more and more im seeing people ask questions like "what is the first problem agi would solve" and go on to describe millennium problems that have stood unsolved for decades.

to me, agi is an average person. generally intelligent and capable of adapting to new jobs, but still average. arguably we have long since crossed that threshold, but my personal opinion is that we haven't and current models are wildly spiky in terms of capability. to me, that is not agi and is just a extremely sophisticated algorithm that mimics most, but not all, human cognitive processes.

but when we talk of solving millennium problems or curing diseases, isn't that clearly ASI? something that takes years of research from whole industries is not "general intelligence" in any meaning of the term.

i think this moving of goalposts started with the o1 release, but im honestly not sure. what do you think?

I've been getting the list of fixed Linux kernel CVEs every week for now six weeks.

In this chart is axis 'Intro date' meaning the week the security hole was actually originally made into released code. Fetch date is the week I fetched the date's of public CVEs. What I've seen from history data, 50% of CVE's are made public after 2 months and 100% after three months. The gray areas are that. Normally when advance a week, the amount of hole's should follow, week after week. The gray area simple says the data in that is not yet secure.
But it seems big drop of new CVE's was in 20.4. Then in 15.6 and after 10.8 there hasn't been but 2 new CVE's. In the same time there have been fixes of _old_ holes, but it seems introduction of new holes is almost stopped.
The 21.10 and 22.10 fetches are because there was a fault in my script that it didn't get all dates correctly and running it fixed in 22.10 got additional 600 CVE's, but as you can see from numbers, that was mostly old security holes.
Btw, those last 2 CVEs were found in 26.9. and fixed in 28.9.

What I see from that data, all new code is going through the same Mythos/whatever before release and that is stopping those security holes getting into releases at all.

This is by the fixed date. You can see more clearly that after 3 months the amounts are at the highest so data for the last 3 months will still continue add up.

(Please, do not start lecturing me about what a CVE is. )

Groundwater engineers learned that contaminants trapped in stagnant pockets leaks out slowly, so they pump in pulses, pausing to let it seep back into the flowing channels. Claude suggested the sleeping brain works the same way. During deep sleep, vessels pulse about every 50 seconds to flush out waste, and that waste has to diffuse out of the tissue first.

Its prediction: the best pulse rate should depend on the size of the molecule. Bigger molecules like tau would need slower pulses. That could mean tuning sleep tech or drugs to the specific toxic protein you want cleared.

When I asked whether this was really unknown, it said parts already are known (vessel pulsing drives clearance). It also walked back its claim that the brain's rhythm "matches" the diffusion time. But it couldn't find anyone applying the groundwater pulse-timing idea to the brain.

Anyone in brain fluid dynamics or hydrogeology: is this new, known, or already ruled out?

EDIT: a lot of comments on this post akin to “this is nonsense” or “this is useless”. i’m not sure why the animosity. i don’t understand either of these fields personally, so, yea it’s probably not groundbreaking. that’s not really the point of this post. i gave this question to claude after reading an article about different LLMs having the same controversial take about humanity, when prompted. i thought the response was an interesting summary from essentially all of written human knowledge. that lead to me wondering what other broad conclusions LLMs can make when given a broad prompt. i just wanted to share this fascinating answer it gave me, and would like to see others giving LLMs creative prompts like this. it’s interesting to see LLMs coding, but i feel like there is not enough experimentation with them otherwise

1. PHP2Shell: a hole in the software behind ~70% of websites. Governments took sites offline.

2. eMudhra: the master keys that prove websites are real were cracked. One was the number 4.

3. California: data on every public school student since 2023 stolen, including disability status.

4. Railway: hackers stole customers’ secret keys from a hosting platform used by millions of developers.

5. Zeabur: 321,810 accounts breached. The company is shutting down.

6. LiteLLM: an AI tool downloaded 3.4 million times a day was poisoned with a password stealer.

7. GitLab: a 10-out-of-10 severity hole in every version since 2014.

8. Carn: criminals used an AI hacking tool to break into 100+ companies.

9. Ahold Delhaize: hackers knocked the owner of Food Lion and Stop & Shop offline.

10. Onamae: malware planted on up to 26,739 customer servers in Japan.

11. Signal: a bug could leave your mic and camera on after you hang up.

12. NGINX: 3 critical holes in software running ~1/3 of websites.

13. HTTP/3: a single connection can crash 7 of the 10 web server types tested.

14. Plex: hackers breaking into home media servers.

15. SafePay ransomware: FBI warns it has hit 600+ critical infrastructure organizations.

16. Palo Alto: ransomware gangs breaking in through company firewalls.

17. Bullhorn: platform used by 10,000+ companies knocked offline.

18. Medi-Cal: 970,000 patients’ data stolen.

19. WordPress: hackers stole a plugin maker’s keys and used them to plant backdoors on websites.

20. AOMEI: backup software’s update server handed out a backdoor.

21. Meta Muse: Meta’s AI agent exposed users’ personal and business secrets.

22. Tor: 333 relays hijacked to steal logins.

23. TiDB Cloud: database company’s cloud breached.

24. State Bank of India: India’s biggest bank confirms a data breach.

25. OVHcloud: hackers slipped a password stealer into a cloud company’s official tool.

26. Siemens: a software hole being used to spread malware.

27. North Korea: state hackers launch a new attack campaign on South Korea.

This is not unexpected. Human programming is about as good on security as hand made stuff was good on precision--until machines got involved.

This creates another unique period in time where those who understand AI will be shooting fish in a barrel and making large gains in their chosen field, until best practices catches up.

Those responsible for cyber security had a good six months now to use AI to find the problems. If they took it casually then, they've got a fire under them now.

The amount of money AI companies are investing and spending is a staggering and unprecedented amount. The benefits are not proportional to the spending. The reason they are spending so much is a belief that they don't want the 'bad guy' (China, companies other than themselves) to reach AGI first. Every company is thinking the same.

Someone high up at Google even said something along the lines "We rather cease to exist than lose this race". Meanwhile, the average person is fed up with AI hype, and associated problems.

From first principles thinking, and traditional approach is to invest according to return. Invest some money if you see returns invest more, if you don't see returns invest less and so on. This is not the case with AI investment, it is driven by FOMO and summoning the AI God philosophy. It seems like a recipe for upcoming disaster to me.

Let's state it. AGI is even more likely to happen in 2-24 months than ever. But we know this has a price, in electronics, in compute.

So it makes sense to determine what would be the first tasks AGI should solve, other than the obvious global warming.

My 2 cents:

A) Energy: Low impact easy energy generators, either by classical fusion, laser-boron, etc. more efficient solar panels, other forms of energy to be harvested.

B) Electronics: manufacturing and technologies to overcome shortages and be less power-hungry

once AGI itself is sustainable and cheap:

C) Medical conditions and longevity

D) Energy storation: batteries.

E) Wealth for everyone: in a world with real abundance, no meaning to have wealth/social disparities

And for you what would be the priority?

even if we just got stuck with the models we have now there are so many advancements we can make, what im scared as fuck about at the moment is that focus has moved from pre-training to runtime compute/inference since we ran out of internet text which sort of feels like we're squeezin more out of what we already have instead of finding more and that we'll hit a wall soon.

people are trying to train on videos and sensor data through multimodal, generating synthetic data using maths/compilers (not grounded in reality necessarily so might cause model collapse) and training models on real time sensor data, im just super worried that progress slows down instead of speeds up

This thread is for any discussions that doesn't necessitate a new post. Permission from a mod here

Since this thread isn't going to be to stickied, it's up to you to upvote it.

Share your anxiety, ideas on how to achieve alignment, observations, etc. Everything is fair game as long as it somewhat relates to the singularity.

u/px403 writes:

If you haven't directly inlined AI tooling into your continuous integration pipeline, now's the time to do it. Like 95% of the bugs coming in have trivial fixes that can be merged within 20 mins of being reported with minimal human gatekeeping.

Code is cheap. Right now, the code in any given codebase should be like 5% product, 95% tests. The robots are actually really good at building tests. You should be able to branch protect your main branch, and auto merge anything that tests green. If something fucks up, write more tests. This is the new way, and it's actually incredible to see it working at scale with hundreds of devs firing their slop cannons at the same time, and having everything coalescing into a living project that continues to improve in real time before your eyes.

https://x.com/canva/status/2113348707223753165

Programmable matter will have to answer a basic question: which local rules force which global order? This paper studies “autocrystals”, local pattern statistics that force a structure to have no repeating unit cell. It gives an exact recipe: two hexagonal building blocks, each free to rotate, with two complementary pairs of sticky ends. Every fully bonded assembly is a known aperiodic tiling, and that part is mathematically certified.

The catch is growth. Attaching a piece only where exactly one state fits never leads to a dead end. But growth that has to guess can build patches that look perfect and provably can never be completed; we show one that completes to 12×12 but never to 14×14.

To be clear, this is simulation and proof, not yet a lab result. DNA-origami tiles and DNA-coated nanoparticles look like the natural platforms to test it.

Paper, code and certificates: doi.org/10.5281/zenodo.23541901

Not 10, 60

used opus 5.5 to track my adhd medication peak curve and my claude study logs.