The limits of machine intelligence

By Henry Shevlin, Karina Vold, Matthew Crosby, Marta Halina. 2019

59 citations recorded by the source corpus.

Despite recent breakthroughs in machine learning, current artificial systems lack key features of biological intelligence. Whether the current limitations can be overcome is an open question, but critical to answer, given the implications for society.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

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In Artificial Intelligence

1. Mehmet Karamanoglu
2. Arpan Kumar Kar

Citations in the corpus

• AI and the Everything in the Whole Wide World Benchmark (2021, neutral): “Researchers now often use the term “generality” to refer to the development of AI systems that are flexible in nature, demonstrating competence on a wide range of tasks and in a wide range of settings, hoping to mirror the adaptive cognitive abilities humans are perceived to possess [Shevlin et al.,”
• Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, background): “This implies that, just like there is a need for standardized benchmarks for validating human AGI, or animal intelligence-mimicking AGI [205], there is also a requirement for performance measures and benchmarks that can accurately evaluate the DT for each industry and application.”