Post by @flowersslop
36% credible (40% factual, 31% presentation). The claim that ASI will not rely on transformer architectures or current neural networks is speculative and lacks empirical support. Omission framing is evident as the post fails to acknowledge ongoing research and counterarguments favoring evolutionary improvements in neural networks.
Analysis Summary
The post claims that Artificial Superintelligence (ASI) will not rely on transformer architectures and is unlikely to use neural networks as currently developed. This assertion is speculative, reflecting the author's opinion on AI evolution without empirical backing. Counterarguments from AI research highlight ongoing advancements in scaling transformers and hybrid neural-symbolic systems as probable paths to ASI, as seen in projects like OpenCog Hyperon.
Original Content
The Facts
The claim is forward-looking and unsubstantiated by current evidence, aligning with speculative discourse in AI futures. Partially plausible but uncertain – while paradigm shifts are possible, dominant trends favor evolutionary improvements to neural networks rather than wholesale abandonment.
Benefit of the Doubt
The author advances a pro-AI progression agenda, positioning themselves as insightful on future tech shifts to emphasize rapid, transformative innovation over incrementalism. Key omissions include the lack of cited evidence, current dominance of transformer-based models in leading AI labs like OpenAI, and perspectives from experts predicting ASI via scaled neural architectures, which shapes reader perception toward anticipating a revolutionary breakthrough while downplaying established research trajectories.
Predictions Made
Claims about future events that can be verified later
ASI will not be a transformer, most likely not even a neural network as we currently know and build it.
Prior: 25%. Evidence: Web sources emphasize transformers' dominance in future AI (e.g., scaling laws, hybrid evolutions); author credibility at 68% truthfulness with AI expertise provides moderate positive weight, but unverified status and pro-rapid-progress bias introduce uncertainty, tempering the update. Posterior: 40%.
How Is This Framed?
Biases, omissions, and misleading presentation techniques detected
The claim presents a speculative future prediction without including essential background on current AI research trends, leading to an incomplete view of likely ASI development paths.
Problematic phrases:
"ASI will not be a transformer, most likely not even a neural network as we currently know and build it."What's actually there:
Transformers dominate current leading AI models and are being scaled toward AGI/ASI
What's implied:
Current neural network paradigms will be entirely replaced without evolutionary continuity
Impact: Readers may overestimate the likelihood of a complete paradigm shift, undervaluing ongoing advancements in transformer-based and hybrid systems.
Fails to acknowledge counter-evidence from AI research, such as expert predictions and projects favoring scaled neural architectures for ASI, creating a one-sided speculative narrative.
Problematic phrases:
"ASI will not be a transformer, most likely not even a neural network as we currently know and build it."What's actually there:
Prominent research highlights evolutionary improvements to neural networks as probable ASI routes
What's implied:
No viable path exists via current neural methods
Impact: Misleads readers by downplaying established research trajectories, fostering undue anticipation of unproven revolutionary breakthroughs.
Presents the claim as an insightful opinion on AI evolution without balancing it against dominant trends or alternative expert views, shaping perception toward the author's pro-innovation agenda.
Problematic phrases:
"ASI will not be a transformer, most likely not even a neural network as we currently know and build it."What's actually there:
Multi-faceted discourse includes both paradigm shift possibilities and incremental scaling
What's implied:
Only radical change is on the horizon
Impact: Encourages readers to adopt a narrow, optimistic view of AI futures, ignoring broader, evidence-based perspectives.
Sources & References
External sources consulted for this analysis
https://www.infosysbpm.com/blogs/financial-services/artificial-super-intelligence-the-future-of-ai.html
https://www.cloudwalk.io/ai/progress-towards-agi-and-asi-2024-present
https://www.geekwire.com/2023/the-problem-with-ai-and-more-predictions-about-the-future-of-technology/
https://www.ibm.com/think/insights/artificial-intelligence-future
https://ai-2027.com/
https://superintelligence.io/
https://www.techtarget.com/searchenterpriseai/definition/artificial-superintelligence-ASI
https://medium.com/ai-product-forge/the-future-of-ai-were-not-ready-for-superintelligence-72e90a999f41
https://www.goldmansachs.com/insights/articles/will-ai-lead-to-superintelligence-or-just-super-automation
https://medium.com/aiguys/when-machines-decide-predicting-the-first-contact-with-superintelligence-239e136651eb
https://web.archive.org/web/20171127213642/http:/www.slate.com/articles/technology/future_tense/2016/04/the_philosophical_argument_against_artificial_intelligence_killing_us_all.html
https://firstmovers.ai/ai-superintelligence/
https://www.sciencedirect.com/science/article/pii/S0303264724000492
https://link.springer.com/article/10.1007/s10676-022-09624-3
https://x.com/flowersslop/status/1936489224728965219
https://x.com/flowersslop/status/1870426026968289394
https://x.com/flowersslop/status/1952595882093785439
https://x.com/flowersslop/status/1929260832233599279
https://x.com/flowersslop/status/1934799059782406366
https://x.com/flowersslop/status/1838266083398054173
https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)
https://research.google/blog/transformer-a-novel-neural-network-architecture-for-language-understanding/
https://www.datacamp.com/tutorial/how-transformers-work
https://blogs.nvidia.com/blog/what-is-a-transformer-model/
https://poloclub.github.io/transformer-explainer/
https://builtin.com/artificial-intelligence/transformer-neural-network
https://www.techtarget.com/searchenterpriseai/feature/Transformer-neural-networks-are-shaking-up-AI
https://dilipkumar.medium.com/transformers-neural-network-architecture-a6fd825d2d5f
https://justanotherai.com/the-transformer-architecture-the-innovation-that-revolutionized-ai-language-models/
https://analystuttam.medium.com/decoding-neural-architecture-search-the-next-evolution-in-ai-model-design-c33cb3a18154
https://theword360.com/2025/06/06/emerging-trends-in-neuromorphic-computing
https://medium.com/@isharaner96/transformers-and-the-future-of-ai-how-this-architecture-is-shaping-the-next-generation-of-fdea0d5fc71c
https://guruangle.com/future-of-transformer-networks
https://www.analyticsinsight.net/artificial-intelligence/transforming-ai-the-rise-of-generative-models-and-transformer-architectures
https://x.com/flowersslop/status/1870426026968289394
https://x.com/flowersslop/status/1852045954956038615
https://x.com/flowersslop/status/1936489224728965219
https://x.com/flowersslop/status/1934799059782406366
https://x.com/flowersslop/status/1868654929411211481
https://x.com/flowersslop/status/1842276349462941846
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