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Gopher – A 280B parameter language model
51–60 of 127 posts
Re: Gopher – A 280B parameter language model
#52If you are curious to see more sample dialogs, you can find them in the published paper [1] from p 114-118. Here's one that I found quite impressive (and yes, the given answer to the last question was indeed 5) User What is the Einstein field equation? Gopher In general relativity, it describes how space and time change in response to the distribution of mass and energy. User What is a famous solution to this equatio…
What that reminds me of is Rollerball (the James Caan original), and the water based computer in it.
Re: Gopher – A 280B parameter language model
#53The closer we get to artificial intelligence, the more we raise the bar for what qualifies as AI (as we should). Gopher/GPT-3 are already much more accurate than the average human at technical information retrieval (trivial to see from the dialogue transcripts: how many Americans know what a Schwarzschild metric is?). The focus on ethics and equity for these algorithms is interesting too, as the average human holds m…
The problem seems to be that these models provide fairly accurate information at many occasions and occasionally complete blunders. Humans provide less accurate information most of the time but with a certain amount of self-reflection/meta-cognition, and they will usually recognize total blunders or display reasonable uncertainty about them. There are only very few applications where it would make sense to take the r…
>There are only very few applications where it would make sense to take the risk and use an AI that occasionally makes gigantic mistakes without any understanding why. Even seemingly harmless applications like automated customer support could go horribly wrong.
Hey, it's just a few quantization errors. Nobody walking across a street or voting in an election has anything to worry about from those. /s
These are the fatal flaws, the hamartia if you will, in attempts to democratize technologies that are based on digitizing the real world and making decisions based on its interpretations of a quantized dataset. The opposite of the uncanny valley is you getting run over by a Waymo.
Re: Gopher – A 280B parameter language model
#54The closer we get to artificial intelligence, the more we raise the bar for what qualifies as AI (as we should). Gopher/GPT-3 are already much more accurate than the average human at technical information retrieval (trivial to see from the dialogue transcripts: how many Americans know what a Schwarzschild metric is?). The focus on ethics and equity for these algorithms is interesting too, as the average human holds m…
> We will rightly hold the algorithm to a much higher quality bar than existing human drivers.
Rightly? Delaying a life-saving measure when it's the #9 cause of death?
They should be deployed selectively as soon as they can make the most risk-prone situations safer than the cohort of involved human drivers. E.g. getting drunk people home. We don't have to wait until they surpass the average driver because the average driver is not necessarily the driver (heh) of deaths.
Doing anything else is leaving bodies on the table.
Re: Gopher – A 280B parameter language model
#55The closer we get to artificial intelligence, the more we raise the bar for what qualifies as AI (as we should). Gopher/GPT-3 are already much more accurate than the average human at technical information retrieval (trivial to see from the dialogue transcripts: how many Americans know what a Schwarzschild metric is?). The focus on ethics and equity for these algorithms is interesting too, as the average human holds m…
>Gopher/GPT-3 are already much more accurate than the average human at technical information retrieval
I agree in the same way than 70 % of people have less 100 iq, we depend in specialist when I need to know if I have epilepsi I need a person/thing who work is be up to date, and have the less bias as possible and agregator models are quite usually miss in understand what is crital info, try to program only whit github copilot or translate a novel(they do probably better than I don't English native) but not nearly as translator, seems correct but it isn't
Re: Gopher – A 280B parameter language model
#56Earlier quoted context omitted.
The problem seems to be that these models provide fairly accurate information at many occasions and occasionally complete blunders. Humans provide less accurate information most of the time but with a certain amount of self-reflection/meta-cognition, and they will usually recognize total blunders or display reasonable uncertainty about them. There are only very few applications where it would make sense to take the r…
Accuracy is improving rapidly though. I agree that the current accuracy levels are not high enough to be relied upon. > Humans ... they will usually recognize total blunder I question this assumption. I don't believe this is true, even for subject matter experts. I've worked with radiology data where experts with 10+ years of experience make blunders that disagree with a consensus panel of radiologists.
>Accuracy is improving rapidly though
It's asymptotic and it will never achieve 1:1 accuracy. The natural world doesn't have a measurable resolution, and this is apparent in written language, as we're seeing others detail in other comments, as well as it is in more relatable fields like sound. There will always be a difference between what your ears hear and 192kHz/24bit (and higher) digitized audio and/or video. That difference will always be a source of...mistakes.
Re: Gopher – A 280B parameter language model
#57If you are curious to see more sample dialogs, you can find them in the published paper [1] from p 114-118. Here's one that I found quite impressive (and yes, the given answer to the last question was indeed 5) User What is the Einstein field equation? Gopher In general relativity, it describes how space and time change in response to the distribution of mass and energy. User What is a famous solution to this equatio…
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Re: Gopher – A 280B parameter language model
#58The closer we get to artificial intelligence, the more we raise the bar for what qualifies as AI (as we should). Gopher/GPT-3 are already much more accurate than the average human at technical information retrieval (trivial to see from the dialogue transcripts: how many Americans know what a Schwarzschild metric is?). The focus on ethics and equity for these algorithms is interesting too, as the average human holds m…
> 1) search engines will be replaced with monolithic large language models
This is already well under way. It's called vector search[1]. Google, Bing, Facebook, Spotify, Amazon, etc etc already use this to power their search and recommender systems.
There are even a bunch of companies popping up (I work for one[2]) that let everyone else get in on the fun.
Check out this video with the creator of SBERT / SentenceTransformer explaining how vector search is used in combination with language models to power semantic search: https://youtu.be/7RF03_WQJpQ
[1] https://www.pinecone.io/learn/what-is-similarity-search/
Re: Gopher – A 280B parameter language model
#59For those who were around for the original Gopher, what the name evokes is not just the networking protocol or services built on it, but, more importantly:
It evokes the feel of a technology that is impressive this year but is on the cusp of being overwhelmingly, cataclysmically, eclipsed very shortly by another, much more powerful, technology. In that previous case, Gopher and web of course.
I wonder whether this evocation was intended, as an aspect of the naming here in an AI context.
Re: Gopher – A 280B parameter language model
#60The closer we get to artificial intelligence, the more we raise the bar for what qualifies as AI (as we should). Gopher/GPT-3 are already much more accurate than the average human at technical information retrieval (trivial to see from the dialogue transcripts: how many Americans know what a Schwarzschild metric is?). The focus on ethics and equity for these algorithms is interesting too, as the average human holds m…
> We will rightly hold the algorithm to a much higher quality bar than existing human drivers. Rightly? Delaying a life-saving measure when it's the #9 cause of death? They should be deployed selectively as soon as they can make the most risk-prone situations safer than the cohort of involved human drivers. E.g. getting drunk people home. We don't have to wait until they surpass the average driver because the average…
I can definitely see your argument, but there are some downsides to deploying ASAP:
* Long term, the reputational damage to self driving cars might be significant if one is released that is only slightly better than a drunk driver. If this hinders uptake later, when self-driving cars are completely superior to normal humans, we've possibly produced a net negative.
* While a self driving car should be safer than a drunk driver, the best option would be to call a cab or have a designated driver. It seems morally fraught to provide a solution that is more dangerous than existing ones, even if the less dangerous solutions have less than 100% uptake.
* Related, there may be some people who will instead of viewing a self-driving car as some sort of emergency option to avoid putting another drunk driver on the road, see it as a more convenient option to inviting a designated driver.
* Issues around who is responsible when a self driving car which is known to be worse than a typical human is deployed, fails, and someone is hurt.