Often used interchangeably, Artificial Intelligence (AI), and Machine Learning (ML) are two very hot buzzwords right now. Though they aren’t the same thing, the perception that they are can sometimes lead to some confusion. For this reason, writing to explain the difference is ‘worth the while.’
Terms that crop up very frequently when the topic is Big Data or analytics, Artificial Intelligence (AI) and Machine Learning (ML) are two varying concepts. AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. On the other hand, Machine Learning is a current application of Artificial Intelligence based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
Renewed Interest in Machine Learning
Machine Learning is making a comeback. In the last few years, there has been renewed interest in machine learning. The revival seems to be driven by strong fundamentals—an incredible amount of data being emitted by sensors across the globe, with cheap storage and lowest ever computational costs! However, not everyone understands what machine learning is. So, it’s important to explain what machine learning is and why it matters.
Understanding the Concept of Machine Learning
A part of artificial intelligence (AI), machine learning enables computers to learn by themselves without being specifically programmed. Upon coming across new data, computer programs learn, change, develop and grow by themselves. A data analysis method, machine learning turn analytical model building into an automated process. Thanks to machine learning and algorithms that constantly learn from data, computers can find insightful information without being programmed to do so.
The concept of machine learning is not new. However, using complicated mathematical calculations for big data has become popular only recently. Cyber fraud detection, offers recommendations from Amazon, Google’s self-driving car, Netflix movie recommendations and friend recommendations in Facebook are some of the instances where machine learning is applied.
The above testifies the vital role that machine learning is playing today. According to a recent McKinsey Global report, machine learning will drive innovation and technological advancement in coming times. Hopefully, by now, you have a better understanding of machine learning.
Where Machine Learning came from
A concept that was born at the same time as AI, machine learning over the years included the following algorithmic approaches:
- Decision tree learning
- Reinforcement learning
- Inductive logic programming
- Bayesian networks
You may or may not be aware of this but none of the aforementioned-approaches achieved AI’s ultimate objective. In fact, for many early ML approaches, even narrow artificial intelligence was out of bounds. Though, there was one ML approach that turned out well: computer vision. But, even that approach had a problem: to perform the job, one had to do a considerable amount of hand-coding.
Using their bare hands, programmers would write classifiers such as edge detection filters to enable the program to identify the starting and ending point of an object. Using the classifiers, the program would develop algorithms to understand the image and how to determine the stop sign. This was good but still far off from what was expected from machine learning or artificial intelligence. Until recently, the aforementioned qualities of machine learning were nowhere near ‘human abilities’. However, time and, the appropriate learning algorithms have turned things around.
How Businesses are Using Machine Learning
Machine learning is at the top of the ‘hype curve’ and there’s no denying it. Machine learning is already forcing massive changes in the way businesses operate. Today, machine learning is helping many businesses to run more efficiently and make more money. Following are some of the ways businesses are using machine learning today.
Making User-Generated Content Valuable
Awful is the word I’d use for the average piece of user-generated content (UGC). To be honest, it’s way worse than you think, with misspellings, vulgarity or flat-out wrong information everywhere. However, by identifying the best and worst UGC, machine-learning can filter out the bad bubble up the good without needing a real person to tag each piece of content.
Find Products Faster
Today, all businesses need smart search results. To show high-quality content to their users, most successful online businesses employ machine learning. Also, there are some businesses that employ machine-learning strategies to give their online customers the benefits of machine learning when they are browsing for products.
Understand Customer Behavior
Sentiment analysis is something machine learning excels at. In fact, machine learning drives a lot of customer-related decisions today. For example, social media listening through machine learning has become standard operating procedure for many businesses today.
What the Future Hold for Machine Learning
To automate their decision processes, many organizations today are using machine-learning based tools. In 2017, corporate investment in artificial intelligence is expected to triple. Moreover, it is predicted to become a $100 billion market by 2025, with machine learning boasting a sizeable share. In 2016 alone, $5 billion were invested in machine learning venture and the numbers are expected to increase each year. So, we can conclude by saying that the future is bright for machine learning and every business that adopts it!