“The number of parameters in a neural network model is actually increasing on the order of 10x year on year. This is an exponential that I’ve never seen before and it’s something that is incredibly fast and outpaces basically every technology transition I’ve ever seen… So 10x year on year means if we’re at 10 billion parameters today, we’ll be at 100 billion tomorrow,” he said. “Ten billion today maxes out what we can do on hardware. What does that mean?”–Naveen Rao, Intel
In 1308, Catalan poet and theologian Ramon Llull completed Ars generalis ultima (The Ultimate General Art), further perfecting his method of using paper-based mechanical means to create new knowledge from combinations of concepts.
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The evolution of AI from theoretical concepts to machine logic to expert systems to machine learning to artificial neural networks and big data-based deep learning.
The trouble with AI is that it lacks a clear definition, that it suffers from the unique nature of its creators’ intelligence and the fuzzy language they use.
Expert systems represented a new stage in the evolution of AI, shifting from its initial emphasis on general problem-solving and reasoning to a new focus was on knowledge.
Catalan poet and theologian Ramon Llull perfected in 1308 his method of using paper-based mechanical means to create new knowledge from combinations of concepts.
The information explosion has turned into the big digital data explosion, enabling deep learning-driven data analysis, today’s AI.
Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted the growth in consumers’ acceptance of chatbots, especially for help with routine tasks; questions about medical AI algorithms missing the worst patient outcomes; and new predictions for 2020 and beyond about the future of AI and work.
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