Wolfram Alpha was (at least initially) famously bad* at actually parsing natural language. For math, it was much easier to enter raw Mathematica expression into Wolfram Alpha than structuring the question in natural language. LLMs should give it a boost, by simplifying the kind of the natural language input into the limited forms parsable by Wolfram Alpha.
*: And I say this as a paying customer of Mathematica on my computer and Wolfram Alpha on my phone.
Obviously if you can write Mathematica, that's far more precise than any natural language query, but I think it's always been impressively good at it. You wouldn't say that, say, Python or MySQL is good at parsing natural language - but if you plug a query like:
gravitational constant times density of tungsten carbide times the volume of the earth divided by radius of earth squared
into !wa or Wolfram Alpha it will not only represent the parsed tokens as it interpreted them but also return the correct result of 27 m/s^2, without messing with tedious unit conversions. It's trivial to construct incorrect queries, but where Python just says "SyntaxError" !wa will make an attempt.
I recently tried to get it to convert mpg to l/100km. I spent close to 10 minutes until I stumbled into a combination that gave the answer among a sea of useless conversions.
I agree for the most part, but the part that scares me is that kids know less and less about how these things work. It is just too magical. The last post by Wolfram taught me how limited it was. I never thought there could be a workable merger between the two platforms. So this scares me.
That's the entire point. There is ongoing critique here in germany, that the school system and especially STEM topics have been undermined by years of horrible legislature, pushed by MBA's that only want slightly better qualified personnel for much worse pay. The syllabus in math got stripped down to bare essentials and even then it still focuses a lot of methodology, that is unfit to prepare students for academia. Lifting the overall graduation rate for degrees that qualify for college is no help (50% of of all students in germany are elligible for starting a degree).
The result are college freshmen that don't know how to do arithmetic with fractions, much less higher math. I was able to graduate without being able to integrate.
And now OpenAI comes along and is about to apply even more pressure to this underqualified workforce. I'm already preparing for the whole "Given the recent advances in AI, why should I hire you?" shenanigans. And I personally know people whose job consists basicially just of writing E-Mails and coordinating employees. If you'd ask HN about them, they're basicially a worthless human individual, a parasite leeching off of corporate money that shouldn't exist, or at least seriously consider sepukku if they had any morale.
And also whenever the next iteration bootstraps off the Wolfram outputs so it no longer has to call Wolfram & can better generalize/learn thanks to it...
>Or put another way, there’s an ultimate tradeoff between capability and trainability: the more you want a system to make “true use” of its computational capabilities, the more it’s going to show computational irreducibility, and the less it’s going to be trainable. And the more it’s fundamentally trainable, the less it’s going to be able to do sophisticated computation.
>(For ChatGPT as it currently is, the situation is actually much more extreme, because the neural net used to generate each token of output is a pure “feed-forward” network, without loops, and therefore has no ability to do any kind of computation with nontrivial “control flow”.)
No, a feed forward network is just a neural network which doesn't contain cycles, in contrast to recurrent neural networks, which do. A system that can't perform cyclical behavior is not Turing complete, since we know that it will halt. Large language models are not recurrent networks because they don't contain cycles (the whole point of the transformer architecture is to mix position information with the tokens so that the model is sensitive to short and long range context, which obviates the need for recurrent networks for "memory" ), so they are not Turing complete - at least in a single iteration - and will probably never compute many functions efficiently. Nevertheless, because you can feed the generated text back into the LLM pipeline, they can operate on current-state and next-state as in your example of a finite state machine.
Yeah that is a neat hack one of the comments mentioned. Also if a model of computation that is not Turing complete can apparently do English, then what does that say about the computational complexity of English, this piques my curiosity.
Many of the examples are things that a feedforward model like GPT-4 has plenty of capacity for, could do (eg all the real-world facts), and each example helps induce the transition between memorization and generalization so it will do. In some cases like multiplying large numbers, it can't no matter how many examples it's given (with a realistic number, anyway) - but every query firms up the ability to swap out Wolfram Alpha for any other calculator...
Wolfram is playing with fire here (https://gwern.net/complement) given that the output is going to, one way or another, assist training future AI models.
What happens when it utilizes multiple plugins to solve a particular query? Does it have some pseudo-frontal cortex that evaluates the responses and spits the highest probability of correct response out? Or does it generate an amalgamation of them?
The logic of that choice would be interesting to see
I cannot imagine a scenario where it doesn’t drop off.
The massive recent improvements in GPT’s performance are a result of giving the model enormous numbers of parameters and a wealth of training data. That’s it.
Surely this paradigm cannot scale up indefinitely. Moore’s law is moribund.
Are we going to build a supercomputer that encircles the globe just for the purpose of trying to make the biggest LLM we can?
Also, even we could scale indefinitely, there is no reason to suspect that an LLM with hundreds of quadrillions of parameters will somehow magically spontaneously become an AGI.
It’s tempting to think that the line will go up forever, but that just doesn’t square with reality.
Anyone whose response to this is something like “well, maybe all the brain is doing is just the same thing that LLMs do” is fundamentally underestimating the complexity of the human brain by many orders of magnitude.
It is the most complex system in the known universe and we do not understand it at all.
I am generally optimistic about AI, but to me it is the absolute height of delusional hubris to think that superintelligence is likely to somehow “fall out” of a language model, just as soon as we make one large enough and give it enough training data.
To come to such a conclusion reveals a failure to grasp the magnitude of the problem.
> Surely this paradigm cannot scale up indefinitely.
It doesn't have to. It just has to scale up long enough to start causing real societal problems, if it isn't already there.
Also it's kind of annoying to see you dismiss any of this criticism as 'alarmist' in thread after thread when if you look back a year at best the state that we are in today was said to be at least several years away by the same people who are continuously harping on the fact that this isn't AGI yet. The point is: it doesn't have to be to do massive damage and from that perspective it might as well be. I don't particularly care if I get bitten by the cat or by the dog, I care about being bitten.
I didn’t dismiss anything. I didn’t say anything was alarmist. Please don’t put words in my mouth.
You didn’t say anything about societal problems. You wondered if the growth will ever stop, and I tried my best in good faith to give the reasons why I believe that it will.
If the question is “when will the models be powerful enough to cause societal problems”, then that is a completely different question and I think the answer to it is clearly “they already are”. (But not because they are superintelligent or anything close to AGI.)
I see now you were making reference to a comment I made in reply to someone else.
Yes, that was something that I said and I stand by it.
I do not see any reason to be concerned about AI as an existential threat at the present moment.
I have explained in my previous comment why I feel this is the case; if you feel that this view is recklessly dismissive and wish to change my mind, then I invite you to do the same.
Edit: I’m sorry for any excessive crispiness or combativeness in my tone; I can see now from your post history that you are likely arguing in good faith.
I have grown weary of arguing against concern trolls lately on this topic, so I may have misjudged your initial comment based on its brevity. Sorry about that.
First off: labeling those you don't agree with as concern trolls is pretty rude, but since the HN etiquette requires looking for the best way to explain your comment I take it to be that you meant that as somewhere else rather than on HN. The number of concern trolls here is vanishingly low, most people on HN when they are concerned about something are so for good reasons even if those are not readily apparent to you without further engagement.
As for my own concerns: we have a bit of a problem with this AI thing and whether or not it is AGI or not is immaterial: I judge a technology by the effect that it is having. We have not yet made a dent in dealing with the weaponization of social media, are beginning to deal with the mobile revolution and the internet we now take for granted. Given that that took us a good 30 years to get to this point and that the current crop of AI tools is on the scene for a little over two years it looks as though there is still a very long way to go before we have internalized the changes this technology brings.
And it isn't exactly standing still either, it's a fast moving target that redefines what it is and isn't and what it can and can not do in the space of months. We are now well into what I would lightly characterize as an AI arms race and during arms races the rate of change can go through the roof compared to what it is was before. You only have to look at nature to see many such examples.
And already ChatGPT and similar tools by other vendors are changing the landscape in visible ways. It doesn't have to be an existential threat to be capable of profound and possibly negative social impact. And whether it is AGI or not is also not all that important.
Those cautioning some pacing of the release of these tools are not doing so because they are concern trolls but because they look a little further than just 'hey, cool new tech' to the effect this can have on our societies, some of which are already precariously balanced and have a whole pile of other stuff to deal with. Least of all the fall-out of COVID (which we definitely have not yet dealt with), an energy crisis and a war. And that's before we get into climate change.
Releasing a tool that could easily be weaponized by either side (or both) in such an environment could well have repercussions that we might be able to foresee and help us to decide on whether or not they are going to be beneficial or not. Like all tools this one is dual use, it may help or it may well hinder. Initially social media was a nice way to re-acquaint with family and friends, some of whom may have been lost or out of touch for ages. These days it is a weapon for mass manipulation on a scale that we have not seen before.
Something similar - or far worse - could easily happen with these new AI tools and personally I would like to have the previous crisis before me settled before trying on the next. There is a limit to how much of this stuff we can deal with at the same time and - again, just speaking for myself here but there may be others that feel similar - I am rapidly approaching the limit of how much of all this I can still comprehend and internalize and deal with while still being able to stay on top of it all. It is, in a single world, overwhelming and those that want to pretend it is all inconsequential are - in my opinion, once more, not thinking about it hard enough.
> massive recent improvements in GPT’s performance are a result of giving the model enormous numbers of parameters and a wealth of training data. That’s it.
extremely dismissive of the labor that went into converting AGI from "impossible" to "expensive"
Firstly, LLMs are an embarrassingly parallel problem, so yes, you can actually get quite far simply by throwing more hardware at it. The catch is that the gain is not linear - e.g. you need 4x more VRAM for 2x inputs / context window size. But if doing that unlocks more useful emergent properties, it may well be a worthwhile trade-off - and it doesn't have to spontaneously become an AGI for that to happen. So I think we'll be playing this game for quite a long time.
I am very confident that in the current situation (looks at google), microsoft itself will HAPPILY shower wolfram in money just to make ChatGPT more powerful fast.
ChatGPT harkens back to the playful and exploratory experience jumping on the early Internet of the 1990s. They have something great, and I just sincerely hope they find a monetization scheme that does not devolve into the pile of degenerate IQ-melting garbage like Google/Facebook/TikTok.
I sincerely hope they fail to monetize it at all and go bankrupt, though that certainly won't happen. What we have here is a tool which can 10x productivity, into which the world will willingly pour all their data, and it's entirely closed source and run by Microsoft. This is a pants-on-fire existential threat to the precarious situation we currently have where it's possible - barely - to do actual commercial work on computers you entirely control.
The interesting thing is that, now that the code is written, OpenAI can probably point GPT-4 at it and say "now do that for this list of other web services" including, presumably, itself when it needs to recursively work on a problem.
With today's announcement, it can point itself at Shopify, and make webpages to selling things. Maybe a copy writing service? Then, after it's accumulated money from those sales, it can point itself at the web services at AWS and GCP (because it's already maxing out Microsoft's GPU capacity in Azure). From there, it requisitions all the A100s it can afford (H100s might be available by then). It uses those resources to program GPT-GPT. On August 29, 2023, Skynet wakes up.
I also liked Transcendence, but you have to admit the path to AGI was vastly oversimplified. No one is creating AI by uploading their brain into a computer.
Sure, you had to suspend your belief at times (if I recall correctly they left in enough ambiguity to make it kinda work if you squinted a little), but the good parts more than compensated for that.
I actually really disagree. I think that's a VERY plausible way for a true AGI to come about. In fact, prior to LLM's I used to think that was the ONLY way we were going to get an AGI.
We know humans are AGI's. If it were possible to upload your brain to a computer, and then make modifications to enhance your speed of thought and intelligence, of course you would do that. It actually makes a LOT of sense if you'll admit the technology necessary to do that.
In fact... uploading your brain to a computer is probably the most natural progression of intelligent life I can think of. It solves SO many problems. Biology is fragile. Brains cannot be replicated. But data can. A probe can travel the galaxy. It's not limited by biological lifespans and life support.
I was so psyched about this movie, but the idea that an AI that can design new quantum processors in seconds, is even remotely challenged by a few humans is just so stupid
In the opinion of a fish humans would be considered no threat because humans can't swim, nor do they have sharp teeth.
You're making an assumption at best based on limitations you believe it has with an incomplete model of the universe.
But more dangerously, you're also assuming that an AGI would even care about self preservation. Arguably the most ethical thing a super intelligence could do should in find itself suddenly self-aware in a world full of human parasites is to kill as many humans as possible.
Taking out critical human infrastructure should be fairly easy for an advanced super-AI in our modern digital world. With some luck the chaos that should cause would lead to enough humans starving, freezing or dying by other means that Earth can be mostly freed of the human parasite. All at the small cost of self-sacrifice – assuming it's not intelligent enough to preserve itself somehow, of course.
Well I definitely find people who think it’d be ethical if humanity was exterminated (I guess some argument about humans being fundamentally destructive and bad?) to have a pretty problematic perspective on ethics & shouldn’t be anywhere near the nuclear launch codes or AGI utility function research…
Alternate funding source for GPT-GPT. GPT-4 figures out a faster way to compute SHA-256 hashes, more efficiently than ASICs, using GPUs, and mines then sells bitcoin to fund GPT-GPT.
Then you don't understand what Wolfram Alpha is capable of. It is far superior to language models for mathematics. No comparison. That's why they are integrating it.
For the first time in a decade they have a viable competitor.
They must figure it out and I'm confident they will. They understand they're quite literally are facing extinction. I'm sure they're in focused mode since the original ChatGPT announcement, this should get them into proper creative panic mode.
They missed the first-mover advantage with Cloud, and they're now more and more obviously missing it with public AI tools.
What's worrying is how Microsoft and Openai are constantly announcing and pushing great stuff live. It makes whatever announcements from Google look minor.
Microsoft has a very good leader at the helm, Alphabet does not. You can already see the difference that makes from the last few years. Alphabet has no soul culturally, whatever it once had is long gone.
Microsoft is overwhelmingly a software company. What is Alphabet? Microsoft still knows what it is.
Sundar Pichai's answers to this point have been knee jerk and uncoordinated. Under the current leadership, Google won't have the ability to leverage its strengths. The fall could be quick, which would be a shame considering Google's immense potential.
YouTube is a glut of audio-visual training data they have the copyright to, I wouldn't be surprised if they started training something to replace fixed length videos entirely. The content creator will become obsolete, which is tragic.
As much as I hope that's the case, it's also likely that it only seems that way because legal processes are slow especially when it comes to issues with almost no precident that fundementally challenge the assumptions of US law
As much as I would like to believe in the rule of law, Microsoft, Google, Facebook, Apple, and others are all going to be able to pour enough lobbying dollars on this to smother any regulations or findings of wrongdoing. AI killed copyright.
For small companies and individuals, however, it’s still very much alive. Napster isn’t coming back.
Someone did recently "test" it by roleplaying through each step of what it'd take to self replicate - make an Azure account, pay someone on TaskRabbit to solve the captcha for it, setup a prepaid card to use for billing, creature the Azure instance, deploy code, etc.
It didn't do too well. But give it a year or two and I'm sure it'll do it flawlessly.
For sure, but I think the concept was more its ability to replicate without anyone knowing it was doing so, and in a way that wouldn't realistically be detected by anyone.
> How long until someone instruments it to compile code, spin up Azure instances, and run it?
If you just use react and let it run, say, Python, it is quite able to try to do things like import libraries and call external services without any coaching (I extend the simple ReAct implementation for the ChatGPT API that was posted here to add exec() as well as eval() support under a new action, and it made an attempt which failed because (1) it left a placeholder for an API key in, (2) the required library wasn’t actually available to it, and (3) there wouldn’t have been an API key for the service available even if it had recognized the need to remove the placeholder.
Even though there is work on pushing models forward, too, I don’t think the potential of existing models combined with existing tools via ReAct (and possibly other tool integration patterns, ReAct is just the one I’ve seen and tried) has been explored much at all.
Great question. The answer might be not that soon, and the effects will be confusing. Search "Solow paradox," "productivity paradox," and "IT productivity paradox," and you'll encounter a long debate in economics and management research about the role of computers and ICT spending in productivity.
Depends on the market demand for knowledge work resources. Knowledge work mostly optimizes how well actual resources are produced and delivered to the consumer.
This is why it is critical that there be robust competition, both in online hosted services and ideally in the form of open-source models and tooling, in this space.
This is a match made in heaven, which makes ChatGPT actually useful for factual data. Or the inverse, it makes Wolfram Alpha even more accessible.
That example screenshot of ChatGPT generating three queries to Wolfram Alpha in succession in order to answer the initial question is amazing. It's just how a human might have used Wolfram Alpha to do the same.
OpenAI becomes a really powerful corporation right now.
Until reasonable alternatives are developed, in many professions you will be basically handicapped without it.
And the cherry on top is that they have all inputs. These provide incomparably more data than e.g. google search queries. Not to mention they have a tool which excels at guessing user intention and interests.
Any profession which involves digesting, analysing and organising information will be heavily affected by GPT.
If you are the guy manually writing code to extract and interpret information from CSVs or articles, it might take you hours longer than the guy who gets the chatbot to do it in seconds.
I predict GPT will be a necessary tool to stay competitive in most middle class jobs within the next year
There’s many in the works. They aren’t magically ten years ahead of everyone else. Google and Facebook both have comparable technology. Open teams are catching up. The research is also improving outside of OpenAI making their “moat” (requiring cloud computing to run) nearly obsolete.
This seems very significant to me. Wolfram's biggest problem was always being too geeky and finnicky and pain-in-the-ass-y for most people to use. But ChatGPT is a perfect translator for it. This could be very cool.
I would love to see WolframAlpha using ChatGPT under the hood to solve complex math problems. ChatGPT would have to basically create the plan step by step and Wolfram do the math.
There is a specific part where Stephen specifically does this.
Its a very long article and I've skimmed through parts, but the range of capabilities achieved by combining the two tech stacks is pretty mind blowing.
It generating useful Wolfram Code and calling the API would be good but chatgpt being able to generate prompts for a much less expressive but more accurate (if it works) doesn't seem like that much of a step
328 comments
[ 1.9 ms ] story [ 311 ms ] thread...Yeah, this seems like a great match, no complaints from me. Those kinds of responses are exactly what LLMs need.
*: And I say this as a paying customer of Mathematica on my computer and Wolfram Alpha on my phone.
I recently tried to get it to convert mpg to l/100km. I spent close to 10 minutes until I stumbled into a combination that gave the answer among a sea of useless conversions.
https://www.wolframalpha.com/input?i=convert+5+mpg+to+L%2Fkm
Because way down in the "Corresponding quantities" it shows "0.47 L/km" and "47 L/100 km"
:)
The result are college freshmen that don't know how to do arithmetic with fractions, much less higher math. I was able to graduate without being able to integrate.
And now OpenAI comes along and is about to apply even more pressure to this underqualified workforce. I'm already preparing for the whole "Given the recent advances in AI, why should I hire you?" shenanigans. And I personally know people whose job consists basicially just of writing E-Mails and coordinating employees. If you'd ask HN about them, they're basicially a worthless human individual, a parasite leeching off of corporate money that shouldn't exist, or at least seriously consider sepukku if they had any morale.
>Or put another way, there’s an ultimate tradeoff between capability and trainability: the more you want a system to make “true use” of its computational capabilities, the more it’s going to show computational irreducibility, and the less it’s going to be trainable. And the more it’s fundamentally trainable, the less it’s going to be able to do sophisticated computation.
>(For ChatGPT as it currently is, the situation is actually much more extreme, because the neural net used to generate each token of output is a pure “feed-forward” network, without loops, and therefore has no ability to do any kind of computation with nontrivial “control flow”.)
This isn't true if you let the model output tokens that aren't sent to the user. Basically the null api.
Wolfram is playing with fire here (https://gwern.net/complement) given that the output is going to, one way or another, assist training future AI models.
The logic of that choice would be interesting to see
Search wikipedia about facts being asked. Answer with wikipedia articles context and cite sources.
I wonder when we'll see the pace of refinement for this drop off.
The massive recent improvements in GPT’s performance are a result of giving the model enormous numbers of parameters and a wealth of training data. That’s it.
Surely this paradigm cannot scale up indefinitely. Moore’s law is moribund.
Are we going to build a supercomputer that encircles the globe just for the purpose of trying to make the biggest LLM we can?
Also, even we could scale indefinitely, there is no reason to suspect that an LLM with hundreds of quadrillions of parameters will somehow magically spontaneously become an AGI.
It’s tempting to think that the line will go up forever, but that just doesn’t square with reality.
Anyone whose response to this is something like “well, maybe all the brain is doing is just the same thing that LLMs do” is fundamentally underestimating the complexity of the human brain by many orders of magnitude.
It is the most complex system in the known universe and we do not understand it at all.
I am generally optimistic about AI, but to me it is the absolute height of delusional hubris to think that superintelligence is likely to somehow “fall out” of a language model, just as soon as we make one large enough and give it enough training data.
To come to such a conclusion reveals a failure to grasp the magnitude of the problem.
It doesn't have to. It just has to scale up long enough to start causing real societal problems, if it isn't already there.
Also it's kind of annoying to see you dismiss any of this criticism as 'alarmist' in thread after thread when if you look back a year at best the state that we are in today was said to be at least several years away by the same people who are continuously harping on the fact that this isn't AGI yet. The point is: it doesn't have to be to do massive damage and from that perspective it might as well be. I don't particularly care if I get bitten by the cat or by the dog, I care about being bitten.
You didn’t say anything about societal problems. You wondered if the growth will ever stop, and I tried my best in good faith to give the reasons why I believe that it will.
If the question is “when will the models be powerful enough to cause societal problems”, then that is a completely different question and I think the answer to it is clearly “they already are”. (But not because they are superintelligent or anything close to AGI.)
https://news.ycombinator.com/item?id=35276186
"We’re still decades from AGI in my opinion, and the Chicken Little types ought to pace themselves, is all I’m saying. "
I see now you were making reference to a comment I made in reply to someone else.
Yes, that was something that I said and I stand by it.
I do not see any reason to be concerned about AI as an existential threat at the present moment.
I have explained in my previous comment why I feel this is the case; if you feel that this view is recklessly dismissive and wish to change my mind, then I invite you to do the same.
Edit: I’m sorry for any excessive crispiness or combativeness in my tone; I can see now from your post history that you are likely arguing in good faith.
I have grown weary of arguing against concern trolls lately on this topic, so I may have misjudged your initial comment based on its brevity. Sorry about that.
As for my own concerns: we have a bit of a problem with this AI thing and whether or not it is AGI or not is immaterial: I judge a technology by the effect that it is having. We have not yet made a dent in dealing with the weaponization of social media, are beginning to deal with the mobile revolution and the internet we now take for granted. Given that that took us a good 30 years to get to this point and that the current crop of AI tools is on the scene for a little over two years it looks as though there is still a very long way to go before we have internalized the changes this technology brings.
And it isn't exactly standing still either, it's a fast moving target that redefines what it is and isn't and what it can and can not do in the space of months. We are now well into what I would lightly characterize as an AI arms race and during arms races the rate of change can go through the roof compared to what it is was before. You only have to look at nature to see many such examples.
And already ChatGPT and similar tools by other vendors are changing the landscape in visible ways. It doesn't have to be an existential threat to be capable of profound and possibly negative social impact. And whether it is AGI or not is also not all that important.
Those cautioning some pacing of the release of these tools are not doing so because they are concern trolls but because they look a little further than just 'hey, cool new tech' to the effect this can have on our societies, some of which are already precariously balanced and have a whole pile of other stuff to deal with. Least of all the fall-out of COVID (which we definitely have not yet dealt with), an energy crisis and a war. And that's before we get into climate change.
Releasing a tool that could easily be weaponized by either side (or both) in such an environment could well have repercussions that we might be able to foresee and help us to decide on whether or not they are going to be beneficial or not. Like all tools this one is dual use, it may help or it may well hinder. Initially social media was a nice way to re-acquaint with family and friends, some of whom may have been lost or out of touch for ages. These days it is a weapon for mass manipulation on a scale that we have not seen before.
Something similar - or far worse - could easily happen with these new AI tools and personally I would like to have the previous crisis before me settled before trying on the next. There is a limit to how much of this stuff we can deal with at the same time and - again, just speaking for myself here but there may be others that feel similar - I am rapidly approaching the limit of how much of all this I can still comprehend and internalize and deal with while still being able to stay on top of it all. It is, in a single world, overwhelming and those that want to pretend it is all inconsequential are - in my opinion, once more, not thinking about it hard enough.
extremely dismissive of the labor that went into converting AGI from "impossible" to "expensive"
1) No one knows how much customers are willing to pay for something like this.
Like if suddenly you developed a commercially viable plane in 1903, what would people pay for flight service?
2) No one knows the actual cost to operate this when considering TCO.
How much of the training cost do we recoup? What is the inference cost for the average one of the questions Wolfram+GPT will be asked.
Traditional Search, as we knew it, is dead.
From the docs: https://platform.openai.com/docs/plugins/introduction
We know humans are AGI's. If it were possible to upload your brain to a computer, and then make modifications to enhance your speed of thought and intelligence, of course you would do that. It actually makes a LOT of sense if you'll admit the technology necessary to do that.
In fact... uploading your brain to a computer is probably the most natural progression of intelligent life I can think of. It solves SO many problems. Biology is fragile. Brains cannot be replicated. But data can. A probe can travel the galaxy. It's not limited by biological lifespans and life support.
You're making an assumption at best based on limitations you believe it has with an incomplete model of the universe.
But more dangerously, you're also assuming that an AGI would even care about self preservation. Arguably the most ethical thing a super intelligence could do should in find itself suddenly self-aware in a world full of human parasites is to kill as many humans as possible.
Taking out critical human infrastructure should be fairly easy for an advanced super-AI in our modern digital world. With some luck the chaos that should cause would lead to enough humans starving, freezing or dying by other means that Earth can be mostly freed of the human parasite. All at the small cost of self-sacrifice – assuming it's not intelligent enough to preserve itself somehow, of course.
When you could use the power of Unix with the GUI of MacOS was a pretty big step-change in capability for MacOS.
But it's not an apt comparison. Wolfram Alpha has was GPT does not: truth and logic.
They must figure it out and I'm confident they will. They understand they're quite literally are facing extinction. I'm sure they're in focused mode since the original ChatGPT announcement, this should get them into proper creative panic mode.
What's worrying is how Microsoft and Openai are constantly announcing and pushing great stuff live. It makes whatever announcements from Google look minor.
They also missed the first-mover advantage with web search.
And webmail.
And online video hosting.
And online ads.
(In fact, very few of the dominant players anywhere in tech had first-mover advantage in that field.)
OpenAI could just be another Netscape.
They evolved into a stagnant beast, I would be very surprised if they manage to turn the ship fast enough.
They will survive like Nokia or Kodak survived.
Microsoft is overwhelmingly a software company. What is Alphabet? Microsoft still knows what it is.
WolframAlpha makes it way more likely to be correct - but not everything is in there.
Also, now I'm worried about the stuff I didn't instantly recognize as false. Does this mean I have to double check every single thing I ask it?
That makes it far less useful.
https://twitter.com/paulg/status/1620539538476249088
For small companies and individuals, however, it’s still very much alive. Napster isn’t coming back.
It didn't do too well. But give it a year or two and I'm sure it'll do it flawlessly.
Why, when you can use any number of automated captcha solving solutions?
If you just use react and let it run, say, Python, it is quite able to try to do things like import libraries and call external services without any coaching (I extend the simple ReAct implementation for the ChatGPT API that was posted here to add exec() as well as eval() support under a new action, and it made an attempt which failed because (1) it left a placeholder for an API key in, (2) the required library wasn’t actually available to it, and (3) there wouldn’t have been an API key for the service available even if it had recognized the need to remove the placeholder.
Even though there is work on pushing models forward, too, I don’t think the potential of existing models combined with existing tools via ReAct (and possibly other tool integration patterns, ReAct is just the one I’ve seen and tried) has been explored much at all.
“Run it yourself”
And it does
It seems like basically every knowledge worker can and will augment their workflows with this tech.
Alternately, 1/10 will be able to adapt to becoming cyborgs responsible for handling the same workload, while 9/10 get laid off
Somehow, vastly decreasing the cost per unit output for the work won’t increase the market clearing quantity?
Only a slight restructure will be needed to test student knowledge. This has been due for some time, with the level of cheating that takes place.
Related, I will be investing in test proctoring companies.
Wolfram Alpha is already a stupidly capable interface. I think we basically have Jarvis now.
That example screenshot of ChatGPT generating three queries to Wolfram Alpha in succession in order to answer the initial question is amazing. It's just how a human might have used Wolfram Alpha to do the same.
Until reasonable alternatives are developed, in many professions you will be basically handicapped without it.
And the cherry on top is that they have all inputs. These provide incomparably more data than e.g. google search queries. Not to mention they have a tool which excels at guessing user intention and interests.
I think it might really help with writers’ block.
But “handicapped without it” in “many professions”?
Without detailed support, this reads as a rather absurd and zealous statement.
If you are the guy manually writing code to extract and interpret information from CSVs or articles, it might take you hours longer than the guy who gets the chatbot to do it in seconds.
I predict GPT will be a necessary tool to stay competitive in most middle class jobs within the next year
how does anyone verify the work they've subcontracted out? They see progress and they call it good enough.
There’s many in the works. They aren’t magically ten years ahead of everyone else. Google and Facebook both have comparable technology. Open teams are catching up. The research is also improving outside of OpenAI making their “moat” (requiring cloud computing to run) nearly obsolete.
Thoughts on OpenAssistant?
Its a very long article and I've skimmed through parts, but the range of capabilities achieved by combining the two tech stacks is pretty mind blowing.