Microservices & APIs – don’t need to do business process and re-engineering
Feature ComputingHardware
Graphic: Transistor Production Has Reached Astronomical Scales
A look at Moore’s Law in action
By Dan Hutcheson
Data Source: VLSI Research
graphic link for Moore's Law special report
In 2014, semiconductor production facilities made some 250 billion billion (250 x 1018) transistors. This was, literally, production on an astronomical scale. Every second of that year, on average, 8 trillion transistors were produced. That figure is about 25 times the number of stars in the Milky Way and some 75 times the number of galaxies in the known universe.
The rate of growth has also been extraordinary. More transistors were made in 2014 than in all the years prior to 2011. Even the recent great recession had little effect. Transistor production in 2009—a year of deep recession for the semiconductor industry—was more than the cumulative total prior to 2007.
The collective pursuit of Moore’s Law has driven this growth. For decades, manufacturing innovation and simple miniaturization have enabled engineers to pack more capability into the same area of silicon. The result has been a steady decrease in manufacturing cost per transistor (transistor price, which is easier to track, is plotted above).
This steady, predictable decline in prices was a self-reinforcing gift. Because electronics manufacturers could depend on Moore’s Law, they could plan further ahead and invest more in the development of new and better-performing products. In ways profound and surprising, this situation promoted economic growth. It has been the ever-rising tide that has not only lifted all boats but also enabled us to make fantastic and entirely new kinds of boats.
This article originally appeared in print as “Transistors, by the Numbers.”
Github, arxiv, Kaggle, even hacker news have made information more accessible to scientists, researchers, engineers and hackers.
Internet collaboration has made the cycle of demonstration, knowledge exchange, experimentation and enhancement much shorter.
Allows us to stand on the shoulders of giants quicker.
Early software systems were whole and complete systems. Early generation Ais ahd to cope with this.
These systems were multifactor, entangled, spaghetti systems that did many things. This mean problems were themselves general and multifactor and ambiguous. This is the sort of problem at which humans excel. Ais don’t. In today’s loosely coupled architectures, APIs and microservices have constrained the AI problem. Essentially these are well-defined contracts between components. This means a human intelligence has less of an advantage; and it becomes more of a level playing field for an AI. And this in part explains the boom in AI as a service startups we’re seeing.
AI systems have become the key to product success in certain classes of products. As soon as AI gets baked in to the the quality of product and its commercial success, the product and Ceo rationale for investing more in the AI increases; creating a lock-in. Competitors need to respond. Because without AI you are now where. Hence Pell’s law of AI lock in.
This has happened in several key industries, such as video games and internet search, where you now simply cannot compete unless you have world-class AI. And these is spreading to more sectors: robotics, defence, finance and more.
Microservices & APIs – don’t need to do business process and re-engineering