What used to be a plot theme for an old science fiction movie, artificial intelligence (AI) has become a modern reality. But AI isn’t just the robotic humanoid that becomes self-aware and comes to save the world. In fact, we use AI every day. From the facial recognition we use to open our smartphones to streaming service show recommendations, you’re seeing AI hard at work — and at the core of AI is machine learning. So, what is machine learning?
An Overview
Let’s start by using Netflix as an example: the company’s recommendation engine is powered by artificial intelligence and uses stored historical data to send you suggestions of what you might be interested in watching — right down to your favorite actors. Learning to code sets the stage for developing machine learning systems, not unlike the one used by Netflix.
After you’ve unlocked your phone (maybe even with facial recognition), how often do you pull up your Facebook, Twitter, Instagram, or Pinterest? And when you do, how do you think it was updated overnight? Not only is artificial intelligence working behind-the-scenes to personalize your feeds, but it’s also determining friend suggestions and pages you may like to follow. With a coding background, working on social media engines and their machine learning makeup is within reach.
Pattern recognition and the idea that computers can learn independently led to the development of machine learning. The history of machine learning dates back to the 1950s when Alan Turing created what’s known as the “Turing Test” — a simple method of inquiry that determines whether or not a machine can exhibit human intelligence. When a machine can engage in a conversation with a human without being detected as a machine, it has demonstrated human intelligence.
In the software development industry, machine learning is a subfield of AI and computer science that provides systems with the capacity to innately learn and evolve from experience without being explicitly programmed by humans. Machine learning uses meticulously designed statistical strategies and algorithms that allow a computer to imitate the behavior of human brains with its deep neural networks and problem-solving skills. Machine learning aims to develop computers that learn from previous computations to make accurate decisions and produce accurate results.
How It Works
Machine learning begins with historical data in the form of numbers, images, symbols, clicks, etc. Things as simple as grocery lists, addresses, bank transactions, sales reports, and previously purchased items, are all data that can be harvested for use.
Once the initial data is obtained, it is then analyzed and prepared so the machine learning model can be properly trained to operate independently. As data is accrued, it provides more opportunities for the model to be tested and corrected.
Moving forward, programmers decide which learning model they wish to proceed with and implement the data so the computer can continue learning on its own.
Depending on the computer’s output, the data is tested against potential error functions to evaluate model predictions. When an error function is observed, the computer can compare and assess the accuracy of the model.
Like human brains, machine learning computers work through a great deal of trial and error to produce precise results. Once inconsistencies are acknowledged, the algorithm will repeat the process to achieve optimization.
To ensure the machine learning computer operates efficiently, human programmers maintain the model, tweaking it if necessary. In some cases, programmers will alter parameters to keep it on track and moving toward producing accurate results.
Real-World Applications
We’ve established the fact that machine learning systems work behind the scenes to adapt and evolve without the need of explicit human input. But, where is this type of technology prevalent in our everyday lives? Some examples of real-world applications include:
Speech Recognition
One of the wonders of machine learning is its capability to translate speech into text. In fact, you’ve most likely interacted with them this very day! (“Siri, please take me to the Kenzie Academy home page.”) There are several common software applications and devices that take your voice commands and download them in the form of a text file by recording your speech and pinpointing voice inflections, volume, and cadence. Some of the most common examples include virtual assistants, speech-to-text applications, in-car roadside assistance, and automated customer service messages.
Image Recognition
Pixels are the tiny, individual elements of a picture that make up an entire image. Machine learning can recognize an image — with or without color — based on an image’s pixel makeup. If you’ve ever used apps that accurately adapt color to a black and white image or altered images with stylized photo filters, then you’ve seen machine learning at work! Pretty cool, huh?
Medical Diagnosis
Where many don’t realize machine learning is on display is in the health care and medical devices field. Doctors and physicians often use chatbots or other voice recognition tools to detect symptom patterns a patient may be experiencing. Image recognition and imaging machines also play a key role in enhancing and innovating these fields. Additionally, Lean Six Sigma methodologies are used to recognize defects in medical devices.
Statistical Arbitrage
Finance is another industry that uses machine learning. With many security and trading firms sifting through thousands of transactions per day, it’s common for them to use what’s called “arbitrage,” which is an automated trading approach. It utilizes a complex trading algorithm that examines different possibilities, trends, and similarities to make sound decisions.
Machine learning can explore the inner workings of a given financial market and its framework, follow potential at-risk models and decide how to interact, and identify the reasons for a failed trade settlement.
Predictive Analytics
By now you’ve learned that machine learning is all about rules, rules, and more rules. When data is gathered, tech professionals can segment the data that follows the previously established rules. Now that each piece of data is categorized, an analyst can find trends and possibilities that could lead to a potential failure. Using predictive analytics, a business can gauge its operational performance and make necessary changes to improve future outcomes.
In this field, machine learning is especially important when forecasting sales quotes by using historical seasonal buying trends, predicting real estate pricing, and determining the authenticity of a business settlement.
Self-Driving Cars
Machine learning allows autonomous cars to recognize objects, understand environments, and make decisions based on object recognition and object classification algorithms.
There are three types of machine learning algorithms methods. Let’s break them down:
