i.e. science.

Comments URL: https://news.ycombinator.com/item?id=47643276

Points: 11

# Comments: 7

https://en.wikipedia.org/wiki/Power_projection

The leader image is ... a US aircraft carrier (the USS Nimitz). That's what the US uses its military power for, to influence events in lands far, far away from its territory.

But, now, tell me which one of the many wars that the US has fought in after WWII did not end in disaster. Afghanistan? Iraq? Korea?

There was a meme doing the rounds the other day: "Name a character who can defeat Captain America". The answer being "Captain Vietnam". The US has faced humiliating defeat after humiliating defeat while bringing death and destruction and immeasurable misery to millions around the world.

That is what HN users seem to have an "anti" sentiment for. If you watch the news you'll be able to tell that this goes far beyond HN. The whole of US society seems to be extremely tired with those "forever wars", those senseless excursions to faraway lands, that not only do not secure US interests but turn world opinion more and more against the US. Even the US' closest allies now fear the US: vide Greenland. Anyone with more than a video game or comic book understanding of how the real world works would do well to be concerned.

Edit: also from EU, btw. Greek but living in the UK.

Comments URL: https://news.ycombinator.com/item?id=47585147

Points: 7

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=47571031

Points: 3

# Comments: 0

As to them all being intercepted, in the 12-day war that seemed to be the plan, i.e. force Israel to waste interceptors on cheap drones [bbc_2]. That seems to have changed in the current conflict.

_______________

[bbc_1] With a maximum range of 2,500km it could fly from Tehran to Athens.

[bbc_2] When Iran attacked Israel with hundreds of drones in 2024, the UK was reported to have used RAF fighter jets to shoot some down with missiles that are estimated to cost around £200,000 each.

Both exceprts from:

https://www.bbc.co.uk/news/resources/idt-b3a272f0-3e10-4f95-...

[google] https://www.google.co.uk/maps/dir/Isfahan,+Isfahan+Province,...

Speaking of self-driving cars' AIs, while they have plenty of machine learning components, e.g. for vision, SLAM, and so on, they are largely hand-coded, rule-based systems. Just like the good old days of GOFAI.

>> The whole notion of "we need to know what intelligence is exactly to reproduce it" is completely and utterly wrong.

Can you explain why it's completely wrong?

Comments URL: https://news.ycombinator.com/item?id=47414795

Points: 4

# Comments: 0

I have no idea why. I have recently started to grow a bit paranoid and wonder whether I am being manipulated by the media I consume. That would not be a huge surprise, I'm willing to bet most people are influenced by some of the things they read online.

Anyway this is an interesting question that has to be answered: why only Gaza, and not the other genocides?

Comments URL: https://news.ycombinator.com/item?id=47407224

Points: 6

# Comments: 0

There's no sense in which symbolic AI is at the end of its life and if you pay close attention you'll see that LLMs are trying to do all the things that symbolic AI is good at: major examples being reasoning, and planning from world models.

And as nextos says in the sibling comment most of the recent successes of LLMs in tasks that go beyond language generation, e.g. solving math olympiad problems, are the result of combining LLMs with symbolic verifiers.

>> While ML is cracking open entirely new fields - and might go all the way to AGI, the way it's going now.

I don't agree. Everything that neural nets do today, speech recognition, object identification in images, machine translation, language generation, program synthesis, game playing, protein folding, research automation, I mean every single thing really, is a task that comes from the depths of AI history. There's a big discussion to be had about why those tasks are "AI" tasks in the first place and what they have to do with "intelligence" in the broader sense (e.g. cats are intelligent but they can't generate any sort of text) but this discussion is constantly postponed as we all breathlessly run up the hill that neural nets are climbing. When we get to the top and find it was the wrong hill to climb, maybe we'll have that discussion at last, or maybe the entire industry, academia in tow, will run after the Next Big Thing in AI™ all over again. But- cracking open new fields? Nah. Not really.

AGI is not going to happen any time soon though. We have no idea what we're doing in terms of reproducing intelligence, that much is clear.

I thought the creator of the Daleks was Davros?

I think you're confusing various different things as "neurosymbolic AI". There is a NeSy symposium and I happen to have met many of the people there, and they are not GOFAI ideologues, rather they recognise the obvious limitations of neural nets (i.e. they're crap at deduction, though great at induction) and they look for ways to address them. Most of that crowd also has a predominantly statistical ML/ neural nets background, with symbolic AI as an afterthought.

I don't think I've ever heard anyone say that "ML is not real AI" and I mainly move in symbolic AI circles. I would check my sources, if I were you.

Anwyay, honestly, this is 2026, there is no sensible reason to be polarised about symbolic vs. statistical AI (or whatever distinction anyone wants to make). An analogy I like to make is as follows: a jetliner is a flying machine, a helicopter is a flying machine. We can use both for their advantages and disadvantages, but a flying machine is something too useful to give up on any one kind for ideological reasons. The practical benefits overwhelmingly make up for any ideological concerns (e.g. "jets bad" or "propellers bad").

And just to be clear, symbolic AI is still in rude health: automated theorem proving, planning and scheduling, program verification and model checking, constraint satisfaction, discrete optimisation, SAT solving, all those are fields where symbolic approaches are dominant, and where neural nets have not made significant inroads in many decades; nor are they likely to, not any more than symbolic approaches are likely to make any inroads in e.g. machine vision, or speech recognition. And that's just fine: lots of tools, lots of problems solved.

To answer dwa3592, it's always possible to set the weights of a neural net by hand, albeit extremely fiddly and normally only done "on paper". This is e.g. how the Turing-completeness of RNNs was shown back in the '90s:

On the computational power of neural nets

https://binds.cs.umass.edu/papers/1992_Siegelmann_COLT.pdf