How To Put Machine Learning To Work For Your Business
Machine learning has been driving innovation in product features for several years, becoming a vital component of enhancing the consumer experience. However, in terms of what machine learning (ML) — and specifically, artificial intelligence (AI) — can do, we’ve only seen the tip of the iceberg.
As a business leader, you may feel a bit like Chevy Chase in Fletch trying to flub your way into appearing knowledgeable about machine learning (it’s all ball bearings nowadays…). Nevertheless, it’s vital to understand what machine learning is and how to get the most out of it for your business.
While it sounds technical, machine learning, in the simplest sense, is a branch of artificial intelligence that uses computer algorithms to learn from data and make better predictions. By utilizing large data sets, machines apply and automatically learn through the analysis of patterns and use that knowledge to improve the algorithm. The techniques behind machine learning have been around for decades, but it’s only recently that we’ve had the computing power to put them to the test. Now that artificial intelligence applications are more ubiquitous, machine learning is coming into its own as an affordable technological tool that even small businesses can leverage.
Machine learning has been behind many significant innovations in the last decade, including self-driving cars and effective web search. Several years ago, we at Rosetta Stone saw an opportunity to utilize natural language processing (NLP), which is the artificial intelligence behind speech recognition, to transform the language learning experience. Through our patented speech recognition technology, users could practice correct pronunciation and get feedback in real time.
Here’s how you can start to power innovative product features through machine learning.
You’ve Got Data — Use It
If there’s one thing companies have no shortage of, it’s data — user data, search trends, product analysis. You can put these massive amounts of data to work with machine learning. Machine learning is a specific way to use a large amount of data to analyze a complex problem without a formula or equation. Using your treasure trove of data for machine learning applications is a process that involves making the data available, analyzing it and splitting it into groups to train and hone your computer models. Some companies hire data scientists to help with the more sophisticated aspects of this work, but there are also a wealth of tutorials online to help you take the first steps toward preparing your data for machine learning applications.
Some examples of machine learning algorithms that use data are Netflix’s movie recommendation engine or the way Amazon discerns what you might be interested in based on your order history. As Netflix and Amazon collect more user data, the personalized recommendations they make grow increasingly targeted and insightful. Google search operates in the same way, with machine learning algorithms that adjust for nuances like location and user intent.
Technological Tools To Employ Machine Learning Are More Accessible Than Ever
Machine learning models have been around for ages, but we’ve only recently had the computing power to make sense of the data that’s been collected. Now that the technology behind using machine learning is accessible and affordable, it puts AI applications within reach of more businesses. Open source tools like TensorFlow and Keras allow companies to use existing platforms and software to integrate machine learning.
Moreover, it’s not just about AI product features. Machine learning can also provide internal gains in efficiency through better data security, improved targeting in ad campaigns and higher-quality customer service. When I was working with a Seattle startup studio, we used OpenCV and supervised machine learning to make predictions for live esports games. Because of the efficiency gains in using machine learning for the development process, we accelerated from a proof of concept to shipping code in a matter of months.
Start Investing Now In Machine Learning’s Next Frontier
The deeper learning that’s associated with more sophisticated AI is machine learning’s next frontier. While much of that technology is on the bleeding edge of innovation, you can still take incremental steps to move your organization to the next level. One of the advantages of machine learning models is that they learn not just the data but the structure of the data. The more computers apply and learn from the successes and failures of the model, the more accurate those models get.
Machine learning has previously been the territory of tech giants like Google and Amazon, but there are many industries that would benefit from applying it. Sectors like the automotive industry and financial services may see the biggest benefits in terms of efficiency of analysis, better predictive models and optimization of production.
Take, for instance, our company’s recent application of machine learning to improve speech recognition. By digitizing the spoken word and the building blocks level of language called phonemes, we applied a sophisticated machine learning model that listens to what language learners say and scores how they said it. This enables our learners to get real-time feedback and to sound more authentic in their target language.
Machine learning has also been driving innovation in medical science, from improving remote diagnosis to assisting in surgical procedures. In 2016, IBM’s Watson began delving into health diagnostics, developing models to sequence genomes and predict disease. What it’s learned has been used to develop remote diagnosis and virtual nursing which, according to a recent Accenture report, has the potential to save the health industry as much as $20 billion annually.
Investing in machine learning now will help your company gain momentum and build a solid foundation in the technology that will drive better, more comprehensive models across myriad industries, including education, service, retail and technology.
All credits to the source below by:
Matt Hulett Forbes Councils