The Six Accelerants of the AI boom
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The Six Accelerants of the AI boom Why is there a boom in artificial intelligence right now? Talk given at The Europas in June 2015
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The Six Accelerants of the AI boom
- 1. @azeem The six accelerants of the AI rocket ship Azeem Azhar, Brandwatch @azeem
- 2. VC funding into pure-play AI startups is booming VC funding into pure-play AI startups @azeem
- 3. AI Moore’s Law Data explosion Internet collaboration Everything is software APIs & microservices Law of AI Lock In @azeem
- 4. We create more transistors every tenth of a second than there are stars in the Milky Way @azeem Source:VLSIResearch
- 5. Watch – more powerful than a Cray 2 Fits in a room Fits in a pocket Fits on a wrist 1.6Gflops 1.9Gflops >3Gflops 1985 2010 2015 @azeem
- 6. 44Zb of data by 2020 – 44x in 11 years 0 5 10 15 20 25 30 35 40 45 50 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Data volume Sources: IDC, Azeem Azhar analysis
- 7. Collaborate faster than ever @azeem
- 8. “Most business problems are now software problems” - me
- 9. Pell’s Observation on Microservices and APIs @azeem
- 10. Pell’s Law of AI Lock In Video games Internet search Mobile UI Defense @azeem Health Finance …
- 11. AI Moore’s Law Data explosion Internet collaboration Everything is software APIs & microservices Pell’s Law of AI Lock In @azeem
- 12. Six accelerants Azeem Azhar Exponential Digest: http://azeem.io/s @azeem azeem@brandwatch.com
Hinweis der Redaktion
- 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