Machine learning is a quite appealing topic for our clients and ourselves. Learning algorithms can be applied to many digitalized activities and can be an integral part of the systems we use daily. The power offered by computers with machine learning capabilities is undeniable. Furthermore, we already use them – or should we rather say we use products based on these algorithms. They are present in many areas of online life – in business, entertainment and domestic life.
Table of contents
1. What is machine learning?
2. What is machine learning good for?
3. What are the advantages and disadvantages of machine learning?
What is machine learning?
Before sharing examples of machine learning applications and systems, here is a short explanation about machine learning. Let’s have a look-
Machine learning is a part of artificial intelligence (AI) science. The whole area of artificial intelligence studies is based on the idea of creating computers that would obtain human-like thinking abilities. And machine learning algorithms are responsible for learning and concluding predictions based on provided data.
Thanks to machine learning, computers can discover patterns and find dependencies that could be missed by the human eye. That is done with advanced algorithms that can analyze significant amounts of data and see similarities. There are many types of machine learning, one of them being deep learning based on neural networks inspired by the human brain.
What is machine learning good for?
Machine learning has applications in all types of industries, including manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services, and energy, feedstock, and utilities. Use cases include:
- Manufacturing. Predictive maintenance and condition monitoring
- Retail. Upselling and cross-channel marketing
- Healthcare and life sciences. Disease identification and risk satisfaction
- Travel and hospitality. Dynamic pricing
- Financial services. Risk analytics and regulation
- Energy. Energy demand and supply optimization
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In addition to recommendation engines, other uses for machine learning include the following:
- Customer relationship management. CRM software can use machine learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses.
- Business intelligence. BI and analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points and anomalies.
- Human resource information systems. HRIS systems can use machine learning models to filter through applications and identify the best candidates for an open position.
- Self-driving cars. Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver.
- Virtual assistants. Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.
What are the advantages and disadvantages of machine learning?
Machine learning has seen use cases ranging from predicting customer behavior to forming the operating system for self-driving cars.
When it comes to advantages, machine learning can help enterprises understand their customers at a deeper level. By collecting customer data and correlating it with behaviors over time, machine learning algorithms can learn associations and help teams tailor product development and marketing initiatives to customer demand.
Some companies use machine learning as a primary driver in their business models. Uber, for example, uses algorithms to match drivers with riders. Google uses machine learning to surface the ride advertisements in searches.
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But machine learning comes with disadvantages. First and foremost, it can be expensive. Machine learning projects are typically driven by data scientists, who command high salaries. These projects also require software infrastructure that can be expensive.
There is also the problem of machine learning bias. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. When an enterprise bases core business processes on biased models it can run into regulatory and reputational harm.
Source: itcrafts, searchenterpriseai.techtarget
