Artificial Intelligence Applications Forecasted to Soar

Artificial Intelligence Applications Forecasted to Soar

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Artificial Intelligence Applications Forecasted to Soar

A recent report from Tractica forecasts that, as enterprise AI deployments gather increasing momentum, cumulative revenue for the sector will total $43.5 billion worldwide during the period from 2015 through 2024.

I tend not to pay much attention to the actual dollar figures in these kinds of forecasts. I just don’t have faith in any set of assumptions drawn from this very early stage in the development of artificial intelligence. I share it, however, because: a) analysts are now trying to quantify the opportunity, which is interesting in itself, and b) the applications outlined at somewhat interesting.

“In almost every industry, including some very traditional ones, new approaches to age-old problems are being trialed using artificial intelligence,” says principal analyst Bruce Daley. “The business questions being addressed range from where to plant crops to how to detect fraud. The most highly affected industries are likely to be those with large amounts of data, where there are high rewards for making decisions quickly.”

Details:

http://www.marketwatch.com/story/artificial-intelligence-technologies-are-quietly-penetrating-a-wide-range-of-enterprise-applications-according-to-tractica-2015-08-19

#artificialintelligence 

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  1. I’m all for the infusion of LOGIC into critical decisions. I’m kinda surprised that we survived this long on sacred cows and office politics…

  2. The Heritage Health Prize discovered insurance redlining. I wonder if that is what Jono Bacon was asking about when he wanted to know the downsides of AI.

  3. Was that done with some sort of machine learning on big datasets, James Salsman​?

  4. Interesting article, James Salsman​. I thought this was particularly surprising:

    What was particularly surprising about the results was that when we examined the ideal distributions for Republicans and Democrats, we found them to be quite similar (see Figure 4). When we examined the results by other variables, including income and gender, we again found no appreciable differences. It seems that Americans — regardless of political affiliation, income, and gender — want the kind of wealth distribution shown in Figure 3, which is very different from what we have and from what we think we have.

    I’m actually working on a piece that I’ll publish next week on the role of the stock market in wealth disparity.

    How do you see this connecting to AI?

  5. Gideon Rosenblatt Art Okun’s mistakes in machine learning techniques in statistics – forecasting – created supply side trickle down economics: http://www.imf.org/external/pubs/ft/fandd/2011/09/berg.htm

  6. His error was because of machine learning? This was back in the seventies, right?

  7. Did you get the actual report Gideon Rosenblatt​? The table of content looks interesting

  8. I did not, Antoine Carriere​. I hate it when I’m forced to sign up just to even see the price of something like this.

  9. Gideon Rosenblatt yes, traditional forecasting was one of the first machine learning applications, implemented as statistical regression on mainframe computers in the 1970s.

  10. Gideon Rosenblatt  I am fascinated with AI but also skeptical. My skepticism is fear of miscreant uses or rogue programming. 

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