Interleaving Fast and Slow Decision Making

Gulati, Aditya; Soni, Sarthak; Rao, Shrisha

doi:10.1109/icra48506.2021.9561562

Cited by 3 publications

(1 citation statement)

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“…However, whereas in biological cognition each of these systems is adapted to play its intended evolutionary role without any conscious effort, the mechanisms that regulate this function in brain-inspired cognitive architectures are far from optimal. Some recent implementations deploy a third system to play a supervisory role, in some cases (e.g., [263]) this involves selecting the solver between systems 1 and 2 without considering the suitability for the problem. This approach may lead to sub-optimal solutions being produced.…”

Section: Discussionmentioning

confidence: 99%

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Mumuni

2022

Prog Artif Intell

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We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-shot learning. Data-driven deep learning models have achieved remarkable performance and demonstrated capabilities surpassing human experts in many applications. Yet, their inability to exploit domain knowledge leads to serious performance limitations in practical applications. In particular, deep learning systems are exposed to adversarial attacks, which can trick them into making glaringly incorrect decisions. Moreover, complex data-driven models typically lack interpretability or explainability, i.e., their decisions cannot be understood by human subjects. Furthermore, models are usually trained on standard datasets with a closed-world assumption. Hence, they struggle to generalize to unseen cases during inference in practical open-world environments, thus, raising the zero-or few-shot generalization problem. Although many conventional solutions exist, explicit domain knowledge, brain-inspired neural network and cognitive architectures offer powerful new dimensions towards alleviating these problems. Prior knowledge is represented in appropriate forms and incorporated in deep learning frameworks to improve performance. Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots. Ultimately, these models achieve better explainability, higher adversarial robustness and data-efficient learning, and can, in turn, provide insights for cognitive science and neurosciencethat is, to deepen human understanding on how the brain works in general, and how it handles these problems.Index Terms-Domain knowledge, cognitive architecture, brain-inspired neural network, explainable AI, adversarial attack, zero-shot generalization. ✦ INTRODUCTION State of deep learningMachine learning is an artificial intelligence (AI) concept that enables computing systems to learn useful relationships from data and then use this information to identify learned patterns and make predictions on new inputs. Deep learning (DL) is a machine learning method that uses a multi-layered arrangement of computational units to learn relevant patterns from data. Many deep learning constructs have been proposed for various use-cases. Some of the most popular ones are deep convolutional neural networks (DCNN) [1], [2] recurrent neural networks (RNN) [3], generative adversarial networks (GANs) [4], deep reinforcement learning [5] and vision transformers [6], [7]). DCNNs are a class of deep neural networks (DNNs) specially designed to handle image data.Recently, deep learning methods have accomplished remarkable milestones in many critical applications. In medical domains, some state-of-the-art models (e.g., as reported

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Section: Discussionmentioning

confidence: 99%

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Mumuni

2022

Prog Artif Intell

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Thinking Fast and Slow in AI: The Role of Metacognition

Ganapini¹,

Campbell²,

Fabiano³

et al. 2023

Lecture Notes in Computer Science

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Nudging is a behavioral strategy aimed at influencing people's thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techniques can targeted at human fast and unconscious thinking, e.g., by using images to generate fear or the more careful and effortful slow thinking, e.g., by releasing information that makes us reflect on our choices. In this paper, we propose and discuss a value-based AIhuman collaborative framework where AI systems nudge humans by proposing decision recommendations. Three different nudging modalities, based on when recommendations are presented to the human, are intended to stimulate human fast thinking, slow thinking, or meta-cognition. Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities. Examples of values are decision quality, speed, human upskilling and learning, human agency, and privacy. Several values can be present at the same time, and their priorities can vary over time. The framework treats values as parameters to be instantiated in a specific decision environment.

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Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning

Mumuni,

Mumuni

2024

Cognitive Systems Research

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Interleaving Fast and Slow Decision Making

Cited by 3 publications

References 9 publications

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Thinking Fast and Slow in AI: The Role of Metacognition

Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning

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