These are terms bandied about quite regularly in the enterprise technology world. Here's why they are inter-connected but definitely not the same
Before we set the ball rolling, it would be pertinent to advise readers that what follows from here on is not meant to be a high-level treatise on technology that has existed for more than a ew years but is now driving change in our lives like no other. So, in case you’re already familiar with these terms, please close this window. If you’ve a nodding acquaintance, read on…
Every time one sees these terms mentioned, the sequence is usually reversed. People refer to them as AI-ML or DL-ML etc. leaving most of us to believe that they’re essentially one and the same. And we often use one set of words to represent the other, leading to a further lack of clarity around what they actually stand for.
Machine Learning: This is a process whereby machines are taught by analyzing data. Algorithms are created to build models that can make predictions. These assist enterprises to make informed decisions through an analysis of sample data sets that are collected from the machines itself. Fortune-500 companies use machine learning to provide users a better experience, especially by throwing up results based on individual preferences. For example, social media applications create curated feeds that are algorithmically created or the streaming apps that provide suggestions to users around what they could watch. Based on the impact of such curated feeds, machine learning models can be tweaked by programmers to fix issues.
Deep Learning: While deep learning can actually be described as a subset of machine learning, there are some differences. For starters, it is an enhanced version of machine learning, which means the logic behind both are similar but the latter has better capabilities. So, how do these operate? Deep learning uses a multi-layered structure of algorithms, whereby the models that are created can resolve challenges that regular machine learning can never do.
For starters, machine learning models need more human intervention and after a time they tend to become better with the results. However, the deep learning models can effectively remove the need for programmers to fix issues of inaccurate predictions. In other words, the machines slowly become smarter by fixing such issues on their own by using much more of the data than a machine learning model does. Which brings us to the term Big Data that gets used during conversations around these topics. Machine learning needs very large amounts of data where machine intelligence gets enhanced with each step delved deeper into data.
One of the best use cases for machine learning and deep learning can be seen from the way in which voice commands are executed by machines. Telling Alexa to switch on the light causes it to search for the word “light” and then respond to it with an action. However, with deep learning, even the absence of a search string with the word “light” in it could engineer the same outcome. So, just by saying “It’s dark out here”, the gadget could turn itself on to provide light.
Artificial intelligence: In both the above examples, you would notice that the machine or gadget involved has learnt something from the voice commands to execute an outcome that is most likely. So, one can safely say that machine learning and deep learning are the means to execute artificial intelligence. In other words, AI is a programmed rule that makes the gadget respond in a specific manner based on the defined situations.
In coding language, artificial intelligence could be described as a bunch of “if-else” statements that allows the machine / gadget to process through them, find the right option and deliver the outcome. Though the term seems to suggest that gadgets would eventually function like a human brain by making intelligent decisions, the same still requires a lot of development. A typical example could be the driverless car whereby it is programmed to move forward till such time as there’s an object / obstacle in front. However, the world is yet to be convinced that AI can actually beat human intelligence in all spheres, especially since emotion is one area where the machines aren’t learning all that fast.
IBM sold out its AI machine Watson earlier this January because they felt it didn’t have the requisite expertise in healthcare. However, they’re still rolling out Watson Order across McDonald’s to automate order taking and fulfillment. The company had tasked its employees to train Watson on regulatory information, back in 2016, to help enterprises automate compliance.