Machine learning and artificial intelligence are often used interchangeably, but have important differences. Learn all about them in this edition of Trends!
You’ve heard of artificial intelligence (AI), but do you know what machine learning (ML) is? If you don’t, don’t be too concerned – the two terms are often used synonymously, which often leads to confusion when pinning down how exactly they differ.
Yet, businesses are starting to take notice of the various ways these technologies both intersect and the different types of capabilities they support. Today, artificial intelligence and machine learning technologies are helping businesses discover and use data for better business decisions, as well as streamlining processes.
In fact, it seems that you can’t work smarter without either AI or ML. From facial recognition features on smartphones to personalized online shopping experiences, AI and machine learning are fast becoming necessary for the businesses that want to take on a competitive edge. And before you take the plunge, it’s important to understand the difference between the two.
Let’s break it down!
How is AI fueling growth in the auto industry? Read our blog for insights!
What is Artificial Intelligence?
According to Northeastern University, “artificial intelligence” is itself a poorly defined term. We’ve explored its definition in previous blogs and essentially, everything AI boils down to machines (like computer systems) that can perform tasks that traditionally required human intelligence to accomplish. When an AI system imitates human behavior, it is problem-solving, learning, and planning by analyzing data and identifying patterns – just like a human brain.
To break AI down even further, AI-powered machines are usually classified into two groups known as general and narrow.
The general artificial intelligence AI machines can intelligently solve problems by following a set of stipulated rules. On the other hand, narrow intelligence AI systems can perform specific tasks very efficiently. However, their applications are limited in scope. An example would be the technology used for classifying images on Pinterest.
Intelligence vs Machine Learning
According to Gartner, machine learning is subset of AI that allows machines to develop problem-solving models by identifying patterns in data instead of being explicitly programmed. “Learning” refers to the training process these systems undergo to function at an optimal rate — the machine learning algorithms identify patterns in big data and then use those patterns to tweak the model, aiming to provide a more accurate output each time.
Further, ML can be supervised, unsupervised or reinforced.
- Supervised learning – Gartner predicts that supervised learning will remain the type of ML utilized by most business leaders through 2022 because it is effective in many business scenarios, like sales forecasting, inventory optimization, and fraud detection. It works by feeding known historical input and output data into ML algorithms. After processing each input-output pair, the algorithm changes the machine learning model to create an output that is as close as possible to the desired result.
- Unsupervised learning – This type of ML is used to develop predictive models from data that consist of input data without historical labeled responses. It can be used to prepare data for supervised learning by identifying patterns or features that can categorize, compress, and reduce the dimensionality of data.
- Reinforcement learning – RL is built on rewarding desired behaviors or punishing undesired ones. Instead of one input producing one output, the algorithm produces a variety of outputs and is trained to select the right one based on certain variables. RL requires less management than supervised learning, which makes it easier to work with than unlabeled datasets. However, practical applications are still being developed.
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Don’t Forget Deep Learning!
Deep learning is yet another subset – this time of machine learning. The term is often invoked when people attempt to describe deep artificial neural networks. These sets of algorithms have set new records in accuracy for problems like image and sound recognition, natural language processing, and more.
The word “deep” refers to the number of layers in a neural network. A shallow network has a hidden layer while a deep network has more than one. With multiple hidden layers, deep neural networks can learn features of data in a feature hierarchy since simple features recombine from one layer to the next to form more complex features. Nets with many layers pass features (input data) through more mathematical operations than nets with few layers. Therefore, these are more computationally intensive to train.
At the end of the day, deep learning tends to result in higher accuracy, requires more training time, and perform well on machine perception tasks that involve unstructured data.
Machine Learning versus AI
One of the best ways to distinguish between AI and ML? Remember that machine learning (and deep learning) are used to implement AI. For instance, if an AI system is a computer that can carry out a set of tasks based on instruction, ML is the machine’s ability to ingest, parse, and learn from that data itself in order to become more accurate or precise when accomplishing that task.
Here are some different ways both AI and ML are used:
- AI can reduce commute times by analyzing the speed of movement of traffic at any given time and providing that information to commuters in real time
- Through the use of machine learning algorithms, email spam is greatly reduced because email spam filters continuously learn from a variety of signals
- ML is being used to prevent plagiarism prevent plagiarism by helping to detect the plagiarism of sources not in a defined database
- AI can prevent fraud by creating systems that learn which types of transactions are fraudulent
- Artificial neural networks are being used to generate personalized product recommendations to online shoppers
- AI assistants like Alexa are powered by voice-to-text technology
In short? Where there is AI, ML is not far behind. And together, they create the intelligent machines that will lead computer science – and society at large – into the next technological frontier.
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Deploying artificial intelligence solutions at your business is a journey you don’t need to take alone. With our SAI suite – short for Stefanini Artificial Intelligence – we can help you design, plan, and build your AI experience, whether it’s a virtual assistant like Sophie, a date science-driven project, or something else. From ideation to implementation to support, Stefanini is there every step of the way.
We are both big enough to address your technological needs while small enough to be an agile, flexible partner. By co-creating AI solutions, we will digitally transform your company so that it achieves more.
