There are two kinds of people in the world -- those who divide the world into groups of two and those who don't. And, here is, now, my take on AI. There are two kinds of AIs -- those that are built using a large set of samples, and the others that "learn" using just the minimal required set of samples. The former is known as Deep Learning or BIG DATA AI, and I call the other kind of AI that learns with minimal data as "MIN DATA AI". In the BIG Data AI, the learning is basically a process that examines a large set of samples and creates a formula using a large number of parameters many of them seem indistinguishable to the naked eye.

In December 2014, I asked whether we were at the beginning of "the end of the Hadoop bubble." I kept updating my Hadoop bubble watch (here and here) through the much-hyped IPOs of Hortonworks and Cloudera. The question was whether an open-source distributed storage technology which Google invented (and quickly replaced with better tools) could survive as a business proposition at a time when enterprises have moved rapidly to adopting the cloud and "AI"--advanced machine learning or deep learning. In January 2019, perennially unprofitable Hortonworks closed an all-stock $5.2 billion merger with Cloudera. In May 2019, another Hadoop-based provider, MapR, announced that it would shut down if it were unable to find a buyer or a new source of funding.

There are plenty of areas where artificial intelligence is helpful and extremely useful. Areas like web marketing, retail product recommendations and consumer help desks can all streamline processes in addition to other benefits when using AI. However, while these areas reap the benefits, is AI the right choice in industrial applications like the Industrial Internet of Things (IIoT) or is there a better option? Many AI methods are self-taught, so they avoid the need for process mapping and other tedious analytical processes, making it seem to be the right fit for IIoT. Yet, only a few methods will apply.

Military officials and industry experts have long discussed how artificial intelligence can benefit the warfighter. The technology promises to crunch mountains of data into easily digestible bites of actionable information and to predict when parts on a vehicle are about to wear out. However, there has been less emphasis on how it can improve modeling and simulations for training purposes -- a market area that is becoming increasingly important as service leaders across the board call for more investment to improve readiness. During the recent National Training and Simulation Association's Interservice/Industry Training, Simulation and Education Conference in Orlando, Florida, military officials and industry experts discussed the benefits of applying the emerging technology to such scenarios. Retired Rear Adm. James Robb, president of NTSA, the host of I/ITSEC and an affiliate of the National Defense Industrial Association, said there was a strong emphasis during the show on big data and artificial intelligence.