Aspect-based sentiment analysis enhances sentiment detection by associating it with specific aspects, offering deeper insights than traditional sentiment analysis. This study introduces a manually annotated dataset of 10,814 multilingual customer reviews covering brick-and-mortar retail stores, labeled with eight aspect categories and their sentiment. Using this dataset, the performance of GPT-4 and LLaMA-3 in aspect based sentiment analysis is evaluated to establish a baseline for the newly introduced data. The results show both models achieving over 85% accuracy, while GPT-4 outperforms LLaMA-3 overall with regard to all relevant metrics.

Last month, Microsoft fired a powerful salvo by launching Viva Sales, an application with embedded generative AI technology designed to help salespeople and sales managers draft tailored customer emails, get insights about customers and prospects, and generate recommendations and reminders. A few weeks later, Salesforce (the company) followed by launching Einstein GPT. Sales, with its unstructured, highly variable, people-driven approach, has been a laggard behind functions such as finance, logistics, and marketing when it comes to utilizing digital technologies. But now, sales is primed to quickly become a leading adopter of generative AI -- the form of artificial intelligence used by OpenAI (the company behind ChatGPT) and its competitors. AI-powered systems are on the way to becoming every salesperson's (and every sales manager's) indispensable digital assistant.

Operationalizing machine learning is a critical step in making AI-powered products and services successful. Let's discuss how MLOps can help businesses resolve issues efficiently. Operationalizing machine learning, or "MLOps", as it is now called, is the latest trend in many industries. Operating is something that businesses do every day; they operate their factories, their offices, their stores, and so on. But what does it mean to "operationalize machine learning"?

Voice AI is expected to reduce global call centre labour costs by as much as US$80 billion by 2026, according to Gartner. However, Australia's adoption of conversational AI for customer service remains relatively low compared to other countries. "Nine-in-ten Australians are now smartphone users, with Siri, Google or Alexa at their fingertips," said Kun Wu, Founder and Managing Director of AI Rudder, a leading voice AI provider in the Asian Pacific (APAC). Voice AI technologies have enhanced consumers' lives in terms of communication, media use, entertainment and information searches. Given this high consumer penetration, Wu says the potential exists for the Australian customer service industry to follow APAC markets in using voice AI to deliver high-quality service.

Artificial intelligence (AI) refers to the capability of machines to perform tasks that would otherwise need human intelligence. This technology has taken the world by storm, with companies like Google, Amazon, and Apple spending billions on R&D in this area. The AI is getting smarter each day as artificial neural networks are trained with large amounts of data which means that it can do more than play games or beat humans at chess. The big data combined with artificial intelligence promises to help companies derive insights never thought possible before, thus allowing them to engage better with their customers. However, organizations need to keep a tab on innovation and stay relevant using artificial intelligence as part of their digital strategy.

The use of artificial intelligence (AI) has become more widespread and is experiencing widespread adoption in all industries. With increasing competitive pressures and observations of their peers' AI successes, more and more businesses are integrating AI into various aspects of their operations. The Machine Learning (ML) models that drive AI systems have become increasingly powerful, displaying superhuman abilities on most tasks. Although AI systems have increased in performance, model complexity has increased as well, making them a black box with decisions that may be difficult for humans to understand. As black box models influence not only business outcomes but also the lives of many people, they can have severe ramifications.

Artificial intelligence (AI) has become increasingly pervasive and is experiencing widespread adoption in all industries. Faced with increasing competitive pressures and observing the AI success stories of their peers, more and more organizations are adopting AI in various facets of their business. Machine Learning (ML) models, the key component driving the AI systems, are becoming increasingly powerful, displaying superhuman capabilities on most tasks. However, this increased performance has been accompanied by an increase in model complexity, turning the AI systems into a black box whose decisions can be hard to understand by humans. Employing black box models can have severe ramifications, as the decisions made by the systems not only influence the business outcomes but can also impact many lives.

Are you looking to land a top-paying job in Data Science, AI & Natural Language Processing? Or are you a seasoned AI practitioner who want to take your career to the next level? Or are you an aspiring data scientist who wants to get Hands-on Data Science and Artificial Intelligence? If the answer is yes to any of these questions, then this course is for you! Data Science is one of the hottest tech fields to be in right now!

Artificial intelligence (AI) has become increasingly pervasive and is experiencing widespread adoption in all industries. Faced with increasing competitive pressures and observing the AI success stories of their peers, more and more organizations are adopting AI in various facets of their business. Machine Learning (ML) models, the key component driving the AI systems, are becoming increasingly powerful, displaying superhuman capabilities on most tasks. However, this increased performance has been accompanied by an increase in model complexity, turning the AI systems into a black box whose decisions can be hard to understand by humans. Employing black box models can have severe ramifications, as the decisions made by the systems not only influence the business outcomes but can also impact many lives.

We are living in an age where we simply need to speak to the VA (voice assistant) and command to get things done for us. This is where NLP or Natural language processing with AI comes into the picture. As the subset of machine learning and an AI component, NLP was first implemented in around 1952 as per the Hodgkin-Hexley model. While, it was Alan Turing in 1950, who first recognized that a'thinking machine' should be able to interpret and understand conversations in the language spoken by humans. As a means to form a bridge between communication for machines and humans, NLP has found diverse applications across the business landscape.