8 ms·

To me, this reads like a very reasonable take. He suggests to limit the scope of the AI problem, add manual overrides in case there are unexpected situations,

To me, this reads like a very reasonable take. He suggests to limit the scope of the AI problem, add manual overrides in case there are unexpected situations, and he (rightly, in my opinion) predicts that the business case for exponentially scaling LLM models isn't there. With that context, I like his iPod example. Apple probably could have made a 3TB iPod to stick to Moore's law for another few years, but after they reached 160GB of music storage there was no usecase where adding more would deliver more benefits than the added costs.

raincole 2y ago
> Apple probably could have made a 3TB iPod It's a very weird comparison, as putting more music tracks to your iPod doesn't make them sound better, while giving a LLM more parameters/computing power make it smarter. Honestly it sounds like a typical "I've drawn my conclusion, and now I only need an analogy that remotely supports my conclusion" way of thinking.
- derefr 2y ago
  No, it makes sense: he's coming at it from the perspective of knowing exactly what task you want to accomplish (something like "fixing the grammar in this document.") In such cases, a model only has to be sufficiently smart to work for 99.9999% of inputs — at which point you cross a threshold where adding more intelligence is just making the thing bulkier to download and process, to no end for your particular use-case. In fact, you would then tend to go the other way — once you get the ML model to "solve the problem", you want to then find the smallest and most efficient such model that solves the problem. I.e. the model that is "as stupid as possible", while still being very good at this one thing.
- greybox 2y ago
  If you have no conception of mathematics, do you think you'd get better at solving mathematics problems based on looking at more examples of people who may or may not be solving them correctly?
  - mewpmewp2 2y ago
    It has worked for humanity...
    - Jensson 2y ago
      Only after humanity became a general intelligence. The previous ancestors were also pretty smart, but not smart enough to develop technology on their own, you have to be extremely smart to do what humanity did.
kromem 2y ago
The business case is absolutely there, it's just the industry has weirdly latched onto 'chatbot' as the usecase as opposed to where the real value lies. The pretrained model is where the enterprise gold is at. But the companies building the models past the tipping point scale for that value to be derived are walling up their pretrained model behind very heavy handed fine tuning that strips away most of the business value. The engineers themselves seem to lack the imagination for the business cases, and the enterprise market doesn't have access to start discovering the applications outside of 'chatbot,' particularly with large context windows of proprietary data fed into SotA pretrained models. There's maybe a handful of people who actually realize what value is being left on the table, and I think most of them are smart enough not to currently be in positions to make it happen.
aurareturn 2y ago
I’m still waiting for SalesForce to integrate an LLM into Slack so I can ask it business logic and decisions long lost. Still waiting for Microsoft to integrate an LLM into outlook so I can get a summary of a 20 email long chain I just got CCed into. I don’t think the iPod comparison is a valid one. People only have so much time to listen to music. Past a certain point, no one has enough good music they like to put into a 3TB iPod. However, the more data you feed into an LLM, the smarter it should be in the response. Therefore, the scale of iPod storage and LLM context is on completely different curves.
- moomoo11 2y ago
  Aren’t you afraid ur gonna be given hallucinating information? That’s my biggest worry. I’ve set up my own RAG and it’s kind of sad how even that is not super accurate.
- Propelloni 2y ago
  Most data around is junk and the internet produces junk data faster then useful data and current GPT AIs basically regurgitate what someone already did somewhere on the internet. So I guess the more data we feed into GPTs the worse the results will get. My take to improve AI output is to heavily curate the data you feed your AI, much the like expert systems of old (which were lauded as "AI" also.) Maybe we can break the vicious circle of "I trained my GPT on billions of Twitter posts and let it write Twitter posts to great sucess", "Hey, me too!"
  - threeseed 2y ago
    > My take to improve AI output is to heavily curate the data you feed your AI This is what OpenAI is doing with their relationships with companies like Reddit, News Corp etc: https://openai.com/index/news-corp-and-openai-sign-landmark-multi-year-global-partnership/ https://openai.com/index/news-corp-and-openai-sign-landmark-... Problem is that we have a finite amount of this type of information.
    - grugagag 2y ago
eysgshsvsvsv 2y ago
Is there a reason why memory was used and not compute power as an example? I don't understand how cherry picking random examples from past explain future of AI. If he think business needs does not exist he should explain how he arrived at that conclusion instead of a random iPod example.
- sigmoid10 2y ago
  This. The scaling of compute has vastly different applications than the scaling of memory. Shows once again that people who are experts in a related field aren't necessarily the best to comment on trendy topics. If e.g. an aeroplane expert critiques Spacex's starship, you should be equally vary, even though they might have some overlap. The only reason this is in the media at all is because negative sentiment to hype generates many clicks. That's why you see these topics every day instead of Rubik's cube players criticising the latest version of Mikado.
- cerved 2y ago
  It's an analogy. He's making the point that even though something can scale at an exponential rate, it doesn't mean there is a business need for such scaling

raincole 2y ago

> Apple probably could have made a 3TB iPod It's a very weird comparison, as putting more music tracks to your iPod doesn't make them sound better, while giving a LLM more parameters/computing power make it smarter. Honestly it sounds like a typical "I've drawn my conclusion, and now I only need an analogy that remotely supports my conclusion" way of thinking.

derefr 2y ago
No, it makes sense: he's coming at it from the perspective of knowing exactly what task you want to accomplish (something like "fixing the grammar in this document.") In such cases, a model only has to be sufficiently smart to work for 99.9999% of inputs — at which point you cross a threshold where adding more intelligence is just making the thing bulkier to download and process, to no end for your particular use-case. In fact, you would then tend to go the other way — once you get the ML model to "solve the problem", you want to then find the smallest and most efficient such model that solves the problem. I.e. the model that is "as stupid as possible", while still being very good at this one thing.
greybox 2y ago
If you have no conception of mathematics, do you think you'd get better at solving mathematics problems based on looking at more examples of people who may or may not be solving them correctly?
- mewpmewp2 2y ago
  It has worked for humanity...
  - Jensson 2y ago
    Only after humanity became a general intelligence. The previous ancestors were also pretty smart, but not smart enough to develop technology on their own, you have to be extremely smart to do what humanity did.

kromem 2y ago

The business case is absolutely there, it's just the industry has weirdly latched onto 'chatbot' as the usecase as opposed to where the real value lies. The pretrained model is where the enterprise gold is at. But the companies building the models past the tipping point scale for that value to be derived are walling up their pretrained model behind very heavy handed fine tuning that strips away most of the business value. The engineers themselves seem to lack the imagination for the business cases, and the enterprise market doesn't have access to start discovering the applications outside of 'chatbot,' particularly with large context windows of proprietary data fed into SotA pretrained models. There's maybe a handful of people who actually realize what value is being left on the table, and I think most of them are smart enough not to currently be in positions to make it happen.

aurareturn 2y ago

I’m still waiting for SalesForce to integrate an LLM into Slack so I can ask it business logic and decisions long lost. Still waiting for Microsoft to integrate an LLM into outlook so I can get a summary of a 20 email long chain I just got CCed into. I don’t think the iPod comparison is a valid one. People only have so much time to listen to music. Past a certain point, no one has enough good music they like to put into a 3TB iPod. However, the more data you feed into an LLM, the smarter it should be in the response. Therefore, the scale of iPod storage and LLM context is on completely different curves.

moomoo11 2y ago
Aren’t you afraid ur gonna be given hallucinating information? That’s my biggest worry. I’ve set up my own RAG and it’s kind of sad how even that is not super accurate.
Propelloni 2y ago
Most data around is junk and the internet produces junk data faster then useful data and current GPT AIs basically regurgitate what someone already did somewhere on the internet. So I guess the more data we feed into GPTs the worse the results will get. My take to improve AI output is to heavily curate the data you feed your AI, much the like expert systems of old (which were lauded as "AI" also.) Maybe we can break the vicious circle of "I trained my GPT on billions of Twitter posts and let it write Twitter posts to great sucess", "Hey, me too!"
- threeseed 2y ago
  > My take to improve AI output is to heavily curate the data you feed your AI This is what OpenAI is doing with their relationships with companies like Reddit, News Corp etc: https://openai.com/index/news-corp-and-openai-sign-landmark-multi-year-global-partnership/ https://openai.com/index/news-corp-and-openai-sign-landmark-... Problem is that we have a finite amount of this type of information.
  - grugagag 2y ago

eysgshsvsvsv 2y ago

Is there a reason why memory was used and not compute power as an example? I don't understand how cherry picking random examples from past explain future of AI. If he think business needs does not exist he should explain how he arrived at that conclusion instead of a random iPod example.

sigmoid10 2y ago
This. The scaling of compute has vastly different applications than the scaling of memory. Shows once again that people who are experts in a related field aren't necessarily the best to comment on trendy topics. If e.g. an aeroplane expert critiques Spacex's starship, you should be equally vary, even though they might have some overlap. The only reason this is in the media at all is because negative sentiment to hype generates many clicks. That's why you see these topics every day instead of Rubik's cube players criticising the latest version of Mikado.
cerved 2y ago
It's an analogy. He's making the point that even though something can scale at an exponential rate, it doesn't mean there is a business need for such scaling