This article was written by Paramita Ghosh.
A 1969 McKinsey article claimed that computers were so dumb that they were not capable of making any decisions. In fact they said, it was human intelligence that drives the dumb machine. Alas, this claim has become a bit of a “joke” over the years, as the modern computers are gradually replacing skilled practitioners in fields across many industries such as architecture, medicine, geology, and education. Artificial Intelligence, Machine Learning, Data Science, and Deep Learning are pushing these changes in ways that are only just being understood.
Google Trends: ML vs Big Data (source: click here)
In the current scenario, many buzzwords are being employed in the evolving IT industry, especially in the various research areas around and within Data Science. For many years, the world has known about experiments (with varying degrees of success) in Artificial Intelligence (AI), but recently, rapid strides were made in this field of study, leading to allied research areas of Machine Intelligence, Machine Learning, and now, Deep Learning. So how are these specialized sub-domains under AI similar to or different from each other? This article takes a look.
A good market application of Machine Learning can be found in Second Spectrum, a California-based start-up that that prepared predictive models of basketball games for the US National Basketball Association. In Europe, the banking sector uses ML techniques to for various banking functions which helped them achieve target business growth and savings. In this article, Quora provides an interesting comparison of Machine Learning and Artificial Intelligence.
The Research Areas in Practice
According to a recent article in Information Week AI and ML are gradually evolving from the science fiction era to on-the-ground reality. Over half of global enterprises are experimenting with Machine Learning, while top enterprises like IBM, Google, and Facebook have invested in open-source ML projects. This article reports that global enterprises are experimenting with “smart computing,” and seriously investigating whether Artificial Intelligence can be applied to business solutions.
A Numenta blog presents a detailed comparative study of the various technologies. The common perception is that there are no standardized definitions for the four terms, and people still use the terms loosely without understanding the scientific significance of each. Also, a significant evolution has taken place in the meaning of the terms. What people meant by AI in 1960 was distinct from what AI means today. Datanami’s How Machine Learning is Eating the Software World explains that the majority of smart applications today depend on Machine Learning to interpret the results in the real world.
In an An Executive's Guide to Machine Learning, Machine Learning 1, 2, and 3 have been aptly described as descriptive, predictive, and prescriptive stages of applications. The predictive stage is happening right now, but the ML 3.0, or the prescriMachine Learning Now provides a clear analysis of this technological preparedness of global businesses.ptive stage, provides a great opportunity for the future. The article titled CIOs Need to Invest in