Artificial Intelligence (AI) is gaining traction in recent years. And because of machine learning (ML) and natural language processing (NLP) technology, AI assists human-computer translation, computer vision, language translation, and business expert systems.
The range of AI applications is extensive, but the five that you should better understand include ML, NLP, computer vision, expert systems, and generative AI.
IBM defines machine learning as a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate how humans learn, gradually improving accuracy.
Machine learning is an essential component of the growing field of data science. Algorithms are trained to make classifications or predictions and to uncover critical insights in data mining projects, which help drive decision-making within applications and businesses.
Company teams don’t have the time or resources to review each candidate’s resume, target candidates, or tailor job descriptions.
But with machine learning, many manual and repetitive recruitment operations can be automated, leaving time for recruitment professionals to focus on more strategic, value-adding tasks.
Specifically, this technology can help with job advertising, resume screening, candidate assessment, candidate engagement, and workforce planning.
Natural Language Processing
Natural Language Processing (NLP) is the branch of computer science, or more specifically, artificial intelligence or AI, concerned with giving computers the ability to understand text and spoken words as human beings can.
NLP combines computational linguistics — rule-based modeling of human language — with statistical machine learning and deep learning models. These technologies enable computers to process human language to understand its whole meaning.
Hiring recruiters must review hundreds, sometimes thousands, of resumes for a single job posting. NLP saves time in analyzing inbound resumes and screening candidates.
NLP can also help remove subconscious bias and increase diversity in candidates, as it is a powerful “listening” technique that HR teams may use to identify employee potential and talent, determine competency, and track behavior trends.
“Automated text analysis can capture skills, personality, and other traits, primarily by using natural language processing, which has a long history of research in linguistics and psychology,” Michael Campion, Ph.D., Herman C. Krannert, Distinguished Professor of Management, said in a recent Talent Select AI interview.
Computer vision is a field of AI that allows computers and systems to derive meaningful information from digital images, videos, and other visual inputs. The technology then takes action to make recommendations based on that information.
Put simply, AI enables computers to think, and computer vision allows them to see, observe, and understand. These vision systems include object detection, image segmentation, and video classification.
One of the most notable factors behind the growth of computer vision is the amount of data generated today used to train and improve computer vision.
In hiring, computer vision technology is harnessed to automate time-consuming tasks like candidate screening and interview scheduling, among other things. In assessments or online interviews, computer vision can help identify body language to see if a candidate is scared or if the movement of lips matches his voice.
Generative AI describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Most of the current buzz around generative AI is on ChatGPT, which is broken down most recently on Student Select AI.
Generative AI model outputs are indistinguishable from human-generated content or can seem uncanny. The results depend on the quality of the model. For example, ChatGPT’s outcomes appear superior to those of its predecessors—and the match between the model and the use case or input.
Mckinsey & Company explains that any business that needs to produce clear written materials can benefit from generative AI- from IT and software organizations that can benefit from the instant, largely correct code to organizations needing marketing copy.
Expert systems are programs that mimic the thinking of human experts who would otherwise have to perform the analysis, design, or monitoring. Most of these programs have a knowledge base, an inference engine, and a user interface.
Expert systems are applied in medicine, finance, engineering, and law. Notably, they can diagnose diseases, forecast financial outcomes, and offer legal counsel.
While Talent Select AI’s product falls under expert systems, we also utilize NLP. With our product, you can analyze the job interview transcript using NLP to provide reliable psychometric insights without that extra step, making it easier to identify and prioritize best-fit candidates quickly – regardless of background.
“An ‘expert AI system’ is one that can emulate the decision-making ability of a human expert. While Talent Select AI’s platform incorporates several types of AI and machine learning methods, our core product is fundamentally an expert system that can assess job candidates as effectively as a trained industrial/organizational psychologist. The biggest benefit of our AI system over expert human professionals is our ability to make our evaluations quickly and at scale,” William Rose, CTO at Talent Select AI, said in a recent interview.
Talent Select AI is an expert system that uses NLP to pull personality, competency and motivational scores right from the transcript of the job interview – no additional tests or manual scoring required.
With Talent Select AI, you can start measuring what matters and increase confidence in hiring decisions while also ensuring each applicant receives a fair, objective, and consistent evaluation.