According to a 2017 survey, 49 percent of companies are already using machine learning to improve their traditional business processes. From the smallest startup to the largest multinational firm, nearly every company can benefit from incorporating machine learning into its business workflow.
Need a quick yet comprehensive introduction to machine learning and how it promises to revolutionize the world of business? This article is for you.
The Definition of Machine Learning (and How People Use Machine Learning)
At its heart, machine learning is the task of making computers more intelligent without explicitly teaching them how to behave. It does so by identifying patterns in data—especially useful for diverse, high-dimensional data such as images and patient health records.
However, “machine learning” is really a broad catch-all term that spans three main subcategories: supervised learning, unsupervised learning, and reinforcement learning.
In supervised learning, the machine has access to a training dataset that consists of example data points and their labels.
For example, suppose that the machine is given an image, as well as the task of recognizing whether a cat is present in the image. The training dataset would then consist of a series of images, along with a note for each one that denotes whether the image contains a cat.
The challenge of supervised learning is for the machine to extrapolate generalized rules based on the training dataset. Adhering too closely to the training dataset, while showing poor performance on real-world examples, is known as “overfitting.” If your training dataset only includes black cats, for instance, the machine may find it harder to recognize the presence of cats of other colors.
Unlike supervised learning, unsupervised learning does not provide the machine with access to a training dataset. Instead, the machine must take unlabeled, unstructured data and find the structure inherent within this information.
One example of unsupervised learning is facial recognition over a large dataset of photographs. Each image contains a person’s face, and there is an unknown number of people present in the dataset.
The machine’s task is therefore to “cluster” the images together, based on the similarity of the faces. In doing so, the machine is recognizing which images are of the same
In reinforcement learning, the machine attempts to find the optimal actions to take while being placed in a set of different scenarios. These actions may have both short-term and long-term consequences, requiring the learner to discover these connections.
The concept of reinforcement learning borrows heavily from psychology experiments on animals, such as rats and birds, in which the animal seeks to obtain a reward such as food without explicitly understanding how to obtain it.
Similarly, reinforcement learning attempts to teach the machine the set of actions that will lead to a positive or negative consequence. Without being explicitly instructed, the machine learns on its own the behaviors that cause a reward or punishment.
Reinforcement learning is extremely important for applications such as self-driving cars that are complex to model. Here, the “reward” can be thought of as a successful trip between two locations, and the “punishment” would be
The Difference Between Machine Learning and Artificial Intelligence
To people unfamiliar with the terms, “machine learning” and “artificial intelligence” might seem like exactly the same concept. In fact, machine learning is a subcategory of artificial intelligence and a particular approach to making machines more intelligent.
Early initiatives in artificial intelligence attempted to define explicit logical rules by which machines should behave; however, these projects had mixed success. Expert systems and knowledge representation are two examples of artificial intelligence techniques that don’t rely on machine learning.
Since the 1990s, a great deal of attention has shifted to machine learning. Rather than using experts to define lines of reasoning, the machine itself relies on vast quantities of data and different experiences to become more intelligent all on its own.
How People Use Machine Learning (And Its Limitations)
Machine learning has revolutionized countless industries; it underlies the technology in your smartphone, from virtual assistants like Siri to predicting traffic patterns with Google Maps.
According to AI expert Andrew Ng, machine learning will likely be very good at tasks that human beings can accomplish in a second or less. Small, repetitive activities can easily be automated using machine learning, saving your business time, effort, and money.
The ability of machine learning models to deal with high-dimensional data is extremely helpful for businesses. AI-enhanced software can do things such as finding patterns in user access data and accurately predicting customer retention, which would otherwise be impossible for a human to do.
However, machine learning also comes with its own set of limitations. For one, it’s only good for certain types of use cases, so your employees won’t be replaced with a robot workforce any time soon. In addition, machine learning can be susceptible to human biases that are present in the training dataset. The data that you train the models on should be large and representative in order to get the best results and avoid overfitting.
In the near future, techniques such as deep neural networks will allow machines not only to classify and cluster
With popular interest and use cases only continuing to increase, the future of machine learning looks bright indeed. For more information about how you can apply machine learning to make your business more efficient and productive, reach out to us today.