In a way, open source and artificial intelligence were born together. Back in 1971, if you’d mentioned AI to most people, they might have thought of Isaac Asimov’s Three Laws of Robotics. However, AI was already a real subject that year at MIT, where Richard M. Stallman (RMS) joined MIT’s Artificial Intelligence Lab. Years later, as proprietary software sprang up, RMS developed the radical idea of Free Software. Decades later, this concept, transformed into open source, would become the birthplace of modern AI. It wasn’t a science-fiction writer but a computer scientist, Alan Turing, who started the modern AI movement. Turing’s 1950 paper Computing Machine and Intelligence originated the Turing Test. The test, in brief, states that if a machine can fool you into thinking that you’re talking with a human being, it’s intelligent. According to some people, today’s AIs can already do this. I don’t agree, but we’re clearly on our way. In 1960, computer scientist John McCarthy coined the term “artificial intelligence” and, along the way, created the Lisp language. McCarthy’s achievement, as computer scientist Paul Graham put it, “did for programming something like what Euclid did for geometry. He showed how, given a handful of simple operators and a notation for functions, you can build a whole programming language.” Lisp, in which data and code are mixed, became AI’s first language. It was also RMS’s first programming love.
Full opinion : Why open source is the cradle of artificial intelligence.
Technology Convergence and Market Disruption: Rapid advancements in technology are changing market dynamics and user expectations. See: Disruptive and Exponential Technologies.
AI Discipline Interdependence: There are concerns about uncontrolled AI growth, with many experts calling for robust AI governance. Both positive and negative impacts of AI need assessment. See: Using AI for Competitive Advantage in Business.
Benefits of Automation and New Technology: Automation, AI, robotics, and Robotic Process Automation are improving business efficiency. New sensors, especially quantum ones, are revolutionizing sectors like healthcare and national security. Advanced WiFi, cellular, and space-based communication technologies are enhancing distributed work capabilities. See: Advanced Automation and New Technologies
Emerging NLP Approaches: While Big Data remains vital, there’s a growing need for efficient small data analysis, especially with potential chip shortages. Cost reductions in training AI models offer promising prospects for business disruptions. Breakthroughs in unsupervised learning could be especially transformative. See: What Leaders Should Know About NLP
