China
Africa
Explore chapters and articles related to this topic
Deep Learning and Economic Prospects in Medical and Pharmaceutical Biotechnology
Published in Hajiya Mairo Inuwa, Ifeoma Maureen Ezeonu, Charles Oluwaseun Adetunji, Emmanuel Olufemi Ekundayo, Abubakar Gidado, Abdulrazak B. Ibrahim, Benjamin Ewa Ubi, Medical Biotechnology, Biopharmaceutics, Forensic Science and Bioinformatics, 2022
Charles Oluwaseun Adetunji, Kingsley Eghonghon Ukhurebor, Olugbemi Tope Olaniyan, Juliana Bunmi Adetunji, Gloria E. Okotie, Julius Kola Oloke
AI has become the emerging science and technology with increasing developmental processes emanating from automobile industries to medical science and manufacturing. Today, AI is used in boosting crop and plant yields and quality, improving weather forecasting, cancer detection, improving industrial growth and productivity, and prediction of an epidemic (LeCun et al., 2015). The application of AI in biomedical science include food science, neuroscience, regenerative medicine, medical imaging, bioinformatics, public health, biological engineering, physiological parameter monitoring, biomechanics, nutrition, drug discovery, neurorobotics, medical informatics, etc. Most models in deep learning are instigated by enhancing the number of layers in a neural network (Giles, 2018). AI is applicable in the following areas:
World models and predictive coding for cognitive and developmental robotics: frontiers and challenges
Published in Advanced Robotics, 2023
Tadahiro Taniguchi, Shingo Murata, Masahiro Suzuki, Dimitri Ognibene, Pablo Lanillos, Emre Ugur, Lorenzo Jamone, Tomoaki Nakamura, Alejandra Ciria, Bruno Lara, Giovanni Pezzulo
Predictive coding is another related theory that recently has become more and more influential [16]. It is heavily influenced by Helmholtz's early theories of perception as a process driven by learning, knowledge, and inference [17]. Predictive coding proposes that the brain infers the external causes of sensations by continuously predicting its input through top-down signals and adapts to minimize prediction error [18, 19]. This substantiates the idea that the brain might use an adaptive world model to support perception. The free energy principle (FEP) also proposes a similar vision. It argues that our brain supports both perception (perceptual inference) and action (active inference) using a form of variational Bayesian inference; in particular, using (variational) free energy, it assesses the quality of the prediction and its conformity to prior beliefs [20]. These ideas, which are currently influential in neuroscience and cognitive science, are also used in cognitive and developmental robotics, neurorobotics [21–23], and artificial intelligence to develop neurodynamics realizing adaptive behaviors and social perception [24].