What is GPT-3? written in layman's terms(tinkeredthinking.com)
tinkeredthinking.com
What is GPT-3? written in layman's terms
https://tinkeredthinking.com/index.php?id=841
69 comments
>> Please stop telling people that neural nets are related to brain neurons. They have essentially no relationship other than the name and it just fosters this fear of Terminator and obscures the real issues that need to be thought about.
Indeed, this is supported by the opinions of the foremost experts in deep learning and neural networks:
IEEE Spectrum: We read about Deep Learning in the news a lot these days. What’s your least favorite definition of the term that you see in these stories?
Yann LeCun: My least favorite description is, “It works just like the brain.” I don’t like people saying this because, while Deep Learning gets an inspiration from biology, it’s very, very far from what the brain actually does. And describing it like the brain gives a bit of the aura of magic to it, which is dangerous. It leads to hype; people claim things that are not true. AI has gone through a number of AI winters because people claimed things they couldn’t deliver.
https://spectrum.ieee.org/automaton/artificial-intelligence/...
Indeed, this is supported by the opinions of the foremost experts in deep learning and neural networks:
IEEE Spectrum: We read about Deep Learning in the news a lot these days. What’s your least favorite definition of the term that you see in these stories?
Yann LeCun: My least favorite description is, “It works just like the brain.” I don’t like people saying this because, while Deep Learning gets an inspiration from biology, it’s very, very far from what the brain actually does. And describing it like the brain gives a bit of the aura of magic to it, which is dangerous. It leads to hype; people claim things that are not true. AI has gone through a number of AI winters because people claimed things they couldn’t deliver.
https://spectrum.ieee.org/automaton/artificial-intelligence/...
Synapses' strength and ANN weights are sort of similar, but the learning process is different (co-activation vs loss propagation). Plus brains have neuroplasticity, but ANNs usually have at least one fully connected layer so it can emulate that, sort of.
Also how the brain's visual circuit parses stuff and how a CNN encodes concepts are sort of similar.
But, that's it so far :)
Also how the brain's visual circuit parses stuff and how a CNN encodes concepts are sort of similar.
But, that's it so far :)
A much more realistic and accurate take on the tech than the article. One combination that can work is to have GPT3 with an unskilled human judge in the loop. The human can discriminate with common sense while the machine generates good jargon with correct grammar.
That seems even worse!
Jargon (sometimes) isn't just obfuscation; it carries important shades of meaning that would be tedious to spell out every time. 'Homicide' and 'murder', for example, are sometimes used interchangeably, but are not actually legally interchangeable. You would not want your involuntary manslaughter plea upgraded to murder, a more serious--and therefore more severely punished offense.
You could, of course, try to train "unskilled labor" to detect things like that. By the time you are done, the labor won't be unskilled and you will have reinvented the paralegal.
Jargon (sometimes) isn't just obfuscation; it carries important shades of meaning that would be tedious to spell out every time. 'Homicide' and 'murder', for example, are sometimes used interchangeably, but are not actually legally interchangeable. You would not want your involuntary manslaughter plea upgraded to murder, a more serious--and therefore more severely punished offense.
You could, of course, try to train "unskilled labor" to detect things like that. By the time you are done, the labor won't be unskilled and you will have reinvented the paralegal.
Yeah, I think those will be some of the first wave of language generation applications. Stuff like Buzzfeed but without as many writers because the language model is generating potential articles and the humans pick which are good (and maybe clean them up). Similarly, disinformation or noise attacks on social media - with just a few people overseeing the language model as it generates potential posts/replies.
Neither scenario at the end of the article (therapist, legal analysis) is particularly futuristic.
From 2019: https://abilitynet.org.uk/news-blogs/eliza-ellie-evolution-a...
From 2018: https://www.techspot.com/news/77189-machine-learning-algorit...
From 2019: https://abilitynet.org.uk/news-blogs/eliza-ellie-evolution-a...
From 2018: https://www.techspot.com/news/77189-machine-learning-algorit...
Neither of those are particularly compelling IMO.
The first is a digital information gatherer that is explicitly not playing the role of a therapist (likely for the reasons I mentioned above around risk/cost of failure, difficulty in evaluating what constitutes 'good advice', and probably legal barriers). There is a world of difference between a chatbot that does information retrieval and an autonomous agent that provides therapy.
The second is also vastly different from the "summarize legislation and detect 'nefarious' clauses" scenario in the article. They are identifying errors in standard NDAs, which is a (comparatively) well-defined, straightforward supervised learning task where the data has a pretty consistent shape unlike congressional legislation and what 'nefarious' means. (I have to make some assumptions here since as far as I can tell there isn't a technical paper on the work).
These sound similar to the tasks in the article, but once you get into the details of implementing and deploying them, I don't think they're very similar. You would still need to solve the problems that have plagued self-driving cars for years: high cost of failure, unpredictable failure modes, a long-tail distribution of data that prevents realistic-to-collect data sets from generalizing well enough, almost no progress in AI for autonomous agents (RL is promising, but it hasn't really made it out of the lab yet AFAICT).
The first is a digital information gatherer that is explicitly not playing the role of a therapist (likely for the reasons I mentioned above around risk/cost of failure, difficulty in evaluating what constitutes 'good advice', and probably legal barriers). There is a world of difference between a chatbot that does information retrieval and an autonomous agent that provides therapy.
The second is also vastly different from the "summarize legislation and detect 'nefarious' clauses" scenario in the article. They are identifying errors in standard NDAs, which is a (comparatively) well-defined, straightforward supervised learning task where the data has a pretty consistent shape unlike congressional legislation and what 'nefarious' means. (I have to make some assumptions here since as far as I can tell there isn't a technical paper on the work).
These sound similar to the tasks in the article, but once you get into the details of implementing and deploying them, I don't think they're very similar. You would still need to solve the problems that have plagued self-driving cars for years: high cost of failure, unpredictable failure modes, a long-tail distribution of data that prevents realistic-to-collect data sets from generalizing well enough, almost no progress in AI for autonomous agents (RL is promising, but it hasn't really made it out of the lab yet AFAICT).
Bx6667(2)
> GPT-3 continuation: A confused voice came from inside. When I opened the door, the person that looked back at me was Hayama Hayato. Why was Hayama, who I only shared memories of me playing soccer with, in my room at this hour of the night? That question immediately flew out from my mouth.
[I knew that name felt familiar](https://oregairu.fandom.com/wiki/Hayato_Hayama). Does that mean GPT-3 was trained on an arbitrary, huge database of text? I wonder how copyright applies here.
[I knew that name felt familiar](https://oregairu.fandom.com/wiki/Hayato_Hayama). Does that mean GPT-3 was trained on an arbitrary, huge database of text? I wonder how copyright applies here.
There's an open question if GPT-3 simply provides a text completion API for the internet. The original paper discusses the risk that datasets for specific evaluation tasks (or equivalents) were simply memorized by the model.
A completion API for the internet could still be an incredibly valuable component.
A completion API for the internet could still be an incredibly valuable component.
yes! if a thing of similar performance as GPT-3 could just spew out links to where its "inspiration" comes from, this would be (super expensive?) real context-aware search. This could be really great.
> Does that mean GPT-3 was trained on an arbitrary, huge database of text?
Yes, 500 billion words, for a model with model 175 billion parameters.
Yes, 500 billion words, for a model with model 175 billion parameters.
So that continues my question: how much of that text would be on the private domain? If there is, is it all legally purchased?
I’m sure some lawyers figured it out (fair use maybe?) but they basically used scraped content from Common Crawl. Trying to figure out how to license that would be harder than trying to get all the land rights for a railroad across the US. So... probably not purchased.
This is an excellent question which applies to a lot of machine learning datasets. AFAIK there is no specific licensing of much of it (licenses you see are generally attached to the labels: the images/text/etc are often not even part of the download and you need to go scrape them yourself) and it's often claimed that the results of the network are free from the copyright of the training data used to create it, but this is contentious and has definitely not been tested in court.
I wonder if that's one reason they can't open it up. Perhaps they are ensuring that responses that come from the AI are sufficiently different from anything in the training corpus, so it can't be queried for sensitive data or large chunks of copyrighted material.
It's basically already open through AI Dungeon. I've had all sorts of conversations so far with copyrighted characters.
That's still coming via the API. They may be blocking any
large chunks of text from being reproduced, rather than individual characters (which would be harder to police).
>> [I knew that name felt familiar](https://oregairu.fandom.com/wiki/Hayato_Hayama). Does that mean GPT-3 was trained on an arbitrary, huge database of text? I wonder how copyright applies here.
It could have lifted the name (and some additional context in the generated sentence) from the fandom wiki you link to, or something similar. It probably wasn't trained with the text of light novels; even though you can er find some of those online, they are generally scans and GPT-3 is trained on text, as far as I can tell.
In any case if it was lifted from sources about Oregairu and not the light novel itself, then it'd most likely be considered fair use. I mean, there's a wikipedia article that describes the characters (including Hayato Hayama) and all.
P.S. I haven't read that one. Is it any good?
It could have lifted the name (and some additional context in the generated sentence) from the fandom wiki you link to, or something similar. It probably wasn't trained with the text of light novels; even though you can er find some of those online, they are generally scans and GPT-3 is trained on text, as far as I can tell.
In any case if it was lifted from sources about Oregairu and not the light novel itself, then it'd most likely be considered fair use. I mean, there's a wikipedia article that describes the characters (including Hayato Hayama) and all.
P.S. I haven't read that one. Is it any good?
One thing I will say after seeing GPT-3 running live is that somehow the examples that you see trotted about in various articles never really seem to capture the spirit of the thing. Interacting with it directly and exploring the universe behind the prompt is one of the more profound computing experiences I've ever had.
Agreed. I've been playing AI Dungeon with their GPT-3 model, and it really does feel like there's a scatterbrained but human DM on the other side.
To me, what GPT-n really tells is how redundant human text in general is. It doesn't provide a lot of new information (if it does, they are accidental), but more or less wandering around the given topic. It is the redundancy that gives the system a space to churn.
The reduntancy is not low. GPT-* are basic "pattern generators" for our language organ. We have pattern generators for our gait for example -- all those synchronized movements of so many muscles can be effectively controlled by a well understood circuit, to the point that even a deafferented animal can still be be made to recognizably walk.
GPT is the equivalent, but for language: it can be used to phrase thoughts. Real language however, has thoughts, GPT doesnt have any thoughts. People are impressed by how many responses it has learned, but forget that it is contains a lot of gigabytes of "compressed" text associations. It needs "something else" to become actually useful.
GPT is the equivalent, but for language: it can be used to phrase thoughts. Real language however, has thoughts, GPT doesnt have any thoughts. People are impressed by how many responses it has learned, but forget that it is contains a lot of gigabytes of "compressed" text associations. It needs "something else" to become actually useful.
I have seen comments on Reddit that smelled as though something like this was used to create them. I don't see how, in future, we are going to be able to meet up and discuss constructively with strangers online, if this thing can be used to emulate people.
Or imagine the amount of useless blog posts for SEO spam. We already suffer from information overload. This will add a lot more.
> We already suffer from information overload
I would suggest this isn't quite right, we actually suffer from content overload, not information overload.
The quantity of useful information in a lot of the content we are offered is depressingly small.
I would suggest this isn't quite right, we actually suffer from content overload, not information overload.
The quantity of useful information in a lot of the content we are offered is depressingly small.
I would suggest this isn't quite right, we suffer from advertisement overload and "content" is actually a "hype" word - itselft a product of advertisement (propaganda).
Voting systems exist to promote insightful comments and suppress the noise. Those systems may or may not need to adapt.
Would it matter if on occasion some value is gleaned from generated content?
Would it matter if on occasion some value is gleaned from generated content?
Let me guess. GPT-3 wrote this article??
That was my thought as well. It has a lot of the idiosyncrasies I've noticed in other GPT-3-generated articles: coherent, but meandering, with a lot of cliches and rhetorical questions that don't really add to the article. Of course, you also see that in human writing. :-)
I think GPT-3 will change the assumption written language or sentences a priory have meanings, because experience taught actions might follow. From now determining the author and their intentions is kind of an survival skill. Just imagine half of the Internet was written by GPT-3 and nobody knows which half.
A comment from r/machinelearning:
"It seems obvious from the demos that GPT-3 is capable of reasoning.
But not consistently.
It would be critical, imo, to see if we can identify a pattern of activity in it associated with the lucid responses vs activity when it prodcues nonsense.
If/when we have such a apattern we would need to find a way to enforce it to happen in every interaction"
And people agree:
"Dunno why you are getting downvoted, I agree with you. It seems like to get GPT-3 to do good reasoning you have to convince it that it is writing about a dialogue between two smart people. Talking to Einstein, giving some good examples, etc. all seem to help. Shaping really seems to matter, but I don’t think we have enough access to the hidden state to determine if there are quantitative differences between when it is more lucid and when it isn’t.
It’s like Gwern said: “sampling cannot prove the absence of knowledge, only the presence of it” (because whenever it fails, maybe with a different context, different sampling parameters, using spaces between letters, etc. it would have worked)"
Its interesting that this kind of speculation is entering the conversation. I think we are on the cusp
"It seems obvious from the demos that GPT-3 is capable of reasoning.
But not consistently.
It would be critical, imo, to see if we can identify a pattern of activity in it associated with the lucid responses vs activity when it prodcues nonsense.
If/when we have such a apattern we would need to find a way to enforce it to happen in every interaction"
And people agree:
"Dunno why you are getting downvoted, I agree with you. It seems like to get GPT-3 to do good reasoning you have to convince it that it is writing about a dialogue between two smart people. Talking to Einstein, giving some good examples, etc. all seem to help. Shaping really seems to matter, but I don’t think we have enough access to the hidden state to determine if there are quantitative differences between when it is more lucid and when it isn’t.
It’s like Gwern said: “sampling cannot prove the absence of knowledge, only the presence of it” (because whenever it fails, maybe with a different context, different sampling parameters, using spaces between letters, etc. it would have worked)"
Its interesting that this kind of speculation is entering the conversation. I think we are on the cusp
I'm not convinced that "capable of reasoning, but not consistently" is a meaningful claim. The examples seem to primarily consist of people spending hours trying things, until eventually GPT-3 outputs a chunk of reasoning they could personally do in seconds. Does that mean that GPT-3 is doing the reasoning, or does it mean that GPT-3 is an English-based lookup table and they managed to find a clever sequence of search keys?
The fact that there could be reasoning going on is certainly exciting by itself. But I don't think it's fair to call it obvious without a compact specification for how to make GPT-3 perform a general class of reasoning. Less "here's a script to make it output stuff about balanced parens", more "here's a strategy to teach it most basic string manipulations".
The fact that there could be reasoning going on is certainly exciting by itself. But I don't think it's fair to call it obvious without a compact specification for how to make GPT-3 perform a general class of reasoning. Less "here's a script to make it output stuff about balanced parens", more "here's a strategy to teach it most basic string manipulations".
> I'm not convinced that "capable of reasoning, but not consistently" is a meaningful claim.
Suppose an entity will consistently do reasoning well, but only when the humidity and temperature are each in a quite narrow range. It seems like it makes sense to say that such an entity is capable of reasoning. Now, suppose we don't know that the conditions for it to do reasoning well are that the humidity and temperature are in that range, we just know that sometimes it looks like it does, sometimes it looks like it doesn't (and maybe we aren't yet sure if it seeming to reason is just an illusion in the way you describe).
I think in such a situation, it would be accurate to say that it can reason, but we haven't yet found a way to make it do so consistently.
So, I think the statement that it is "capable of reasoning, but not consistently" is a meaningful statement.
However, whether it is an accurate statement is a very different question, and one which I am not claiming an answer to.
Suppose an entity will consistently do reasoning well, but only when the humidity and temperature are each in a quite narrow range. It seems like it makes sense to say that such an entity is capable of reasoning. Now, suppose we don't know that the conditions for it to do reasoning well are that the humidity and temperature are in that range, we just know that sometimes it looks like it does, sometimes it looks like it doesn't (and maybe we aren't yet sure if it seeming to reason is just an illusion in the way you describe).
I think in such a situation, it would be accurate to say that it can reason, but we haven't yet found a way to make it do so consistently.
So, I think the statement that it is "capable of reasoning, but not consistently" is a meaningful statement.
However, whether it is an accurate statement is a very different question, and one which I am not claiming an answer to.
Have you seen the database prompt?
https://www.gwern.net/GPT-3#the-database-prompt
https://www.gwern.net/GPT-3#the-database-prompt
As I mentioned, I don't think any single prompt can demonstrate the presence of true reasoning. If the prompt isn't shown to broadly generalize, it might just be doing a text match to something that was said before on the depths of the internet. You can see this in the next section; Kevin Lacker gets GPT-3 to demonstrate it knows some basic trivia questions, but it "knows" any prompt with the same textual structure as a basic trivia question, even if the prompt is nonsense. This strongly suggests that it's parsing out key words and doing a lookup on them rather than accessing a consistent internal model.
I think the downvotes are from people who don't want to believe that AGI is possible, or that we're taking baby steps towards it.
It's not a matter of belief at this time. The evidence is right in front of our eyes. But it takes intelligence to recognize intelligence. Contextual extension by abstract inference is the basic (and hardest to achieve) building block of AGI, and we have achieved it. The rest is about utilizing this same power for the querying (priming) part of the reasoning loop, within the contexts we are interested in.
Part of me is glad we're not exactly there yet, because the thought of this running autonomously in a thinking cycle is downright scary. What will you find when you sit behind the console in the morning? It took us months to start understanding this in its current one-shot mode.
I don't care about the ideological downvotes, but we will do better if we start taking this very seriously. It's no longer theoretical that this (and machine learning as such) will have unprecedented (and impossible to predict) impact on everything we know, and the timeline is now measured in months instead of years or decades.
Part of me is glad we're not exactly there yet, because the thought of this running autonomously in a thinking cycle is downright scary. What will you find when you sit behind the console in the morning? It took us months to start understanding this in its current one-shot mode.
I don't care about the ideological downvotes, but we will do better if we start taking this very seriously. It's no longer theoretical that this (and machine learning as such) will have unprecedented (and impossible to predict) impact on everything we know, and the timeline is now measured in months instead of years or decades.
>> Contextual extension by abstract inference is the basic (and hardest to achieve) building block of AGI, and we have achieved it.
What is "contextual extension by abstract inference" and why do you say it's "the basic building block of AGI"? Can you point to an authoritative source for the two parts of the statement (i.e. a source that defines "contextual extension by abstract inference" and a source that asserts this is "the basic building block of AGI")?
What is "contextual extension by abstract inference" and why do you say it's "the basic building block of AGI"? Can you point to an authoritative source for the two parts of the statement (i.e. a source that defines "contextual extension by abstract inference" and a source that asserts this is "the basic building block of AGI")?
> So embeddings are bits of binary code that are associated with the words and word-snippets that the computer ‘reads’. These embeddings never change.
I'm not entirely sure but I think this definition of embedding is wrong: first they are not binary, they are floats (as the other parameters/weights) and they do change, as the error backpropagates through them. They are simply "swappable parameters", explicitly corresponding to each word, thus it's possible to detach and reuse them for other purposes after training, which is not necessarily easy (or meaningful) with any other weight matrix in a model.
I'm not entirely sure but I think this definition of embedding is wrong: first they are not binary, they are floats (as the other parameters/weights) and they do change, as the error backpropagates through them. They are simply "swappable parameters", explicitly corresponding to each word, thus it's possible to detach and reuse them for other purposes after training, which is not necessarily easy (or meaningful) with any other weight matrix in a model.
> If prompted correctly it responds identically a human.
It appears to be grammatically incorrect. It made me think "ok, probably written by a human." But then also realized that typos and grammar issues are probably prevalent in the data set. How would they manifest? Will they reinforce emerging changes in language? (eg, think of how the meanings of words have slowly mutated). And how much of the scraped content is itself written by a AI?
It appears to be grammatically incorrect. It made me think "ok, probably written by a human." But then also realized that typos and grammar issues are probably prevalent in the data set. How would they manifest? Will they reinforce emerging changes in language? (eg, think of how the meanings of words have slowly mutated). And how much of the scraped content is itself written by a AI?
Apologies i realize this is a bit of a tangent but what i would like and can't find is an understandable description of how Generative neural networks work. My very basic understanding of neural networks is as a classifier. Cat and dog pictures in, likelihood out. I can't get any intuition about how this can generate new cat pictures.. is it a search problem? How does that search work?
Classifiers are just one example of output, one where the final layer of output is interpreted to be probabilities of different items. Using a different loss function (or reward function), you can train for the output to be different things.
But in fact text generators work basically identically to classifiers. You train the model to classify texts according to which single word comes next. Then you append a word to the text according to that output, and repeat.
But in fact text generators work basically identically to classifiers. You train the model to classify texts according to which single word comes next. Then you append a word to the text according to that output, and repeat.
Good article. I was half expecting at the end to read that the article itself was generated by GPT3. Luckily GPT3 can’t explain itself just yet.
Just wrote about something similar and what we can learn about GPT-3 inputs. Applicable for lots of domains but written to product managers.
https://productsolving.substack.com/p/openais-gpt-3-will-cha...
https://productsolving.substack.com/p/openais-gpt-3-will-cha...
I'm curious how long this prompting UI into language models like GPT-3 will continue. It's so fiddly and imprecise and unpredictable. It's better than nothing, of course, but there's a lot of art in it.
The idea of garbage in, garbage out is worth considering in the context of human minds and priming. Advertisements and algorithmic curation could contribute to hazardous information environments. Perhaps GPT agents may assist in revealing this dynamic and simulate the extent, and consequences of manipulation?
"Generate text that could believably have been written by a human" is a task we're getting very good at. I'm curious how far it extends towards concrete applications. When can we start using it to fill functionality gaps between the functions of a human-coded framework?
In about the average life span of a redwood tree.
I'm probably going to be dead before Google Assistant can correctly interpret "take me to the McDonald's beside the Target"
I reckon that's 3 years away...
Current assistant features rely on trying to match your query fuzzily to an action template. Eg. "Set alarm for 10pm" might match the template "Make alarm at time {TIME}", with $TIME=22:00. Minor transformations of parameters can occur (Converting the time from 10pm to 22:00), but those are mostly hard-coded.
Future assistants will use a neural network to do the matching and parameter conversion, with the networks big enough to encode world data. So things like "Mcdonalds beside the target" can be encoded by the neural net so your query matches "Navigate to ${ADDRESS}", with $ADDRESS="Mcdonalds, 21 Foo Avenue".
It's all do-able today, but nobody has done it yet. Probably 3 years away I'd guess.
Current assistant features rely on trying to match your query fuzzily to an action template. Eg. "Set alarm for 10pm" might match the template "Make alarm at time {TIME}", with $TIME=22:00. Minor transformations of parameters can occur (Converting the time from 10pm to 22:00), but those are mostly hard-coded.
Future assistants will use a neural network to do the matching and parameter conversion, with the networks big enough to encode world data. So things like "Mcdonalds beside the target" can be encoded by the neural net so your query matches "Navigate to ${ADDRESS}", with $ADDRESS="Mcdonalds, 21 Foo Avenue".
It's all do-able today, but nobody has done it yet. Probably 3 years away I'd guess.
Looks like they also made a podcast.
"#828 - What is GPT-3?"
https://podcasts.apple.com/us/podcast/828-what-is-gpt-3/id13...
"#828 - What is GPT-3?"
https://podcasts.apple.com/us/podcast/828-what-is-gpt-3/id13...
Great introduction to GPT-3 and machine learning for non-technical people. Interesting implications/possible use cases described at the end, including therapy and regulatory capture.
GPT-3 probably has thousands of different skills like these two, we just haven't explored it much yet.
Images in the article are not showing up.
Isn't this a text-only article? My issue is rather with the title of the page, "Tinkered Thinking", instead of "Tinkered Thinking - What is GPT-3?", which harms bookmarking.
Don't just chuck your bookmarks into a pile. Sort them, label them - curate them.
Otherwise it's pretty much "garbage in, garbage out".
Otherwise it's pretty much "garbage in, garbage out".
Sounds like it’s similar to the tool that creates these deep dream images, but for text.
How long until someone builds GPT-4 with several trillion parameters? How much would that cost?
Training GPT-3 costs only a few million dollars. Scaling up is still pretty cheap. I wouldn't be surprised if we see a quadrillion parameter model in 5 years, given the potential value.
Google has actually already trained a trillion parameter model IIUC [1], though that was a Mixture of Experts so was way cheaper to train.
[1] https://twitter.com/lepikhin/status/1278174444528132098
Google has actually already trained a trillion parameter model IIUC [1], though that was a Mixture of Experts so was way cheaper to train.
[1] https://twitter.com/lepikhin/status/1278174444528132098
I expect similar things exist inside Google/Facebook, and they haven't been opened up to the public because nobody has figured out how to make them not say racist things, which isn't good for the reputation of a big tech company.
where one should start learning how to leverage OPENAPI to build some complex logic? i've seen gym, but it seems to me that they are predefined envs where you can just play.
More specifically:
- training on data is a lossy process. In your examples, GPT would actually have a worse memory than your lawyer or therapist. There is no way to combine language models and something more abstract like 'facts'.
- GPT has shown zero ability to do anything consistently successfully without a human-in-the-loop. When it comes to bring AI models into production, this matters a lot. There's no way autonomous therapists are coming from GPT-3 when half the time the model spews out potentially dangerous garbage. You can't teach GPT-3 to not hurt people because it has no concept of people or hurting them. It JUST knows the shape of English.
- GPT is an unsupervised (in terms of data labelling work required) model. It has not made any breakthroughs in requiring labelled data for fine-tuning the model to do a specific task. Which remains a gigantic problem for productionalizing models. Like how are you going to build an autonomous therapist? That data remains as inaccessible and impossible to label as ever.
- Please stop telling people that neural nets are related to brain neurons. They have essentially no relationship other than the name and it just fosters this fear of Terminator and obscures the real issues that need to be thought about. This is just my personal opinion but I'm so tired of having to spend my time telling otherwise smart people who don't know better that we aren't close to Terminator.
GPT is an impressive technical accomplishment, but it's impact on the world has been exaggerated quite a bit IMO. Some of the demos I've seen are almost certainly smoke and mirrors or very carefully chosen, human-in-the-loop examples.