A brief overview for beginners, by a beginner.
Unless you’re a member of an isolated ancient tribe living under one of the six remaining trees in what used to be the Amazon rain forest, you have almost certainly heard the term “Machine Learning” floating past within the last few years.
From 2013 there was a huge hype for Big Data. In fact, according to Gartner’s Hype Cycle that year, big data was at the “peak of inflated expectations.”
Fast forward to 2017 and the hype around big data had mostly quieted, only to be replaced by machine learning, a sub-branch of Artificial Intelligence (AI).
The hype is everywhere — from promises of an AI-fueled utopia to the impending Skynet apocalypse. Clients frequently inquire how they can harness AI to unlock potential business value. Friends and colleagues outside tech ask me about AI and what it means for our lives. AI systems are becoming a brand. IBM Watson is a household name! TensorFlow is the latest cool new toy. Countless companies are looking to inject AI into their business and a lot of funding is pouring into AI startups now.
I am seeing job postings with titles changed from Big Data Analyst to AI Analyst. There are even active discussions amongst some data scientists whether or not their title is really appropriate or if they should change it to “machine learning engineers.”
Expectations about AI are extremely high. We are constantly hearing that “AI will change the world.” The big data hype wave felt very similar — big data was the new business intelligence, big data would solve everything. But the harsh reality is that the majority of companies were just not ready to handle, store, or use massive amounts of data. I saw many companies hire data scientists first, without thinking what those first data projects should be, or the larger goal of integrating how to integrate that data in a valuable way.
In those early years of big data, the outcome was always less than perfect. Most of the work ended up in PowerPoint presentations without ever going into production because most teams simply did not have the right infrastructure or culture to maintain them. Rather, many companies just bought into the trend and invested in big data because they had the fear of missing out. Behavioral economist Dan Ariely also succinctly described the big data hype thusly:
The same thing applies to AI now. And before we go any further, let’s clear one thing up: when people in business say “AI” they really mean machine learning — which is the outcome everyone was envisioning when they got excited about big data all those years ago.
The fear of missing out on AI is so high that everyone wants to be part of this wave even though they are not ready for this. I’ve seen companies who don’t have the basis for even the simplest machine learning algorithm — let alone the right people and culture — but they believe if they pour in enough money, they will get their AI transformation. They spend millions on tools, and yet they don’t have an infrastructure that can handle complex algorithms or deploy changes to it in a fast, iterative manner.And some opportunists are ready to capitalize on this FOMO.
Just like it happened for big data, one can expect this bubble to burst. And after that we’ll get a new trend. Will it be quantum computing? I don’t know yet, but I hope that after the AI hype storm there will be some calm.
I anticipate that after this passes, we can start to do the right thing — focusing on using machine learning to build things that are meaningful and realistic. Like driver-less cars, which will go on to shape the future of mobility for generations. We can work on improving medical diagnosis, education retention, and much more. Those are just some examples; there are many ways machine learning can help, and many will be realistic applications that improve our everyday lives. We just need the right people, culture, and infrastructure to execute. Instead of surfing the wave which will just take us back to the beach we’ve always known, we can sail towards new ground, a promising shore.
Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two very popular concepts Machine Learning and Deep Learning. But lately, Deep Learning is gaining much popularity due to it’s supremacy in terms of accuracy when trained with huge amount of data.
Just to show you the kind of attention Deep Learning is getting, here is the Google trend for the keyword:
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PS: This is my first article and it may contain errors and miss certain details. I apologize for the same and welcome any suggestions to improve my articles.