Hallucination in Large Language Models (LLMs) refers to the generation of content that may deviate from factual accuracy or the provided source content. While these instances may occur, it’s crucial to recognise that LLMs are primarily designed for general-purpose language understanding and generation, not for delivering absolute precision. Originally published at LinkedIn Pulse on 7 January 2024. Photo by Andrea Piacquadio @ Pexels.com Consider an example where an image gen

Synthetic data has become a buzzworthy topic in recent times, offering a glimmer of hope for addressing the challenge of limited high-quality data for training AI and ML models. The other day, an enthusiastic salesperson came to me with a pitch for a product that claimed to generate synthetic data. Now, don’t get me wrong, AI and ML models are undoubtedly going to shape the future of work. However, I have some reservations about relying solely on synthetic data to build these

When I embarked on my first data role over a decade ago, my organisation was supported by a team of 30 business analysts responsible for generating insight reports by extracting a month’s worth of transaction data from an Oracle database, conducting analysis, creating visualisations, and documenting observed trends. They used a custom web interface with different parameters to extract the data from the database. Unfortunately, this entire process, from start to finish, took a

In today’s highly competitive environment, the rush to harness the transformative power of Artificial Intelligence (AI) is apparent. However, beneath the surface of many organisations, there is a subtle, often hidden, threat: Shadow AI. This isn’t the stuff of sci-fi thrillers; it involves the widespread use of AI tools and initiatives by individual teams or departments, such as marketing, HR, or customer service, without central oversight, approval, or understanding from IT

The honeymoon phase is over. What comes next will reshape every company on earth. There is a quiet revolution underway inside enterprise technology—and it bears no resemblance to the breathless headlines about artificial general intelligence or trillion-parameter models. It looks like budget reviews. It looks like an infrastructure audit. It looks like boards are demanding to know why AI investments are producing inconsistent returns at unsustainable cost. The era of deployin