Moreover, this looks like it is going to be happening sooner rather than later.
As long as it comes across some reasoning process that have not been seen before in the training wordset, which can be as easy as a middle school math question, it fails. Because it has no ability to extrapolate logic.
If it manages to pass Bar test, that says more about the Bar test than it says about GPT.
During the industrialization, machines did not replace all jobs, but they replaced or changed most jobs. The same will happen here.
A typical office job will have a few hours a week of actual, intensive thought. The vast majority of time will be spent doing simple, repetitive work. This work can be automated, or at least significantly sped up, using technology like GPT.
“write an API client for …”, “integrate APIs … and …” can easily be automated. Yes, you'll still have to write the business logic, but that's not the majority of your work today. You could even have it write unit tests based on the JIRA ticket description.
The same applies to many other jobs.
This is a wonderful point: writing unit tests is exactly the kind of mind-numbing tedium that I'm super excited to automate away.
Like the point above; that says more about the work.
It’s going to be really interesting how the middle-class narrative pushes back on AI revealing how little work is actually done during office hours.
GPT can be of use there, as long as you're working with languages that use strict static types and have proper tests, it's easy to automate and ensure there are no mistakes.
* Understanding complex language does not require logic/reasoning,
* There are infinitely many forms of logic/reasoning or at least more than those existing in a vast training set.
Neither of which is likely true.
What do you think of the Minerva system, which can solve multi-step quantitative reasoning questions better than many competent students and most adults?
https://ai.googleblog.com/2022/06/minerva-solving-quantitati...
Note: If you look at LSAT test samples, many questions are tests of complex logical reasoning, a requisite for legal professions.
I also really strongly disagree that it’s basically doing some sort of information retrieval design where based on language it regurgitates some sort of markov expectations. You can ask it to do very complex translations of a concept from one domain to another and expressed in a form that’s certainly never been done before and it does it with alacrity. At the very minimum it “remembers” things from the past in the conversation and can associate the semantic ideas across prompts and synthesize cogent responses - that in itself implies it has some semantic “understanding” of the structure of the language. That is a huge missing piece in our tool kit to date.
Frankly I feel these threads expose just how jaded and unable to dream we have become, that even when a wonder walks up and hits you in the nose we can’t even see it.
There are no magic, it is just a more complicated transpose, created by training over perhaps 10% of all available text on the internet.
It does have a lot of use, for one I think it would probably put grammarly out of business, and maybe even do some work for law firms.
The key is understanding. It does not need to, it has already seen the question asked in a 100 different ways, it also seen the answer to all of those. It just rephrases those answer via a neural network and that happen to pass the bar test.
> There are infinitely many forms of logic/reasoning or at least more than those existing in a vast training set.
More importantly, differences between forms are subtle and cannot be understood, that's why ChatGPT confidently give wrong answers on stackoverflow: https://meta.stackoverflow.com/questions/421831/temporary-po...
Src: scored 99.8th percentile on LSAT, tutored it, now working at major law firm
The radiology AIs are technically more accurate than radiologists on any sufficiently large dataset, and yet they still have not replaced radiologists (or even are anywhere close to).
Humans are bad at reciting things, but a lot better (compared to GPT) at reasoning.
Is this true even if you tell it to show its working? In my experience that drastically improves its ability to do math problems.
Which means whatever apparent logic you're getting out of it is from text that it has learned. not reasoning embedded within those text, but the actual text itself.
Answer: To solve this equation, we can use the quadratic formula:
x = (-b +/- sqrt(b^2 - 4ac)) / (2a)
Plugging in the values for a, b, and c, we get:
x = (-11 +/- sqrt(11^2 - 4130)) / (2*1)
x = (-11 +/- sqrt(121 - 120)) / 2
x = (-11 +/- sqrt(1)) / 2
x = (-11 +/- 1) / 2
x = -5 or x = -6
So the solutions to the equation are x = -5 and x = -6.
Gun to my head where I had to put money down, I would put it on "Brains are not nearly as special as we (they?) think they are." No fairy dust or supernatural beings required, brains are just another AI model (and likely not even a particularly great one).
An AI that survives that long surely has to be great. Probably you meant that human brains are not made for the world of today.
Yes, it doesn't have reasoning ability, but being able to manage knowledge and information in the way that these models can is still an amazing feat.
It doesn’t matter to me if they have “reasoning” capabilities or not if the outcome is the same.
I think we are a long ways off from AGI still.
Which tasks? The output of some crafter with a limited number of modules, and of somebody who can assess the output, cannot be the same - unless you would have accepted the output of a mentally crippled entity in the first place.
He says it's still years away. His interview with Lex Fridman[0] was pretty tame - I didn't learn much new from it. Kurzweil deflected the Singularity segment to be a discussion about the history of computer power.
Remember that Kurzweil is Director of Engineering[1] at Google, with the mandate to "bring natural language understanding to Google"[2]. He started there in 2012, just after publishing his book, "How to Create a Mind"[3], and that's exactly what he and his team have been doing for ten years. Publication of his new book, "The Singularity is Nearer"[4] is now pushed out to mid 2023. Maybe he'll change the title to "Here" by then. (It's hard to believe that OpenAI is actually ahead of Google.)
Fridman made the point that maybe we won't realize at the time that the Singularity is passing, and only understand later that it did. Kurzweil didn't disagree.
[0] https://www.youtube.com/watch?v=ykY69lSpDdo
[2] https://en.wikipedia.org/wiki/Ray_Kurzweil
[3] https://www.amazon.com/How-Create-Mind-Thought-Revealed-eboo...
[4] https://www.amazon.com/s?k=kurzweil+singularity+is+nearer
Are Google's LLMs available for us to test out? From what I've gleaned, they've locked them up - I'd love to compare GPT vs Google's LLMs.
However we know they have been working on AI longer than OpenAI, with better datasets than anyone, with top shelf talent, essentially infinite funding, custom hardware, and what we do see publicly is incredible.
It's a pretty safe bet that Google is ahead of the pack, perhaps even with some distance, but it's not yet clear what they intend to do long term with their projects. What is clear is that they don't want or need the public playing with it.
By this I don't mean an AI as in the story acting by itself with its own motivations, I'm only talking about the subversion of established verification & communication methods used by it by humans with malicious purposes.
Essentially, if you do anything security related, we might only be O(months) away from you needing to stop using basically any electronic communication for your purposes. Companies can't have online meetings anymore in which decisions are made, everything will have to be more analog, more in-person.
Look at the kind of access the Russian comedians Vovan & Lexus [1] have gotten. Without advanced AI, just a little social engineering, they got heads of state on the phone. Now combine this with the kind of text/audio/video synthesis we're not too far away from, and you have an absolute recipe for disaster ...
Now that's been reduced to pointing out minor flaws that the next generation of AI artists will trivially resolve, and sharing memes beseeching other humans to participate in a boycott.
There's real pain and angst there, and I don't want to be callous about it with a comparison to buggy-whip manufacturers or something. But I wish the participants in these types of discussions were able to zoom out a bit and see that there's a larger societal issue here around automation, and that the real solution is going to be rethinking the basic economics of how we distribute wealth in a time of extraordinary machine-driven productivity— productivity that is no longer just about assembly lines and primary industries, but now also includes an increasing bite out of realms previously classified as "knowledge work".
Programming was an outlet, if not a gold rush, for many people as the basic technical skills to create Software with the already sophisticated tooling available today presented an economic opportunity, but if "describe your problem, get crappy app" becomes viable, it may squeeze the market for junior developers.
For as long as it has existed, Software has been subject to the Jevons Paradox [1], and every advancement in making its development cheaper and its supply more abundant has only made it so more activities become powered by Software and Software developers, but it's hard to tell how this will impact the job market, especially if Software was absorbing people who didn't find more opportunities in the broader service sector.
Basically it's the same story as everywhere else, where technological augmentation has already created a huge squeeze, and now suddenly even the senior people are wondering if the writing is on the wall for them too.
Inability to recognize intelligence is and will be devastating.
It's a pop-culture quote from a movie that was no masterpiece, I know, but "I, Robot" presented in two sentences an argument for having more sober expectations on what machine intelligence could be capable of, and of our own
> Detective Del Spooner: "Can a robot write a symphony? Can a robot turn a… canvas into a beautiful masterpiece?"
> Sonny: "Can you?"
We're discrediting the capabilities of current machine learning models for being unable of producing the thoughts that many, many people are unable to either.
Alright, so the models are not at the level that us HN philosopher kings hold ourselves to be, and they won't be Senior Architects of distributed systems or what have you very soon, but what does it say about Average Joe, slightly-above-Average Joe, and their economic prospects? Specially since in the West and much of the developing world, we were taking solace in the idea that a service economy comprised of knowledge workers would provide plenty of opportunities on a political and economic landscape where manufacture was gone, or had never arrived.
-- Pseudo Sonny: "Can you?"
-- Pseudo Detective Del Spooner: "Ha-ha. So what the #!@! is a robot doing there, not doing what is required? I cannot, and I do not stand there clueless"
What is being engineered, toys for the satisfaction of some idle decadent sympathy urge? Have cats disappeared from the world?
> We're discrediting
We are shocked that an overly large number of individuals expect stones to bleed, and intelligence to pour out of machines that do not have intelligence coded inside, and that instead have unintelligence - acritical repetition - coded inside.
> what does it say about Average Joe
That he should catch up with his nature, if he shows the critical capacities of a simulacrum that has none.
> not at the level
No, no, no: it is not a matter of quantity but of quality: if you do not implement it or its origin, it will not be there.
> [Asimov]
Asimov is relevant. For example, I remember his idea that the State comes from Agriculture (~10000 BC), in the need to plan irrigation, or that the Abel vs Cain story could be a parallel of the political consequences of lands denied to pastors. Now: those seem to be good ideas, and their production can be an interesting goal. But there is something /before/ "creativity", or "advanced pattern recognition": it is /intelligence/, meaning that Asimov, after having spawned those hypoteses, has /vetted/ them as a required duly activity before confirming them in his set of founded hypotheses. You have to use intelligence, you have to have intelligence, and if you want to do AGI, you have to implement intelligence!!!
If we're setting the bar of personhood or dignity to being exceptional researchers and engineers, it doesn't bode well for the masses that aren't and won't be. Maybe this will result in a society of leisure where everyone can be that! I wouldn't bet on it, there's already more PhDs in the sciences and humanities than society can fit, and humans may just not work that way.
You're already dismissing concerns about the welfare of the merely average, for being unfit when competing with the Machine Learning models we may have in the near future.
This writer individually: no, not literally «dismissing», it is just that I could not grasp precisely your point in this specific area. And I would say, as I wrote just earlier, «Inability to recognize intelligence is and will be devastating»: it already happens that an inability to discriminate ("It takes it to see it") will hide from the sight to some manager the critical risks that the underdeveloped sense of some workers will pose, and such risk will increase when they will have to compete with even riskier and less endowed entities that may be confused for acceptable - since this is what has been showing even here in the past times.
This issue comes from a devaluation of actual intelligence.
> If we're setting the bar of personhood or dignity to being exceptional researchers and engineers
Not really. Look, a few weeks ago this HN member had some heavy exchange with others to which it was said "there is no intelligence if there is no critical thinking", and some arrived to call that position "delirious". Now a rebuttal would have been, "Ask your grandmother". Because there is a "high culture", that of the Professor and the Professional, and "low culture", that of the Teacher and of the Relative, it does not take the former to have good judgement - the latter suffices plentifully, when not polluted.
So, you do not need to have the bar set to «exceptional researchers and engineers» - just a good grandmother. Who could have been an «exceptional researchers and engineer», in case, if life so determined - because "the requirements were there", available.
the point was very definitely not about «hav[ing] been denied opportunities», the idea of the relevance of a «gender» and gender issues is completely only thrown in by the reader, that of the «1950s» confirms misunderstanding because the point was not localized in space and time:
I very literally stated that "you can set the bar to" «just a good grandmother». The reference to «exceptional researchers and engineers» related to the grandmother was just that "you do not need a Professor, but something that has the basic requirements - good sense, intelligence - to become one in case, suffices.
It is not the bar "«of personhood or dignity»" as the poster originally proposed: it is the bar to be a proper social actor. And it is a requirement that has always been there, and which today is in the highlight, given that some are advancing the idea that a pseudo-parrot may suffice.
"Good sense" should better return as a definite Value.
Who created ours?
And if it's god (which god?), who created theirs?
If you want to implement it directly, good;
if you want to implement what will spawn it, good;
if you want to implement an "[evolutionary] genetic algorithm" as said spawner - so that the population of the entities in need to find solutions in the solution space will progressively develop a model of said world and a logic that works in it -, good.
If you built a mannequin and wanted to call it a woman... Bad.
I genuinely don't know if anyone knows the answer to that.
But I do know the perceptron is a toy model of an organic neuron, and that deep learning is a toy model of larger structures such as a cortical column.
And I do know some AI (not sure about GPT in particular) are trained via genetic algorithms.
You appear to be awfully confident we haven't implemented intelligence, even by the standard stated in your reply.
We already did, it is called "Case Based Reasoning" within Decision Support Systems.
And that is obvious, if you ask one of these models, a meta question like for example: "If a person says I am lying, are they lying or saying the truth?"
You will see these models will spit a canned elegant response, talking how a question could possibly be true or false, some persons not being able to attest if another one is truthful or not...But no mention of the Liar Paradox.
So we are not yet ready to say: "Your Honor its not fair! My Lawyer is version 2.2.3 with SP1 while the Prosecution is version 4.0 with an additional Cloud Based Elastic Inference! "
ChatGPT's response to me asking "If a person says I am lying, are they lying or saying the truth?"
I tried several times as I have been investigating sending some of the "meta" questions to these models. I just tried again and this is what I got:
"If a person says "I am lying," it could mean either that they are telling the truth about the fact that they are lying, or it could be a lie itself. It is important to consider the context in which the statement was made and to try to assess the credibility of the person making the statement. It is also important to remember that people may not always be honest or sincere, and it is always a good idea to verify information before accepting it as true."
I could never make it mention the Liar Paradox. Is that all that you sent?