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IBM bets big on cognitive; ropes in Watson for manufacturing

Soumik Ghosh | June 6, 2017
Organisations today face rising resource costs in traditionally low-cost production markets. Cognitive manufacturing helps swing the balance back in favor of manufacturers, believes Bruce Anderson, IBM.

When you start to unlock a lot of the data using IoT-like technologies, you wind up with a richer data set in order to analyse the problem. 

What you really need is the ability to derive meaningful insights with all the data, and have the experience go in to the repository for the next guy to get answers to the problems.

When you apply cognitive to a problem, you can get a range of answers immediately, about how you can solve that problem. What we're doing here is augmented intelligence, where a human being makes the decisions on which strategy to use.

So, this eliminates the need for people to manually collect the data. In addition to this, we were able to shrink the time taken down by a factor of 24 or more.


All swell, but why haven't manufacturers embraced cognitive yet?
The survey indicates that 38 percent of companies haven't yet implemented cognitive, but have plans of doing so; 26 percent responded saying that they have roped in cognitive to a limited degree; whereas 24 percent of companies said that they already have a pilot program in place.

One of the biggest reasons is insufficiently skilled human resource. So, the people in manufacturing have to have the kind of background to implement cognitive. 

You will as a company, still go through that evolution of starting with basic analytics, then move towards predictive, as you learn your problem better. You'll start to get to a prescriptive approach from a preventive one. And then finally, you'll start using cognitive as your data set is actually in shape to do that.

Companies in India that have invested in data collection got a head start - it's a data-hungry process. What you need to remember is that when you just get the raw data in, you get about 30 percent of the questions right. What you really need to do is train the data. So, you have human experts who work with the data till you come up with the right answers. 

After we trained the data, the accuracy went up to the high 90s - we were able to rank-order all the right answers.


So how does one go about training the data?
Basically, the algorithms can sift through all of the text, and it's able to understand nouns and verbs based on natural language processing. It can ask you questions, and your answers remain in its memory.

In each domain of manufacturing, there's a certain taxonomy. You have to spend enough time with it so that Watson can build a taxonomical understanding of the information.

So, it's going to build the taxonomy and apply it to the data you've already given it. And the next time, it's going to ask you fewer questions, till it comes to a point where it asks you almost no questions. It will still want to know from its rank-order answers if it's doing a good job, though.


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