I think it would be great to augment this tool to diagram the council meetings with questions, discussions, and motions. This would make it easier to track what city staff presents, which questions are asked by which council members, and finally how the motions are made and modified for any action items. I find that keeping track of all this during a meeting is very hard to do. It can make your head spin.
University of Washington actually has a neat project where they drove around Seattle and Milwaukee to try and do International Mobile Subscriber Identity (IMSI)-Interceptor detection. Basically, find cars or buildings with mobile towers that intercept and redirect your cell signal through a man-in-the-middle observation. [2] Notably, United States Citizenship and Immigration Services building (USCIS) apparently operates one. The pictures are rather pretty.
Some of those may not actually be law-enforcement IMSI interceptors. Some government buildings have legitimate microcell antennas that are installed and operated by the telco.
> To covertly transmit on the same frequencies as the normal cellular network, IMSI-catchers may mimic the identifying properties (mcc, mnc, cell id, etc.) of legitimate cell towers. [2]
On the surface this is a great idea, but my experience with city councils and planning commissions is that real decision making is done outside chambers and in private. What happens at public meetings is performance art. I've never seen public discussion impact decisions, even when very compelling legal arguments were made. Searchable minutes are probably adequate for most purposes.
My own opinion is that convening in-person meetings with Robert's Rules were necessary before telecommunications, but they're long past their useful life.
this may be true but is also a "gravity always wins" sort of statement. Some legal venues have "sunshine rules" to mitigate and dampen the inevitable. I have seen exactly as you say, and on different councils, quite a lot of involvement.
Interesting idea for making public meetings more accessible to the public. I know 3pm on a random weekday doesn't work for most citizens and yet... that's when it feels like many council meetings are.
Side note: Spotting a bit of lorem ipsum on the site which seems odd.
This is the kind of tool that makes me excited about the AI boom. I can imagine this being helpful for journalists. Think of a tool that you can say "Watch all the CSPAN footage and send me an email when someone talks about $TOPIC"
The people who are in a position to do this are probably in a position to do anything else they want to do with said phone calls. What's changing is that random joes can do this stuff now.
No, modern AI tools make dragnet surveillance much easier. Imagine that the State bans a certain religion, and has the ability to automatically detect apostatic or heretical speech in private conversations without a tap, without an agent reviewing the recording, and with a fraction of the compute needed in the past
No, I'm not really worried about Joe doing that, just Uncle Sam
Google does this quite efficiently. I am confident that everything going over Voice service (and probably Fi as well) is transcribed in real-time and dumped to the correct agencies.
I can reliably trigger a disconnect for my calls if I blurt out a series of keywords. Distinct behavior from my mobile carrier's neutrality.
It feels more like it's going to make it easier to deceive people. It seems like, as people rely more and more on automated systems like this, it would be easy to own and change the output on the fly.
Attention is all you need. But not the way we here at HN expect.
If you investigate police statistics, you'll see that the "story" about the statistics is often dictated by the availability of the statistics. Go to a "safe" city and review the statistics on the police department website. The availability of those statistics is all over the place, and one city will claim they can't publish the information from past years because of COVID-19, and the next city will say that they can only publish information from the past because of COVID-19. One city will claim impossible to verify outcomes. And, another city will publish information which will be used to prove political points by failing newspapers.
This feels like it could really shift attention to processing information and bringing attention to when that information isn't available.
Humorous fact: half of Asher Avenue was renamed to Colonel Glenn Avenue in Little Rock to confound crime reporting that was linked to street names. Lies, damned lies, and statistics...
Wouldn't you then be reliant on community members to track down that information and feed it into the system? In which case the end result would still be a matter of how determined the community is to keep police accountable, which I don't see changing much.
It's probably my own biases, but I feel like the community is going to be a lot more reliable and motivated to publish information than the organizations which are scrutinized by that information being public.
> Despite targeted outreach, we've noticed that community members are not returning to use the tool after their initial interaction. There has been minimal engagement apart from a spike during a mid-November focus group. This indicates that Sawt is not helpful enough yet for people to want to come back.
Perhaps they have trouble thinking of additional questions to ask? Their first few interactions probably cover the things they care most about. They get an answer and that’s it. Maybe a “subscribe to updates whenever this topic comes up in a meeting again” feature would be useful?
Your tool is too effective. It actually answered our questions. Don't you know the money's in treating the symptoms? Partial joke, yet interesting to see the jump to a result (weird when they're all about bias).
The suggestion's a cool idea. In addition to generic warning updates, you could also do "send me a summary" every time it appears. Kinda RSS feed summaries. Since they're integrating the news, they could also do full news coverage of the day summaries. Might actually be helpful for some people. Daily summary of "what happened in New Orleans yesterday?"
Right - my intuition is that most people don't have questions that they want answered in this way very often.
Learning how to use this tool involves learning what kind of questions can be answered by this data, and formulating those questions requires a pretty in-depth knowledge of how city politics actually works.
The same way you don’t care about plumbers and their issues, you already have your own, a job ( or two ), family and need to rest.
On top of that people ( rightfully) feel they have no power to change anything on their own without requiring a multi-year almost full time job pressuring and bringing awareness to the public, which is part of being a politician, which is exactly what they don’t have the time for and the will to deal with those type of people, which are being paid for and you don’t.
The problem with a text search is that you have to get your keywords exactly right. With LLMs you ask inexact questions like 'has the topic of X ever been discussed?'[1] without needing to have an exact match on X. An LLM front end which could return references to the full text seems like the best use of both.
[1] For example, your query might have had X be 'crime' and the transcript would have references to multiple specific types of crime such as 'muggings', 'vandalism' etc. which a full text search isn't going to match. Further, with the LLM front-end you could refine the query to ask about violent crime etc.
For many cases yes. With llm based embeddings you get “semantic search”, so for example if someone searches for “pets” they will most likely get results that include “dogs” and “cats”. This is not the case for regular text search.
The prompt seems reasonable enough but considering the groups somewhat partial agenda [1] and the massively loaded supplementary datasets [2, 3, etc], I'd be very cautious about using this tool without significant testing for bias.
New Orleans has a fairly large Palestinian diaspora population especially on the West Bank across from the City (and I think mostly from the other West Bank by chance).
The described technique of RAG is not only expensive, but also prone to hallucinations.
I would have liked more discussion on hallucinations, which is the ultimate pitfall of LLMs. This is critical for discovery-based public-facing chatbots.
I'm also very skeptical of real-world HyDE applications as they depend on the underlying model to properly answer the question, and can easily drift from the intention.
I've seen a lot of people (including people who I trust) say RAG is the best current mitigation we have for hallucinations, because grounding the LLM in additional context makes it much less likely it will make something up as opposed to use the information that has been passed to it along with the user's question.
I spent a bit of time playing with h2ogpt, which is a popular RAG framework. I gave it all our architectural documentation for our software and then tried asking it questions that transcended basic search (so the answer is not directly in there, but you could formulate it if you put disparate parts together). It started hallucinating pretty fast and told me all kinds of BS about how our software works.
I think RAG can be used in a way that eliminates or drastically reduces hallucinations, but to do that you have to do quite a lot of work to constrain the context and structure the prompting to address very specific questions. When you apply these more general frameworks they pump in large amounts of context in an unstructured manner and you just end right back at hallucinations again because the context isn't constrained enough.
So RAG is useful to me but not a silver bullet. It doesn't solve the original problem of wanting all the features of an LLM but without the hallucinations. It gives you some targeted way to use the LLM that doesn't hallucinate but misses a lot of the functionality people want.
RAG conceptually is the solution for hallucinations. I'm being critical of the implementations used to achieve it and the lack of awareness for hallucinations.
Fat citation needed on RAG being expensive. Most embeddings models these days are smaller than most LLM models, and run more cheaply and more effectively than the ones provided by OpenAI.
If you mean that increasing token counts in expensive, I suppose sure - but the retrieval side itself is not the cost center in general.
