It took AI just a couple of years to go from undercurrent to mainstream. But despite rapid progress on many fronts, AI still is something few understand and fewer yet can master. Here are some pointers on how to make it work for you, regardless of where you are in your AI journey.
From big data to AI- In 2016, the AI hype was just beginning, and many people were still cautious when mentioning the term “AI”. After all, many of us have been indoctrinated for years to avoid this term, as something that had spread confusion, over-promised, and under-delivered. As it turned out, the path from big data and analytics to AI is a natural one.
Not just because it helps people relate and adjust their mental models, or because big data and analytics were enjoying the kind of hype AI has now, before they were overshadowed by AI. But mostly because it takes data — big or not-so-big — to build AI.
It also takes some other key ingredients. So, let’s revisit Big Data Spain (BDS), one of the biggest and most forward-thinking events in Europe, which marked the transition from big data to AI a couple of years back, and try to answer some questions on AI based on what we got from its stellar lineup and lively crowd last week.
Can you fake it till you make it?
Short answer: No, not really. One of the points in that Gartner analytics maturity model was that if you want to build AI capabilities (the predictive and prescriptive end of the spectrum), you have to do it on a solid big data foundation (the descriptive and diagnostic end of the spectrum).
Part of that is all about the ability to store and process massive amounts of data, but that really is just the tip of the iceberg. Technical solutions for this are in abundance these days, but as fellow ZDNet contributor Tony Baer put it, to build AI, you should not forget about people and processes.
More concretely: Don’t forget about data literacy and data governance in your organization. It has been pointed out time and again, but these really are table stakes. So, if you think you can develop AI solutions in your organization by somehow leapfrogging the evolutionary chain of analytics, better think again.
As Oscar Mendez, Stratio CEO, emphasized in his keynote, to go beyond flashy AI with often poor underpinnings, a holistic approach is needed. Getting your data infrastructure and governance right, and finding and training the right machine learning (ML) models on this can yield impressive results. But there is a limit to how far these can take you, amply demonstrated by everyday fails by the likes of Alexa, Cortana, and Siri.
The key message here is that bringing context and reasoning capabilities in play is needed to more closely emulate human intelligence. Mendez is not alone in this, as this is something shared by AI researchers such as Yoshua Bengio, one of Deep Learning’s top minds. Deep Learning (DL) excels in pattern matching, and the data and compute explosion can make it outperform humans in tasks based on pattern matching.