Big data, deep learning, artificial intelligence, machine learning. I can bet these buzzwords show up at least once a week in your feed and have been for the past few years. Our radar can’t escape them even if we wanted to. By now everyone has some idea of what those words mean, but can you really explain their meaning if someone asked you? Or better yet, do you know enough that you could apply those concepts to your work? In this article, I’ll go through a brief review of machine learning and then explain some of its newest use cases for business.
In short, machine learning (ML) is the study of statistical methods and algorithms used by computers in order to perform a task without explicitly being told. The ‘learning’ part means that the computer tries to find patterns in the data it’s provided with. The way it learns is through algorithms we devise.
Machine learning is closely related to artificial intelligence. In fact, it can be seen as a discipline within it. The history of machine learning can help us understand it better so let us go through a quick overview.
As is often the case with new technologies, it is hard to pinpoint exactly when in time the birth of ML took place. Often, people who go as far as assigning a date to its inception are really just telling you the date some underlying concept was introduced.
Here we adopt a more practical view:
We have seen rapid development, especially in the last few years. Google, Facebook, and Amazon make investments in AI and ML, in particular with the creation of Google’s X Lab, DeepFace algorithm and Amazon’s own Machine Learning platform.
In 2018, Google introduces AutoML, which enables people with less expertise to benefit from machine learning methods. Google’s AutoML trains your data-sets with custom models. AutoML currently focuses on models for Natural Language Processing, Visual Object Recognition, and Translation.
Similarly, MindsDB, in our mission to democratize machine learning, provides an AI service that does the work that a machine learning expert would normally do, training the data with different models & delivering results to users based on their queries.
As Ethem Alpaydin put in his book Introduction to Machine Learning:
“… we all became producers of data. Every time we buy a product, every time we rent a movie, visit a web page, write a blog, or post on social media, even when we just walk or drive around, we are generating data.”
And machine learning is all about learning from data. The algorithms machine learning experts use usually fall under the following categories:
“The technologies and techniques of AI and ML are still so new that the main adopters of the techniques are the large software companies able to hire and to invest in the necessary expertise”
Despite machine learning applications being in their early stages, in recent years machine learning adoption has begun to rapidly accelerate as more organizations see the benefits that this technology can bring to their business.
For instance, O’Reilly recently surveyed more than eleven thousand people who worked with AI, Data Science, and related fields. It reports that about half of the respondents said they are ‘just looking’ into the technology and more than one-third have been working with machine learning models for at least the past 2 years. That means about two-thirds of the respondents are already in touch with the technology at some level.
Machine learning is being used in a variety of sectors and use cases are showing up in a wide range of areas. The specific use cases are diverse; they range from adjusting paywall pricing for different readers based on the probability of readers subscribing to reducing scrap rates in semiconductor manufacturing. Here are some of the major ways machine learning is helping organizations:
While the history of machine learning is quite recent even when compared to traditional computing, its adoption has accelerated over the last several years. It’s becoming more and more clear that machine learning methods are helpful to many types of organizations in answering different kinds of questions they might want to ask and answer using data. As technology develops, the future of corporate machine learning lies in is ability to overcome some of the issues that, as of now, still prevent the widespread adoption of machine learning solutions, namely explainability and access to people beyond machine learning engineers.
Explainability refers to the degree to which a given outcome can be explained. In other words, why does this model work and what its limitations are? MindsDB actually already solves this by identifying values where it has enough information to make predictions and, what is perhaps even more important, where it doesn’t have information to make predictions. From there it learns from these values to understand why things didn’t work.
The second point, the democratization of machine learning, has already seen efforts by Google and MindsDB to provide solutions, or at least facilitate, the use of machine learning by a larger group of developers. In particular, MindsDB allows people outside the niche of machine learning experts to use the tool and benefit from the predictions neural networks can make.
Finally, with more access to the tools machine learning provides and a better understanding of how the algorithms work, machine learning experts (and others who use machine learning) can serve as better consultants and managers can make better decisions, which is the ultimate goal of every business.
A huge thank you to Amie, Richard, George & Jorge
Adam Carrigan is the co-founder and CEO of MindsDB. Prior to founding MindsDB in 2017, Adam ran deep learning start-up Real Life Analytics (RLA) in London which applied deep learning computer vision to the advertising & marketing industries. Before joining RLA, Adam was a researcher & management consultant for Deloitte working with some of the world’s largest organizations to help solve their complex data challenges. Adam earned an MPhil from the University of Cambridge where his dissertation involved using an early form of natural language processing to help predict stock market movements. He also has degrees in Economics and Finance from the Australian National University and the University of Queensland from his adopted homeland Australia.
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