AGI wolf calls have done their job. Anyone who believed CEOs were bearers of wisdom can take a moment, breathe, and guard their wallets until the next time Silicon Valley’s siren songs resurface.
That depends on how you look. The fundamental limit is that no amount of induction (statistics) gets you deduction (symbolic logic), even if you can approximate close. The other way around is true too as we found out 40-20 years ago.
Symbolic logic may be seen as part of our brain's efficiency apparatus, that is, it for instance prevents infinite scaling issues (e.g. the outlier problem, but also others).
The symbolic logic people had it the wrong way around. Stuff like emotions doesn't emerge from large amounts of discrete facts and rules (the problem being that 'large' here in effect is 'infinite'), but discrete facts and rules are created out of the non-discrete, messy, chaotic, stuff below. You can create Q out of R, but not the other way around.
In that sense it may be evolutionary related to our 'conviction' efficiency apparatus, such as convictions that 'incremental work' 'will' inspire new directions 😀. Or convictions that these 'new directions' — when mentioned now — are the equivalent of vapourware.
Which is true. And here you are close to why systems with massive discrete logic (like the bits and operands of digital computers) always have some trouble in messy reality. What holds for that scaling symbolic logic doesn't get you there probably is true to for discrete logic in general. (Just riding a hobby horse 😀).
There seems to be a fundamental gap in understanding how *meaning* is produced. AI systems are incapable of producing meaning, and therefore cannot achieve 'AGI' in any capacity no matter how much training data they are subjected to.
There is no “understanding” either by the AIs OR by the folks developing them (who don’t understand — and don’t seem to even care — precisely why they seem so convincing)
The latter fact leads to all the hyperbolic claims that have no grounding in reality.
It’s all very weird for a field that calls itself “science”
Grounded meaning requires no symbols... that's where the disconnect lies, between reality (natural intelligence) and ALL of AI-to-date including the latest dot-product-based "generative" ['computational', really] fakes.
They don't (have any bearing on intelligence). For the past 70 to 75 years, it's all been about mimicking intelligent behavior. Whether symbol processing systems or ANNs, everyone has been and is still developing complex functions, trying to mimic the input-output behavior of thinking humans. And, not surprisingly, all they have to show for it to date is narrow AI. That's why people like Peter Norvig, who should know better, given their stature in the field, are claiming that AGI is already here: because they don't want to believe that all they've been doing their entire careers is developing complex functions, instead of making inroads toward understanding intelligence. If the AI community (or psychologists or neuroscientists) understood the nature of intelligence, no one would continue to develop systems the way they have been and still are (except perhaps to model some function of the mind-brain) if their intention is to achieve AGI.
But as I said in my previous post, what you describe above is just mimicking intelligent behavior, albeit improved behavior. However, it is no different in principle than AI in the latter part of the 20th century. Just different processes and representations. Basically, history is repeating.
TL;DR: AICYC's Semantic AI Model (SAM) revolutionizes artificial intelligence by combining a multi-lingual knowledge graph with large language models (LLMs), ensuring users receive verifiable, accurate information in response to questions. SAM addresses the shortcomings of traditional AI models by promoting transparency, multilingual access, verifiable knowledge, and continual learning. AICYC provides a secure, decentralized infrastructure governed by the AICYC DAO to protect the privacy and rights of its users, democratizing AI for lifelong learning.
What separates the modeling from the modeled? This: all models of phenomena can be undone, in the stack sense - previous states (eg of fluid flowing) can be reversed merely by setting the model variables to past states, all the way to the start. In contrast, reality can never, ever, ever, be undone - time doesn't flow backwards.
True meaning/understanding would require qualitative experience imo. Meaning for humans is done, at least initially, by mapping symbols to qualia and stringing symbols that are mapped together to produce concepts.
A camera hooked up to ai won’t even do this, as it’s just computing raw digital data.
From the ongoing timeline these A.I guys should be part of WWE hype promos! Just give it to us do not keep telling us. I will not be waiting. The human is superior for me till further notice.
I feel like the whole "AGI" debate is moving into absurdity. So, tech bros get to decide for everyone else with brute force? AGI is not real and has no meaning...hell it can't even be defined in contextualized linguistics/language...I think we need Noam Chomsky right now to bring some intellectual clarity and stop this science fiction nonsense.
You don't need a linguist. You need a philosopher. I recommend Robert Brandom, who wrote regarding rationality: "Rational beings are ones that ought to have reasons for what they do, and ought to act as they have reason to. They are subjects of rational obligations, prohibitions, and permissions." Given this definition it is obvious what the problem is: LLMs lack this normative dimension. They are not capable of binding themselves by what they believe or say in the way that any rational being would.
It looks really cool, if hacky in the way that all deep learning based attempts at performing deductive logic are hacky. Notice that there is an element of deduction involved: AI-generated proposed solution steps are turned into Python code and then run to see if they actually work.
Ultimately, this is still a probabilistic "does this solution resemble solutions from the training?" approach, but done in a principled manner so as to avoid the usual problems in using language models to answer logic questions.
I'd say this is a good example of a well-established phenomenon: the more narrowly an AI is tailored to solving a specific kind of problem, the better it will perform.
GPT-5 should arrive right around the time those "few simple words" refuting Hubert Dreyfus McCarthy promised us back in the late 1960s are finally written and published.
I meant for all those capital expenditures not by the data center buildouts. I meant those corporations who are buying into all this and choosing vendors due to AI investing being requirement by c suite to not fall behind. where is their ROI?
Zoom has already rebranded as an AI-first company just by incorporating a couple AI "assistants" into their software. I highly doubt that it will be a good long-term decision.
What’s with the version numbering anyway? Isn’t it mere marketing to call it GPT 5 instead of GPT 4.7, for example? Sorry if this is a dumb question, I’m not a software developer.
Changes in the transformer architecture, I guess. Turbo was 'long context'. .5 may have been dimensional increases, like token dictionary size, embedding size, parameter volume)
Hi Gary - more and more I find articles written with AI tools - such as this one: https://www.sencha.com/blog/how-ui-components-help-developers-create-scalable-and-user-friendly-web-apps/ . These articles are dry and devoid of the meaning that other articles written with a clear intent by humans have. I was thinking of inventing a term for this type of articles, or maybe it already exists. Do you know? They are like zombies.
Even worse are YouTube videos of which the script is written by a LLM and the voiceover is produced by AI reading the script. The footage consists of cut-and-paste jobs or even AI-generated videos and imagery. Dreadful, dystopian stuff.
AGI will be here when Sam Altman decides it's time to call OpenAI's latest model "AGI". The believers will insist it meets the qualifications, the non-believers will mock it, and the term itself will finally be permitted to show the world its meaninglessness.
Gary, the consummate beat reporter, has put his thumb on the matter. The Eureka! moments of random walk scaling are done. But, as Ilya Sutskever says, now is the time for 'scaling the right thing' - a more deliberative walk. Deliberation has long been fraught, but lessons have been learned in contracting to ease the way forward.
I just wrapped up a small review of research on AI agents and how they fare in high-level roles and the challenges are significant. Even if we get LLMs that are much better task planners there is something about the social level and the ability of LLMs to operate in that space with information asymmetry that seems quite a reach right now - so yeah I would be surprised if AGI showed up this year https://www.agentsdecoded.com/p/research-roundup-can-ai-agents-actually
AGI wolf calls have done their job. Anyone who believed CEOs were bearers of wisdom can take a moment, breathe, and guard their wallets until the next time Silicon Valley’s siren songs resurface.
Little Red Writing Hood:
“Grandma, what big AI’s you have.”
Silicon Valley Wolf: “ The better to fleece you with my dear”
Fleece Navidad
Merry Shipmas
https://futurism.com/the-byte/enron-banker-parallels-openai
Nor will we see it in 2026, 2027, ...
(It's not an incremental issue, it is a fundamental issue. 'Wide' AI will not become'General' AI)
That depends on how you look. The fundamental limit is that no amount of induction (statistics) gets you deduction (symbolic logic), even if you can approximate close. The other way around is true too as we found out 40-20 years ago.
Symbolic logic may be seen as part of our brain's efficiency apparatus, that is, it for instance prevents infinite scaling issues (e.g. the outlier problem, but also others).
The symbolic logic people had it the wrong way around. Stuff like emotions doesn't emerge from large amounts of discrete facts and rules (the problem being that 'large' here in effect is 'infinite'), but discrete facts and rules are created out of the non-discrete, messy, chaotic, stuff below. You can create Q out of R, but not the other way around.
In that sense it may be evolutionary related to our 'conviction' efficiency apparatus, such as convictions that 'incremental work' 'will' inspire new directions 😀. Or convictions that these 'new directions' — when mentioned now — are the equivalent of vapourware.
Which is true. And here you are close to why systems with massive discrete logic (like the bits and operands of digital computers) always have some trouble in messy reality. What holds for that scaling symbolic logic doesn't get you there probably is true to for discrete logic in general. (Just riding a hobby horse 😀).
There seems to be a fundamental gap in understanding how *meaning* is produced. AI systems are incapable of producing meaning, and therefore cannot achieve 'AGI' in any capacity no matter how much training data they are subjected to.
There is no “understanding” either by the AIs OR by the folks developing them (who don’t understand — and don’t seem to even care — precisely why they seem so convincing)
The latter fact leads to all the hyperbolic claims that have no grounding in reality.
It’s all very weird for a field that calls itself “science”
Exactly.
Grounded meaning requires no symbols... that's where the disconnect lies, between reality (natural intelligence) and ALL of AI-to-date including the latest dot-product-based "generative" ['computational', really] fakes.
They don't (have any bearing on intelligence). For the past 70 to 75 years, it's all been about mimicking intelligent behavior. Whether symbol processing systems or ANNs, everyone has been and is still developing complex functions, trying to mimic the input-output behavior of thinking humans. And, not surprisingly, all they have to show for it to date is narrow AI. That's why people like Peter Norvig, who should know better, given their stature in the field, are claiming that AGI is already here: because they don't want to believe that all they've been doing their entire careers is developing complex functions, instead of making inroads toward understanding intelligence. If the AI community (or psychologists or neuroscientists) understood the nature of intelligence, no one would continue to develop systems the way they have been and still are (except perhaps to model some function of the mind-brain) if their intention is to achieve AGI.
But as I said in my previous post, what you describe above is just mimicking intelligent behavior, albeit improved behavior. However, it is no different in principle than AI in the latter part of the 20th century. Just different processes and representations. Basically, history is repeating.
We are back around to symbolic AI.
http://aicyc.org/2024/11/08/revolutionizing-artificial-intelligence-the-essential-role-of-semantic-ai/
TL;DR: AICYC's Semantic AI Model (SAM) revolutionizes artificial intelligence by combining a multi-lingual knowledge graph with large language models (LLMs), ensuring users receive verifiable, accurate information in response to questions. SAM addresses the shortcomings of traditional AI models by promoting transparency, multilingual access, verifiable knowledge, and continual learning. AICYC provides a secure, decentralized infrastructure governed by the AICYC DAO to protect the privacy and rights of its users, democratizing AI for lifelong learning.
It's not about the modeling - it's about conflating the model with the modeled.
What separates the modeling from the modeled? This: all models of phenomena can be undone, in the stack sense - previous states (eg of fluid flowing) can be reversed merely by setting the model variables to past states, all the way to the start. In contrast, reality can never, ever, ever, be undone - time doesn't flow backwards.
True meaning/understanding would require qualitative experience imo. Meaning for humans is done, at least initially, by mapping symbols to qualia and stringing symbols that are mapped together to produce concepts.
A camera hooked up to ai won’t even do this, as it’s just computing raw digital data.
From the ongoing timeline these A.I guys should be part of WWE hype promos! Just give it to us do not keep telling us. I will not be waiting. The human is superior for me till further notice.
I feel like the whole "AGI" debate is moving into absurdity. So, tech bros get to decide for everyone else with brute force? AGI is not real and has no meaning...hell it can't even be defined in contextualized linguistics/language...I think we need Noam Chomsky right now to bring some intellectual clarity and stop this science fiction nonsense.
You don't need a linguist. You need a philosopher. I recommend Robert Brandom, who wrote regarding rationality: "Rational beings are ones that ought to have reasons for what they do, and ought to act as they have reason to. They are subjects of rational obligations, prohibitions, and permissions." Given this definition it is obvious what the problem is: LLMs lack this normative dimension. They are not capable of binding themselves by what they believe or say in the way that any rational being would.
So what do you think about things like this? https://huggingface.co/papers/2501.04519
Looks overwhelming and a little scary. Do you think it's real or they just overtrained on that exact benchmark?
It looks really cool, if hacky in the way that all deep learning based attempts at performing deductive logic are hacky. Notice that there is an element of deduction involved: AI-generated proposed solution steps are turned into Python code and then run to see if they actually work.
Ultimately, this is still a probabilistic "does this solution resemble solutions from the training?" approach, but done in a principled manner so as to avoid the usual problems in using language models to answer logic questions.
I'd say this is a good example of a well-established phenomenon: the more narrowly an AI is tailored to solving a specific kind of problem, the better it will perform.
GPT-5 should arrive right around the time those "few simple words" refuting Hubert Dreyfus McCarthy promised us back in the late 1960s are finally written and published.
And what is the ROI for all this investment?
I meant for all those capital expenditures not by the data center buildouts. I meant those corporations who are buying into all this and choosing vendors due to AI investing being requirement by c suite to not fall behind. where is their ROI?
I have been a manager at Adobe and a Masa Softbank co. You are incorrect. They will and have.
Zoom has already rebranded as an AI-first company just by incorporating a couple AI "assistants" into their software. I highly doubt that it will be a good long-term decision.
What’s with the version numbering anyway? Isn’t it mere marketing to call it GPT 5 instead of GPT 4.7, for example? Sorry if this is a dumb question, I’m not a software developer.
Changes in the transformer architecture, I guess. Turbo was 'long context'. .5 may have been dimensional increases, like token dictionary size, embedding size, parameter volume)
The “.5” means it’s half-baked.
Hi Gary - more and more I find articles written with AI tools - such as this one: https://www.sencha.com/blog/how-ui-components-help-developers-create-scalable-and-user-friendly-web-apps/ . These articles are dry and devoid of the meaning that other articles written with a clear intent by humans have. I was thinking of inventing a term for this type of articles, or maybe it already exists. Do you know? They are like zombies.
Even worse are YouTube videos of which the script is written by a LLM and the voiceover is produced by AI reading the script. The footage consists of cut-and-paste jobs or even AI-generated videos and imagery. Dreadful, dystopian stuff.
AGI will be here when Sam Altman decides it's time to call OpenAI's latest model "AGI". The believers will insist it meets the qualifications, the non-believers will mock it, and the term itself will finally be permitted to show the world its meaninglessness.
Gary, the consummate beat reporter, has put his thumb on the matter. The Eureka! moments of random walk scaling are done. But, as Ilya Sutskever says, now is the time for 'scaling the right thing' - a more deliberative walk. Deliberation has long been fraught, but lessons have been learned in contracting to ease the way forward.
"Gary Marcus wishes the media would hold those who make unrealistic promises to account. Because they *rarely do...." right?
I just wrapped up a small review of research on AI agents and how they fare in high-level roles and the challenges are significant. Even if we get LLMs that are much better task planners there is something about the social level and the ability of LLMs to operate in that space with information asymmetry that seems quite a reach right now - so yeah I would be surprised if AGI showed up this year https://www.agentsdecoded.com/p/research-roundup-can-ai-agents-actually
Any thoughts about the impact that Titans (from Google) could have?
https://arxiv.org/abs/2501.00663
GPT-5 is more of a cryptid than a product.