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Superintelligence
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=== Pathways to superintelligence === Philosopher [[David Chalmers]] argues that AGI is a likely path to ASI. He posits that AI can achieve equivalence to [[human intelligence]], be extended to surpass it, and then be amplified to dominate humans across arbitrary tasks.{{sfn|Chalmers|2010|p=7}} More recent research has explored various potential pathways to superintelligence: # Scaling current AI systems β Some researchers argue that continued scaling of existing AI architectures, particularly transformer-based models, could lead to AGI and potentially ASI.<ref>{{Cite arXiv |last1=Kaplan |first1=Jared |last2=McCandlish |first2=Sam |last3=Henighan |first3=Tom |last4=Brown |first4=Tom B. |last5=Chess |first5=Benjamin |last6=Child |first6=Rewon |last7=Gray |first7=Scott |last8=Radford |first8=Alec |last9=Wu |first9=Jeffrey |last10=Amodei |first10=Dario |title=Scaling Laws for Neural Language Models |year=2020|class=cs.LG |eprint=2001.08361 }}</ref> # Novel architectures β Others suggest that new AI architectures, potentially inspired by neuroscience, may be necessary to achieve AGI and ASI.<ref>{{Cite journal |last1=Hassabis |first1=Demis |last2=Kumaran |first2=Dharshan |last3=Summerfield |first3=Christopher |last4=Botvinick |first4=Matthew |title=Neuroscience-Inspired Artificial Intelligence |journal=Neuron |volume=95 |issue=2 |year=2017 |pages=245β258 |doi=10.1016/j.neuron.2017.06.011|pmid=28728020 }}</ref> # Hybrid systems β Combining different AI approaches, including symbolic AI and neural networks, could potentially lead to more robust and capable systems.<ref>{{Cite arXiv |last1=Garcez |first1=Artur d'Avila |last2=Lamb |first2=Luis C. |title=Neurosymbolic AI: The 3rd Wave |year=2020|class=cs.AI |eprint=2012.05876 }}</ref>
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