7 ms·

> potential applications > if you ... > for example ... Yes there seems to be lots of potential. Yes we can brainstorm things that should work. Yes there is a

by afro88 2y ago

> potential applications > if you ... > for example ... Yes there seems to be lots of potential. Yes we can brainstorm things that should work. Yes there is a lot of examples of incredible things in isolation. But it's a little bit like those youtube videos showing amazing basketball shots in 1 try, when in reality lots of failed attempts happened beforehand. Except our users experience the failed attempts (LLM replies that are wrong, even when backed by RAG) and it's incredibly hard to hide those from them. Show me the things you / your team has actually built that has decent retention and metrics concretely proving efficiency improvements. LLMs are so hit and miss from query to query that if your users don't have a sixth sense for a miss vs a hit, there may not be any efficiency improvement. It's a really hard problem with LLM based tools. There is so much hype right now and people showing cherry picked examples.

jihadjihad 2y ago
> Except our users experience the failed attempts (LLM replies that are wrong, even when backed by RAG) and it's incredibly hard to hide those from them. This has been my team's experience (and frustration) as well, and has led us to look at using LLMs for classifying / structuring, but not entrusting an LLM with making a decision based on things like a database schema or business logic. I think the technology and tooling will get there, but the enormous amount of effort spent trying to get the system to "do the right thing" and the nondeterministic nature have really put us into a camp of "let's only allow the LLM to do things we know it is rock-solid at."
- sdesol 2y ago
  > "let's only allow the LLM to do things we know it is rock-solid at." Even this is insanely hard in my opinion. The one thing that you would assume LLM to excel at is spelling and grammar checking for the English language, but even the top model (GPT-4o) can be insanely stupid/unpredictable at times. Take the following example from my tool: https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples=5 https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples... 5 models are asked if the sentence is correct and GPT-4o got it wrong all 5 times. It keeps complaining that GitHub is spelled like Github, when it isn't. Note, only 2 weeks ago, Claude 3.5 Sonnet did the same thing. I do believe LLM is a game changer, but I'm not convinced it is designed to be public-facing. I see LLM as a power tool for domain experts, and you have to assume whatever it spits out may be wrong, and your process should allow for it. Edit: I should add that I'm convinced that not one single model will rule them all. I believe there will be 4 or 5 models that everybody will use and each will be used to challenge one another for accuracy and confidence.
  - SimianSci 2y ago
    > "I see LLM as a power tool for domain experts, and you have to assume whatever it spits out may be wrong, and your process should allow for it." this gets to the heart of it for me. I think LLMs are an incredible tool, providing advanced augmentation on our already developed search capabilities. What advanced user doesnt want to have a colleague they can talk about their specific domain capacity with? The problem comes from the hyperscaling ambitions of the players who were the first in this space. They quickly hyped up the technology beyond want it should have been.
    - netdevnet 2y ago
      Welcome to capitalism. The market forces will squeze max value out of them. I imagine that Anthropic and OpenAI will be in the future fully downsized and acquired by their main investors (Microsoft and Amazon) and will simply becoming part of their generic and faceless AI & ML Teams once the current downwards stage of the hype cycle completes it closure in the next 5-8 years.
      - parineum 2y ago
        > Welcome to capitalism. The market forces will squeze max value out of them. What a ringing endorsement.
  - larodi 2y ago
    Those Apple engineers stated in a very clear tone: - every time a different result is produced. - no reasoning capabilities were categorically determined. So this is it. If you want LLM - brace for different results and if this is okay for your application (say it’s about speech or non-critical commands) then off you are. Otherwise simply forget this approach, and particularly when you need reproducible discreet results. I don’t think it gets any better than that and nothing so far implicated it will (with this particular approach to AGI or whatever the wet dream is)
    - marcellus23 2y ago
- deleted 2y ago
  [deleted]
VeejayRampay 2y ago
really agree with this and I think it's been the general experience: people wanting LLMs to be so great (or making money off them) kind of cherry picking examples that fit their narrative, which LLMs are good at because they produce amazing results some of the time like the deluxe broken clock that they are (they're right many many times a day) at the end of the day though, it's not exactly reliable or particularly transformative when you get past the party tricks
archiepeach 2y ago
To be fair in the human-based teams I've worked with in startups I couldn't show you products with decent retention.
fnordpiglet 2y ago
We have built quite a few highly useful LLM applications in my org that have reduced cost and improved outcomes in several domains - fraud detection, credit analysis, customer support, and a variety of other spaces. By in large they operate as cognitive load reducers but also handle through automation the vast majority of work since in our uses false negatives are not as bad as false positives but the majority of things we analyze are not true positives (99.999%+). As such the LLMs do a great job at anomaly detection and allow us to do tasks it would be prohibitively expensive with humans and their false positive and negative rates are considerably higher than LLMs. I see these statements often here about “I’ve never seen an effective commercial use of LLMs,” which tells me you aren’t working with very creative and competent people in areas that are amenable to LLMs. In my professional network beyond where I work now I know at least a dozen people who have successful commercial applications of LLMs. They tend to be highly capable people able to build the end to end tool chains necessary (which is a huge gap) and understand how to compose LLMs in hierarchical agents with effective guard rails. Most ineffectual users of LLMs want them to be lazy buttons that obviate the need to think. They’re not - like any sufficiently powerful tool they require thought up front and are easy to use wrong. This will get better with time as patterns and tools emerge to get the most use out of them in a commercial setting. However the ability to process natural language and use an emergent (if not actual) abductive reasoning is absurdly powerful and was not practically possible 4 years ago - the assertion such an amazing capability in an information or decisioning system is not commercially practical is on the face absurd.
- andai 2y ago
  >compose LLMs in hierarchical agents with effective guard rails Could you elaborate? Is this related to the "teams of specialized LLMs" concept I saw last year when Auto-GPT was getting a lot of hype?
- topicseed 2y ago
  Do they build guardrails themselves or do they use an llm guardrail api like Modelmetry or Langwatch?
- mhuffman 2y ago
  >We have built quite a few highly useful LLM applications in my org that have reduced cost and improved outcomes in several domains Apps that use LLMs or apps made with LLMs? In either case can you share them? >which tells me you aren’t working with very creative and competent people > In my professional network beyond where I work now I know at least a dozen people who have successful commercial applications of LLMs. Apps that use LLMs or apps made with LLMs? In either case can you share them? No one doubts that you can integrate LLMs into an application workflow and get some benefits in certain cases. That has been what the excitement and promise was about all along. They have a demonstrated ability to wrangle, extract, and transform data (mostly correctly) and generate patterns from data and prompts (hit and miss, usually with a lot of human involvement). All of which can be powerful. But outside of textual or visual chatbots or CRUD apps, no one wants to "put up or shut" a solid example that the top management of an existing company would sign off on. Only stories about awesome examples they and their friends are working on ... which often turn out to be CRUD apps or textual or visual chatbots. One notable standout is generative image apps can be quite good in certain circumstances. So, since you seem to have a real interest and actual examples of this, I am curious to see some that real companies would gamble that company on. And I don't mean some quixotic startup, I mean a company making real money now with customers that is confident on that app to the point they are willing to risk big. Because that last part is what companies do with other (non LLM) apps. I also know that people aren't perfect and wouldn't expect an LLM to be, just want to make sure I am not missing something.
physicsguy 2y ago
We’ve found that the text it generates in our RAG application is good, but it cocks up probably 5-10% of the time doing the inline references to the documents which users think is a bug and which we aren’t able to fix. This is static rather than interactively generated too
anilgulecha 2y ago
LLMs are not hype. In education at least, we've actively improved efficiency by ~25% across a large swath of educators (direct time saved) - agentic evaluators, tutors and doubt clarifiers. The wins in this industry are clear. And this is that much more time to spend with students. I also know from 1-1 conversation with my peers in large-finance world, and there too the efficiency improvements on multiple fronts are similar.
- netdevnet 2y ago
  They are partially hype though. That's what people here are arguing. There are benefits but their valuation is largely hype driven. AI is going to transform industries and humanity, yes. But AI does not mean LLM (even if LLM means AI). LLM raw potential was reached last year with GPT-4. From here on, the value will lie on exploiting the potential we already have to generate clever applications. Just like the internet provided a platform for new services, I expect LLMs to be the same but with a much smaller impact
jacobr1 2y ago
This is why we are only at the start of exploring the solution space. What applications don't require 100% accuracy? What tooling can we build that enables a human in the loop to choose between options? What options do we have to better testing or checking accuracy? There is a lot more to be done to invest hybrid systems that use other types of models or novel training date or heuristics or human workflows in novel ways that shore up the shortcomings ... but in aggregate allow us to do new things. It will take many years for us to figure where this makes the most sense.

by afro88 2y ago

jihadjihad 2y ago

> Except our users experience the failed attempts (LLM replies that are wrong, even when backed by RAG) and it's incredibly hard to hide those from them. This has been my team's experience (and frustration) as well, and has led us to look at using LLMs for classifying / structuring, but not entrusting an LLM with making a decision based on things like a database schema or business logic. I think the technology and tooling will get there, but the enormous amount of effort spent trying to get the system to "do the right thing" and the nondeterministic nature have really put us into a camp of "let's only allow the LLM to do things we know it is rock-solid at."

sdesol 2y ago
> "let's only allow the LLM to do things we know it is rock-solid at." Even this is insanely hard in my opinion. The one thing that you would assume LLM to excel at is spelling and grammar checking for the English language, but even the top model (GPT-4o) can be insanely stupid/unpredictable at times. Take the following example from my tool: https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples=5 https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples... 5 models are asked if the sentence is correct and GPT-4o got it wrong all 5 times. It keeps complaining that GitHub is spelled like Github, when it isn't. Note, only 2 weeks ago, Claude 3.5 Sonnet did the same thing. I do believe LLM is a game changer, but I'm not convinced it is designed to be public-facing. I see LLM as a power tool for domain experts, and you have to assume whatever it spits out may be wrong, and your process should allow for it. Edit: I should add that I'm convinced that not one single model will rule them all. I believe there will be 4 or 5 models that everybody will use and each will be used to challenge one another for accuracy and confidence.
- SimianSci 2y ago
  > "I see LLM as a power tool for domain experts, and you have to assume whatever it spits out may be wrong, and your process should allow for it." this gets to the heart of it for me. I think LLMs are an incredible tool, providing advanced augmentation on our already developed search capabilities. What advanced user doesnt want to have a colleague they can talk about their specific domain capacity with? The problem comes from the hyperscaling ambitions of the players who were the first in this space. They quickly hyped up the technology beyond want it should have been.
  - netdevnet 2y ago
    Welcome to capitalism. The market forces will squeze max value out of them. I imagine that Anthropic and OpenAI will be in the future fully downsized and acquired by their main investors (Microsoft and Amazon) and will simply becoming part of their generic and faceless AI & ML Teams once the current downwards stage of the hype cycle completes it closure in the next 5-8 years.
    - parineum 2y ago
      > Welcome to capitalism. The market forces will squeze max value out of them. What a ringing endorsement.
- larodi 2y ago
  Those Apple engineers stated in a very clear tone: - every time a different result is produced. - no reasoning capabilities were categorically determined. So this is it. If you want LLM - brace for different results and if this is okay for your application (say it’s about speech or non-critical commands) then off you are. Otherwise simply forget this approach, and particularly when you need reproducible discreet results. I don’t think it gets any better than that and nothing so far implicated it will (with this particular approach to AGI or whatever the wet dream is)
  - marcellus23 2y ago
deleted 2y ago
[deleted]

VeejayRampay 2y ago

really agree with this and I think it's been the general experience: people wanting LLMs to be so great (or making money off them) kind of cherry picking examples that fit their narrative, which LLMs are good at because they produce amazing results some of the time like the deluxe broken clock that they are (they're right many many times a day) at the end of the day though, it's not exactly reliable or particularly transformative when you get past the party tricks

archiepeach 2y ago

To be fair in the human-based teams I've worked with in startups I couldn't show you products with decent retention.

fnordpiglet 2y ago

We have built quite a few highly useful LLM applications in my org that have reduced cost and improved outcomes in several domains - fraud detection, credit analysis, customer support, and a variety of other spaces. By in large they operate as cognitive load reducers but also handle through automation the vast majority of work since in our uses false negatives are not as bad as false positives but the majority of things we analyze are not true positives (99.999%+). As such the LLMs do a great job at anomaly detection and allow us to do tasks it would be prohibitively expensive with humans and their false positive and negative rates are considerably higher than LLMs. I see these statements often here about “I’ve never seen an effective commercial use of LLMs,” which tells me you aren’t working with very creative and competent people in areas that are amenable to LLMs. In my professional network beyond where I work now I know at least a dozen people who have successful commercial applications of LLMs. They tend to be highly capable people able to build the end to end tool chains necessary (which is a huge gap) and understand how to compose LLMs in hierarchical agents with effective guard rails. Most ineffectual users of LLMs want them to be lazy buttons that obviate the need to think. They’re not - like any sufficiently powerful tool they require thought up front and are easy to use wrong. This will get better with time as patterns and tools emerge to get the most use out of them in a commercial setting. However the ability to process natural language and use an emergent (if not actual) abductive reasoning is absurdly powerful and was not practically possible 4 years ago - the assertion such an amazing capability in an information or decisioning system is not commercially practical is on the face absurd.

andai 2y ago
>compose LLMs in hierarchical agents with effective guard rails Could you elaborate? Is this related to the "teams of specialized LLMs" concept I saw last year when Auto-GPT was getting a lot of hype?
topicseed 2y ago
Do they build guardrails themselves or do they use an llm guardrail api like Modelmetry or Langwatch?
mhuffman 2y ago
>We have built quite a few highly useful LLM applications in my org that have reduced cost and improved outcomes in several domains Apps that use LLMs or apps made with LLMs? In either case can you share them? >which tells me you aren’t working with very creative and competent people > In my professional network beyond where I work now I know at least a dozen people who have successful commercial applications of LLMs. Apps that use LLMs or apps made with LLMs? In either case can you share them? No one doubts that you can integrate LLMs into an application workflow and get some benefits in certain cases. That has been what the excitement and promise was about all along. They have a demonstrated ability to wrangle, extract, and transform data (mostly correctly) and generate patterns from data and prompts (hit and miss, usually with a lot of human involvement). All of which can be powerful. But outside of textual or visual chatbots or CRUD apps, no one wants to "put up or shut" a solid example that the top management of an existing company would sign off on. Only stories about awesome examples they and their friends are working on ... which often turn out to be CRUD apps or textual or visual chatbots. One notable standout is generative image apps can be quite good in certain circumstances. So, since you seem to have a real interest and actual examples of this, I am curious to see some that real companies would gamble that company on. And I don't mean some quixotic startup, I mean a company making real money now with customers that is confident on that app to the point they are willing to risk big. Because that last part is what companies do with other (non LLM) apps. I also know that people aren't perfect and wouldn't expect an LLM to be, just want to make sure I am not missing something.

physicsguy 2y ago

We’ve found that the text it generates in our RAG application is good, but it cocks up probably 5-10% of the time doing the inline references to the documents which users think is a bug and which we aren’t able to fix. This is static rather than interactively generated too

anilgulecha 2y ago

LLMs are not hype. In education at least, we've actively improved efficiency by ~25% across a large swath of educators (direct time saved) - agentic evaluators, tutors and doubt clarifiers. The wins in this industry are clear. And this is that much more time to spend with students. I also know from 1-1 conversation with my peers in large-finance world, and there too the efficiency improvements on multiple fronts are similar.

netdevnet 2y ago
They are partially hype though. That's what people here are arguing. There are benefits but their valuation is largely hype driven. AI is going to transform industries and humanity, yes. But AI does not mean LLM (even if LLM means AI). LLM raw potential was reached last year with GPT-4. From here on, the value will lie on exploiting the potential we already have to generate clever applications. Just like the internet provided a platform for new services, I expect LLMs to be the same but with a much smaller impact

jacobr1 2y ago

This is why we are only at the start of exploring the solution space. What applications don't require 100% accuracy? What tooling can we build that enables a human in the loop to choose between options? What options do we have to better testing or checking accuracy? There is a lot more to be done to invest hybrid systems that use other types of models or novel training date or heuristics or human workflows in novel ways that shore up the shortcomings ... but in aggregate allow us to do new things. It will take many years for us to figure where this makes the most sense.