This article provides a comprehensive, beginner‑friendly walkthrough of the landmark 2017 paper “Attention Is All You Need,” covering its authors, historical context, the shortcomings of RNNs and CNNs, the birth of self‑attention, the Transformer architecture, and its transformative impact on modern AI.
This comprehensive guide traces AI from the origins of human intelligence and the first computers, through the birth of artificial intelligence, the rise of machine learning and large language models, to the emergence of agents, multimodal systems, and the challenges that lie ahead.
This article traces the seventy‑year evolution of Symbolic AI, explains its core physical symbol system hypothesis, contrasts it with connectionist approaches, examines historic milestones such as the Logic Theorist, MYCIN and XCON, discusses the symbol‑grounding problem, and shows how modern neural‑symbolic systems are reviving its relevance in high‑stakes domains requiring accuracy, interpretability and safety.
The article explains how the terms “neural network” and “deep learning” originated, why they were once treated as distinct branches of AI, and how historical biases and naming politics eventually merged them into a single research direction.
This article chronicles the evolution of artificial intelligence over eight decades, spotlighting seminal figures such as Alan Turing, Allen Newell, Marvin Minsky, John McCarthy, Yoshua Bengio, Geoffrey Hinton, Andrew Ng and Yann LeCun, and explains how their groundbreaking work shaped modern AI.
This article examines the historical roots of large language models, highlighting Jeremy Howard’s ULMFiT as a pioneering work, its influence on GPT‑1, and subsequent debates about which model truly qualifies as the first true LLM, supported by citations and expert commentary.
This article recounts the little‑known 2012 AI auction in which Baidu, Google, Microsoft and DeepMind vied for Geoffrey Hinton’s fledgling DNNResearch, revealing how the bidding drama propelled deep learning into the mainstream and set the stage for today’s AI arms race.
Geoffrey Hinton, the Canadian cognitive psychologist and computer scientist known as the father of deep learning, rose from a distinguished scientific family, endured decades of skepticism, pioneered deep belief networks, mentored future AI leaders, and now warns of AI risks after leaving Google, embodying a lifelong commitment to humanity and ethical AI.
In this talk, Wu Jun explains the decades‑long evolution of language models, why ChatGPT sparked hype yet isn’t a breakthrough, how massive compute and data power it, and what practical effects it has on creators, energy use, and the tech industry.
From Deep Blue’s 1997 chess victory to today’s generative AI breakthroughs like GPT‑3, DALL‑E, and large‑scale models, this article traces the rapid rise of artificial intelligence, highlighting key milestones, the impact of massive compute and data, and the societal implications of AI’s expanding capabilities.
This article explores what artificial intelligence is, traces its historical development, examines current data, algorithm, and hardware breakthroughs driving AI adoption, discusses whether AI could surpass humans, and highlights key learning challenges for newcomers.
The article outlines AI’s three historical waves—search and reasoning, expert systems, and machine‑learning/deep‑learning—detailing their core technologies, achievements, and limitations, while emphasizing how past cycles inform today’s narrow AI advances and the renewed relevance of computing power and data‑driven methods.
This article traces the evolution of computer chess from the 18th‑century automaton "The Turk" through early programs by Turing, Shannon, and McCarthy, to landmark systems like Deep Blue, AlphaGo, and AlphaZero, highlighting key algorithms, milestones, and their impact on AI research.
This article defines artificial intelligence as a branch of computer science that aims to simulate and extend human intelligence, outlines its key research areas such as robotics and natural language processing, and presents a detailed visual timeline from TalkingData that charts the technology’s major milestones.
The article traces humanity’s long‑standing quest for intelligent machines—from early mechanical curiosities and Turing’s seminal test to modern breakthroughs in deep learning, highlighting how hierarchical feature learning, massive data, and collaborative open‑source efforts are reshaping AI and its future impact.
