Machine learning is quickly becoming one of the largest fields in the tech industry. In fact, a report states that the market share for machine learning will exponentially grow to over $8 billion by the year 2022. Yet despite how big ML’s projected application and impact is to the future of humanity, a large chunk of the global population remains oblivious to what machine learning algorithms are and what it is actually capable of doing. Here are 10 things you didn’t but should know about ML.
Relationship With AI
Many people use artificial intelligence interchangeably with machine learning. However, ML is only a subset or branch of AI. Machine learning tackles a computer’s ability to learn and adapt to user behaviors. Artificial intelligence, on the other hand, encompasses a broader spectrum that is interested in creating systems that can display some level of human intelligence, including consciousness and emotionality.
Quantum ML
Quantum machine learning is a lesser-known application of ML. It combines the governing principles of quantum physics with the disciplines of machine learning. Since quantum physics works well with minute details, using it with machine learning can boost algorithm performance.
Cybercrime Deterrent
As companies scramble for better solutions to thwart cybersecurity threats, ML is being pushed into the spotlight as a primary contender. Based on a report by Cybersecurity Ventures, cybercrime is expected to cause roughly $6 trillion in damages worldwide by 2021. Incorporating machine learning into cybersecurity tools can help boost their effectiveness in storing and securing information in a company database.
Content Moderator
Misinformation is a growing problem in today’s day and age when society has this information superhighway that is the Internet. Information resources are popping up left and right, each guided by its own self-interest and biases. As a result, more fake content is being circulated now more than ever. Machine learning algorithms are now being deployed to help with content moderation by scanning through conversations for any controversial claims and inaccurate data.
Image and Video Manipulation
Using ML’s generative adversarial networks, you can take an image or video of a clear summer day and then turn it into a winter-themed backdrop. While this nifty trick might not even be remotely beneficial to societal’s progress, it serves as a testament to how technology is getting closer to making edited images and videos in instantaneous fashion, which may be used by film production companies to cut down production costs.
Earthquake Predictor
The National Center for Research on Earthquake Engineering is using machine learning algorithms to identify the magnitude of incoming earthquakes. The team uses a machine to detect and interpret micro-seismic P-waves that are essentially invisible to humans.
Data is an Invaluable Resource
ML algorithms that work with more data can beat a technically more superior algorithm that is working with fewer data. One of the main reasons why better algorithms produce smaller payoffs is that, to a first conjecture, they all work on the same problem sets and therefore produce the same results. As a general rule of thumb, it makes logical sense to implement the most basic learners first, i.e. naive Bayes, K-nearest neighbor, logistic regression, etcetera.
ML Platforms
Third-party machine learning platforms, like Appen, allow people to start their ML projects without the need to collect datasets from scratch. High-quality training data is an invaluable resource that takes time, energy, and expertise to collect and label. ML platforms give you the training data you need to bootstrap your ML project.
Learner Models
A learner is essentially a program designed to process an ML model from datasets. During the early days of ML, engineers and scientists handpicked one learner model as their favorite and found biased reasons to claim it as the most dominant of the bunch. But over time, systematic hands-on comparisons revealed that the best learner models differ from one project or application to the next.
Supervised Learning
Supervised learning is one of the two main types of machine learning, the other being unsupervised learning. Supervised learning involves exposing the learner model to large chunks of labeled data, such as images of a cat or dog. Over time, the supervised learning model learns to detect the image of a cat or dog based on the clusters of pixels and shapes that comprise the image.
Knowing these 10 things won’t necessarily make you an expert in the field of machine learning. However, it does open your mind up to the opportunities and limitations that ML is bound by.
