The frequency with which 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 that 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, tools that recognise and understand human language, and learn and analyze our behaviors 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 negative ideas and clichés.
Artificial Intelligence (AI) actually started as a research area linked to computer science in the 1950s, and it is constantly evolving. It is a discipline that, through the processing and analysis f data and logical processes, aims at creating machines and technologies that can replicate the functioning of the human brain.
AI studies are oriented towards the development and programming of 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. In this sense, its goals 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 situation based on new data, but also to improve the functioning of their algorithm 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 a great 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. By simulating the activity of human neural networks, these artificial systems replicate the functioning of the brain.
The technologies of artificial intelligence, machine learning and deep learning have been applied in various sectors and have brought important contributions in terms of performance improvement, more precision and productivity, acceleration of working times, and greater amount of data available for companies.
According to the report “The Global AI agenda” conducted by MIT Technology Review Insight, which examines how companies are using artificial intelligence today and what they will do in the future, interesting 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 specifically towards Talent Acquisition. The company benefits directly due to the positive impact on the company’s brand (Employer Branding) and consequently the candidates that it attracts. But AI can do more than just attract talent. Through specific algorithms, it is able to analyze a large amount of candidate data and information (activity which also makes use of Sentiment Analysis and Computer Vision), evaluate candidate skills and the candidate’s motivation to work. In addition, predictive analysis can be a very useful tool for monitoring employee retention.
In addition to the advantages of AI, Inda also includes a number of features that a re 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 fora 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 that can also be applied to improve performance within the whole company.
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