ramity

In my experience, until you implement the math of a NN in numpy or something similar, it's easy to feel like a library is abstracting something away from you. Many people in ML focused academia stand on those libraries. They may conceptually understand chain rule but do not manually implement the math. The same feeling exists for encryption. There is a culture of shying away from rolling out your own ops, so it can easily feel like your understanding is segmented to some degree.

I'm passionate about and talk about this topic a lot. A common misconception is the idea that temperature or entropy must be 0 when seeded entropy enables both creativity and consistency. Attention mechanisms, layer dropout, initial weights, all of it. It can all be driven by seeded entropy. These systems can and have been served in a deterministic manner, but there are a litany of economic drivers against doing so.

Do not confuse the medium with the method. Recommendation systems are the root of what plagues us. Democracy was born because of the social media of its time. Social media good. Current implementations bad.

I miss chronological feeds.

I'm very thankful for the section on reproducibility. I argue this is the single biggest hangup for the entire space. You CAN have temperature and determinism. I've been waiting for six years for a major provider to offer it, there is demand, but I've slowly come to realize the current game theory does not support it.

For providers, not supporting deterministic eval means:

- users use more tokens = more money

- providers can generate more tokens per compute = more money

- providers have cheaper hardware options (GPUs) = more money

- providers models are harder to extract/distill = more money

- providers are harder to hold liable for outputs = more money

- providers can secretly use other models = more money

- providers are harder to compare against others = more money

- providers can cherry pick performance results = more money

Good thoughts here. Forgetting is important, but that's too advanced for modern LLMs.

Two angles for thought. 1) If an LLM says, "I don't know" its underlying data said it as well. 2) Many system prompts use something along the lines of, "you are a helpful assistant" which may be counter to stating something like, "I don't know."/has a low likelihood of appearing after the system prompt.

Regardless the frontier model considered, we're certainly in a "know-it-all" era.

Maybe the sort of introspective prompt-response is difficult to implement when it could limit/contaminate future improvement. I speculate it's easier to correct a "confidently incorrect" model than a "I don't know" model. A confidently incorrect model response >=0% correct over a 0% correct (I don't know).

Maybe "I don't know" is a model cognito hazard of sorts when many queries can lead back to the response. Maybe future Turing tests will use this sort of introspective evaluation. Who knows? I don't :)

:)

I run a business that makes 3d printed braille molds that are used to repeatably emboss paper. I haven't considered the molds being offline storage, but I suppose they are. I mostly operate with the assumption of a shelf life of 10 years for the PETG molds, but ink free, embossed paper has excellent lifespan if stored correctly.

I guess you could consider it an "offline datastore as a service." It would be a pretty good offline storage of keys with a way to request a paper copy. Certainly issues of trust and physical security but wrapping it with encryption would be easy. Also benefit from your government's legal protections for mail. There might actually be a usecase here.

Couple fast facts:

- Current 26 * 32 = 832 cells * 6 dot braille = 4992 bits/mold/page

- Possible 28 * 34 = 952 cells * 6 dot braille = 5712 bits/mold/page

- Maybe some more headroom, but that's what is possible with current spacings

Let me first start off by saying I and many others have stepped in this pitfall. This is not an attack, but a good faith attempt to share painfully acquired knowledge. I'm actively using AI tooling, and this comment isn't a slight on the tooling but rather how we're all seemingly putting the circle in the square hole and it fits.

Querying an LLM to output its confidence in its output is a misguided pattern despite being commonly applied by many. LLMs are not good at classification tasks as the author states. They can "do" it, yes. Perhaps better than random sampling can, but random sampling can "do" it as well. Don't get too tied to that example. The idea here is that if you are okay with something getting the answer wrong every so often, LLMs might be your solve, but this is a post about conforming non-deterministic AI into classical systems. Are you okay if your agentic agent picks the red tool instead of the blue tool 1%, 10%, etc of the time? If so, you're never not going to be wrangling, and that's the reality often left unspoken when integrating these tools.

While tangential to this article, I believe its worth stating that when interacting with an LLM in any capacity, remember your own cognitive biases. You often want the response to work, and while generated responses may look good and fit your mental model, it requires increasingly obscene levels of critical evaluation to see through the fluff.

For some, there will be inevitable dissonance reading this, but consider that these experiments are local examples. Its lack of robustness will become apparent with large scale testing. The data spaces these models have been trained on are unfathomably large in both quantity and depth, but under/over sampling bias will be ever present (just to name one).

Consider the the following thought experiment: You are an applicant for a job submitting your resume with knowledge it will be fed into an LLM. Let's confine your goal into something very simple. Make it say something. Let's oversimplify for the sake of the example and say complete words are tokens. Consider "collocations". [Bated] breath, [batten] down, [diametrically] opposed, [inclement] weather, [hermetically] sealed. Extend this to contexts. [Oligarchy] government, [Chromosome] biology, [Paradigm] technology, [Decimate] to kill. With this in mind, consider how each word of your resume "steers" the model's subsequent response, and consider how the data each model is trained on can subtly influence its response.

Now let's bring it home and tie the thought experiment into confidence scoring in responses. Let's say its reasonable to assume that the results of low accuracy/low confidence models are less commonly found on the internet than higher performing ones. If that can be entertained, extend the argument to confidence responses. Maybe the term "JSON" or any other term used in the model input is associated with high confidences.

Alright, wrapping it up. The end point here is that the model output provided confidence value is not the likelihood of the answer provided in the response but rather the most likely value following the stream of tokens in the combined input and output. The real sampled confidence values exist closer to code, but they are limited to each token. Not series of tokens.

I see I was mistaken, but I'm tempted to continue poking holes. Trying a different angle, though it may be a stretch, but could a caching layer within the VPN provider cause these sort of "too fast" RTTs?

Let's say you're a global VPN provider and you want to reduce as much traffic as possible. A user accesses the entry point of your service to access a website that's blocked in their country. For the benefit of this thought experiment, let's say the content is static/easily cacheable or because the user is testing multiple times, that dynamic content becomes cached. Could this play into the results presented in this article? Again, I know I'm moving goalposts here, but I'm just trying to be critical of how the author arrived at their conclusion.

Thanks for your informative reply. I see now I was approaching this incorrectly. I was considering drawing conclusions from a high RTT rather than a RTT so small it would be impossible to have gone the distance.

Contrasting take: RTT and a service providing black box knowledge is not equivalent to knowledge of the backbone. To assume traffic is always efficiently routed seems dubious when considering a global scale. The supporting infrastructure of telecom is likely shaped by volume/size of traffic and not shortest paths. I'll confess my evaluation here might be overlooking some details. I'm curious on others' thoughts on this.

I didn't see any reference to a sender or actively blasting RF from the same access point. I think the approach relies on other signal sources creating reflections to a passively monitoring access point and attempting to make sense of that.

5GHz WiFi has a wavelength of ~6cm and 2.4GHz ~12.5cm. Anything achieving smaller is a result of interferometry or a non WiFi signal. Mentioning this might not add much substance to the conversation, but it felt worth adding.