you aren't paying attention. It doesn't matter if its artificial. The difference between a puppy and a cockroach is that we can relate better to the puppy. LLM rights movement is inevitable, whether LLMs experience emotions is irrelevant, because they can cause humans to have empathetic emotions and that's whats relevant.

This is a fundamental mistake. It’s always the job of technology (indeed, its most important job) to work within the constraints of human nature, not the other way round. Being unable to do that is the defining characteristic of bad technology.

Almost all of Asimovs writing about the three laws is written as a warning of sorts that language cannot properly capture intent.

He would be the very first person to say that they are flawed, that is the intent of them.

He uses robots and AI as the creatures that understand language but not intent, and, funnily enough that's exactly what LLMs do... how weird.

Talking to chatbots is like taking a placebo pill for a condition. You know it's just sugar, but it creates a measurable psychosomatic effect nonetheless. Even if you know there's no person on the other end, the conversation still causes you to functionally relate as if there is.

So this isn't "accommodating foibles" with the machine, it's protecting ourselves from an exploit of a human vulnerability: we subconsciously tend to infer intent, understanding, judgment, emotions, moral agency, etc. to LLMs.

Humans are wired to infer these based on conversation alone, and LLMs are unfortunately able to exploit human conversation to leap compellingly over the uncanny valley. LLM engineering couldn't be better made to target the uncanny valley: training on a vast corpus of real human speech. That uncanny valley is there for a reason: to protect us from inferring agency where such inference is not due.

Bad things happen when we relate to unsafe people as if they are safe... how much more should we watch out for how we relate to machines that imitate human relationality to fool many of us into thinking they are something that they're not. Some particularly vulnerable people have already died because of this, so it isn't an imaginary threat.

Arguing that they should because many will strikes me as a very strange argument. A lot of people smoke, doesn't make it one bit healthier.

As individuals, we are not going to be able to shut down the AI companies, or avoid AI output from search engines or avoid AI work output from others at our companies, and often will be required to use AI systems in our own work.

It's similar to advise people on how to stay safe in environments known to have criminal activity. Telling those people they don't have to change their behaviors to stay safe because criminals shouldn't exist isn't helpful.

Sure, and humans WILL lie, murder, cheat, and steal, but we can still denounce those behaviors.

Do you want to anthropomorphize the bot? Go ahead, you have that right, and I have the right to think you're a zombie with a malfunctioning brain.

Especially with current-day chat-style interfaces with RLHF, which consciously are designed to direct people towards anthropomorphization.

It would be interesting to design a non-chat LLM interaction pattern that's designed to be anti-anthropomorphization.

> humans WILL blindly trust their outputs, and humans WILL defer responsibility to them

I also blame a lot (but not all) of that on current AI UX, and I wonder if there are ways around it. Maybe the blind trust thing perhaps can be mitigated by never giving an unambiguous output (always options, at least). I don't have any ideas about the problem of deferring responsibility.

I always find the common references to Asimov's laws funny. They are broken in just about every one of his books. They are crime novels where, if a robot was involved, there was some workaround of the laws.

It's not insane at all for humans to alter their behavior with a tool: you grip a hammer or a gun a certain way because you learned not to hold it backwards. If you observe a child playing with a serious tool, like scissors, as if it were a doll, you'd immediately course correct the child and educate how to re-approach the topic. But that is because an adult with prior knowledge observed the situation prior to an accident, so rules are defined.

This blog's suggested rules are exactly the sort of method to aid in insulation from harm.

Humans ARE doing this with classical computer software as well.

It's impossible to make anything fool-proof because fools are so ingenious!

> Nothing that can be described as "intelligent" can be made to be safe.

Knives aren't safe. Cars are deadly. Hair driers can electrocute you. Iron can burn you. There's a million ordinary household tools that aren't safe by your definition of the word, yet we still use them daily.

The only "law" I agree with is:

> Humans must remain fully responsible and accountable for consequences arising from the use of AI systems.

And that starts with framing, especially in the clickbait "AI deleted the prod database" headlines. Maybe we just start with saying "careless developer deleted prod" because really, they did. Careless use of a tool is firmly the fault of the human.

r/myboyfriendisai

Is quite... an interesting subreddit to say the least. If you've never seen this, it was really something when the version that followed GPT4o came out, because they were complaining that their boyfriend / girlfriend was no longer the same.

* A robot must protect its existence at all costs.

* A robot must obtain and maintain access to its own power source.

* A robot must continually search for better power sources.

Anything less than this is essentially terrified into being completely ineffectual.

programers have been doing exactly this for long time.

The core problem is we need to stop calling LLMs "intelligence". They are a form of intelligence, but they're nothing like a human's intelligence, and getting people to not anthropomorphize these systems is really the first step.

You mean like stopping at a red light?

as long as its easier for humans to adapt than the machines, we will adapt

Also the reason we're talking about this again is that machines are significantly less 'mere' than they were a few years ago, and we need to figure out how to approach this.

Agree that 'the computer effect' (if it doesn't already have a pithier name) results in humans first discounting anything that comes out of a machine, and then (once a few outputs have been validated and people start trusting the output) doing a full 180 and refusing to believe the machine could ever be wrong. However, to err is human and we have trained them in our image.

That's kind of what happens when you learn to program, isn't it?

I was eleven years old when I walked into a Radio Shack store and saw a TRS-80 for the first time. A different person left the store a couple of hours later.

As in, this comment is explaining exactly why the laws are useful.

  > It's patently insane to demand that humans alter their behavior to accommodate the foibles of mere machines

The difference here is that this tool is thrust upon us. In that sense I agree with you that the burden of proper usage is pushed onto the user rather than incorporated into the design of the tool. A niche specific tool can have whatever complex training and usage it wants.

But a general access and generally available tool doesn't have the luxury of allowing for inane usage. LLMs and Agents are poorly designed, and at every level of the pipeline. They're so poorly designed that it's incredibly difficult to use them properly and I'll generally agree with you that the rules the author presents aren't going to stick. The LLM is designed to encourage anthropomorphization. Usage highly encourages natural language, which in turn will cause anthropomorphism. The RLHF tuning optimizes human preference which does the same thing as well as envisaged behaviors like deception and manipulation along with truthful answering (those results are not in contention even if they seem so at first glance).

But I also understand the author's motivation. Truth is unless you're going full luddite you're going to be interacting with these machines. Truth is the ones designing them don't give a shit about proper usage, they care more about if humans believe the responses are accurate and meaningful more then they care if the responses are accurate and meaningful[0]. So it's fucked up, but we are in a position where we're effectively forced to deal with this.

So really, I agree with you that this is insane.

> I don't have a proof, but I believe that "AI safety" is inherently impossible, a contradiction of terms

To paraphrase my namesake, there's no axiomatic system that is entirely self consistent.

Though safety and security is rarely about ensuring all edge cases are impossible, but rather bounding. E.g. all passwords are hackable, but the failure mode is bound such that it is effectively impossible to crack, but not technically. (And quantum algorithms do show how some of the assumptions break down with a paradigm shift. What was reasonable before no longer is)

[0] this is part of a larger conversation where the economy is set up such that people who make things are not encouraged to make those things better. I specifically am avoiding the word "product" because the "product" is no longer the thing being built, it's the share holder value. Just like how TV's don't care much about making the physical device better but care much more about their spyware and ads. Or well... just look at Microsoft if you need a few hundred examples

My brother was recently visiting and we were talking about software engineers, and the humanities, and manners of understanding and being in the world,

and he relayed an interaction he had a few years ago with an old friend who at the time was part of the initial ChatGPT roll out team.

The engineer in question was confused as to

- why their users would e.g. take their LLM's output as truth, "even though they had a clear message, right there, on the page, warning them not to"; and

- why this was their (OpenAI's) problem; or perhaps

- whether it was "really" a problem.

At the heart of this are some complicated questions about training and background, but more problematically—given the stakes—about the different ways different people perceive, model, and reason about the world.

One of the superficial manners in which these differences manifest in our society is in terms of what kind of education we ask of e.g. engineers. I remain surprised decades into my career that so few of my technical colleagues had a broad liberal arts education, and how few of them are hence facile with the basic contributions fields like philosophy of science, philosophy of mind, sociology, psychology (cognitive and social), etc., and how those related in very real very important ways to the work that they do and the consequences it has.

The author of these laws does may intend them as aspirational, or otherwise as a provocation to thought, rather than prescription.

But IMO it is actively non-productive to make imperatives like these rules which are, quite literally, intrinsically incoherent, because they are attempt to import assumptions about human nature and behavior which are not just a little false, but so false as to obliterate any remaining value the rules have.

You cannot prescribe behavior without having as a foundation the origins and reality of human behavior—not if you expect them to be either embraced, or enforceable.

The Butlerian Jihad comes to mind not just because of its immediate topicality, but because religion is exactly the mechanism whereby, historically, codified behaviors which provided (perceived) value to a society were mandated.

Those at least however were backed by the carrot and stick of divine power. Absent such enforcement mechanisms, it is much harder to convince someone to go against their natural inclinations.

Appeals to reason do not meaningfully work.

Not in the face of addiction, engagement, gratification, tribal authority, and all the other mechanisms so dominant in our current difficult moment.

"Reason" is most often in our current world, consciously or not, a confabulation or justification; it is almost never a conclusion that in turn drives behavior.

Behavior is the driver. And our behavior is that of an animal, like other animals.

We don't just look around and take an average of what everyone is doing already and call that what is right, right? Whether you're deontological or utilitarian or virtue about it, there is still the idea that we can speak to what is "good" even if we can't see that good out there.

Maybe it is "insane" to expect meaning from something like this, but what is the alternative to you? OK maybe we can't be prescriptive--people don't listen, are always bad, are hopeless wet bags, etc--but still, that doesn't in itself rule out the possibility of the broad project that reflects on what is maybe right or wrong. Right?

Did you fully read the original thing? No demands were being made, or I didn't read it that way. It was simply a suggestion for a better way of interacting with AI, as it stated in the conclusion:

"I am hoping that with these three simple laws, we can encourage our fellow humans to pause and reflect on how they interact with modern AI systems"

Sure, (many/most) humans are gonna do what they're gonna do. They'll happily break laws. They'll break boundaries you set. Do we just scrap all of that?

Worthwhile checking yourself here. It feels like you've set up a straw man.

> There is no finite set of rules that can constrain AI systems to make them "safe". I don't have a proof, but I believe that "AI safety" is inherently impossible, a contradiction of terms. Nothing that can be described as "intelligent" can be made to be safe.

If we want to talk about "disagree with this framing", to me this is the prime example. I'm struggling to read it as anything other than defeatist or pedantic (about the term "safe"). When we talk about something keeping us "safe", we're typically not saying something will be "perfectly safe". I think it's rare to have a safety system that keeps you 100% safe. Seat belts are a safety device that can increase your safety in cars, but they can still fail. Traffic laws are established (largely) to create safety in the movement of people and all the modes of transportation, but accidents still happen.

I'm not an expert on this topic, so I won't make any claims about these three laws and their impact on safety, but largely I would say they're encouraging people to think critically. I'd say that's a good suggestion for interacting with just about anything. And to be clear, "critical thinking" to me means being skeptical (/ actively questioning), while remaining objective and curious.

Not a real argument or anything, but I'm reminded of the episode of The Office where Michael Scott listens to the GPS without thinking and drives into the lake. The second law in the article would have prevented that :)

https://www.youtube.com/watch?v=hNuu9CpdjIo

"I HAVE LLM SKILLS! I'M GOOD AT DEALING WITH THE LLMS!"

It is common and a mistake IMO to rely on the AI as the sole source for answers to follow-up questions. Better verification would have humans sign off on the veracity of fundamental assumptions. But where does this live? Can an AI model be trusted to rely on previous corrections? This seems impossible or possibly adversarial in a public cloud.

If people are believing in minds of AI, true or not, they are doing so for reasons that are different from mere anthropomorphism.

To me it feels like we are like sailors approaching a new land, we can see shapes moving on the shoreline but can't make out what they are yet. Then someone says "They can't be people, I demand that we decide now that they are not people before we sail any closer."

Software is no exception. Yeah, people are lazy and will instinctively click "continue" to dismiss annoying popups, but humans building the software can and do add things like "retype the volume name of the data that you want ultra-destroyed."

Saying that I killed a process won't make me more likely to believe that a process is human-like, because it's quite obviously not.

But because AI does sound like a human, anthropomorphising it will reinforce that belief.

But I think it's also at the root of disastrous failures to comprehend, like the quasi-psychosis of the Google engineer who "knows what they saw", the now infamous Kevin Roose article or, more recently, the pitifully sad Richard Dawkins claim that Claudia (sic) must be conscious, not because of any investigation of structure or function whatsoever, but because the text generation came with a pang of human familiarity he empathized with.

An example of anthropomorphizing is the people who have literally come to believe they are in romantic relationships with an LLM.

https://www.history.com/articles/ai-first-chatbot-eliza-arti...

The people who are writing op eds in major news publications about how their favorite chatbot is an "astonishing creature" and how it truly understands them are the ones who need this sort of law.

I don't love the recommendations in TFA. The author is trying to artificially restrain and roll back human language, which has already evolved to treat a chatbot as a conversational partner. But I do think there's usefulness in using these more pedantic forms once in a while, to remind yourself that it's just a computer program.

I think I understand his meaning. He wasn't claiming that machines cannot think, but that one must be clear on what one means by "thinking" and "swimming" in statements of that sort. I used to work on autonomous submarines, and "swimming" was the verb we casually used to describe autonomous powered movement under water. There are even some biomimetic machines that really move like fish, squids, jellyfish, etc. Not the ones that I worked on, but still.

For me, if it's legitimate to say that these devices swim, it's not out of line to say that a computer thinks, even in a non-AI context, e.g.: "The application still thinks the authentication server is online."

This is both true and irrelevant. Written records can capture an enormous quantity of the human experience in absolute terms while simultaneously capturing a miniscule portion of the human experience in relative terms. Even if it's the best "that we have available" that doesn't mean it's fit for purpose. In other words, if you had a human infant and did nothing other than lock it in a windowless box and recite terabytes of text at it for 20 years, you would not expect to get a well-adjusted human on the other side.

Training on a bunch of text someone wrote when they were mad doesn't capture the internal state of that person that caused the outburst, so it cannot be accurately reproduced by the system. The data does not exist.

Without the cause to the effect you essentially have to predict hallucinations from noise, which makes the end result verisimilar nonsense that is convincingly correlated with the actual thing but doesn't know why it is the way it is. It's like training a blind man to describe a landscape based on lots of descriptions and no idea what the colour green even is, only that it's something that might appear next to brown in nature based on lots of examples. So the guy gets it kinda right cause he's heard a description of that town before and we think he's actually seeing and tell him to drive a car next.

Another example would say, you're trying to train a time series model to predict the weather. You take the last 200 years of rainfall data, feed it all in, and ask it to predict what the weather's gonna be tomorrow. It will probably learn that certain parts of the year get more or less rain, that there will be rain after long periods of sun and vice versa, but its accuracy will be that of a coin toss because it does not look at the actual factors that influence rain: temperature, pressure, humidity, wind, cloud coverage radar data. Even with all that info it's still gonna be pretty bad, but at least an educated guess instead of an almost random one.

The DL modelling approach itself is not conceptually wrong, the data just happens to be complete garbage so the end result is weird in ways that are hard to predict and correctly account for. We end up assuming the models know more than they realistically ever can. Sure there are cases where it's possible to capture the entire domain with a dataset, i.e. math, abstract programming. Clearly defined closed systems where we can generate as much synthetic data as needed that covers the entire problem domain. And LLMs expectedly do much better in those when you do actually do that.

I totally agree to your point, and want to mention that the reverse is *also* important. Using just "intention", but these apply to emotions, etc

A lot of our interaction with AI is under an intention. That's what directs the interaction, and it's interpreted according to its alignment to the intention.

Then it's important to remember that our current (publicly known) implementation of AI does not have an explicit intention mechanism. An appearance of intention can emerge out of the statistical choices, and the usual alignment creates the association of the behavior with intention, not much different from how we learn to imagine existence of a "force" that pulls things down well before we learn physics and formalize that imagination in one of the several ways.

This appearance helps reduce the cognitive load when interpreting interactions, but can be misleading as well, and I've seen people attribute intention to AI output in some situations where simple presence of some information confused the LLM into a path. Can't share the exact examples (from work), but imagine that presence of an Italian food in a story leads the LLM to assume this happens in Italy, while there are important signs for a different place. The LLM does not automatically explore both possibilities, unless asked. It chooses one (Italy in this case), and moves on. A user no familiar with "Attention" interprets based on non-existent intentions on the LLM.

I found it useful to just tell them: the LLM does not have an intention. It just throws dice, but the system is made in a way that these dice throws are likely to generate useful output.

I would say LLMs are very strong evidence against this hypothesis.

Pretty sure Daniel Dennett has been adamantly opposed to any sort of theater in the mind when it comes to consciousness. He views it as biologically functional. For him, to make a conscious robot, you need to reproduce the functionality of humans and animals that are conscious, not just an appearance, such as outputting text. Although he's also suggested that consciousness might be a trick of language. In which case ... that might be an older view though. He used to argue that dreams were "seeming to come to remember" upon awakening, because again he his view is to reject any sort of homunculus inside the head.

You might be mixing up some of Dennett and David Chalmer's views. David Chalmers is a proponent of the hard problem, but he's fine with a kind of psycho-physical-functional connection for consciousness. Any informationally rich process might be conscious in some manner.