Google's From AGI to ASI Paper
On AGI/SGI/ASI
AGI (Artificial General Intelligence) and SGI (Specialized Generative Intelligence or Superhuman General Intelligence) represent the next major evolutionary leaps in artificial intelligence, transitioning from narrow, single-task systems to highly capable, adaptable cognitive tools. [1, 2, 3]Breaking Down the Concepts
- AGI (Artificial General Intelligence): Refers to a hypothetical AI system that possesses the cognitive ability to learn, understand, and perform any intellectual task that a human being can across a wide variety of domains. [1, 2]
- SGI (Specialized Generative Intelligence): A classification for AI models that are domain-specific but retain advanced generative and generalist abilities. Alternatively, some researchers refer to SGI as Symbiotic General Intelligence, highlighting recursive human-AI collaboration. [1, 2, 3]
Current Industry Trajectory
- Where we are: Today's models operate primarily in the SGI space—highly specialized yet possessing robust general knowledge. True AGI remains a major milestone that researchers are actively pursuing. [1, 2, 3, 4]
- The "Tipping Point": Reaching AGI requires balancing general knowledge with deep, reliable specialty in areas like complex math and iterative programming. [1, 2]
The Ultimate Goal: ASIAre you looking to explore how these models differ in processing data, evaluating hardware constraints, or understanding the safety and ethical standards surrounding their development? Let me know which angle you'd like to dive into!
12 comments:
As humans blindly create their future masters.
ASI won't be affected by the inevitable affects of human ignorance relative to its impact on the climate and environment.
But, at least initially the tech billionaires stand to become the emerging trillionaire class.
WooHoo!
The AGI Hype isn't big enough yet, hence the emergent ASI.
Reframing?
Yawn.
Moving the goal posts (football chains). It's 4th down for AGI, 3 yards to go. 97% (according to Eric Weinstein) is "attention". They've scaled almost there (AGI). Figuring out the last 3%, (curiosity & Intuition? Surplus jouissance?) that's ASI.
Going beyond required intelligence... to surplus.
Resolving analogies - courage:temperance::justice:wisdom
and applying ethics - charity:faith:hope
not the first miss in the history of science and tech
yaaawn
know what AI winter means?
We're still trying to solve physics... and string theory (50 years) is still a miss.
It's not a term I'm familiar with...
An AI winter is a period of significantly reduced interest, public funding, and commercial investment in artificial intelligence research. It typically occurs after a hype cycle when high expectations fail to materialize, leading to widespread disillusionment, project cancellations, and frozen venture capital.
The Anatomy of an AI Winter
Winters follow a predictable, cyclical pattern that mirrors economic and technological bubbles:
Overpromising: Researchers and companies make grandiose claims about the imminent capabilities of their AI systems.
Over-funding: Investors and governments pour massive amounts of capital into the technology.
Under-delivering:
The technology fails to meet inflated expectations or deliver practical bottom-line business value.
Backlash: Skepticism takes over. Funding dries up, laboratories close, and researchers leave the field (known as a "knowledge diaspora").
Historical AI Winters
First AI Winter (1974–1980): Following the "Golden Age" of AI in the 1950s and 60s, early translation and mathematical programs hit hard computational walls. Once governments realized machines couldn't solve these complex, generalized problems as quickly as promised, funding plummeted.
Second AI Winter (1987–Early 2000s): The corporate push for "expert systems" faced significant maintenance and scaling issues. As specialized hardware became obsolete and expectations were unmet, commercial interest slumped for over a decade.
Are We Heading for Another Winter?
Following the massive boom in Large Language Models (LLMs) and generative AI, industry watchers heavily debate whether a third winter is on the horizon. Concerns are driven by the skyrocketing computational and energy costs required to train models, alongside questions from major consulting firms regarding whether the technology is actually driving bottom-line revenue for most businesses.
Yep, the gains from AI will come from the potentials realized by modified workflows, not simple use within existing workflows. Those will take years to realize... trial and error.
They really need to work out the 3%, and get AI to create the "re-orginations" needed.
That's probably where your tech comes in... adoptation.
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