Have you ever wondered whether machines could be programmed to fix their own flaws and build more robust programs? Today, they can.

The method of data analysis that allows this intelligence is called machine learning. Machine learning encourages perpetual analysis of data that allows it to iteratively learn from the data without relying on a programmed solution to be implemented by engineers. It allows for iterative new model building that pushes the boundaries of where computer science can take us.

Machine Learning Isn’t New

The idea of machine learning is an old one, but the algorithms used today are far removed from the machine “learning” used in the past.

First taking hold as pattern recognition, machine learning of old was based on a theory that computers could learn and adapt without interference from humans if they could recognize patterns. If a computer could recognize a pattern on its own, it could implement a solution.

Today, the process goes further because it is iterative. Patterns still exist and inform, but today, products learn from new and existing data to independently produce a repeatable product as a result. Thus, while not new, the iterative nature of today’s machine learning means it can take the world further than previously anticipated.

More importantly, machine learning is more accessible than ever. A strong mathematics background is the only pre-requisite required for learning the algorithms used in machine learning. Those with a keen interest can easily find tutorials on coding for machine learning across the internet including on learning forums like Coursera.

Machine Learning Impacts Your Life – Right Now

If you use the internet, then your life is directly impacted by the latest developments in machine learning.

Netflix is a stellar example of the way these programs work. Sign up for a Netflix account, and you’ll be asked questions about your taste in entertainment. Netflix’s algorithm will then recommend programs to you based on the information provided – it will also send you after its own content because it needs to be promoted.

Your initial data serves as the foundation for your early entertainment experiences, but as you watch more hours of TV or film, Netflix learns more about you. It learns:

  • What categories you prefer
  • What actors you like
  • When you watch entertainment
  • Whether you watch one or ten episodes in a sitting
  • Whether you watch things all at once or frequently pause or switch programs
  • Who you share your account with – and what they watch
  • Where you watch entertainment

With all this information, Netflix can provide you with a custom selection of programs on your home screen. And it continues to learn as your tastes change. Switched from a week of romantic comedies to just plain comedy? Netflix takes note and adjusts your queue. In fact, some users may never venture further into the Netflix vault because their next program is only the push of a button away.

Entertainment is only the beginning of the ways machine learning and artificial intelligence will impact our lives. Here are a few more ways:

  • Google Maps uses AI to provide better traffic routes
  • AI autopilots mean the average flight requires only seven minutes of human steering
  • Email providers use AI to strengthen spam filters and categorize emails
  • Schools use machine learning for plagiarism checks and robo-reading
  • Banks deposit checks through smartphones using machine learning and AI
  • Robo-investors trade stocks for newbie investors based on AI-first approaches

Machine Learning, Artificial Intelligence, and Big Data

Machine learning isn’t new – it’s widespread use dates back several decades. The advent of machine learning in the public mind, however, is aided by what’s called “big data.”

Big data is a general term referring to the huge amount of data generated, collected, stored, and used on a daily basis. The volume of data, while massive, is important because it is laden with limitations, but what’s more critical for changing the world is the way data is used.

Big data can be used to make better decisions and generate better strategies. It can help businesses achieve profitability. It may someday help scientists cure cancer. But the potential of big data is limited without 1) a way to sort and analyze the data and 2) a way to learn from the data.

It’s here that big data and machine learning/artificial intelligence converge.

Here’s how:

Data is plugged into machine learning programs where it’s analyzed to produce intelligence and create data visibility. Machine learning enables data mining and therefore deep learning which works together with the deep neural networks to influence artificial intelligence.

Artificial intelligence is slated to be the most disruptive technology to emerge over the next ten years – and it couldn’t arrive without the help of big data and present and future machine learning capabilities.

Yet, many wonder what this means for us – as people, as employees, and as scientists. The dystopian futures predicted by many from Elon Musk to the European Union suggest this great shift could lead to the biggest culling of jobs in modern economic history. While these three trends will disrupt the workplace, there will also be many advantages: most importantly, it will create better scientists.

The collaboration between artificial intelligence, big data, and machine learning allow for human scientists to learn and evolve and make gains in the market and technology. While it’s true that machines can ingest more data and learn at an unfathomable pace, only humans have the emotional intelligence required to put the final stamp on important decisions.

The difference? We’ll be able to reach those decisions faster and hopefully make fewer mistakes along the way.

Algorithms Used by Every Machine Learning Developer

Machine learning has exploded, and algorithms have grown along with it. But both new and experienced engineers rely on a set of algorithms that serve as the foundation of AI programs that can be broken down generally into supervised and unsupervised learning.

Supervised learning is similar to learning when an answer key already exists. It’s simple a matter of finding the fastest, most efficient way to the answer based on data with Y labels.

Unsupervised learning focuses on finding the underlying structure of the dataset you’re working with. The goal is to summarize, categorized, and represent the data in a useful format. It’s unsupervised because unlike supervised learning, you don’t start with labelled data.

Some of these algorithms include but are not limited to the following:

Decision Trees

Decision trees are a supervised learning algorithm that create tree graphs that map out decisions and potential consequences. It doesn’t stop there: the graphs also include resource costs, chance-event outcomes, and utility measurements of potential outcomes.

The premise of the tree is to provide a pathway to find the minimum number of questions needed to make a correct decision as often as possible. It also provides a structured way to approach a problem and makes it simple to find the point of departure when something goes wrong

Ordinary Least Squares Regression

Ordinary Least Squares Regression is a supervised learning algorithm based on the idea of linear regression, a simple statistics method. Linear regression involves fitting a task across a straight line and directing it through a set of points. Least squares is a method of performing this in a machine context: the goal is to find a point where the line fits through points where the sum of distances between the points and the line is small.

Naïve Bayes Classification

The Naïve Bayes classification is based on the application of Bayes’ theorem when strong independence assumptions exist between the features. The classifiers used are simple probabilistic classifiers that allow for sorting.

Naïve Bayes classification is used in sorting emails with spam filters and facial recognition software.

Clustering Algorithms

Clustering algorithms differ from the first three algorithms because it’s a form of unsupervised learning. In these algorithms, the code groups a set of objects in the same groups when it identifies objects that are more similar to each other than objects in other groups.

Clustering algorithms are simple in principle, but the algorithms themselves are very diverse. Some include neural networks, deep learning, probabilistic, connective-based, and density-based algorithms.

Independent Component Analysis

Independent component analysis (ICA) uses statistics to reveal the hidden factors found in sets of random measurements, signals, or variables. It’s a model for observing multivariate data gathered from a large sample database. The model assumes the data variables are linear mixtures of currently unknown variables and an unknown mixing system.

ICA is used when traditional methods of finding underlying factors fail. It’s used to identify variables in document and image database and when working with psychometric measurements

Machine Learning Is the Future

Machine learning is here, and it’s already changing the way people lives their lives. It makes decisions at home, at work, and even when shopping. The combination of machine learning with big data and artificial intelligence presents exciting opportunities for the present and the future. Those opportunities come with concerns, but it’s even possible that the capabilities of the amalgamation of these three concepts could even alleviate these concerns.

Do you work in an industry heavily impacted by machine learning? Share your thoughts in the comments below.