AI: Business 'on steroids'

The hype around big data and AI is quite overwhelming and companies are not really sure how to react.

In my experience there are a lot of companies currently that fulfill this axiom "You have to fake it till you make it” and this is a common observation from many others as Dan Ariely, Professor of Psychology and Behavioral Economics says: “Big Data is like Teenage Sex: everyone talks about it, nobody really knows how to do it..., only some really know how to do it. Everyone thinks everyone else is doing it. So, everyone claims they’re doing it.

The Data Science world has come crazy since I started JaraTech in 2010 building a professional profiles Search engine, that it becomes a Big Data in 2012. Since then, this world hasn't stop creating new AI buzzwords weekly. 

By those days, it was the Big Data wave, now it has evolved to something that has become a must for all of us. 

Currently I'm working on Qrowd Makers creating AI solutions to increase the profits to many customers. One of this solutions is 'LinkedIn Bots' the first chatbot engine in LinkedIn although we also develop projects on demand.


My world now is AI, Machine in Learning, Chatbots, Cognitive computing and the many variations around this new wave. Obviously I'm deeply involved in all this, but it can seem difficult for many professionals and business owners to get ahold of what applications are viable, and which are hype, hyperbole or hoax. Mostly because there are a lot of gurus, IT consultancies, and "experts" selling IA as the Philosopher's stone.


One year ago I began teaching in Data Science Programs, in some Universities and Business Schools, about the entrepreneurship opportunity in Data Science, which are the opportunities, which advantages a tiny startup / company can provide to Big ones, and why none of this benefits comes from the amount of money invested.

The challenge is that implementing AI isn’t as easy as installing software. It requires expertise, vision, and information that isn’t easily accessible.

To clarify and simplify how to approach to an AI model, it is very important to know that it is only as good as the data it receives. And it is able to interpret that data only within the narrow confines of the supplied context. Furthermore, It doesn’t “understand” what it has analyzed, so it is unable to apply its analysis to scenarios in other contexts. Therefore, I'm very sorry if you are a fan of science fiction movies, and you are worried about the Singularity (is the hypothetical future creation of super-intelligent machines. Super-intelligence is defined as a technologically-created cognitive capacity far beyond that possible for humans). Nowadays AI is more like an Excel spreadsheet “on steroids” than a brainy specialist.

Yes, the Data is key if your AI has to solve a problem, or answer a complex question with many fuzzy responses. This is your first insight: start by building a Big Data strategy if you want to continue with AI.

'AI' is hard to learn, difficult to develop, and its deployment strategy in the organization requires highly specialized and skilled professionals, although... if you make it right, the results may exceed your expectations, and generate the wave of a new digital transformation that will empower your company to achieve the market share desired for the next years. In addition, this wave may become one of the main barriers to competition. All those companies with the illness of lack of vision will not break into this technological and strategic leap.

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