Do Artificial Intelligence Systems Understand?
Not on the map. Why?
Abstract (arXiv)
Are intelligent machines really intelligent? Is the underlying philosophical concept of intelligence satisfactory for describing how the present systems work? Is understanding a necessary and sufficient condition for intelligence? If a machine could understand, should we attribute subjectivity to it? This paper addresses the problem of deciding whether the so-called "intelligent machines" are capable of understanding, instead of merely processing signs. It deals with the relationship between syntaxis and semantics. The main thesis concerns the inevitability of semantics for any discussion about the possibility of building conscious machines, condensed into the following two tenets: "If a machine is capable of understanding (in the strong sense), then it must be capable of combining rules and intuitions"; "If semantics cannot be reduced to syntaxis, then a machine cannot understand." Our conclusion states that it is not necessary to attribute understanding to a machine in order to explain its exhibited "intelligent" behavior; a merely syntactic and mechanistic approach to intelligence as a task-solving tool suffices to justify the range of operations that it can display in the current state of technological development.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 7 of its 37 references that are in the map. First posted on arXiv 22 Jul 2022.
Builds on
7 of 37 references.
References · 7
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
- 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
- 2021A Survey of Transformerscited by 399
- 2020From Statistical Relational to Neuro-Symbolic Artificial Intelligencecited by 63
- 2020A Machine Consciousness architecture based on Deep Learning and Gaussian Processescited by 4
Built on by
Citing papers per year: 2022: 2; 2023: 2.
4 papers.
- 2022On the independence between phenomenal consciousness and computational intelligencecited by 3
- 2023Simulating H.P. Lovecraft horror literature with the ChatGPT large language modelcited by 3
- 2023Best uses of ChatGPT and Generative AI for computer science researchcited by 1
- 2022Optimizing Integrated Information with a Prior Guided Random Search Algorithmcited by 0
- 2023Best uses of ChatGPT and Generative AI for computer science research18 Nov 2023
- 2023Simulating H.P. Lovecraft horror literature with the ChatGPT large language model5 May 2023
- 2022Optimizing Integrated Information with a Prior Guided Random Search Algorithm8 Dec 2022
- 2022On the independence between phenomenal consciousness and computational intelligence3 Aug 2022