Machine learning races forward
Machine learning has flourished over the past few years into a powerful tool. The process itself is a form of artificial intelligence that uses statistical methods to find patterns in datasets, allowing analysts to go beyond descriptive results for predictive and prescriptive feedback. Although the biggest names in tech and industry are investing in research and development, they’ve only scratched the surface of applications and insight this technology can offer.
Utilizing machine learning has risen to the top of many organization’s tech priorities, but there’s a lot of uncertainty about how and when it can be applied. Marketing, farming, security and deep learning are among the most intriguing areas of key growth in which machine learning is reshaping culture, industry and science.
Marketing: Big data innovation is not new to marketers. SEO and complex market research developments have already transformed the way businesses think about their brands and consumers. Today, though, machine learning is being put into action by automating systems that analyze consumer needs and then meet them. Machines are beginning to power the majority of consumer-brand interactions. Customization has revolutionized the customer experience: Amazon makes personalized book recommendations, basic customer service operations no longer require employee involvement, and search engines results are rigorously defined. Data can even augment in-person brand experiences, as machines are increasingly capable of drawing correlations between consumers’ behaviors and their ultimate intent.
Farming: Though less glamourous than some of the other applications, machine learning’s impact on agriculture has the potential to revolutionize human existence around the world. Much of modern crop management now relies on highly automated systems. New advances are further enabling farmers to adjust moisture and chemical use based on input not from fields or regions, but from individual plants. This capability could both automate use and inform farmers so they can make appropriate decisions on steps forward during extreme conditions. Such advancements would minimize water waste and pesticide use, reducing world hunger and helping protect the environment.
Security: Machine learning has already found a valuable place in data, financial and communications security. It’s now established practice to run linear algorithms to detect fraud, but the reach of big data can spread farther. PayPal, for example, relies on a blend of linear, neural network and deep learning algorithms to assess “risky” threats to its system. Beyond identifying financial scams, researchers are also exploring ways to apply machine learning to government practices. One study of declassified State Department communications discovered that algorithms were able to identify classified content and even locate confidential information erroneously labeled as unclassified. These findings open doors to possible applications for monitoring emails and cables and for preventing human error in defense and national security.
Deep learning: Interest is growing in deep learning, which is behind many practical applications of machine learning. The method develops artificial “neural networks” that mimic the neocortex’s vast array of interconnected, multilayer neurons. It is powering developments in self-driving cars, speech recognition and healthcare. Higher-level capabilities like identifying visual patterns and beating humans at complex strategy games repeatedly demonstrate the vast power that deep learning may reveal in years to come.
Concentrix Catalyst uses machine learning, mathematical modeling and statistics to cut through complexity and translate the findings into practical, easy-to-understand information for our customers. Advanced analytics techniques, including machine learning, are also an essential part of many of our Internet of Things initiatives. Learn more about our Intelligence and Analytics capabilities.Tags: Deep Learning, Edge, Machine Learning