The frequency we hear about artificial intelligence, machine learning and deep learning demonstrates how much this technology has entered our lives. From digital assistants on our smartphones and chatbots, we query on websites to intelligent driving systems and automation tools used in the home, financial, HR and tech sectors. Every day, even unconsciously, we use tools that exploit artificial intelligence systems, recognise and understand human language, and learn and analyze our behaviours to predict our choices or actions. But what are we talking about specifically? Let’s try to clarify.
When we talk about artificial intelligence, we immediately think of robots: intelligent machines that, with guidance, can carry out different activities. This, however, can lead to a negative impression of artificial intelligence because it is connected with opposing ideas and clichés.
Artificial Intelligence (AI) started as a research area linked to computer science in the 1950s and is constantly evolving. It is a discipline that, through the processing and analysis of data and logical processes, aims at creating machines and technologies that can replicate the functioning of the human brain.
AI studies are oriented towards developing and programming machines with characteristics typical of human intelligence. This assumes that AI has the same role in computational machines as the brain has in the human body. Its goal is to simulate human activities and responses: seeing, understanding language, reasoning and making decisions, solving problems, learning, etc.
From the discussion and study of artificial intelligence, we move on to the related subjects of Machine Learning (ML) and its subset, Deep Learning (DL).
Machine Learning – also known as artificial learning – refers to the ability of computers to self-learn. The purpose is to allow machines and software to learn how to respond to different situations based on new data and improve their algorithm’s functioning over time by updating their acquired knowledge (data) and experience.
The study and development of activities such as those mentioned above were possible due to the excellent availability of data following the spread of Big Data. This has allowed a subset of Machine Learning, Deep Learning, or in-depth learning to solve complex tasks by resorting to the processing of artificial neural networks. These synthetic systems replicate the brain’s functioning by simulating the activity of human neural networks.
Artificial intelligence, machine learning and deep learning technologies have been applied in various sectors and have brought essential contributions in terms of performance improvement, more precision and productivity, acceleration of working times, and a more significant amount of data available for companies.
According to the report “The Global AI Agenda” conducted by MIT Technology Review Insight, which examines how companies use artificial intelligence today and what they will do in the future, exciting data emerges. In the section dedicated to Europe, it is reported that
While customer service will remain a leading area of AI use for three- quarters of businesses, between 2019 and 2022 the fastest areas of AI growth will be in IT management, sales and marketing, and human resources
The study estimates a growth in the use of AI systems in human resources, racing from 14% to 44% over three years (up to 2022). This data confirms the contribution that artificial intelligence, machine learning and deep learning algorithms can make to improve the recruitment and selection process.
With this in mind, an AI solution such as Inda (Intelligent Data Analysis) can help the company attract talent and focus hiring activities on Talent Acquisition. The company benefits directly due to its positive impact on its brand (Employer Branding) and, consequently, the candidates it attracts. But AI can do more than attract talent. Through specific algorithms, it can analyze a large amount of candidate data and information (an activity that also uses Sentiment Analysis and Computer Vision) and evaluate candidate skills and the candidate’s motivation to work. In addition, predictive analysis can be a handy tool for monitoring employee retention.
In addition to the advantages of AI, Inda also includes several features that are advantageous to the recruitment process. Inda has the following functionalities: extracting information and digitising candidate data (Information Extraction); semantic search algorithms to refine the candidate search in line with open positions; Job Matching for a better match between the job announcement and the candidate profile; and searching for specific profiles that are similar to the profile being sought.
These features and advantages of artificial intelligence can make a big difference in the world of HR, but they can also be applied to improve performance within the whole company.