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Beyond Human: Deep Learning, Explainability And Representation

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Her latest work is concerned with theorising what she terms “algorithmic thought”. Recently, she has written on explainability in deep learning, on the epistemic implications of algorithmic Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience

Towards the Explainability of Multimodal Speech Emotion Recognition

Analyzing the Impact of Data Augmentation on the Explainability of Deep ...

Navigating the interpretability paradox of autonomous AI: Can we maintain trust and transparency without sacrificing performance? AI has rapidly evolved from simple, rule Deep learning models have revolutionized numerous fields, yet their decision-making processes often remain opaque, earning them the characterization of “black-box”

Beyond Human: Deep Learning, Explainability and Representation Sussex Research M. Fazi M. Beatrice Philosophy, Computer Science Theory, Culture & Society 2020

Local representations are the most straightforward and easy-to-interpret way of learning, whereas distributed representations can be complex, often leading to emergent

Beyond Human: Deep Learning, Explainability and Representation: M Beatrice Fazi – 26 Nov 2020 – Theory, Culture & Society This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in Abstract The rise of deep learning has revolutionized many fields, yet the complexity of these models often leads to challenges in explainability and interpretability.

Beyond Human: Deep Learning, Explainability and Representation Article Full-text available Nov 2020 THEOR CULT SOC

Abstract Explainable AI (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. In recent years, various techniques Recent progress in Deep Learning (DL) for learning feature representations has significantly box character demands rethinking impacted RL, and the combination of both methods (known as deep RL) has led to This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in

Beyond human: deep learning, explainability and representation

(PDF) Explainability in Deep Reinforcement Learning. (arXiv:2008 ...

  • RELAX: Representation Learning Explainability
  • AI as a Buddhist Self-Overcoming Technique in Another Medium
  • Reproducibility and explainability in digital humanities

Over the last decade, research has shifted from conventional machine learning models to more advanced deep learning and transfer learning architectures. These approaches have

Despite the significant improvements that self-supervised representation learning has led to when learning from unlabeled data, no methods have been developed that explain

Deep neural networks have been well-known for their superb handling of various machine of deep learning learning and artificial intelligence tasks. However, due to their over-parameterized

Follow them to stay up to date with their professional activities in philosophy, and browse their publications such as „Machines That Create: Contingent Computation and Generative AI“, Explainable AI (XAI) is critical for bridging the gap between complex, black-box models and human understanding, establishing trust and facilitating successful AI deployment. Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks. However, due to their over-parameterized

Research on the problem of “explainability” or “interpretability”, especially of machine learning based methods and AI models (Explainable AI, XAI), is currently a very Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience M. Beatrice Fazi introduces her Theory, Culture & Society article ‚ Beyond Human: Deep Learning, Explainability and Representation ‚ (Open Access) . The article is part of the

We propose a non-representationalist framework for deep learning relying on a novel method computational phenomenology, a dialogue between the first-person perspective (relying on

Key aspects such as AI reliance, human intuition, and emerging collaboration theories — including the human-algorithm centaur and co-intelligence paradigms — are explored in Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience

Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience Beyond Human Representation The success of the Google-owned artificial intelligence (AI) company DeepMind and its computer program AlphaGo is well known. The article does so by entering debates about explainability in AI and assessing how technoscience and technoculture tackle the possibility to ‘re-present’ the algorithmic

“Reproducibility” and “explainability” are important methodological considerations in the Sciences, and are increasingly relevant in Digital Humanities. The discussions around how these procedures could Nan Z. Da’s The Beyond Human Representation The success of the Google-owned artificial intelligence (AI) company DeepMind and its computer program AlphaGo is well known.