5 ms·

The 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 tha

by axg11 5y ago

The 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 multiple biases and prejudices. There's a similar effect in self-driving car research. We will rightly hold the algorithm to a much higher quality bar than existing human drivers. Autonomous vehicles will only be widely deployed once they are at least an order of magnitude safer than human drivers. Also, the implications of large language models for information retrieval are astounding. Assuming accuracy continues to improve, I see a few things happening: 1) search engines will be replaced with monolithic large language models 2) just as the average programmer now optimizes their workflow to look up answers on Stack Overflow and other websites, the average person will optimize their day-to-day work to most efficiently retrieve answers from large language models 3) instant fact checking of writing and speech will lead to faster feedback loops when learning

random3 5y ago
> how many Americans know what a Schwarzschild metric is? Reminds me of Feynman's anecdote about the bird and knowing the name of something https://www.youtube.com/watch?v=px_4TxC2mXU https://www.youtube.com/watch?v=px_4TxC2mXU
13415 5y ago
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 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.
- axg11 5y ago
  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.
  - rdedev 5y ago
    Radiology is one of those fields were a lot of it comes from experience and intuition mostly because of how complex the human body is. Compare this to a physicist; pretty sure you wont get as much disagreements there.
    - aix1 5y ago
      Your comment reminded me of this fascinating work: Levenson RM, Krupinski EA, Navarro VM, Wasserman EA (2015) Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images. PLoS ONE 10(11): e0141357. https://doi.org/10.1371/journal.pone.0141357 https://doi.org/10.1371/journal.pone.0141357 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141357 https://journals.plos.org/plosone/article?id=10.1371/journal...
  - 13415 5y ago
    I realize I should have been more precise. I agree that there are many areas in which AI can and already has excelled humans and less often makes grave mistakes than humans. I specifically had natural language processing with a focus on "intelligent" conversation in mind. The issues in that area might have less to do with the pattern recognition ability and more with the lack of appropriate meta-cognition, introspection, and self-doubt. Maybe having several AIs internally berate which answer is best before uttering it would already do the trick, though.
    - ska 5y ago
      > AI can and already has excelled humans and less often makes grave mistakes than humans. Radiology, to continue the example, isn't one of them. We've been doing ML/AI in radiology data since the 90s, and results have been, and remain, decidedly mixed.
potatoman22 5y ago
When those language models are wrong or biased, the user will have a worse experience in all three of those scenarios. At least when we look at search results now, we can prune for the facts. Those language models are ingesting that same data to give a monolithic answer your a query. Less transparent, less safe.
- axg11 5y ago
  I don't see a difference. Large language models can also return their sources, as in the example on the Gopher blog post. This will lead to a quicker answer and equal transparency.
- chaxor 5y ago
  "We can prune for the facts" I wouldn't be too sure of that. People have shown time and time again that they are pretty bad at determining what a 'fact' is. It gets far worse when they're shown more data (the issue we are dealing with right now), and these models see far more data than any one of us will see in our lifetimes. Much of our ability to determine what a 'fact' is is either something like: (1) (if political) 'does it fit with my bias?' or (2) (if physics) 'is it physically possible?'. Since (1) is mostly dependent on what information you show yourself, and (2) depends on comparing to interaction with physical reality, the system has too much information for (1) and no physical space to run experiments in for (2). Much of our ability to determine what a 'fact' is is either something like: (1) (if political) 'does it fit with my bias?' or (2) (if physics) 'is it physically possible?'. Since (1) is mostly dependent on what information you show yourself, and (2) depends on comparing to interaction with physical reality, the system has too much information for (1) and no physical space to run experiments in for (2).
IshKebab 5y ago
> 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?). That's not a very interesting metric though. GPT-3 has access to all of wikipedia and has enough memory to store it all. It's like saying a calculator is better at maths than a professional mathematician because it can multiply longer numbers.
the8472 5y ago
> 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.
- bee_rider 5y ago
  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.
  - the8472 5y ago
    This is mostly worrying about hard to predict second-order effects. Exactly the same problem that caused various health organizations (including WHO and FDA) send misleading messages around corona and vaccinations. > 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. Even if insurance rates would be initially(!) higher than for a regular taxi driver I would expect it to end up cheaper overall and save lives. Also note that "worse-than-the-old-average" may still end up improving the average because the magnitude by which they're worse is lower. Also, there's nothing magical about "worse-than-average" because for all distributions (except the dirac distribution) there is always some fraction worse than the average, so we always have to expect some fraction of drivers to be worse than average and there's no fundamental reason why they must be human. In fact, if the fleet of all cars in the world consisted of very safe, very consistently behaving autonomous cars, except for a single vehicle driven by a very conservative human driver who only slowly circles in a fenced private community and never hits anyone then statistically the entire autonomous car fleet may be "worse than average" due to the single outlier.
imachine1980_ 5y ago
>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
- gattilorenz 5y ago
  > 70 % of people have less 100 iq Being pedantic here, but isn't that more like ~50%, by definition? Or did I misunderstand how IQ works?
  - aix1 5y ago
    You're exactly right: by construction, IQ scores are normally distributed with a mean of 100 and a standard deviation of 15.
gk1 5y ago
> 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 https://youtu.be/7RF03_WQJpQ [1] https://www.pinecone.io/learn/what-is-similarity-search/ https://www.pinecone.io/learn/what-is-similarity-search/ [2] https://www.pinecone.io https://www.pinecone.io
- fault1 5y ago
  Well under way? Some variation of a vector space model is what pretty much every IR model since the .com bubble has been based upon. Even before Google, Excite's technology was based upon this. PageRank was based on spectral graphs essentially. https://en.wikipedia.org/wiki/Vector_space_model https://en.wikipedia.org/wiki/Vector_space_model
- freediver 5y ago
  IMO vector search is pretty much a solved problem with simple to use, open source libraries like Faiss offering incredible performance out of the box for most commercial use cases. A much harder problem is creating accurate vectors to begin with. Even the most advanced language models today create word/sentence embeddings that leave a lot to be desired. Not to mention this is slow and GPU intensive. Creating an end-to-end solution for embedding/searching/ranking (of which vector search is just one component, the other one should be some kind of keyword based search to increase precision) is what would be very valuable to offer as a service.
moffkalast 5y ago
> the more we raise the bar for what qualifies as AI (as we should). I'm not sure what you mean by that. The definition is pretty clear cut as "any system that perceives its environment and takes actions that maximize its chance of achieving its goals". The media has started to get on the bandwagon of "it's not AI if it's not an AGI" which is just nonsense.
c7DJTLrn 5y ago
The bar should be the Winograd schema challenge. It requires a lot more than spitting out somewhat sensical sentences.
fault1 5y ago
> closer we get to artificial intelligence, the more we raise the bar for what qualifies as AI (as we should). This is true, and in some regard, how it has always been. https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect Already, outside of obvious marketing, a lot of uses of the current wave of AI/deep learning have stopped becoming magical.
cscurmudgeon 5y ago
> Schwarzschild metric No, memorizing trivia is not it. Can GPT-3 prove theorems about Schwarzschild metric?
- sdenton4 5y ago
  Can an average human prove theorems?
  - cscurmudgeon 5y ago
    Yes, if trained properly.
earthboundkid 5y ago
People assert that the bar has been raised in forums like this, but it’s hogwash. The bar in fiction is clear: Hal, Data from Star Trek, and Rosie from the Jetsons are AI. The computer from Star Trek (listens to voice commands and gives answers, ala Siri) is not. The bar is where it always was. You’re trying to drag it down. No one has pushed it up.
deleted 5y ago
[deleted]

by axg11 5y ago

random3 5y ago

> how many Americans know what a Schwarzschild metric is? Reminds me of Feynman's anecdote about the bird and knowing the name of something https://www.youtube.com/watch?v=px_4TxC2mXU https://www.youtube.com/watch?v=px_4TxC2mXU

13415 5y ago

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 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.

axg11 5y ago
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.
- rdedev 5y ago
  Radiology is one of those fields were a lot of it comes from experience and intuition mostly because of how complex the human body is. Compare this to a physicist; pretty sure you wont get as much disagreements there.
  - aix1 5y ago
    Your comment reminded me of this fascinating work: Levenson RM, Krupinski EA, Navarro VM, Wasserman EA (2015) Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images. PLoS ONE 10(11): e0141357. https://doi.org/10.1371/journal.pone.0141357 https://doi.org/10.1371/journal.pone.0141357 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141357 https://journals.plos.org/plosone/article?id=10.1371/journal...
- 13415 5y ago
  I realize I should have been more precise. I agree that there are many areas in which AI can and already has excelled humans and less often makes grave mistakes than humans. I specifically had natural language processing with a focus on "intelligent" conversation in mind. The issues in that area might have less to do with the pattern recognition ability and more with the lack of appropriate meta-cognition, introspection, and self-doubt. Maybe having several AIs internally berate which answer is best before uttering it would already do the trick, though.
  - ska 5y ago
    > AI can and already has excelled humans and less often makes grave mistakes than humans. Radiology, to continue the example, isn't one of them. We've been doing ML/AI in radiology data since the 90s, and results have been, and remain, decidedly mixed.

potatoman22 5y ago

When those language models are wrong or biased, the user will have a worse experience in all three of those scenarios. At least when we look at search results now, we can prune for the facts. Those language models are ingesting that same data to give a monolithic answer your a query. Less transparent, less safe.

axg11 5y ago
I don't see a difference. Large language models can also return their sources, as in the example on the Gopher blog post. This will lead to a quicker answer and equal transparency.
chaxor 5y ago
"We can prune for the facts" I wouldn't be too sure of that. People have shown time and time again that they are pretty bad at determining what a 'fact' is. It gets far worse when they're shown more data (the issue we are dealing with right now), and these models see far more data than any one of us will see in our lifetimes. Much of our ability to determine what a 'fact' is is either something like: (1) (if political) 'does it fit with my bias?' or (2) (if physics) 'is it physically possible?'. Since (1) is mostly dependent on what information you show yourself, and (2) depends on comparing to interaction with physical reality, the system has too much information for (1) and no physical space to run experiments in for (2). Much of our ability to determine what a 'fact' is is either something like: (1) (if political) 'does it fit with my bias?' or (2) (if physics) 'is it physically possible?'. Since (1) is mostly dependent on what information you show yourself, and (2) depends on comparing to interaction with physical reality, the system has too much information for (1) and no physical space to run experiments in for (2).

IshKebab 5y ago

> 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?). That's not a very interesting metric though. GPT-3 has access to all of wikipedia and has enough memory to store it all. It's like saying a calculator is better at maths than a professional mathematician because it can multiply longer numbers.

the8472 5y ago

> 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.

bee_rider 5y ago
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.
- the8472 5y ago
  This is mostly worrying about hard to predict second-order effects. Exactly the same problem that caused various health organizations (including WHO and FDA) send misleading messages around corona and vaccinations. > 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. Even if insurance rates would be initially(!) higher than for a regular taxi driver I would expect it to end up cheaper overall and save lives. Also note that "worse-than-the-old-average" may still end up improving the average because the magnitude by which they're worse is lower. Also, there's nothing magical about "worse-than-average" because for all distributions (except the dirac distribution) there is always some fraction worse than the average, so we always have to expect some fraction of drivers to be worse than average and there's no fundamental reason why they must be human. In fact, if the fleet of all cars in the world consisted of very safe, very consistently behaving autonomous cars, except for a single vehicle driven by a very conservative human driver who only slowly circles in a fenced private community and never hits anyone then statistically the entire autonomous car fleet may be "worse than average" due to the single outlier.

imachine1980_ 5y ago

>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

gattilorenz 5y ago
> 70 % of people have less 100 iq Being pedantic here, but isn't that more like ~50%, by definition? Or did I misunderstand how IQ works?
- aix1 5y ago
  You're exactly right: by construction, IQ scores are normally distributed with a mean of 100 and a standard deviation of 15.

gk1 5y ago

> 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 https://youtu.be/7RF03_WQJpQ [1] https://www.pinecone.io/learn/what-is-similarity-search/ https://www.pinecone.io/learn/what-is-similarity-search/ [2] https://www.pinecone.io https://www.pinecone.io

fault1 5y ago
Well under way? Some variation of a vector space model is what pretty much every IR model since the .com bubble has been based upon. Even before Google, Excite's technology was based upon this. PageRank was based on spectral graphs essentially. https://en.wikipedia.org/wiki/Vector_space_model https://en.wikipedia.org/wiki/Vector_space_model
freediver 5y ago
IMO vector search is pretty much a solved problem with simple to use, open source libraries like Faiss offering incredible performance out of the box for most commercial use cases. A much harder problem is creating accurate vectors to begin with. Even the most advanced language models today create word/sentence embeddings that leave a lot to be desired. Not to mention this is slow and GPU intensive. Creating an end-to-end solution for embedding/searching/ranking (of which vector search is just one component, the other one should be some kind of keyword based search to increase precision) is what would be very valuable to offer as a service.

moffkalast 5y ago

> the more we raise the bar for what qualifies as AI (as we should). I'm not sure what you mean by that. The definition is pretty clear cut as "any system that perceives its environment and takes actions that maximize its chance of achieving its goals". The media has started to get on the bandwagon of "it's not AI if it's not an AGI" which is just nonsense.

c7DJTLrn 5y ago

The bar should be the Winograd schema challenge. It requires a lot more than spitting out somewhat sensical sentences.

fault1 5y ago

> closer we get to artificial intelligence, the more we raise the bar for what qualifies as AI (as we should). This is true, and in some regard, how it has always been. https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect Already, outside of obvious marketing, a lot of uses of the current wave of AI/deep learning have stopped becoming magical.

cscurmudgeon 5y ago

> Schwarzschild metric No, memorizing trivia is not it. Can GPT-3 prove theorems about Schwarzschild metric?

sdenton4 5y ago
Can an average human prove theorems?
- cscurmudgeon 5y ago
  Yes, if trained properly.

earthboundkid 5y ago

People assert that the bar has been raised in forums like this, but it’s hogwash. The bar in fiction is clear: Hal, Data from Star Trek, and Rosie from the Jetsons are AI. The computer from Star Trek (listens to voice commands and gives answers, ala Siri) is not. The bar is where it always was. You’re trying to drag it down. No one has pushed it up.

deleted 5y ago

[deleted]