Tag: Machine Understanding

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(Featured) Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use

Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use

Christoph Durt, Thomas Fuchs, and Tom Froese investigate the astonishing capacities of Large Language Models (LLMs) to mimic human-like responses. They begin by acknowledging the unprecedented feats of these models, particularly GPT-3, which have led some to assert that they possess common-sense reasoning and even sentience. They caution, however, that these claims often overlook the instances where LLMs fail to produce sensical responses. Even as the models evolve and mitigate some of these limitations, the authors urge circumspection regarding the attribution of understanding and sentience to these systems.

The authors argue that the progress of LLMs invites a reassessment of long-standing philosophical debates about the limits of AI. The authors challenge the view, expressed by philosophers such as Hubertus Dreyfus, that AI is inherently incapable of understanding meaning. Given the emergent linguistic capabilities of these models, they query whether these advancements warrant attributing understanding to the computational system. Contrary to Dreyfus’s assertion that any formal system cannot be directly sensitive to the relevance of its situation, the authors propose that LLMs seem to exhibit this sensitivity in a pragmatic sense.

While the article explores the philosophical debates surrounding AI understanding and sentience, it does not definitively conclude whether LLMs truly understand or are sentient. The authors suggest that the human-like behaviour exhibited by these models may lead to the inference of a human-like mind. However, they argue that more nuanced and empirically informed positions are required. The authors further advocate for a more comprehensive assessment of LLM output, rather than relying on selective instances of impressive performance.

This research brings into focus the broader philosophical implications of our interaction with AI, particularly the ontological and epistemological assumptions we make when interacting with LLMs. The debate surrounding AI sentience and understanding illuminates the complexities inherent in defining consciousness and understanding, a philosophical quandary that dates back to Descartes and beyond. It forces us to interrogate the nature of understanding – is it a purely human phenomenon, or can it be replicated, even surpassed, by silicon-based entities? Moreover, it challenges our anthropocentric views of cognition and compels us to consider alternate forms of intelligence and understanding.

Looking forward, the study of AI and philosophy would benefit from an even deeper exploration of these questions. More empirical research is needed to understand the extent and limitations of LLMs’ capacities. Concurrently, philosophical inquiry can help define and refine the metrics by which we measure AI understanding and sentience. As we delve further into the AI era, it is crucial that we continue to scrutinize and challenge our assumptions about AI capabilities, not only to enhance our technological advancements but also to enrich our philosophical understanding of the world.

Abstract

Large language models such as ChatGPT are deep learning architectures trained on immense quantities of text. Their capabilities of producing human-like text are often attributed either to mental capacities or the modeling of such capacities. This paper argues, to the contrary, that because much of meaning is embedded in common patterns of language use, LLMs can model the statistical contours of these usage patterns. We agree with distributional semantics that the statistical relations of a text corpus reflect meaning, but only part of it. Written words are only one part of language use, although an important one as it scaffolds our interactions and mental life. In human language production, preconscious anticipatory processes interact with conscious experience. Human language use constitutes and makes use of given patterns and at the same time constantly rearranges them in a way we compare to the creation of a collage. LLMs do not model sentience or other mental capacities of humans but the common patterns in public language use, clichés and biases included. They thereby highlight the surprising extent to which human language use gives rise to and is guided by patterns.

Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use
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(Featured) Machine Understanding and Deep Learning Representation

Machine understanding and deep learning representation

Michael Tamir and Elay Shech embark on an ambitious journey to explore the notion of understanding in the context of deep learning algorithms. They attempt to ascertain if the impressive achievements of deep learning, particularly in areas where these algorithms can compete with human performance, can be construed as an indication of genuine understanding. To this end, the authors delve into the philosophy of understanding and seek to establish criteria for evaluating machine understanding. This is done in the hope of determining whether the trends and patterns exhibited by deep learning algorithms in representation and information compression can be equated with partial or full satisfaction of these criteria.

The authors identify three key factors in understanding: reliability and robustness, information relevance, and well-structured representation. They argue that these factors can be observed in deep learning algorithms, providing a basis for evaluating their presence in direct task performance or in analyzing the representations learned within the neural networks’ hidden layers. In order to assess understanding in machines, the authors draw upon various concepts and techniques from the realm of deep learning, such as generalization error, information bottleneck analysis, and the notion of disentanglement.

The authors consider and address three possible objections to their arguments. First, they acknowledge the narrow scope of deep learning models’ success in achieving human competitive performance, as well as their limitations in other tasks. However, they contend that their goal is to provide a framework for evaluating potential machine understanding, rather than claiming that any specific algorithm exhibits a certain degree of understanding. Second, they respond to concerns about the constitutive nature of the three key factors by emphasizing that their work serves as a critical tool for quantifying and comparing evidence of understanding, rather than making conclusive judgments. Finally, the authors address the objection that understanding requires a “mentality,” highlighting the value of an aspect-sensitive account of machine understanding that is independent of presupposed mentality.

When it comes to broader philosophical issues, the paper contributes to ongoing debates surrounding the nature of understanding in both human and artificial agents. The authors draw connections between deep learning algorithms and philosophical accounts of understanding, showing how concepts from the latter can be utilized to develop evaluation criteria for the former. By doing so, they provide a valuable philosophical framework for approaching the topic of machine understanding, allowing for a more nuanced analysis of the similarities and differences between human and machine cognition.

The paper’s findings open up several avenues for future research and investigation. One possible direction is to delve deeper into the interplay between the key factors of understanding, exploring how they might be combined or weighted to better assess the relative strengths and weaknesses of various deep learning algorithms. Another promising area for exploration is the application of the authors’ framework to other types of artificial intelligence, such as reinforcement learning or unsupervised learning. Additionally, examining the potential impact of advancements in AI hardware, neural network architectures, or training methodologies on the key factors of understanding could further enrich our understanding of the relationship between deep learning and the philosophy of understanding.

Abstract

Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for identifying these factors in deep learning representations provides a framework for discussing and critically evaluating potential machine understanding given the continually improving task performance enabled by such algorithms.

Machine understanding and deep learning representation