Known for decades, artificial intelligence is not an innovative technology. Combined with the rise of Big Data, it offers new perspectives to companies. Machine learning automates actions through data analysis and can be applied to areas such as e-commerce, customer relations, security, or even health.
Machine learning refers to an artificial intelligence system based on learning and reasoning.
Already used in many applications, machine learning is used daily without necessarily noticing it: voice recognition systems from Google or Apple are part of it.
How a machine learning tool works
The mechanisms of a machine learning device are relatively complex but can be explained by the principle of learning by example.
The massive collection of data (Big Data) and data annotation is used to “feed” the machine learning algorithm. The machine does not, therefore, need to have been programmed explicitly beforehand to complete a task. It works independently: it analyzes the data, establishes correlations between them, and can automate actions based on the results obtained.
Machine learning, for what type of business?
The principle of machine learning is potentially applicable to any type of business.
Data collection and predictive analytics can be useful regardless of company size and industry to gain responsiveness and increase profits. You just need to arm yourself with artificial intelligence professionals to get started and keep in mind that human assistance will always be necessary to help the robot improve.
Examples of use and benefits of machine learning
Chatbots are one of the most well-known machine learning applications. These virtual assistants are robots but can make conversation with a human being through pre-set scenarios and predictive data analysis. Chatbots do not necessarily replace human advisers, but they offer many advantages in terms of customer relations:
– responsiveness thanks to a presence outside working hours;
– saving time by responding to simple issues;
– continuous improvement thanks to the analysis of data and semantics: the more the chatbot interacts, the more its analyzes are refined.
In addition, the customer is not required to go through a third-party application that they should download and install. He can find answers through the company’s website or on Facebook Messenger which offers chatbot programming.
Another application of machine learning in e-commerce: personalized recommendation based on the preferences of each customer.
In a physical store, salespeople are able to anticipate the wishes of their most loyal customers because they know them well. In online sales, machine learning algorithms can deliver the right product to each customer after aggregating a massive amount of data (customer behavior, purchase history, trends).
This application of artificial intelligence makes it possible to better orient customers, obtain qualified traffic and potentially increase sales. It also works for physical outlets that receive a large number of customers.
With their ability to analyze big data in a limited time, machine learning algorithms are able to identify threats faster than humans, even if they are new or unknown. To obtain optimal threat detection, the use of a single algorithm is not enough: it is necessary to use several different algorithms, each specialized in a family of malware.
Even though a doctor’s consultation remains essential today, machine learning can help make precise medical diagnoses by taking into account clinical trials, studies, research and by relating them to the data of each individual. Here again, it is the analysis of thousands of data by the machine which takes advantage of the human being like Watson, the artificial intelligence of IBM.
There are many other applications but what has been described above can give us a “proof” that machine learning is an important part of the human future.