Hot take: the problem here is not the one implied or AFAICT assumed by the author.
The problem is not that LLM or, say, generative AI tools such as Midjourney, "have no imagination" and collapse upon the same solution. Anyone who has spent any serious time exploring what was originally called the "latent space" Midjourney provided a view into, is aware that the combinatorics around what these systems have ingested is inconceivably large and full of wonders and surprises... what we might well describe as unexpected, novel, dare I say creative, imagery [etc.]
The problem AFAICT is asking for examples of something without providing any process or instruction.
Vibe-coded single-shot prompts are inevitably (unless this is patched) going to converge on statistically-likely or contextually hinted examplars.
And this is true all the way down. It's true of secondary and tertiary features (what camera angle do we have on the pelican? Is it a juvenile or injured? Is it riding to the left or right? etc.)
The fact that humans are a heterogeneous pool with different experiences and capabilities, means if you sample humans, you get high variability. If you ask contemporary LLM, they are converging, indeed, but not in the way implied; they are converging because the corpus they are trained on is comprehensive and coextensive.
What you get is the centroids in the space.
Until you simply ask for something else.
If you want more "creative" examples, of pelicans or anything else, it's more or less trivial to get them. And if you detect durable secondary or tertiary qualities you want to see varied, you ask.
More seriously,
if what you want is to interact with large capable models/systems which by default exhibit variability , you do the (minimal) work to make that happen.
Midjourney has from very early on exposed explicit dials to turn in exactly this way.
Very good. I have a listening experience close to elevenlabs. But sometimes you can get "failed" segments so you need to detect them and retry/repair automatically. One example is the fading volume.
This framing suggests that this is the sum of their business strategy, and strongly implies that it's being pursued cynically.
The risks posed by open access to highly capable AI are unprecedented.
The answer is not to ban open models; nor is it do nothing.
That we don't have answers technical political social or otherwise to how to navigate these risks, is not Anthropic's fault.
It's actively unhelpful IMO to amplify cynical takes which wave off or sideline the risk, or the reality that this is a very hard problem, among the hardest our societies have had to negotiate and navigate. It encourages a naive primary color tribalism that actively prevents process and progress.
It's depressing how poorly most internet spaces promote sincere dialogue. There are so many cynical hot takes that don't seem to accomplish much beyond scoring social points.
Not a bad metaphor: mostly fine, arguably legitimate benefits [when you're lucky], potentially really bad.
The biggest place the metaphor breaks is that when you drink bad milk, the consequences are local.
Note: this is not an argument that oligopolies or the state should control or have sole access to AI. It is a reflection of that fact that we as a society have no good answer nor even any process towards one, about how to balance the competing goods and risks of widespread access to capable AI.
Why would anyone believe this (also) is not simply example N+1 of this is the worst it will ever be, as opposed to recognizing this as what will almost certainly prove to be an awkward moment, soon to be replaced by another order of magnitude of cleaner, clearer, more intelligible, etc.?
Ximm's Law: every critique of AI assumes to some degree that contemporary implementations will not, or cannot, be improved upon.
Devin's Law: every defense of AI which rests on "it will get better, trust me" is in many ways indistinguishable from 2010s crypto hype or "level 5 self driving is right around the corner"
1) Predicting the future is hard, but so far everyone who was saying that it would get better turned out to be right. It is getting better
2) Waymo exists
3) That doesn't mean flying cars will within your lifetime
I don't think anyone is saying it can't or won't get better, but the question is how much better, on what timescale, and are there fundamental parts of the problem which will remain extraordinarily difficult to improve?
The comment I was responding to suggested a guarantee of an "order of magnitude" jump right around the corner. There is no guarantee of this, and if you view doomers as fools for having doubts, then we ought to look upon the folks who are sure of this sort of progress in the same way.
Your original comment was about self-driving cars, not flying ones. If you wanted unattainable goalposts you should’ve started with that, not ended with it.
Respectfully, I disagree with the way you're characterizing my comment. I was demonstrating that just because you have a level 4 Waymo doesn't mean flying cars are right around the corner. This is again a reference to the original comment I was replying to, the one that suggested of course we're going to get an order of magnitude improvement.
In that case, one would expect to see some progress in this direction, but AFAICT that hasn't shown up yet? If anything, it's getting worse, though that could just be the increasing scale and decreasing cleanup efforts.
Already the unit distance proof was substantially human-edited (per Thomas Bloom). Then with the ten problems from Astra you started getting the citation issues. Then Navier-Stokes was a rushed 160 pages with barely any citations, and some of the related papers were called (by their "authors") the ugliest mess they've ever seen.
And now here we are. At least it seems that mathematical ability and communication with a mathematical audience are independent skills, and progress in the first does not imply the second.
This doesn't surprise me much, given two analogies: 1) many smart people are nonetheless horrible lecturers. (You can't quite get the opposite extreme, since to explain math well you have to be able to do it.) 2) AI writing in general hasn't improved. The models have annoying verbal tics ("honestly") and have no sense of which part of what they say is obvious and which is relevant.
I'm thinking of somebody like Grant Sanderson (3blue1brown), doing pure exposition extremely well. For that you at least need to be able to work through examples, or to present why an intuitive approach might fail, and these things can be little theorems themselves. It doesn't have to be publishable in the current culture of novel results, but you do need a lot of competence with the tools.
Yes, Grant Sanderson might be a great example. I am not doubting the competence of the "good lecturers" I was referring to. But that is different from being able to introduce the big new concepts that give the big new results.
>The operation sought to exploit and amplify existing public concerns about energy prices and local impacts of data center development, but we found no evidence of meaningful breakout beyond its own activity.
>What X didn't disclose, according to Darren Linvill, a researcher at Clemson University, is that at least 59 of those bots had zero engagement and were likely quickly deactivated by X before they could garner a following.
How can you possibly determine the efficacy of this operation?
Is it not more likely that there is a grassroots rejection of building the infrastructure to replace labor, with no upside to the American worker? And a few hundred Chinese sock puppets join in the online fray, but this conversation is not just happening online, and Chinese propaganda is not raising electricity rates or burning illegal natural gas generators.
The dislike of giant data centers is also authentic though. They are generally bad neighbors, for a wide variety of reasons (water/electricity usage, loud, over lit, generally cheap construction with no aesthetics). There are ways to be more neighborhood friendly - you often drive by traditional data centers without even knowing.
Like Russia's efforts to aid Donald Trump in 2016, it's hard to tell how much actual impact the propaganda is actually having on the outcome. There are many, many legitimate reasons to oppose data centers, from the local nuisance they create to the energy they consume to their contribution to an already critical level of global warming.
But I think the most damning argument against them is that they aren't needed. The cost of serving models is dropping faster than data centers can be built, and that trend seems likely to continue---AI is such a new technology that we have barely scraped the surface in finding efficiencies. And most of the innovations in efficiency that have emerged have come from the smaller players, because the big AI labs believe they can simply scale their way out of capacity constraints. An increasingly troubling trend is that they are not scaling their way out with cash flow, but with massive amounts of borrowed money. Seen in that light, data centers are not only a physical liability, they could become a catastrophic economic liability.
There were not many many legitimate reasons to support Trump tho. That vote says something very bad about those who cast it and that is all there is to it.
If you truly believe that, then all I can say is you have not put nearly enough effort into understanding Trump voters' views. I know a few people who voted for Trump, and while I disagree with their respective choices, it doesn't just come down to "it says something bad about them".
I did put effort into reading and listening what conservatives actually say. I am from conservative environment originally. And yes, it does says a lot of bad about them.
It is tempting to whitewash and rationalize and excuse them and take bullshit at the face value. At best, they believed to easy to see lies. At worst they wanted exactlt what happened, except with lower gas price.
That would be a false similarity in some important ways.
This was a very specific single technical problem, requiring a silo from basic science to theoretical physics to advanced engineering, etc., with each layer having its own space of failure modes.
Something like the reverse is true with sufficiently-capable and sufficiently-efficient AI, which we are rapidly evolving to.
In our current situation, the attack surface is quite literally our entire highly interdependent civilization and its components. AI does not need to be autonomous, it just needs to employed, either will wilful bad intent, or, poorly stated goals, or, poor alignment, or, simple mistakes and errors, to break things.
The challenge with security is always that the attacker need only succeed once.
This does not mean doomerism has it right, especially in narratives based on poor understanding or extapolation beyond what is now konwn. The chance of "extinction" in particular is overstated.
The chance of one or more compounding "Chernobyl scale" gray swan events however is ever increasing.
Personally I don't think that's remotely contestable.
As a civilization and society and industry we need to have process, and public process at that, about the nature of the risks faced.
That process has not yet emerged in any coherent way; a hundred letters have been signed and such but to no actual effect, bar one performative "executive order" this week.
The problem is not that LLM or, say, generative AI tools such as Midjourney, "have no imagination" and collapse upon the same solution. Anyone who has spent any serious time exploring what was originally called the "latent space" Midjourney provided a view into, is aware that the combinatorics around what these systems have ingested is inconceivably large and full of wonders and surprises... what we might well describe as unexpected, novel, dare I say creative, imagery [etc.]
The problem AFAICT is asking for examples of something without providing any process or instruction.
Vibe-coded single-shot prompts are inevitably (unless this is patched) going to converge on statistically-likely or contextually hinted examplars.
And this is true all the way down. It's true of secondary and tertiary features (what camera angle do we have on the pelican? Is it a juvenile or injured? Is it riding to the left or right? etc.)
The fact that humans are a heterogeneous pool with different experiences and capabilities, means if you sample humans, you get high variability. If you ask contemporary LLM, they are converging, indeed, but not in the way implied; they are converging because the corpus they are trained on is comprehensive and coextensive.
What you get is the centroids in the space.
Until you simply ask for something else.
If you want more "creative" examples, of pelicans or anything else, it's more or less trivial to get them. And if you detect durable secondary or tertiary qualities you want to see varied, you ask.
More seriously,
if what you want is to interact with large capable models/systems which by default exhibit variability , you do the (minimal) work to make that happen.
Midjourney has from very early on exposed explicit dials to turn in exactly this way.
I am surprised the author does not know this.
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