Nexus: A Brief History of Information Networks from the Stone Age to AI

Nexus is chiefly concerned with the sudden rise of AI. Artificial Intelligence (AI) has evolved at an astonishing pace, significantly transforming industries, economies, and societies. 

The concept of AI dates back to the mid-20th century, with pioneers like Alan Turing and John McCarthy laying the theoretical groundwork for machines that could simulate human intelligence. Turing’s 1950 paper “Computing Machinery and Intelligence” introduced the Turing Test, a way of evaluating whether a machine could exhibit intelligent behavior indistinguishable from a human. In 1956, the term “artificial intelligence” was coined by McCarthy at the Dartmouth Conference, marking the beginning of AI as a field of study. 

The true breakthrough in AI’s rise came in the late 2000s and 2010s with the development and popularization of machine learning (ML), particularly deep learning, a subset of ML. Machine learning allows computers to learn patterns from vast amounts of data and to improve performance over time without explicit programming. This shift was fueled by advancements in computational power, the availability of large datasets, and the rise of neural networks. 

Deep learning, which mimics the structure of the human brain through artificial neural networks, was instrumental in enabling machines to perform tasks such as image recognition, natural language processing, and speech synthesis at a superhuman level. In 2012, a landmark moment occurred when a deep neural network developed by Geoffrey Hinton and his team at the University of Toronto achieved a stunning victory in the ImageNet competition, drastically reducing error rates in image classification tasks.

By the mid-2010s, AI-powered systems such as virtual assistants (e.g., Siri, Alexa), recommendation algorithms (e.g., Netflix, Amazon), and self-driving cars (e.g., Tesla, Google’s Waymo) became commonplace, signaling the widespread integration of AI into consumer technology. In 2024, AI is revolutionizing healthcare, helping to diagnose diseases, develop new treatments, and improve patient care. 

The growing sophistication of generative AI models has raised important questions about intellectual property, creativity, and the future of work in the creative sector. Legal frameworks and industry standards are evolving to address these challenges. AI’s rise has also raised economic concerns, such as the displacement of jobs due to automation. Bias in AI systems is another major issue, as is AI’s role in surveillance and privacy. In Nexus, Yuval Noah Harari explores these issues and threats, while also speculating on the potentiality of AI.