TECHNOLOGY
Conclusion AI is a topic which has sparked debate. While some people are optimistic about the benefits and opportunities that AI could bring and are eager to learn more, others are more cautious and believe AI will replace humans in the workplace. For the most part, UK media coverage tends to centre on new industry products, announcements and initiatives that include AI, therefore focussing on the commercial aspect of AI. However, the government has also declared its interests in AI and its future capabilities, so AI is a technology we should all learn to embrace. n
Chatbots Some of the services and communications employees often need from payroll can be covered by AI-powered chatbots, along with payslip access and salary information. In addition, AI can help human resource (HR) departments streamline hiring processes, simplify onboarding, enhance compliance and improve the accuracy of data passed from HR to payroll, ensuring a higher degree of accuracy. All these benefits will save time and avoid under or overpayments to employees. Although AI isn’t a replacement for human expertise and judgment, it can be used to augment and enhance the work of payroll staff
and other professionals across the organisation, who have an input into the payroll process. Impact on payroll professionals As AI takes over routine payroll tasks, concerns arise about the potential displacement of payroll professionals. However, it’s essential to recognise that AI cannot entirely replace human expertise. Instead, payroll professionals can transition into more strategic positions, focussing on: l interpreting AI-generated insights l analysing complex data patterns l providing valuable financial advice to businesses.
Full name
Acronym (if applicable) What is it?
AI is the science of creating machines which can perform tasks that typically require human intelligence. AI technology can process large amounts of data in ways that are different from humans, and it can do things considered to be ‘smart’, such as recognising patterns, making decisions and judging like humans.
Artificial intelligence
AI
GAI is a type of AI which can generate text, images or other media using generative models. Some examples of generative AI systems include ChatGPT, Copilot, Gemini and LLaMA.
Generative AI
Gen AI/ GAI
ML is a type of AI which allows computers to learn from data and make decisions without being explicitly programmed to do so. It’s like teaching a computer to recognise patterns and make predictions based on those patterns. A cluster is a group of data points which share similar characteristics or attributes. Clustering is a ML technique that involves grouping sets of objects in such a way that objects in the same group, called a cluster, are more like each other than those in other groups. Clustering algorithms tend to work well in environments where the answer doesn’t need to be perfect, it just needs to be similar or close to be an acceptable match. AI clustering can be particularly effective in identifying patterns in unsupervised learning. Some common applications of clustering are in HR, data analysis, recommendation systems and social science. NLP is a type of computer science which helps computers understand and interpret human language. It combines knowledge from computer science and linguistics to teach computers how to read, write and speak like humans. A chatbot is a computer program that simulates human conversation with an end user. Chatbots can be designed to respond to text or voice interactions and can use artificial AI techniques like NLP and ML to create better customer experiences. An algorithm is a set of instructions that a computer program follows to solve a specific problem. AI algorithms are designed to learn from data and make predictions or decisions based on that data. There are many different types of AI algorithms, such as supervised learning, unsupervised learning and reinforcement learning. Supervised learning is a type of ML, where the algorithm learns from labelled data to make predictions or decisions based on the input and output features. In other words, the algorithm is trained on a dataset that has both input and output data, and it learns to map the input data to the correct output data.
Machine learning
ML
Cluster
Natural language processing
NLP
Chatbots
Bots
Algorithm
Supervised learning
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| Professional in Payroll, Pensions and Reward |
Issue 99 | April 2024
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