What is Machine Learning?

Machine Learning (ML) is a type of Artificial Intelligence (AI) that is characterized as the use and development of computer systems that are able to learn and adapt without following explicit instructions. Machine learning uses algorithms and statistical models to analyze and draw inferences from patterns in data.

Deploying machine learning as part of an application requires a special blend of strong engineering practices and an analytical mindset—not to mention a significant amount of creativity. 

Artificial Intelligence, Machine Learning, and Deep Learning: What’s the Difference?

The world of tech is really exploding with new terminology. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can be difficult to distinguish. Here’s how they relate and how you can tell them apart.

Artificial Intelligence

Artificial Intelligence is a relatively broad branch of computer science/statistical analysis that explores ways of creating systems and machines that are able to perform tasks that are typically done by humans. For example, in video games, the enemies that you may encounter, are likely developed using AI principles. These enemies are not typically learning from your actions, but instead following a very specific script programmed by a developer to respond to your actions.

Programs and machines using AI are created with different levels of sophistication. AI has a lot of subset areas, including deep learning, machine learning, and natural language processing.

Machine Learning

Machine Learning is a very popular type of AI that uses algorithms that learn from input data.  Ideally, Machine Learning concentrates on creating machines and systems that are able to learn from experience without being explicitly programmed. Classification tasks are a great example of machine learning.

You probably interact with software and programs supported by machine learning more than you think. Your email spam filter, streaming service recommendations, and credit card’s fraud detection system are all real-world examples of machine learning. Machine Learning algorithms are able to extract and improve knowledge from the data that is available to them.

Deep Learning

Deep Learning (DL), is a subset of Machine Learning that aims to develop algorithms that connect more than one layer to solve a specific problem. The knowledge traverses the layers until an optimal solution is found. 

Deep learning is essentially a neural network with 3 or more layers. Unlike machine learning–which leverages structured, labeled data–deep learning eliminates some of the data preprocessing. Deep learning can process unsupervised data–like images and text–and extract observations. Elevate uses Deep Learning in our facial expression recognition algorithms. We use video input of human faces and provide live, facial expression analysis without requiring any delays or excessive human intervention. We’ve automated this process to better serve users.

The differences between AI, Machine Learning, and Deep learning picture as a stacked venn diagram

Classifying Machine Learning—Supervised and Unsupervised Learning

The process of machine learning involves an interaction between the learner (machine) and the environment. Learning tasks can be divided according to the nature of the interaction with the environment. In ML, it is important to note the difference between supervised and unsupervised learning.

Supervised Machine Learning

Most machine learning is supervised. Supervised learning can be described as the learning scenario of user experience to gain expertise. In this case, the experience is a well-defined significant set of information.

Here’s a basic supervised machine learning formula:


You have input variables (x) and output variables (Y). You use an algorithm to learn the mapping function from the input to the output.

The goal is to approximate the mapping function and be able to predict the output (Y) from any input (x).

Types of Supervised Learning

There are two main types of supervised learning: classification and regression. Here’s the difference:

  • Classification: Classification is a method of predictive modeling that refers to training a model on a labeled dataset to assign data points to classes or to classify new data points. The output variables are categorical–i.e. “blue”, “purple:, “red”. Elevate uses classification to train our neural networks.
  • Regression: Regression is another method of prediction modeling. In regression, the model is similar to classification. The main difference is that regression has real value outputs–i.e. “weight”, “length”, and “dollars”. (Towards Data Science has a great guide to Regression modeling for beginners.)

Unsupervised Machine Learning

In unsupervised machine learning, there is no distinction between training and test data. The learner in unsupervised machine learning processes inputs data with the goal of coming up with a summary of the data. Recommender systems, anomaly detection, and customer segmentation are great examples of unsupervised machine learning in daily life.

a side by side comparison of unspervised and supervised machine learning

Challenges in Machine Learning

So what does the rapidly advancing field of machine learning mean for our society? Machine learning can be a difficult concept to grasp and can even sound scary. It is important to remember that machine learning–like all concentrations of AI–should follow a strict code of ethics. Here are some of the challenges that machine learning developers must face when creating new technologies.

Privacy and Security

Privacy and security are growing consumer concerns. From sold data to breaches, it makes sense that people are more concerned than ever about the security of their personal data. This is exactly why Elevate utilizes edge-AI. Edge-Ai processes locally, as close to the end user’s device as possible. We never send images to the cloud and don’t store images locally. We do this to ensure that your video and image inputs are as secure as possible. Edge-Ai also allows our algorithms to provide real-time analytics. Win-win.

Bias and Discrimination

AI is able to discriminate, and often this discrimination is completely unintentional. Recent studies show that AI can further deepen racial and economic inequality. How does this happen? Well, AI is built by humans whose unconscious biases can make their way in. From auto rejecting applicants to misidentifying people of color in images, AI that is not carefully developed to mitigate bias can have very real and negative implications. How do we solve this? Elevate uses carefully curated datasets that intentionally combine a wide variety of ethnicities, genders, and ages. To do this, we had to expand from public datasets to proprietary datasets to ensure diversity.  We believe that human-centric AI means that our AI must work for everyone, equally.

Technological Singularity

Will robots take over the world? Can computers be sentient enough to have opinions? Short answer, no. We love the Matrix, too, but it’s not really possible. Technological singularity doesn’t just refer to a post-apocalyptic takeover though. There is more nuance. What happens when a self-driving car gets into an accident? Should we put a pause on fully autonomous cars and instead lean further into semi-autonomous cars? These ethical debates persist.

Answer: Machine Learning is a type of AI that uses trained algorithms to analyze data and perform tasks.

Machine learning is a type of artificial intelligence (AI) incorporated into Elevate’s AI. We use machine learning, deep learning, and neural networks to train our algorithms. Our human-centric multimodal AI analyzes verbal and non-verbal human communication: facial expressions, body language, and voice analysis to help bridge the communication gap in video settings. All of these facets of AI are deep and growing with exponential practical uses. How do you use machine learning? 

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