Machine learning (ml) has become a buzzword in the tech industry in recent years. It is often used in conversation with search engine algorithms and the artificial intelligence behind Alexa and Siri. But what are the actual applications of machine learning, and how does it affect our daily lives? While the uses of machine learning are vast and constantly expanding, we’ve focused on three major applications that businesses and organizations are using today to optimize their desired outputs and performance levels.
Some of the best search engine features such as image recognition and speech recognition are the product of machine learning algorithms engaging in a process called deep learning. This unsupervised learning allows ML programs to have more accurate predictions about what people are searching and what web page will be the most accurate for that search.
Natural language processing software has allowed search engines to get better and better at making predictions about the natural language we speak like humans, which is full of slang and informal speech that many programs have difficulty analyzing. ML programs give search engines stronger, more accurate results when it comes to voice searches, voice dictation on smartphones, and translations.
The advancements that machine learning has made in the search engine industry have rippled into other industries as well. The image recognition that allows you to search with a digital image rather than text has been instrumental to breakthroughs in video surveillance technology in the security industry and face recognition software used by smartphones and social media platforms. Social media companies also use the deep neural networks of machine learning programs to connect profiles with similar interests or mutual friends.
Accurate data is important in all industries, but perhaps it’s most important in the healthcare industry. Machine learning technology has allowed doctors to analyze historical data to help them provide a more accurate medical diagnosis to their patients.
Thanks to the data engineering capabilities of ML technologies, doctors and researchers can produce and analyze complex statistical models as they conduct their research. This means a quicker turnaround time for treatments that require the analysis of complex, big data sets like pharmaceutical medications, antibiotics, and vaccines.
The useful information that ML programs have been able to give businesses of all types has allowed for quicker transactions, efficient logistics, and happier customers worldwide. This is because an ML program can parse through the millions of data points in a companies database faster and with more efficiency than data scientists ever could. In today’s world, all successful companies need to be able to make meaning out of the large amount of data that they have access to. Machine learning also aids companies in turning their raw data into profit by helping them monetize their data assets.
Companies are also able to perform at the highest level of efficiency as machine learning helps businesses organize themselves in cost-efficient ways. Many companies waste huge sums of money due to inefficiency such as a bad delivery schedule, overstaffing, and unhelpful customer support teams.
With chatbots and other customer service tools, machine learning helps provideinsight to companies on product recommendations that the customer will actually want. In the transport industry, companies like Uber and Lyft have used machine learning to create self-organizing maps that allow for quicker turnarounds that keep customers, drivers, and investors happy.
This is by no means an exhaustive list of all the ways machine learning technology has been able to improve our lives. We are seeing deep learning programs being implemented in any field from education to professional sports. This is because human beings are intuitive beings, and huge data sets can overwhelm us. That’s why we’ve created artificial intelligence and machine learning: to make meaning out of the raw data for us so that we can spend more time doing what we do best, and less time on the boring or confusing data.