The Fast-evolving Landscape of Deep Learning: What’s New and What’s Next | Chapter 3 | Engineering Research: Perspectives on Recent Advances Vol. 10

 

Deep learning is rapidly evolving with transformative breakthroughs in foundation models (GPT-4, Gemini), generative AI (diffusion models, video synthesis), and self-supervised learning, reducing reliance on labelled data. Key advances in efficient AI (edge computing, model compression) and reinforcement learning (robotics, autonomous systems) are expanding practical applications. Emerging frontiers like neurosymbolic AI and AGI research highlight both progress and unresolved challenges. This chapter examines these cutting-edge developments, offering insights into deep learning’s current state and future trajectory.

 

 

Author(s) Details

K. Sridhar
Department of Computer Science and Engineering, Malla Reddy (MR) Deemed to be University, Maisammaguda (H), Gundlapochampally (V), Medchal - Malkajgiri District, Telangana, India.

 

Ravikumar Thallapalli
Department of Computer Science and Engineering, Vaageswari College of Engineering (Autonomous) Accredited by NAAC A+, Beside LMD Police Station, Karimnagar, Telangana, India.

 

M. Srinivas
Department of Computer Science and Engineering (AI&ML), Vaageswari College of Engineering (Autonomous) Accredited by NAAC A+, Beside LMD Police Station, Karimnagar, Telangana, India.

 

P. Venkateshwarlu
Department of Computer Applications, Vaageswari College of Engineering (Autonomous) Accredited by NAAC A+, Beside LMD Police Station, Karimnagar, Telangana, India.

 

Please see the book here :-https://doi.org/10.9734/bpi/erpra/v10/6079