The belief that the third time something happens is more likely to succeed than the previous two attempts. It is also a good luck charm – spoken just before trying something for the third time.
We are upon the third AI winter, Gartner has predicted it according to the hype cycle, Forbes suggested in an March 2019 and then again in October 2019, but in September 2019, Tiernan Ray, a Forbes Contributor indicated that ‘it’s different for a simple reason: artificial intelligence, in its latest incarnation, called deep learning, has become “industrialized.” For the first time ever, AI is part of how companies work.’ Ray also hinted that ‘even if these new (AI) startups don’t deliver on their explicit promises, namely, to optimize everything, they have a seat at the table by being involved with the data.’
Two issues will bring forth the third AI winter, and they are (1) the data we have collected is not as good as expected and, (2) we need to create a specific business case. There is a bright lining, and this AI winter will be very productive. Startups will have time to optimize and re-calibrate data that needs to be collected. Innovation will come ‘outside-the-box’ business case.
There were two previous AI winters from 1974 – 1980 [1] and 1987 – 1993 [2] but this third winter is different, as suggested by Tiernan Ray (Forbes contributor). This third winter is a milestone moment for me and Emerson H Lee Accounting Intelligence (EHLAI). To keep busy during this third AI winter, I am surrounding myself with plenty of internal and externally available data, lots cheap storage, cloud, and virtualization technology. I also have a staff that is ‘all in’ to automation, 400 very loyal small business clients, and 8,000 individuals that can help me fine-tune the algorithm for interaction with the ELA our expert system chatbot.
We are also blessed to be in the age of Facebook, Twitter
and GitHub. We are more connected than our parents and we have access to some
of the largest open-source code libraries in history to test our algorithms. AI
winter also means quiet time for me to reflect on the failures of not having a
strategy, plan for the next steps in sales, traditional financing and best of
all less hype noise.
[1] shortcomings of machine translation in 1966 (ALFAC report indicate that machine translation did not meet expectations) and DARPA’s budget cutbacks 1974 .
[2] The end of LISP machine beginning in 1987 and rise of the minicomputer.