There are two kinds of AI: BIG Data or MIN Data

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 the AI. There are two kinds of AIs — those that are built using BIG DATA and others that are NOT.

The BIG DATA AI

In simplistic terms, the BIG Data powered AI models “learn” without being explicitly programed to do so. 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. It is a time-consuming trial and error process where the parameters are continually adjusted until training data with the same labels consistently yield similar outputs. A classic example of this is using a data set that has 50000 images of digits (0 to 9) and the AI model learns to recognize the digit.

The BIG DATA AI is all about "mathematically recognizing" incredibly subtle patterns within the mountains of data.

Using this "pattern recognition technique" you can now "provide" an answer, a decision or a prediction to an input data that you have never seen before. It is a great tool but is unlike the human brain in basic learning. These algorithms require mountains of data and high CPU/GPU power to train. It has no common sense, conceptual learning, creativity, planning, human-like intuition, imagination, cross-domain thinking, self-awareness, emotions, etc. It has trouble when it comes to extreme edges of data for which it had limited samples.

BIG DATA AI is basically an optimizer based on a large volume of data for a very specific vertical single task.

The MIN DATA AI

In MIN DATA AI, the AI models "learn" with minimal data that is needed to learn. A formula is indeed created however with minimal data. A classic example of this is how some people say they never forget a face even if they met in passing, or, how you recognize a taste even if you have experienced it only once before.

MIN DATA AI is closer to how we learn and do problem-solving. I don't need to drink 10,000 OLD FASHIONED (cocktail) to tell the drink in my hand is old fashioned (although may be in my case I already have) or to recognize that the old fashioned is from HaberDasher San Jose or NOT (and, yes, HaberDasher is a great place). With this AI, you will be able to make bets even though data says something else. Your learning can be super fast.

Here is how you can start your journey for MIN DATA AI:

1. Stop looking for BIG DATA. Like any other addiction, it will be difficult to cope with BIG DATA addiction but you have to do it.
2. Focus more on algorithms.
3. Innovate how to create BIG DATA from MIN DATA.

In next 3 years, AI will rely less on BIG DATA and more on MIN DATA. This new MIN Data approach of AI will enable a whole new set of use cases that seemed unsuited before.

MIND is perhaps MIN D(ata)

Future of AI making devices, processes and automation intelligent (and not just high confidence level decision making based on BIG Data) is not far. If you are struggling with lack of BIG Data for your AI, or would like to brainstorm on how MIND works, or for that matter how SECOMIND does AI, please feel free to reach out to me.