Generative AI's most prominent skeptic doubles down
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Vancouver (AFP) – Two and a half years since ChatGPT rocked the world, scientist and writer Gary Marcus still remains generative artificial intelligence's great skeptic, playing a counter-narrative to Silicon Valley's AI true believers.
Marcus became a prominent figure of the AI revolution in 2023, when he sat beside OpenAI chief Sam Altman at a Senate hearing in Washington as both men urged politicians to take the technology seriously and consider regulation.
Much has changed since then. Altman has abandoned his calls for caution, instead teaming up with Japan's SoftBank and funds in the Middle East to propel his company to sky-high valuations as he tries to make ChatGPT the next era-defining tech behemoth.
"Sam's not getting money anymore from the Silicon Valley establishment," and his seeking funding from abroad is a sign of "desperation," Marcus told AFP on the sidelines of the Web Summit in Vancouver, Canada.
Marcus's criticism centers on a fundamental belief: generative AI, the predictive technology that churns out seemingly human-level content, is simply too flawed to be transformative.
The large language models (LLMs) that power these capabilities are inherently broken, he argues, and will never deliver on Silicon Valley's grand promises.
"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there. And I think the companies running it are not mostly the best people in the world."
His skepticism stands in stark contrast to the prevailing mood at the Web Summit, where most conversations among 15,000 attendees focused on generative AI's seemingly infinite promise.
Many believe humanity stands on the cusp of achieving super intelligence or artificial general intelligence (AGI) technology that could match and even surpass human capability.
That optimism has driven OpenAI's valuation to $300 billion, unprecedented levels for a startup, with billionaire Elon Musk's xAI racing to keep pace.
Yet for all the hype, the practical gains remain limited.
The technology excels mainly at coding assistance for programmers and text generation for office work. AI-created images, while often entertaining, serve primarily as memes or deepfakes, offering little obvious benefit to society or business.
Marcus, a longtime New York University professor, champions a fundamentally different approach to building AI -- one he believes might actually achieve human-level intelligence in ways that current generative AI never will.
"One consequence of going all-in on LLMs is that any alternative approach that might be better gets starved out," he explained.
This tunnel vision will "cause a delay in getting to AI that can help us beyond just coding -- a waste of resources."
Instead, Marcus advocates for neurosymbolic AI, an approach that attempts to rebuild human logic artificially rather than simply training computer models on vast datasets, as is done with ChatGPT and similar products like Google's Gemini or Anthropic's Claude.
He dismisses fears that generative AI will eliminate white-collar jobs, citing a simple reality: "There are too many white-collar jobs where getting the right answer actually matters."
This points to AI's most persistent problem: hallucinations, the technology's well-documented tendency to produce confident-sounding mistakes.
Even AI's strongest advocates acknowledge this flaw may be impossible to eliminate.
Marcus recalls a telling exchange from 2023 with LinkedIn founder Reid Hoffman, a Silicon Valley heavyweight: "He bet me any amount of money that hallucinations would go away in three months. I offered him $100,000 and he wouldn't take the bet."
Looking ahead, Marcus warns of a darker consequence once investors realize generative AI's limitations. Companies like OpenAI will inevitably monetize their most valuable asset: user data.
"The people who put in all this money will want their returns, and I think that's leading them toward surveillance," he said, pointing to Orwellian risks for society.
"They have all this private data, so they can sell that as a consolation prize."
Marcus acknowledges that generative AI will find useful applications in areas where occasional errors don't matter much.
"They're very useful for auto-complete on steroids: coding, brainstorming, and stuff like that," he said.
"But nobody's going to make much money off it because they're expensive to run, and everybody has the same product."
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Vancouver (AFP) – Two and a half years since ChatGPT rocked the world, scientist and writer Gary Marcus still remains generative artificial intelligence's great skeptic, playing a counter-narrative to Silicon Valley's AI true believers.
Marcus became a prominent figure of the AI revolution in 2023, when he sat beside OpenAI chief Sam Altman at a Senate hearing in Washington as both men urged politicians to take the technology seriously and consider regulation.
Much has changed since then. Altman has abandoned his calls for caution, instead teaming up with Japan's SoftBank and funds in the Middle East to propel his company to sky-high valuations as he tries to make ChatGPT the next era-defining tech behemoth.
"Sam's not getting money anymore from the Silicon Valley establishment," and his seeking funding from abroad is a sign of "desperation," Marcus told AFP on the sidelines of the Web Summit in Vancouver, Canada.
Marcus's criticism centers on a fundamental belief: generative AI, the predictive technology that churns out seemingly human-level content, is simply too flawed to be transformative.
The large language models (LLMs) that power these capabilities are inherently broken, he argues, and will never deliver on Silicon Valley's grand promises.
"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there. And I think the companies running it are not mostly the best people in the world."
His skepticism stands in stark contrast to the prevailing mood at the Web Summit, where most conversations among 15,000 attendees focused on generative AI's seemingly infinite promise.
Many believe humanity stands on the cusp of achieving super intelligence or artificial general intelligence (AGI) technology that could match and even surpass human capability.
That optimism has driven OpenAI's valuation to $300 billion, unprecedented levels for a startup, with billionaire Elon Musk's xAI racing to keep pace.
Yet for all the hype, the practical gains remain limited.
The technology excels mainly at coding assistance for programmers and text generation for office work. AI-created images, while often entertaining, serve primarily as memes or deepfakes, offering little obvious benefit to society or business.
Marcus, a longtime New York University professor, champions a fundamentally different approach to building AI -- one he believes might actually achieve human-level intelligence in ways that current generative AI never will.
"One consequence of going all-in on LLMs is that any alternative approach that might be better gets starved out," he explained.
This tunnel vision will "cause a delay in getting to AI that can help us beyond just coding -- a waste of resources."
Instead, Marcus advocates for neurosymbolic AI, an approach that attempts to rebuild human logic artificially rather than simply training computer models on vast datasets, as is done with ChatGPT and similar products like Google's Gemini or Anthropic's Claude.
He dismisses fears that generative AI will eliminate white-collar jobs, citing a simple reality: "There are too many white-collar jobs where getting the right answer actually matters."
This points to AI's most persistent problem: hallucinations, the technology's well-documented tendency to produce confident-sounding mistakes.
Even AI's strongest advocates acknowledge this flaw may be impossible to eliminate.
Marcus recalls a telling exchange from 2023 with LinkedIn founder Reid Hoffman, a Silicon Valley heavyweight: "He bet me any amount of money that hallucinations would go away in three months. I offered him $100,000 and he wouldn't take the bet."
Looking ahead, Marcus warns of a darker consequence once investors realize generative AI's limitations. Companies like OpenAI will inevitably monetize their most valuable asset: user data.
"The people who put in all this money will want their returns, and I think that's leading them toward surveillance," he said, pointing to Orwellian risks for society.
"They have all this private data, so they can sell that as a consolation prize."
Marcus acknowledges that generative AI will find useful applications in areas where occasional errors don't matter much.
"They're very useful for auto-complete on steroids: coding, brainstorming, and stuff like that," he said.
"But nobody's going to make much money off it because they're expensive to run, and everybody has the same product."
"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there.
Ehhh. I don't know if I'd go quite that far. I think that LLMs might be a component of a functional AGI. But just running a larger LLM model on bigger hardware is not going to suddenly barf out something that can act in the same sort of general way a human can, I agree there.
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"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there.
Ehhh. I don't know if I'd go quite that far. I think that LLMs might be a component of a functional AGI. But just running a larger LLM model on bigger hardware is not going to suddenly barf out something that can act in the same sort of general way a human can, I agree there.
If AGI is made of components, you could argue that it isn't "general". Which would be fine, as most psychologists would say the same about human intelligence
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Vancouver (AFP) – Two and a half years since ChatGPT rocked the world, scientist and writer Gary Marcus still remains generative artificial intelligence's great skeptic, playing a counter-narrative to Silicon Valley's AI true believers.
Marcus became a prominent figure of the AI revolution in 2023, when he sat beside OpenAI chief Sam Altman at a Senate hearing in Washington as both men urged politicians to take the technology seriously and consider regulation.
Much has changed since then. Altman has abandoned his calls for caution, instead teaming up with Japan's SoftBank and funds in the Middle East to propel his company to sky-high valuations as he tries to make ChatGPT the next era-defining tech behemoth.
"Sam's not getting money anymore from the Silicon Valley establishment," and his seeking funding from abroad is a sign of "desperation," Marcus told AFP on the sidelines of the Web Summit in Vancouver, Canada.
Marcus's criticism centers on a fundamental belief: generative AI, the predictive technology that churns out seemingly human-level content, is simply too flawed to be transformative.
The large language models (LLMs) that power these capabilities are inherently broken, he argues, and will never deliver on Silicon Valley's grand promises.
"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there. And I think the companies running it are not mostly the best people in the world."
His skepticism stands in stark contrast to the prevailing mood at the Web Summit, where most conversations among 15,000 attendees focused on generative AI's seemingly infinite promise.
Many believe humanity stands on the cusp of achieving super intelligence or artificial general intelligence (AGI) technology that could match and even surpass human capability.
That optimism has driven OpenAI's valuation to $300 billion, unprecedented levels for a startup, with billionaire Elon Musk's xAI racing to keep pace.
Yet for all the hype, the practical gains remain limited.
The technology excels mainly at coding assistance for programmers and text generation for office work. AI-created images, while often entertaining, serve primarily as memes or deepfakes, offering little obvious benefit to society or business.
Marcus, a longtime New York University professor, champions a fundamentally different approach to building AI -- one he believes might actually achieve human-level intelligence in ways that current generative AI never will.
"One consequence of going all-in on LLMs is that any alternative approach that might be better gets starved out," he explained.
This tunnel vision will "cause a delay in getting to AI that can help us beyond just coding -- a waste of resources."
Instead, Marcus advocates for neurosymbolic AI, an approach that attempts to rebuild human logic artificially rather than simply training computer models on vast datasets, as is done with ChatGPT and similar products like Google's Gemini or Anthropic's Claude.
He dismisses fears that generative AI will eliminate white-collar jobs, citing a simple reality: "There are too many white-collar jobs where getting the right answer actually matters."
This points to AI's most persistent problem: hallucinations, the technology's well-documented tendency to produce confident-sounding mistakes.
Even AI's strongest advocates acknowledge this flaw may be impossible to eliminate.
Marcus recalls a telling exchange from 2023 with LinkedIn founder Reid Hoffman, a Silicon Valley heavyweight: "He bet me any amount of money that hallucinations would go away in three months. I offered him $100,000 and he wouldn't take the bet."
Looking ahead, Marcus warns of a darker consequence once investors realize generative AI's limitations. Companies like OpenAI will inevitably monetize their most valuable asset: user data.
"The people who put in all this money will want their returns, and I think that's leading them toward surveillance," he said, pointing to Orwellian risks for society.
"They have all this private data, so they can sell that as a consolation prize."
Marcus acknowledges that generative AI will find useful applications in areas where occasional errors don't matter much.
"They're very useful for auto-complete on steroids: coding, brainstorming, and stuff like that," he said.
"But nobody's going to make much money off it because they're expensive to run, and everybody has the same product."
I'm a fan of AI, but I still think this guy is right as far as investment and hype goes. It's a useful tool. It cannot do all the things they are promising well. Both can be true.
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"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there.
Ehhh. I don't know if I'd go quite that far. I think that LLMs might be a component of a functional AGI. But just running a larger LLM model on bigger hardware is not going to suddenly barf out something that can act in the same sort of general way a human can, I agree there.
AGI is artificial GENERAL intelligence,
The subject of the article is GENERATIVE AI or GAI.GAI: is a subfield of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.
(AGI)—sometimes called human‑level intelligence AI—is a type of artificial intelligence that would match or surpass human capabilities across virtually all cognitive tasks
-
Vancouver (AFP) – Two and a half years since ChatGPT rocked the world, scientist and writer Gary Marcus still remains generative artificial intelligence's great skeptic, playing a counter-narrative to Silicon Valley's AI true believers.
Marcus became a prominent figure of the AI revolution in 2023, when he sat beside OpenAI chief Sam Altman at a Senate hearing in Washington as both men urged politicians to take the technology seriously and consider regulation.
Much has changed since then. Altman has abandoned his calls for caution, instead teaming up with Japan's SoftBank and funds in the Middle East to propel his company to sky-high valuations as he tries to make ChatGPT the next era-defining tech behemoth.
"Sam's not getting money anymore from the Silicon Valley establishment," and his seeking funding from abroad is a sign of "desperation," Marcus told AFP on the sidelines of the Web Summit in Vancouver, Canada.
Marcus's criticism centers on a fundamental belief: generative AI, the predictive technology that churns out seemingly human-level content, is simply too flawed to be transformative.
The large language models (LLMs) that power these capabilities are inherently broken, he argues, and will never deliver on Silicon Valley's grand promises.
"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there. And I think the companies running it are not mostly the best people in the world."
His skepticism stands in stark contrast to the prevailing mood at the Web Summit, where most conversations among 15,000 attendees focused on generative AI's seemingly infinite promise.
Many believe humanity stands on the cusp of achieving super intelligence or artificial general intelligence (AGI) technology that could match and even surpass human capability.
That optimism has driven OpenAI's valuation to $300 billion, unprecedented levels for a startup, with billionaire Elon Musk's xAI racing to keep pace.
Yet for all the hype, the practical gains remain limited.
The technology excels mainly at coding assistance for programmers and text generation for office work. AI-created images, while often entertaining, serve primarily as memes or deepfakes, offering little obvious benefit to society or business.
Marcus, a longtime New York University professor, champions a fundamentally different approach to building AI -- one he believes might actually achieve human-level intelligence in ways that current generative AI never will.
"One consequence of going all-in on LLMs is that any alternative approach that might be better gets starved out," he explained.
This tunnel vision will "cause a delay in getting to AI that can help us beyond just coding -- a waste of resources."
Instead, Marcus advocates for neurosymbolic AI, an approach that attempts to rebuild human logic artificially rather than simply training computer models on vast datasets, as is done with ChatGPT and similar products like Google's Gemini or Anthropic's Claude.
He dismisses fears that generative AI will eliminate white-collar jobs, citing a simple reality: "There are too many white-collar jobs where getting the right answer actually matters."
This points to AI's most persistent problem: hallucinations, the technology's well-documented tendency to produce confident-sounding mistakes.
Even AI's strongest advocates acknowledge this flaw may be impossible to eliminate.
Marcus recalls a telling exchange from 2023 with LinkedIn founder Reid Hoffman, a Silicon Valley heavyweight: "He bet me any amount of money that hallucinations would go away in three months. I offered him $100,000 and he wouldn't take the bet."
Looking ahead, Marcus warns of a darker consequence once investors realize generative AI's limitations. Companies like OpenAI will inevitably monetize their most valuable asset: user data.
"The people who put in all this money will want their returns, and I think that's leading them toward surveillance," he said, pointing to Orwellian risks for society.
"They have all this private data, so they can sell that as a consolation prize."
Marcus acknowledges that generative AI will find useful applications in areas where occasional errors don't matter much.
"They're very useful for auto-complete on steroids: coding, brainstorming, and stuff like that," he said.
"But nobody's going to make much money off it because they're expensive to run, and everybody has the same product."
Yet for all the hype, the practical gains remain limited.
. . . He dismisses fears that generative AI will eliminate white-collar jobs, citing a simple reality: "There are too many white-collar jobs where getting the right answer actually matters."
This. Plus everyone’s selling the same thing and none of it can be sold at a profit.
It’s just like watching people use lots of feathers on sticks to try and fly. Flight will probably be possible someday, but not like that, and if you idiots are going all in on feather-sticks now, you’re horrible businesspeople.
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If AGI is made of components, you could argue that it isn't "general". Which would be fine, as most psychologists would say the same about human intelligence
If AGI is made of components, you could argue that it isn’t “general”.
Can you elaborate? I don't get what you mean by this.
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If AGI is made of components, you could argue that it isn’t “general”.
Can you elaborate? I don't get what you mean by this.
The idea of general intelligence (g) is that you've got some overall capacity that can be turned to any task. Human intelligence is probably much more like a big toolbox of skills, though, and I can't see a version of computer "intelligence" that's any different to that. I worry that people who get excited about AI are kidding themselves a bit as it isn't going to be general - it's going to be a toolbox at best: an LLM for writing, a totally different system for drawing, another for identifying birdsong, and and yet another for maths... And at that point you've not got some special interesting AGI - you've just reinvented the idea of apps
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AGI is artificial GENERAL intelligence,
The subject of the article is GENERATIVE AI or GAI.GAI: is a subfield of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.
(AGI)—sometimes called human‑level intelligence AI—is a type of artificial intelligence that would match or surpass human capabilities across virtually all cognitive tasks
The article literally mentioned AGI, which is why he commented about the relationship between LLM and AGI. I agree with him, LLM by itself will never be an AGI, but it can definitely be a part of it, especially if it takes the role similar to the human language centre.
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If AGI is made of components, you could argue that it isn't "general". Which would be fine, as most psychologists would say the same about human intelligence
Isn’t human intelligence exactly what most people mean by "general intelligence"? It becomes ASI (Artificial Superintelligence) once its capabilities surpass those of humans - which I’d argue would happen almost immediately.
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The idea of general intelligence (g) is that you've got some overall capacity that can be turned to any task. Human intelligence is probably much more like a big toolbox of skills, though, and I can't see a version of computer "intelligence" that's any different to that. I worry that people who get excited about AI are kidding themselves a bit as it isn't going to be general - it's going to be a toolbox at best: an LLM for writing, a totally different system for drawing, another for identifying birdsong, and and yet another for maths... And at that point you've not got some special interesting AGI - you've just reinvented the idea of apps
I think you've misunderstood what AGI means.
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If AGI is made of components, you could argue that it isn't "general". Which would be fine, as most psychologists would say the same about human intelligence
The human brain is made of components, so why not an AI brain?
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Vancouver (AFP) – Two and a half years since ChatGPT rocked the world, scientist and writer Gary Marcus still remains generative artificial intelligence's great skeptic, playing a counter-narrative to Silicon Valley's AI true believers.
Marcus became a prominent figure of the AI revolution in 2023, when he sat beside OpenAI chief Sam Altman at a Senate hearing in Washington as both men urged politicians to take the technology seriously and consider regulation.
Much has changed since then. Altman has abandoned his calls for caution, instead teaming up with Japan's SoftBank and funds in the Middle East to propel his company to sky-high valuations as he tries to make ChatGPT the next era-defining tech behemoth.
"Sam's not getting money anymore from the Silicon Valley establishment," and his seeking funding from abroad is a sign of "desperation," Marcus told AFP on the sidelines of the Web Summit in Vancouver, Canada.
Marcus's criticism centers on a fundamental belief: generative AI, the predictive technology that churns out seemingly human-level content, is simply too flawed to be transformative.
The large language models (LLMs) that power these capabilities are inherently broken, he argues, and will never deliver on Silicon Valley's grand promises.
"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there. And I think the companies running it are not mostly the best people in the world."
His skepticism stands in stark contrast to the prevailing mood at the Web Summit, where most conversations among 15,000 attendees focused on generative AI's seemingly infinite promise.
Many believe humanity stands on the cusp of achieving super intelligence or artificial general intelligence (AGI) technology that could match and even surpass human capability.
That optimism has driven OpenAI's valuation to $300 billion, unprecedented levels for a startup, with billionaire Elon Musk's xAI racing to keep pace.
Yet for all the hype, the practical gains remain limited.
The technology excels mainly at coding assistance for programmers and text generation for office work. AI-created images, while often entertaining, serve primarily as memes or deepfakes, offering little obvious benefit to society or business.
Marcus, a longtime New York University professor, champions a fundamentally different approach to building AI -- one he believes might actually achieve human-level intelligence in ways that current generative AI never will.
"One consequence of going all-in on LLMs is that any alternative approach that might be better gets starved out," he explained.
This tunnel vision will "cause a delay in getting to AI that can help us beyond just coding -- a waste of resources."
Instead, Marcus advocates for neurosymbolic AI, an approach that attempts to rebuild human logic artificially rather than simply training computer models on vast datasets, as is done with ChatGPT and similar products like Google's Gemini or Anthropic's Claude.
He dismisses fears that generative AI will eliminate white-collar jobs, citing a simple reality: "There are too many white-collar jobs where getting the right answer actually matters."
This points to AI's most persistent problem: hallucinations, the technology's well-documented tendency to produce confident-sounding mistakes.
Even AI's strongest advocates acknowledge this flaw may be impossible to eliminate.
Marcus recalls a telling exchange from 2023 with LinkedIn founder Reid Hoffman, a Silicon Valley heavyweight: "He bet me any amount of money that hallucinations would go away in three months. I offered him $100,000 and he wouldn't take the bet."
Looking ahead, Marcus warns of a darker consequence once investors realize generative AI's limitations. Companies like OpenAI will inevitably monetize their most valuable asset: user data.
"The people who put in all this money will want their returns, and I think that's leading them toward surveillance," he said, pointing to Orwellian risks for society.
"They have all this private data, so they can sell that as a consolation prize."
Marcus acknowledges that generative AI will find useful applications in areas where occasional errors don't matter much.
"They're very useful for auto-complete on steroids: coding, brainstorming, and stuff like that," he said.
"But nobody's going to make much money off it because they're expensive to run, and everybody has the same product."
I'd argue that image generation does have a use, especially for generating reference images. Sure, there might be some weirdness, but you can fix that when you do whatever art you're using it for.
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"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there.
Ehhh. I don't know if I'd go quite that far. I think that LLMs might be a component of a functional AGI. But just running a larger LLM model on bigger hardware is not going to suddenly barf out something that can act in the same sort of general way a human can, I agree there.
I don't think so, and I believe not even the current technology used for neural network simulations will bring us to AGI, yet alone LLMs.
