Intentional Forgetting: An Emerging Field in AI and Beyond

Beierle, Christoph; Timm, Ingo J.

doi:10.1007/s13218-018-00574-x

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…In multi-task program induction we have the inverse problem: catastrophic remembering, the inability for a learner to forget knowledge. We therefore need intentional forgetting (Beierle and Timm 2019).…”

Section: Related Workmentioning

confidence: 99%

Forgetting to Learn Logic Programs

Cropper

2020

AAAI

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Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other programs. To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK. We consider forgetting in an inductive logic programming (ILP) setting. We show that forgetting can significantly reduce both the size of the hypothesis space and the sample complexity of an ILP learner. We introduce Forgetgol, a multi-task ILP learner which supports forgetting. We experimentally compare Forgetgol against approaches that either remember or forget everything. Our experimental results show that Forgetgol outperforms the alternative approaches when learning from over 10,000 tasks.

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Section: Related Workmentioning

confidence: 99%

Forgetting to Learn Logic Programs

Cropper

2020

AAAI

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“…Data accuracy stands as the cornerstone of reliable and impactful decision-making. However, as AI systems become more sophisticated, several challenges emerge in ensuring the accuracy of the data they process based on four major challenges (18,19): • Model Forgetfulness: It is a tendency of AI models to gradually lose previously acquired knowledge over time, particularly when exposed to evolving datasets, especially when it is supposed to predict or forecast something. • User Acceptability: It is the willingness of end-users to trust and use AI-driven solutions in their decision-making, depending on the transparency of the LLMs and its features.…”

Section: Ensuring Data Accuracy and Challengesmentioning

confidence: 99%

Large language models in physical therapy: time to adapt and adept

Naqvi,

Shaikh,

Mishra

2024

Front. Public Health

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Healthcare is experiencing a transformative phase, with artificial intelligence (AI) and machine learning (ML). Physical therapists (PTs) stand on the brink of a paradigm shift in education, practice, and research. Rather than visualizing AI as a threat, it presents an opportunity to revolutionize. This paper examines how large language models (LLMs), such as ChatGPT and BioMedLM, driven by deep ML can offer human-like performance but face challenges in accuracy due to vast data in PT and rehabilitation practice. PTs can benefit by developing and training an LLM specifically for streamlining administrative tasks, connecting globally, and customizing treatments using LLMs. However, human touch and creativity remain invaluable. This paper urges PTs to engage in learning and shaping AI models by highlighting the need for ethical use and human supervision to address potential biases. Embracing AI as a contributor, and not just a user, is crucial by integrating AI, fostering collaboration for a future in which AI enriches the PT field provided data accuracy, and the challenges associated with feeding the AI model are sensitively addressed.

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“…Over the years, the desire to abstract over details led to different theories (e.g., (Giunchiglia and Walsh 1992)) and applications of abstraction in various areas of AI, among many are planning (Knoblock 1994), constraint satisfaction (Bistarelli, Codognet, and Rossi 2002), and model checking (Clarke, Grumberg, and Long 1994). Getting rid of (ir)relevant details through forgetting continues to motivate works in different subfields of AI (Beierle and Timm 2019), such as knowledge representation and reasoning (KR) (Eiter and Kern-Isberner 2018) and symbolic machine learning (Siebers and Schmid 2019). Recent examples of forgetting Copyright c 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org).…”

Section: Introductionmentioning

confidence: 99%

A Unified View on Forgetting and Strong Equivalence Notions in Answer Set Programming

Saribatur,

Woltran

2024

AAAI

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Answer Set Programming (ASP) is a prominent rule-based language for knowledge representation and reasoning with roots in logic programming and non-monotonic reasoning. The aim to capture the essence of removing (ir)relevant details in ASP programs led to the investigation of different notions, from strong persistence (SP) forgetting, to faithful abstractions, and, recently, strong simplifications, where the latter two can be seen as relaxed and strengthened notions of forgetting, respectively. Although it was observed that these notions are related, especially given that they have characterizations through the semantics for strong equivalence, it remained unclear whether they can be brought together. In this work, we bridge this gap by introducing a novel relativized equivalence notion, which is a relaxation of the recent simplification notion, that is able to capture all related notions from the literature. We provide the necessary and sufficient conditions for relativized simplifiability, which shows that the challenging part is for when the context programs do not contain all the atoms to remove. We then introduce an operator that combines projection and a relaxation of SP-forgetting to obtain the relativized simplifications. We furthermore provide complexity results that complete the overall picture.

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Intentional Forgetting: An Emerging Field in AI and Beyond

Cited by 10 publications

References 8 publications

Forgetting to Learn Logic Programs

Forgetting to Learn Logic Programs

Large language models in physical therapy: time to adapt and adept

A Unified View on Forgetting and Strong Equivalence Notions in Answer Set Programming

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