Over the past two years, I have spearheaded multiple Generative AI (GenAI) initiatives to enhance customer service, streamline internal processes, and gain a competitive edge. These projects have spanned various applications, from deploying customer service chatbots to automating internal workflows. Originally published at LinkedIn Pulse on 24 March 2024. Photo by Tara Winstead @ Pexels.com This journey has provided us with critical insights, revealing both the immense poten

In 2023, the field of generative artificial intelligence (AI) experienced remarkable progress, with significant advancements in natural language processing, computer vision, and audio synthesis. These developments have unlocked a plethora of new opportunities and applications, shaping the future trajectory of technology across a wide range of sectors. This article provides an overview of the ethical considerations and responsible strategies for the development, deployment, an

Generative AI is a branch of artificial intelligence that focuses on creating new content or data from scratch, such as images, text, music, code, diagrams, etc. Generative AI has been making impressive advances in recent years, thanks to the development of deep learning models such as generative adversarial networks (GANs), autoencoders, transformers, and others. Originally published at LinkedIn Pulse on 8 January 2024. Photo by Google DeepMind @ Pexels.com Generative AI ha

Once businesses have started collecting and combining all kinds of data, the next elusive step is to extract value from it. Collected data may hold tremendous potential, but no value can be created unless insights are uncovered and translated into actions or business outcomes. Originally published at LinkedIn Pulse on 23 December 2019. Photo credit: www.pexels.com Data storytelling is the process of translating data analyses into layman’s terms to influence business decision

I have seen many articles on types of analysis. Most of these articles discuss four types: descriptive, exploratory, diagnostic (usually either exploratory or diagnostic!), predictive, and prescriptive analysis. But throughout my early career, I’ve personally experienced eight types of analysis. I encourage all aspiring data scientists to be familiarised with all these types of analysis and, if necessary, apply them. Originally published at LinkedIn Pulse on 22 December 2019

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

To drive value from data, analytics need to be operationalised. Ad-hoc data exploration can be unclear, depends on each individual functional team’s preferences to generate insight, acted on and then forgotten under the pile of documentation. The preference may vary from the way the experiments are conducted, tools being used, and the finding has been disseminated. Every project starts from scratch. And if someone with a vast knowledge leaves the organisation, the prior knowl

Multiple recent recruitment surveys revealed that the demand for data engineers has recently exceeded the previous demand for data scientists. The Dice 2020 Tech Job Report said data engineer was the fastest growing job in technology with a 50% year-over-year growth in the number of open positions. Many mentees asked me how do I perceive this shifts in demand, and what area should they pursue. Actually, I see a course correction happening in the industry. Most organisations t

The pervasive notion that sophisticated Artificial Intelligence (AI) systems, particularly predictive models and large language models (LLMs), operate under a regime of continuous, autonomous self-improvement is one of the most persistent and potentially damaging misconceptions in contemporary technology discourse. This belief, often propagated through media narratives and science fiction precedents like Recursive Self-Improvement (RSI), a concept involving a system autonomou

Imagine a world where information and opportunities are not just a click away but are rather confined to certain demographics and regions. Sadly, such a world existed well before the late 20th century when the term 'digital divide' was coined. It served as a stark reminder of the gaping chasm that separated those who had the privilege of accessing modern information and communications technology from those who were left behind, either with no access or restricted access. Orig

With the advent of the data science practises, especially the likes of Artificial Intelligence (AI) or machine learning and their adaptation in sensitive areas, has drawn massive attention to this technology in the recent years. Whether it is cancer diagnosing system, Amazon’s AI recruiting tool or the US court’s profiling system Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), we can always disagree to the decision made by the AI systems. In fac

Artificial Intelligence (AI), or more precisely Machine Learning (ML), has become an industry trend in the past 10 years. From a buzzword to a new way of automation and decision-making, ML has become mainstream. I have had conversations with multiple organisations keen to use ML, even without a real-world use case. Graduate schools are offering lightweight courses to the masses, consulting companies are providing services on adopting ML, and technology companies are building

There are many definitions of data science and is enough to confuse anyone, the question remains what is data science? So, before discussing this topic I often lay out the definition I go by. Data science is an interdisciplinary area about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, like Data Mining or Knowledge Discovery in Databases (KDD) . My definition of data science is rather s

Defining success is a crucial part of managing a data science experiment. Of course, success is context-specific. However, some aspects of success are general enough to merit discussion. A list of hallmarks of success includes: New knowledge is created. Decisions or policies are made based on the outcome of the experiment. A report, presentation, or app with impact is created. It is learned that the data cannot answer the question being asked of it. Some more negative outcome