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> it is now quite well established that GPT-4 has impressive out-of-sample performance Err... I can show this is false, kinda trivially. People who engage in p
by mjburgess 3y ago
> it is now quite well established that GPT-4 has impressive out-of-sample performance
Err... I can show this is false, kinda trivially. People who engage in prompt-confirmation-bias aren't aware of what the in-sample is.
It's basically everything ever digitised: you can ask it for the first paragraph of every dickens novel, to what the average petal length of an iris flower is -- etc.
How are you measuring the in-sample here?
If you engage in straightfoward reasoning from first principles, and are basically aware of what the training data is, you can show in 10 seconds critical failures of generalisation.
If you want a recipe: go find some fringe api docs. Establish that it has been trained on them. Then, since they're fringe there wont be much code on github, etc. Now ask it do something non-trivial with that API. It will fail, and the mechanism will be obvious: it'll jam in correlated code that lacks relevance.
Do the same on a popular API, and see it succeed.
The in-sample will be obvious for both, and the bounday of generalisation
- kristiandupont 3y agoYou can make it invent a new language: https://maximumeffort.substack.com/p/i-taught-chatgpt-to-invent-a-language https://maximumeffort.substack.com/p/i-taught-chatgpt-to-inv... I am sure you will continue to argue that this is still in line with everything-thats-ever-written prediction but my opinion is that at that point, it's a meaningless distinction. The human brain is also just a machine.
- intended 3y agoThe brain is a machine, the issue is the difference between 2 claims LLMs are enough to be a brain LLMs are not enough to be a brain.
- deleted 3y ago[deleted]
- mjburgess 3y agoSo I was with a financial researcher recently, and he wanted to use ChatGPT to summarise some reference financial data -- and it did so, actually correctly. Being sceptical, as every person ought in these matters, I changed the finical data and performed the same analysis (both in a new tab, and within the same convo). The results were the same! How strange? Well, in being reference financial data ChatGPT was reporting prior reference summaries of it. When that data was changed it was reporting the very same reference summaries (which were now wrong). Since it's incapable of actually summarising financial data. It's only capable of selecting combinations of pieces of its training set. Now, is this distinction "meaningless" ? No, it's the difference between this guy being fired for causing a massive loss on a major project; and this guy keeping his job and doing it well.
- kristiandupont 3y ago>Since it's incapable of actually summarising financial data It's not, though. It is in fact able to summarize financial data, just as it's able to write code and diagnose a medical condition. It makes mistakes, yes, even grave ones, much more so than experts in those fields would.
- mjburgess 3y agoIt isnt making mistakes ... its never actually doing it. Do you see a difference between the process of adding numbers and dividing by their count (taking a mean) and emitting numeric tokens which are most probable for a given input? The former is called "taking a mean" the latter isnt. This system never engages in any method to summarise financial data. It's method is always the same: to emit tokens most probable given a set of historical tokens. It's the difference between saying "the average of 1,2,3" is 2 because that sentence occurs 1,000,000 times and saying it's 2 because you've literally computed it. This system does not run financial summary algorithms. It's a trick
- sweetgiorni 3y agoTo add to your point: try asking ChatGPT to do basic arithmetic on numbers it hasn't seen before. You'll see just how good it is at computation.
- Txmm 3y agoBut “everything ever digitised” includes a tonne of linguistics information - it’s still in sample.