That's really what these are: something analogous to JPEG for language, and queryable in natural language.
Tangent: I was thinking the other day: these are not AI in the sense that they are not primarily intelligence. I still don't see much evidence of that. What they do give me is superhuman memory. The main thing I use them for is search, research, and a "rubber duck" that talks back, and it's like having an intern who has memorized the library and the entire Internet. They occasionally hallucinate or make mistakes -- compression artifacts -- but it's there.
So it's more AM -- artificial memory.
Edit: as a reply pointed out: this is Vannevar Bush's Memex, kind of.
I've been looking at it as an "instant reddit comment". I can download a 10G or 80G compressed archive that basically contains the useful parts of the internet, and then I all can use it to synthesize something that is about as good and reliable as a really good reddit comment. Which is nifty. But honestly it's an incredible idea to sell that to businesses.
And so what would the point be of anyone actually posting on the internet if no one actually visits the sites because large corps have essentially stolen and monetized the whole thing.
And I'm sure they have or will have the ability to influence the responses so you only see what they want you to see.
That's the next step after algorithmic content feeds - algorithmic/generated comment sections. Imagine seeing an entirely different conversation happening just to get you to buy a product. A product like Coca-Cola.
Imagine scrolling through a comment section that feels tailor-made to your tastes, seamlessly guiding you to an ice-cold Coca-Cola. You see people reminiscing about their best summer memories—each one featuring a Coke in hand. Others are debating the superior refreshment of Coke over other drinks, complete with "real" testimonials and nostalgic stories.
And just when you're feeling thirsty, a perfectly timed comment appears: "Nothing beats the crisp, refreshing taste of an ice-cold Coke on a hot day."
Algorithmic engagement isn’t just the future—it’s already here, and it’s making sure the next thing you crave is Coca-Cola. Open Happiness.
Why Coca Cola though? Sure it is refreshing on a hot day but you know what is even better? Going to bed on a nice cool mattress. So many are either too hard or too soft. They aren’t engineered to your body so you are virtually guaranteed to get a poor nights sleep.
Imagine waking up like I do every morning. Refreshed and full of energy. I’ve tried many mattresses and the only one that has this property is my Slumber Sleep Hygiene mattress.
The best part is my partner can customize their side using nothing more than a simple app on their smartphone. It tracks our sleep over time and uses AI to generate a daily sleep report showing me exactly how good of a night sleep I got. Why rely on my gut feelings when the report can tell me exactly how good or bad of a night sleep I got.
I highly recommend Slumber Sleep Hygiene mattresses. There is a reason it’s the number one brand recommended on HN.
Or, war is good peace is bad, nuclear war is winnable, don't worry and start loving the bomb. The enemy are not human anyway, your life will be better with fewer people around.
Look at the people who want to control this, they do not want to sell you Coke.
“Vannevar Bush's 1945 article "As We May Think". Bush envisioned the memex as a device in which individuals would compress and store all of their books, records, and communications, "mechanized so that it may be consulted with exceeding speed and flexibility".
The memex was a deterministic device to consult documents - the actual documents. The "LLM" is more like a dumb archivist that came with it ("Yes, see for example that document, it tells you that q=M·k...").
I grew up with physical encyclopedia, then moved on to Encarta, then Wikipedia dumps and folders full of PDFs. I still prefer curated information repository over chat interfaces or generated summaries. The main goal with the former is to have a knowledge map and keywords graph, so that you can locate any piece of information you may need from the actual source.
I believe LLMs are both data and processing, but even humans reasoning is based in strong ways on existing knowledge. However, for the goal of the post, indeed it is the memorization that is the key value, and the fact that likely in the future sampling such models can be used to transfer the same knowledge to bigger LLMs, even if the source data is lost.
I'm not saying there is no latent reasoning capability. It's there. It just seems to be that the memory and lookup component is much more useful and powerful.
To me intelligence describes something much more capable than what I see in these things, even the bleeding edge ones. At least so far.
I offer a POV that is in the middle: reasoning is powerful to evaluate which solution is better among N in the context. Memorization allows sampling of many competing ideas from the problem space, than the LLM picks the best, making chain of thoughts so effective. Of course zero shot reasoning also is a part of the story but somewhat weaker, exactly like we are not often able to spit the best solution before evaluation of the space (unless we are very accustomed to the specific problem).
That's the problem with the term "intelligence". Everyone has their own definition, we don't even know what makes us humans intelligent and more often than not it's a moving goalpost as these models get better.
I can ask a LLM to write a haiku about the loss function of Stable Diffusion. Or I can have it do zero shot translation, between a pair of languages not covered in the training set. Can your "language JPEG" do that?
I think "it's just compression" and "it's just parroting" are flawed metaphors. Especially when the model was trained with RLHF and RL/reasoning. Maybe a better metaphor is "LLM is like a piano, I play the keyboard and it makes 'music'". Or maybe it's a bycicle, I push the pedals and it takes me where I point it.
I regularly pushback against casual uses of the word “intelligence”.
First, there is no objective dividing line. It is a matter of degree relative to something else. Any language that suggests otherwise should be refined or ejected from our culture and language. Language’s evolution doesn’t have to be a nosedive.
Second, there are many definitions of intelligence; some are more useful than others. Along with many, I like Stuart Russell’s definition: the degree to which an agent can accomplish a task. This definition requires being clear about the agent and the task. I mention this so often I feel like a permalink is needed. It isn’t “my” idea at all; it is simply the result of smart people decomplecting the idea so we’re not mired in needless confusion.
I rant about word meanings often because deep thinking people need to lay claim to words and shape culture accordingly. I say this often: don’t cede the battle of meaning to the least common denominators of apathy, ignorance, confusion, or marketing.
Some might call this kind of thinking elitist. No. This is what taking responsibility looks like. We could never have built modern science (or most rigorous fields of knowledge) with imprecise thinking.
I’m so done with sloppy mainstream phrasing of “intelligence”. Shit is getting real (so to speak), companies are changing the world, governments are racing to stay in the game, jobs will be created and lost, and humanity might transcend, improve, stagnate, or die.
If humans, meanwhile, can’t be bothered to talk about intelligence in a meaningful way, then, frankly, I think we’re … abdicating responsibility, tempting fate, or asking to be in the next Mike Judge movie.
We never would have been able to create science, if it weren't for focusing on the kinds of thinking that can be made logical. There's a big difference. What you're doing, with this whole "let's make a bullshit word logical" is more similar to medieval scholasticism, which was a vain attempt at verbal precision. https://justine.lol/dox/english.txt
Yikes, maybe we can take a step back? I'm not sure where this is coming from, frankly. One anodyne summary of my comment above would be:
> Let's think and communicate more clearly regarding intelligence. Stuart Russell offers a nice definition: an agent's ability to do a defined task.
Maybe something about my comment got you riled up? What was it?
You wrote:
> What you're doing, with this whole "let's make a bullshit word logical" is more similar to medieval scholasticism, which was a vain attempt at verbal precision.
Again, I'm not quite sure what to say. You suggest my comment is like a medieval scholar trying to reconcile dogma with philosophy? Wow. That's an uncharitable reading of my comment.
I have five points in response. First, the word intelligence need not be a "bullshit word", though I'm not sure what you mean by the term. One of my favorite definitions of bullshitting comes from "On Bullshit" by Harry Frankfurt:
> Frankfurt determines that bullshit is speech intended to persuade without regard for truth. The liar cares about the truth and attempts to hide it; the bullshitter doesn't care whether what they say is true or false. - Wikipedia
Second, I'm trying to clarify the term intelligence by breaking it into parts. I wouldn't say I'm trying to make it "logical" (in the sense of being about logic or deduction). Maybe you mean "formal"?
Third, regarding the "what you're doing" part... this isn't just me. Many people both clarify the concept of intelligence and explain why doing so is important.
Fourth, are you saying it is impossible to clarify the meaning of intelligence? Why? Not worth the trouble?
Fifth, have you thought about a definition of intelligence that you think is sensible? Does your definition steer people away from confusion?
You also wrote:
> We never would have been able to create science, if it weren't for focusing on the kinds of thinking that can be made logical.
I think you mean _testable_, not _logical_. Yes, we agree, scientists should run experiments on things that can be tested.
Russell's definition of intelligence is testable by defining a task and a quality metric. This is already a big step up from an unexamined view of intelligence, which often has some arbitrary threshold.* It allows us to see a continuum from, say, how a bacteria finds food, to how ants collaborate, to how people both build and use tools to solve problems. It also teases out sentience and moral worth so we're not mixing them up with intelligence. These are simple, doable, and worthwhile clarifications.
Finally, I read your quote from Dijkstra. In my reading, Dijkstra's main point is that natural language is a poor programming interface due to its ambiguity. Ok, fair. But what is the connection to this thread? Does it undercut any of my arguments? How?
* A common problem when discussing intelligence involves moving the goal post. Whatever quality bar is implied has a tendency to creep upwards over time.*
I just wanted to share an essay I liked. I didn't think you'd pay it much mind. But I can see now that you are a person devoted to science. If you want to know what I believe, I think computers in the 50's were intelligent. I think gpt2 probably qualified as agi if you take the meaning of the acronym literally. At this point we've blown so far past all expectations in terms of intelligence that I've come to agree with Karpathy that the time has come to start moving the goalposts to other words, like agency, since agents are an unsolved problem, and agency is proving to possibly be more important/powerful/rare/difficult than intelligence.
I reacted negatively to the idea earlier that agency should be considered an aspect of intelligence. I think separating the concepts helps me better understand people, their unique strengths, and puzzles like why sometimes people who aren't geniuses who know everything and can rotate complex shapes are sometimes very successful, but most importantly, why LLMs continue to feel like they're lacking something, compared to people, even though they're so outrageously intelligent. It's one thing to be smart, another thing entirely to be useful.
> I reacted negatively to the idea earlier that agency should be considered an aspect of intelligence.
In the hopes of clarifying any misunderstandings of what I mean... I said "agent" in Russell's sense -- a system with goals that has sensors and actuators in some environment. This is a common definition in CS and robotics. (I tend to shy away from using the word "agency" because sometimes it brings along meaning I'm not intending. For example, to many, the word "agency" suggests free will combined with the ability to do something with it.)
> My own motivation for studying AI is to create and understand intelligence as a general property of systems, rather than as a specific attribute of humans. I believe this to be an appropriate goal for the field as a whole...
To continue my earlier comment... I prefer not to call an LLM "intelligent" much less "outrageously intelligent". Why? The main reason is communication clarity -- and by communication I mean the notion of a sender communicating a meaning to a receiver. Not just symbolic information (a la Shannon), but a faithful representation in the recipient. The phrase "outrageously intelligent" can have many conflicting interpretations in one's audience. Doing so generates more confusion than clarity.
To say my point a different way, intelligence is contextual. I'm not using "contextual" as some sort of vague excuse to avoid getting into the details. I'm not saying that intelligence cannot be quantified at all. Quite the opposite. Intelligence can be quantified fairly well (in the statistical sense) once a person specifies what they are talking about. Like Russell, I'm saying intelligence is multifaceted and depends on the agent (what sensors it has, what actuators it has), the environment, and the goal.
So what language would I use instead? Rather than speaking about "intelligence" as one thing that people understand and agree on, I would point to task- and goal-specific metrics. How well does a particular LLM do on the GRE? The LSAT?
Sooner or later, people will want to generalize over the specifics. This is where statistical reasoning comes in. With enough evaluations, we can start to discuss generalizations in a way that can be backed up with data. For example, might say things like "LLM X demonstrates high competence on text summarization tasks, provided that it has been pretrained on the relevant concepts" or "LLM Y struggles to discuss normative philosophical issues without falling into sycophancy, unless extensive prompt engineering protocols are used".
I think it helps to remember this: if someone asks "Is X intelligent?", one has the option to reframe the question. One can use it as an opportunity to clarify and teach and get into a substantive conversation. The alternative is suboptimal. But alas, some people demand short answers to poorly framed questions. Unfortunately, the answers they get won't help them.
Intelligence is closely related to the concept of attractiveness and gravitas. You say it depends on the agent. I say it's in the eye of the beholder. People aren't very good at explaining what attracts them either.
The closest thing we have to a definition for intelligence is probably the LLMs themselves. They're very good at predicting words that attract people. So clearly we've figured it out. It's just such a shame that this definition for intelligence is a bunch of opaque tensors that we can't fully explain.
LLMs don't just defy human reasoning and understanding. They also challenge the purpose of intelligence itself. Why study and devise systems, when gradient descent can figure it out for you? Why be cleverer when you can just buy more compute?
I don't know what's going to make the magical black pill of machine learning more closely align with our values. But I'm glad we have them. For example, I think it's good that people still hold objectivity as a virtue and try to create well-defined benchmarks that let us rank the merits of LLMs using numbers. I'm just skeptical about how well our efforts to date have predicted the organic processes that ultimately decide these things.
> Intelligence is closely related to the concept of attractiveness and gravitas.
Interesting. I wonder why you make this connection. Do you know?
Your choice of definition seems to be what I would call "perception of intelligence". But why add that extra layer of indirection; why require an observer? I claim this extra level of indirection is not necessary. I eschew definitions with unnecessary complexity (a.k.a "accidental complexity" in the phrasing of Rich Hickey).
Here are some examples that might reveal problems with the definition above:
- DeepBlue (decisively beating Kasparov in 1997) showed a high level of intelligence in the game of chess. The notion of "being good at the game" is simpler (conceptually) than the notion of "being attractive to people who like the game of chess". See what I mean?
- A group of Somali pirates working together may show impressive tactical abilities, including the ability to raid larger ships, which I would be willing to call a form of tactical intelligence to achieve their goals. I grant the intelligent behavior even though I don't find it "attractive", nor do I think the pirates need any level of "gravitas" to do it. Sure, the pirates might use leadership, persuasion, and coordination to accomplish their goals but these concepts are a means to an end accomplishing the goal. But these traits are not necessary. Since intelligent behavior can be defined without using those concepts, why include them? Why pin them to the definition?
- The human brain is widely regarded as a intelligent organ in a wide variety of contexts relating to human survival. Whether or not I find it "attractive" is irrelevant w.r.t. intelligence, I say. If the neighboring tribe wants to kill me and my tribe (using their tribally-oriented brains), I would hardly call their brains attractive or their methods being nuanced enough to use "gravitas".
My claim is then: Intelligence should be defined by functional capability which leads to effectiveness at achieving goals, not by how we feel about the intelligence or those displaying it.
Intelligence is an amalgamation of things. I read somewhere once that scientists tried to figure out which gene is the high IQ gene and found many contributed. It isn't a well defined game like chess. Being good at chess is to intelligence like having great legs might be to attractiveness.
You're don't like pirates? You're either in the Navy or grandstanding. People love pirates and even killers. But only if they're successful. Otherwise One Piece wouldn't be the most popular manga of all time.
Achieving goals? Why not define it as making predictions? What makes science science? The ability to make predictions. What does the brain organ and neural networks do? They model the world to make predictions. So there you have it.
This whole conversation has been about reducing intelligence to its defining component. So I propose this answer to your question. Take all the things you consider intelligent, and order them topologically. Then define intelligence as whatever thing comes out on top. Achieving goals depends on the ability to make predictions. Therefore it's a better candidate for defining intelligence.
> Achieving goals? Why not define it as making predictions?
Because "achieving goals" subsumes "making predictions". Remember, Russell's goal is to find a definition of intelligence that is broader than humans -- and even broader than sentient beings. But using the "achieving goals" definition, one can include system that accomplishes goals, even if we can't find any way to verify it is making predictions. For example, even a purely reactive agent (e.g. operating on instincts) can display intelligent behavior if its actions serve its purposes.
If you are seeking one clear point of view about the nature of intelligence, I highly recommend Russell's writing. You don't have to "agree" with his definition, especially not at first, but if you give it a fair reading, you'll probably find it to be coherent and useful for the purposes he layes out.
Russell has been thinking about and teaching these topics for probably 40+ years in depth. So it is sensible to give his ideas serious consideration. Also, there are scholars who disagree with Russell's definition or accentuate different aspects. Wherever a person lands, these various scholars provide a clear foundation that is all too often lacking in everyday conversation.
> This whole conversation has been about reducing intelligence to its defining component.
Not really, but I can see why you might say this. Neither Russell nor I are attempting to define "the one component" of intelligence -- we're saying that there is no single kind of intelligence. Only when one defines a particular (agent, environment, goal) triple can one can start to analyze it statistically and tease apart the related factors. You and I agree that the result will be multifaceted.
I wouldn't say I'm trying to "reduce" anything. I would say I've been attempting to explain a general definition of intelligence that works for a wide variety of types of intelligence. The goal is to reduce unnecessary confusion about it. It simply requires taking some extra time to spell out the (agent, environment, goal).
Once people get specific about a particular triple, then we have a foundation and can start to talk about patterns across different triples. If one is so inclined, we can try to generalize across all intelligent behavior, but frankly, only a tiny fraction of people have put in the requisite thought to do this rigorously. Instead, many people latch onto one particular form of intelligence (e.g. abstract problem solving or "creativity" or whatever) and hoist these preferred qualities into their definition. This is the tail wagging the dog in my opinion. But this is another topic.
Tangent: I was thinking the other day: these are not AI in the sense that they are not primarily intelligence. I still don't see much evidence of that. What they do give me is superhuman memory. The main thing I use them for is search, research, and a "rubber duck" that talks back, and it's like having an intern who has memorized the library and the entire Internet. They occasionally hallucinate or make mistakes -- compression artifacts -- but it's there.
So it's more AM -- artificial memory.
Edit: as a reply pointed out: this is Vannevar Bush's Memex, kind of.