Machine Learning

It is an AI technique to improve the outcome of systems by learning from large databases. The key difference between machine learning and standard algorithms is adaptability and constant development. The more data and information an algorithm gathers, the more accurate its analytics will be.
Examples of using machine learning in business:

• Chatbots that advise users

Machine learning helps to develop the knowledge base of a chatbot, and in 6-12 months a virtual consultant can answer almost all questions.

• Personalize and improve customer experience

Machine learning improves customer engagement and satisfaction. For example, it offers personalized product selection based on recent purchases.

• Verifying resumes and documentation

Machine learning allows you to create a profile of the ideal candidate, simplify the hiring process, and speed up resume analysis. As a result, the amount of routine work for HR specialists is reduced.

Neural networks

The most frequent representatives of artificial intelligence in business. Essentially, it is a program code that processes data and imitates the work of the human brain. Neural networks have found wide application in design, marketing, copywriting, customer service, statistics, calculations, industry, banking.
For example, neural networks for SEO have completely changed the rules by which companies work with search engines Google and Yandex. Previously, only humans could create content. Now that powerful AI-based tools have emerged, it has become clear that neural networks are great at writing SEO texts, translating articles, and generating all kinds of media. And at the same time, they do everything cheaper and faster.

• What can the simplest and cheapest neural network do?
• Writes texts based on specified keywords;
• Creates product descriptions, headlines, meta tags.
• Extracts from texts.
• Generates images based on text descriptions
• Creates scripts for YouTube

NLP natural language processing

A machine learning technology that gives computers the ability to understand human language. Modern companies have huge amounts of voice and text data - email correspondence, messages, social media news, video, audio, etc. NLP technology is used to process all of this and use it to the advantage of the business.

For example, NLP is able to recognize three basic types of emotions - positive, negative and neutral - with 95% accuracy. If predictions are to be believed, by 2025, half of online advertising will be based on this technology. Disney already determines whether viewers like content using a streaming platform. And Ping An claims to have reduced financial losses on loans by 60% thanks to new algorithms.