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1. Current and Near-Term AI as a Potential Existential Risk Factor

   https://doi.org/10.1145/3514094.3534146  

   Bucknall, Benjamin S.; Dori-Hacohen, Shiri (July 2022, AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society)

   There is a substantial and ever-growing corpus of evidence and literature exploring the impacts of Artificial intelligence (AI) technologies on society, politics, and humanity as a whole. A separate, parallel body of work has explored existential risks to humanity, including but not limited to that stemming from unaligned Artificial General Intelligence (AGI). In this paper, we problematise the notion that current and near-term artificial intelligence technologies have the potential to contribute to existential risk by acting as intermediate risk factors, and that this potential is not limited to the unaligned AGI scenario. We propose the hypothesis that certain already-documented effects of AI can act as existential risk factors, magnifying the likelihood of previously identified sources of existential risk. Moreover, future developments in the coming decade hold the potential to significantly exacerbate these risk factors, even in the absence of artificial general intelligence. Our main contribution is a (non-exhaustive) exposition of potential AI risk factors and the causal relationships between them, focusing on how AI can affect power dynamics and information security. This exposition demonstrates that there exist causal pathways from AI systems to existential risks that do not presuppose hypothetical future AI capabilities. 

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2. Artificial Conscious Intelligence

   https://doi.org/10.1142/S270507852050006X  

   Reggia, James A.; Katz, Garrett E.; Davis, Gregory P. (March 2020, Journal of Artificial Intelligence and Consciousness)

   The field of artificial consciousness (AC) has largely developed outside of mainstream artificial intelligence (AI), with separate goals and criteria for success and with only a minimal exchange of ideas. This is unfortunate as the two fields appear to be synergistic. For example, here we consider the question of how concepts developed in AC research might contribute to more effective future AI systems. We first briefly discuss several past hypotheses about the function(s) of human consciousness, and present our own hypothesis that short-term working memory and very rapid learning should be a central concern in such matters. In this context, we then present ideas about how integrating concepts from AC into AI systems to develop an artificial conscious intelligence (ACI) could both produce more effective AI technology and contribute to a deeper scientific understanding of the fundamental nature of consciousness and intelligence. 

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3. Visual Sequence Repetition Learning is Not Impaired in Signing DHH Children

   https://doi.org/10.1093/deafed/enab007  

   Terhune-Cotter, Brennan P; Conway, Christopher M; Dye, Matthew W (May 2021, The Journal of Deaf Studies and Deaf Education)

   Abstract The auditory scaffolding hypothesis states that early experience with sound underpins the development of domain-general sequence processing abilities, supported by studies observing impaired sequence processing in deaf or hard-of-hearing (DHH) children. To test this hypothesis, we administered a sequence processing task to 77 DHH children who use American Sign Language (ASL) and 23 hearing monolingual children aged 7–12 years and found no performance difference between them after controlling for age and nonverbal intelligence. Additionally, neither spoken language comprehension scores nor hearing loss levels predicted sequence processing scores in the DHH group, whereas ASL comprehension scores did. Our results do not indicate sequence processing deficits in DHH children and do not support the auditory scaffolding hypothesis; instead, these findings suggest that factors related to experience with and/or proficiency in an accessible language during development may be more important determinants of sequence processing abilities. 

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4. Individual differences in comprehension of contextualized metaphors

   https://doi.org/10.1080/10926488.2020.1821203  

   Stamenković, D.; Ichien, N.; Holyoak, K. J. (January 2020, Metaphor and symbol)

   null (Ed.)

   We report a study examining the role of linguistic context in modulating the influences of individual differences in fluid and crystalized intelligence on comprehension of literary metaphors. Three conditions were compared: no context, metaphor-congruent context, and literal-congruent context. Relative to the baseline no-context condition, the metaphor-congruent context facilitated comprehension of the metaphorical meaning whereas the literal-congruent context impaired it. Measures of fluid and crystalized intelligence both made separable contributions to predicting metaphor comprehension. The metaphor-congruent context selectively increased the contribution of crystalized verbal intelligence. These findings support the hypothesis that a supportive linguistic context encourages use of semantic integration in interpreting metaphors. 

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5. Do Multi-Document Summarization Models Synthesize ?

   https://doi.org/10.1162/tacl_a_00687  

   DeYoung, Jay; Martinez, Stephanie C; Marshall, Iain J; Wallace, Byron C (September 2024, Transactions of the Association for Computational Linguistics)

   Louis, Annie (Ed.)

   Abstract Multi-document summarization entails producing concise synopses of collections of inputs. For some applications, the synopsis should accurately synthesize inputs with respect to a key aspect, e.g., a synopsis of film reviews written about a particular movie should reflect the average critic consensus. As a more consequential example, narrative summaries that accompany biomedical systematic reviews of clinical trial results should accurately summarize the potentially conflicting results from individual trials. In this paper we ask: To what extent do modern multi-document summarization models implicitly perform this sort of synthesis? We run experiments over opinion and evidence synthesis datasets using a suite of summarization models, from fine-tuned transformers to GPT-4. We find that existing models partially perform synthesis, but imperfectly: Even the best performing models are over-sensitive to changes in input ordering and under-sensitive to changes in input compositions (e.g., ratio of positive to negative reviews). We propose a simple, general, effective method for improving model synthesis capabilities by generating an explicitly diverse set of candidate outputs, and then selecting from these the string best aligned with the expected aggregate measure for the inputs, or abstaining when the model produces no good candidate. 

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