Limitations of Deep Learning vs. Human Intelligence: Training Data, Interpretability, Bias, and Ethics

Abstract

Deep Learning (DL) has brought a paradigm shift in innumerable fields and allowed machines to learn and decide to a very high extent. Its advantage is that it can analyze large sets of data, identify rather complex patterns, and learn from experience. DL models are widely used to perform complicated tasks [1], [2]. The state of the art in DL includes neural networks with two or more tiers and it has made progressive improvements in fields like image and voice identification, writing comprehension, language generation, and even decision-making self-governing systems [3]. These have led to growing concern and anticipation in the efficiency and rate of change that AI can bring about in industries and people [4].

Ähnliche Arbeiten