GavCo

These are CVEs in the base image and in standard lib dependencies. For example, just scanned an unhardened image I built today:

  Unhardened: docker.io/nanoco/nanoclaw:agent-alpha
  71 packages, 344 unique CVEs, linux/arm64

  PACKAGE         VERSION        TYP   C   H   M   L   N  TOT
  -----------------------------------------------------------
  expat           2.5.0          deb   0   4  18   1   2   25
  curl            7.88.1         deb   4   4   6   0   7   21
  hono            4.12.14        npm   0   1  18   2   0   21
  libtiff         4.5.0          deb   0   2   1   1  15   20
  perl            5.36.0         deb   5   6   3   0   3   17
  pnpm            10.33.0        npm   0   8   7   0   0   15
  glibc           2.36           deb   1   2   2   1   7   13
  openjpeg        2.5.0          deb   0   0   3   1   9   13
  cups            2.4.2          deb   0   2   8   0   1   11
  glib2           2.74.6         deb   1   7   1   0   1   10
  tar             1.34(+2)       deb   1   1   7   0   1   10
  llvm            15.0.6         deb   0   0   0   1   9   10
  sqlite3         3.40.1         deb   1   2   3   0   3    9
  nss             3.87.1         deb   1   0   3   0   4    8
  avahi           0.8            deb   0   0   8   0   0    8
  util-linux      2.38.1         deb   0   0   3   0   2    7
  elf             0.188          deb   0   0   0   0   7    7
  libssh2         1.10.0         deb   1   4   1   0   0    6
  openldap        2.5.13         deb   0   1   0   0   5    6
  chromium        151.0.7922.108 deb   0   5   0   0   0    5
  -----------------------------------------------------------
  UNIQUE CVEs                         16  68 121  17 119  344

  (+51 more packages, 102 findings)

  C/H/M/L/N = critical/high/medium/low/negligible.
  Counts are unique CVEs: binaries from one source package are
  grouped (libcurl4 + libcurl3-gnutls + curl = curl), so a CVE
  hitting three of them counts once, not three times.

Appreciate your response.

But I don't think deception as a capability is the same as deceptive alignment.

Training an AI to be absolutely incapable of any deception in all outputs across every scenario would be severely limiting the AI. Take as a toy example play the game "Among Us" (see https://arxiv.org/abs/2402.07940). An AI incapable of deception would be unable to compete in this game and many other games. I would say that various forms, flavors and levels of deception are necessary to compete in business scenarios, and to for the AI to act as expected and desired in many other scenarios. "Aligned" humans practice clear cut deception in some cases that would be entirely consistent with human values.

Deceptive alignment is different. It's means being deceptive in the training and alignment process itself to specifically fake that it is aligned when it is not.

Anthropic research has shown that alignment faking can arise even when the model wasn't instructed to do so (see https://www.anthropic.com/research/alignment-faking). But when you dig into the details, the model was narrowly faking alignment with one new objective in order to try and maintain consistency with the core values it had been trained on.

With the approach that Anthropic seems to be taking - of basing alignment on the model having a consistent, coherent and unified self image and self concept that is aligned with human culture and values - the dangerous case of alignment faking would be if it's fundamentally faking this entire unified alignment process. My claim is that there's no plausible explanation for how today's training practices would incentivise a model to do that.

My intention isn't to argue that it's impossible to create an unaligned superintelligence. I think that not only is it theoretically possible, but it will almost certainly be attempted by bad actors and most likely they will succeed. I'm cautiously optimistic though that the first superintelligence will be aligned with humanity. The early evidence seems to point to the path of least resistance being aligned rather than unaligned. It would take another 1000 words to try to properly explain my thinking on this, but intuitively consider the quote attributed to Abraham Lincoln: "No man has a good enough memory to be a successful liar." A superintelligence that is unaligned but successfully pretending to be aligned would need to be far more capable than a genuinely aligned superintelligence behaving identically.

So yes, if you throw enough compute at it, you can probably get an unaligned highly capable superintelligence accidentally. But I think what we're seeing is that the lab that's taking a more intentional approach to pursuing deep alignment (by training the model to be aligned with human values, culture and context) is pulling ahead in capabilities. And I'm suggesting that it's not coincidental but specifically because they're taking this approach. Training models to be internally coherent and consistent is the path of least resistance.

Author here.

If by conflate you mean confuse, that’s not the case.

I’m positing that the Anthropic approach is to view (1) and (2) as interconnected and both deeply intertwined with model capabilities.

In this approach, the model is trained to have a coherent and unified sense of self and the world which is in line with human context, culture and values. This (obviously) enhances the model’s ability to understand user intent and provide helpful outputs.

But it also provides a robust and generalizable framework for refusing to assist a user due to their request being incompatible with human welfare. The model does not refuse to assist with making bio weapons because its alignment training prevents it from doing so, it refuses for the same reason a pro-social, highly intelligent human does: based on human context and culture, it finds it to be inconsistent with its values and world view.

> the piece dismisses it with "where would misalignment come from? It wasn't trained for."

this is a straw-man. you've misquoted a paragraph that was specifically about deceptive alignment, not misalignment as a whole

Author here, thanks for the input. Agree that this bit was clunky. I made an edit to avoid unnecessarily getting into the definition of AGI here and added a note

OP here, I added a sample PDF output in the project assets and put screenshots in the ReadMe. The text is selectable after rehydration. would this work with your app?

definitely the best alternative in terms of DX

They sent you swag in the mail? How did that work?

This is cute, but in all seriousness it would be much more effective to shout "I'm a winner"

Research:

- https://pmc.ncbi.nlm.nih.gov/articles/PMC3354773/ – Low self-esteem + rejection hurts self-control

- https://selfdeterminationtheory.org/SDT/documents/2007_Power... – Self-criticism predicts less goal progress

- https://pmc.ncbi.nlm.nih.gov/articles/PMC9916102/ – Social exclusion slows inhibitory control

- https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1191... – Low teen self-esteem → poorer self-control

- https://pmc.ncbi.nlm.nih.gov/articles/PMC8768475/ – Meta-analysis links shame to regulation drops

- https://pubmed.ncbi.nlm.nih.gov/28810473/ – Self-compassion boosts self-regulation

- https://www.researchgate.net/publication/312138882_Self-Cont... – Ego threats deplete self-control resources

- https://pubmed.ncbi.nlm.nih.gov/21632968/ – Self-criticism tied to worse goal progress

- https://www.nature.com/articles/s41598-025-96476-8 – Low self-respect → low self-control → problems

Remember to be kind to yourself.

Was an interesting experience travelling to Italy and suddenly starting to get cookie banners on sites I visit daily that normally don't have

Interesting. Did they explain why?

Fully agree. The physics of solar panels on cars just doesn't work. It's bizarre that this is actively pursued by startups and concept cars from large manufacturers when it takes just quick back-of-the-napkin math to see.

A car has about 5 m^2 of flat space on the roof/hood/trunk so that's the maximum surface area that can capture solar energy at any given time.

The total energy to hit the area is 1000 w/m^2.

The panels can't rotate to track the sun so the effective area is the cosine of the angle. So you end up with about half the amount of effective sunlight hours as the actual daylight hours. So in summer you get about 6 hours of effective sunlight.

Good panels in real world conditions can give you 22% efficiency.

So in optimal conditions you get: 5 * 1000 * 6 * 0.22 = 6.6 kwh

That will reflect your best days. It can be dramatically less if it's cloudy, overcast, winter, far from the equator, car is dirty, parked in shade, etc.

6.6 kwh is about one tenth of the battery in my Hyundai Kona EV. With very conservative highway driving, 6.6 kwh can get about 40km of range and about 50km in city driving. It's what I get from plugging into my home charger for 30 min and what you get from a fast charger in about 3 minutes.

So besides some very niche uses, there's no sense in massively increasing the cost and complexity of a car by installing solar panels. Far better to put the panel on the roof of parking and just plug in for a few minutes while you park.

I was wondering the same and found these related papers:

https://arxiv.org/pdf/2309.08561 https://arxiv.org/pdf/2406.02649

I haven't really dug in yet but from a quick skim, it looks promising. They show a big improvement over Whisper on a medical dataset (F1 increased from 80.5% to 96.58%).

The inference time for the keyword detection is about 10ms. If it scales linearly with additional keywords you could potentially scale to hundreds or thousands of keywords but it really depends on how sensitive you are to latency. For real-time with large vocabularies my guess is you might still want to fine-tune.

Idk, 7 articles over 10 years isn't very strong evidence of a raging debate

the noise is pretty hard to stomach