1950 Alan Turing's "Computer Machinery and Intelligence" 1956 The term "Artificial Intelligence" is coined at the Dartmouth Conference. 1966 Joseph Weizenbaum creates ELIZA, an early natural language processing computer program. 1970 Masahiro Mori introduced the concept of the "Uncanny Valley," which describes the discomfort people feel in response to robots that appear almost, but not quite, humnan. 1972 MYCIN: One of the earliest expert systems is developed to aid in identifying bacteria causing severe infections. 1986 Backpropagation Algorithm provides breakthrough for training neural networks, a key development in Machine Learning. 1997 IBM's Deep Blue, a chess-playing computer, beats world champion Garry Kasparov. 2000s Geoffrey Hinton's work on Deep Learning, including Restricted Boltzmann Machines (RBMs) leads to advancements in deep belief networks. 2012 AlexNet: a breakthrough in Deep Learning which significantly improves image recognition. 2016 Google's AlphaGo defeats world champion Go players Lee Se-dol and Ke Jie. 2017 Introduction of Transformer Architecture: A novel neural network architecture that relies on a "self-attention" mechanism. This becomes the foundational technology for most modern Large Language Models (LLMs), including GPT and BERT, revolutionizing natural language processing.. 2020 With a staggering 175 billion parameters, GPT-3 represents a quantum leap in AI's ability to generate human-like text, write code, translate languages, and more, making advanced AI capabilities accessible via API. 2023 The European Union makes significant progress on the EU AI Act, aiming to establish a comprehensive legal framework for AI based on risk levels. Discussions around AI ethics, safety, and governance intensify worldwide. 2024 Demis Hassabis and John Jumper (DeepMind) are awarded the Nobel Prize in Chemistry for their work on AlphaFold and protein folding prediction. 2025 The concept of AI agents that can autonomously plan and execute complex tasks on behalf of users becomes a major trend, with early prototypes and tools emerging.

"Deep Learning" by Goodfellow, Bengio, Courville A comprehensive guide covering theoretical and practical aspects of deep learning. "Reinforcement Learning: An Introduction" by Sutton, Barto A foundational textbook introducing key concepts and algorithms in reinforcement learning. "Artificial Intelligence: A Modern Approach" by Russell, Norvig Explores broad AI topics including machine learning, NLP, and robotics.

Explore and track cutting-edge discoveries in AI & Robotics. 🔍 How to Find Research arXiv.org – Free preprint server for CS, AI, robotics, and more. Use categories like cs.AI, cs.RO, cs.LG, and eess.SY. IEEE Xplore – Peer-reviewed robotics & engineering papers (some paywalled). Semantic Scholar – AI-powered academic search tool with citation graphs. Papers With Code – Tracks state-of-the-art papers & their implementations. Google Scholar – General academic search across all disciplines. Top 5 Research Papers (Must-Reads) “Attention is All You Need” (Vaswani et al., 2017)→ Introduced the Transformer architecture, foundational to modern LLMs. “ImageNet Classification with Deep Convolutional Neural Networks” (AlexNet, Krizhevsky et al., 2012)→ Sparked the deep learning revolution with CNNs. “Playing Atari with Deep Reinforcement Learning” (Mnih et al., 2013)→ Showed how deep Q-networks (DQN) could learn game strategies from pixels. “Robotic Grasping and Manipulation Benchmarking” (Mahler et al., 2017, DexNet)→ Advanced data-driven robot grasping using synthetic training. “Planning Algorithms” (Steven M. LaValle, book, 2006)→ Canonical reference for motion planning, search, and robotics control.