Science-fiction movies since birth have tried to lead our imaginations and predict how the future world and technologies would look like. We all have in our minds some images of the intelligent supercomputers from the movies,
for example, the Deep Thought in 2005 movie The Hitchhiker’s Guide to the Galaxy (see pic) or the intelligent master control system in 2008 movie Eagle Eye, known as the government’s intelligent gathering supercomputer ARIIA or ARIA(see pic below).

In many of these movies, a common theme had been that when such an intelligent “machine being” became overly powerful or misguided by evils, human heroes had the obligation to destroy it before it could destroy mankind. Although there is a distinct possibility that such a super intelligent machine being could exist in our real future, increasing evidences from today suggest that its picture be totally different. It will not be a centralized physical super machine or system as in the movies, but rather more likely it will take an invisible form living in the future clouds – in the complex webs of networked systems that could exist everywhere, on earth, in orbits or even on remote stars. The plot in the movie series The Matrix (1999 and 2003) seemed closer to this scenario. It would become a lot harder to be destroyed though if indeed evil thoughts took control. Let’s wish the age of ultra-capable RoboCops or human surrogates (as in 2009 movie Surrogates) that could draw intelligence and power through such invisible all-around machine forces won’t come into reality too soon before we find out the answer to this age-old question yet: Could one day machine-learnt intelligence indeed surpass human intelligence?

Machine Learning (ML) is a branch of Artificial Intelligence (AI). It’s the study of using machines’ computing and large data processing power, analyzing past and present data through programming and algorithms, to offer predictive capabilities without the inputs of human intuitions. The next stage will lead to more advanced AI that allows the simulations of the cognitive powers of the human brain by the machines. In fact these desires and concepts, as shown in generations of sci-fi movies, have existed for a very long time and nothing is new. Many commercial companies, including Google, Microsoft, Amazon, IBM, etc., have been playing with these concepts in their data-mining related product and service offerings such as search, cross-selling, online gaming, etc. People and countries also have been building better and faster supercomputers for decades to shrink the computing time. However only with the recent compounding growth of the compute power by clouds or clusters, these ideas, and many more enhanced possibilities in advanced AI, are becoming closer and closer to reality, and exciting again.

Machine Learning and AI are great examples of those fields in which when science and technology merge together, unlimited potentials emerge. Even with increasingly scalable and seemingly unlimited compute power, machines can only learn as intelligently as the algorithms that direct them. That’s the field of Data Science, the multi-disciplinary science of extracting insights and knowledge from data. Math and statistics are only part of what Data Science needs. Versatile skills in many areas are needed to truly make intelligent sense out of the amount of data in our hands today and the gigantic yet-to-come in order to predict the future or build human capabilities in machines.

Although still in the basic stage, IBM’s $1 billion investment announced early this year in Watson, a cognitive ML technology on cloud, and the coming July release of Microsoft Azure ML, are seen as the start of the large-scale commercial propagation of Machine Learning, both as part of the cloud offerings on their individual cloud platform. Once these facilitating tools and services become available to the masses, the power of science and technology coupling will become even more evident.

At least in our current age, there is no doubt that humans are definitely still in control of the machines.