Preface

Comparing the capacity of computers to the capacity of the human brain, I've often wondered, where does our success come from? The answer is synthesis, the ability to combine creativity and calculation. into a whole that is much greater than the sum of its parts.

How Life Imitates Chess, Garry Kasparov (2007)

Data analytics is a crucial process in today's data-driven world. It involves collecting, cleaning, transforming and analyzing data to uncover useful information, insights, trends and patterns which inform business strategy, decision-making and process optimization. Traditionally, data analytics was a manual process requiring data scientists and analysts to prepare and process data before analyzing them. This was both tedious and time-consuming. The advent of machine learning and artificial intelligence (AI) has transformed data analytics by automating some parts of the process.

Generative AI models, like ChatGPT, are at the forefront of this automation revolution in data analytics. These large language models can understand human prompts and generate coherent and human-like textual responses. They are trained with massive text datasets, enabling them to perform a variety of language-based tasks. Generative models, such as ChatGPT, can be fine-tuned for specific applications, including data analytics.

We can consider these generative AI models as children with unlimited potential. The data scientist's role is to nurture these models, train them and help them grow - just as parents do with their children. In the beginning, these models are like children - they have immense capabilities, but need guidance to realize their potential.

The data scientist trains them step-by-step, teaching them the different tasks, operations and functionalities required for data analytics. This includes data preprocessing, cleaning, feature engineering, modeling, evaluation and explanation. Models are trained thoroughly, across diverse datasets, learning the nuances of each analytical task.

Gradually, just as a toddler learns to walk, talk and eat solid foods, generative models become capable with regard to various data analytics workflows. With each iteration, their skills improve: they learn to handle diverse datasets, manage missing values, transform features, select optimal models, critically assess performance and generate data-driven insights.

After extensive training on a wide range of datasets and analytical tasks, these generative models evolve from toddlers into mature analytical assistants. They move from simple memorization of problem-solving techniques to the development of true conceptual understanding. The models understand why particular data transformations, models and evaluations are appropriate for given scenarios.

In a way, they develop inductive reasoning and deductive logic, just like humans. They understand the principles and evidence-based principles that underlie data analytics workflows, rather than simply memorizing mechanical instructions. This conceptual understanding is what distinguishes generative AI from prior rule-based expert systems.

Thus, when a data scientist prompts a mature, well-trained model like ChatGPT to perform analysis, it deeply understands the request, rather than simply matching keywords. It draws on its conceptual knowledge to analyze the dataset, select optimal techniques, generate insights and explain the reasoning behind them. And it does so at superhuman speeds, leveraging the computing power of AI.

But does that make these generative models smarter than humans? The answer is no. At least not yet. Although they can outperform humans on narrow tasks within their training domain, these AI models lack generalized intelligence. Human cognition crucially remains far more advanced.

Unlike generative models, humans possess common sense, intuition, imagination, social intelligence, sensitivity and generalized reasoning capacities. We can creatively solve new and open problems that intersect several domains. Humans also have better judgment, wisdom and morality, which temper our technical capabilities with ethics and responsibility.

So, while ChatGPT can analyze datasets and quickly generate information, it lacks the general critical thinking skills to deeply understand implications and assess ethics. Its intelligence is circumscribed by its training data and purpose. It cannot reason its way through completely new scenarios, as humans can through transfer learning.

That said, narrow AI models offer complementary advantages to human intelligence. Their prodigious memory and computational speed enable exhaustive data analytics. Their lack of bias and fatigue ensure consistent performance. In this way, they endow humans with superhuman data processing capabilities.

Rather than competing with AI, we can collaborate with it - combining human wisdom and ethics with AI's productivity and precision. Together, we evolve data analytics and make it more insightful and responsible. But humans must remain in the loop to provide guidance, assess implications and ensure alignment with ethics.

An ideal symbiosis is one where humans manage creative and strategic tasks requiring reason, ethics and imagination, while AI accelerates repetitive analytical tasks requiring memory, computation and precision. Similar to Iron Man deploying the AI assistant JARVIS to enhance his human capabilities.

So, while the gap between human intelligence and AI persists, narrow AI models like ChatGPT are still in their infancy. Their capabilities will continue to grow exponentially thanks to increased data size, computing power and algorithmic advances. One day, they may even cross the threshold into artificial general intelligence (AGI).

But for the time being, generative AI enhances rather than replaces humans when it comes to data analytics process. It takes care of the tedious parts, allowing data scientists to focus on creative, high-added-value tasks. It is becoming an indispensable analytical assistant that continues to learn - like a child who grows into an adult over the years with careful attention.

The key is for humans to guide the development of these generative models in a thoughtful and ethical way. We need to focus on beneficial objectives and monitor for harmful biases or abuse. With judicious care and training, AI can usher in an era of augmented analytics - where human and machine intelligences meet and converge for more powerful, yet ethical, data insights. But the human must remain the supervising parent for the AI child.

Rather than wondering when AI will surpass human intelligence, we should focus on how to cultivate beneficial and ethical AI applications today. Generative models like ChatGPT are impressionable children that will shape the future based on the guidance they receive. Data scientists have a profound opportunity and responsibility: to educate these AI "children", so that they become responsible and collaborative allies instead of impenetrable adversaries.

Just as teaching helps humans to consolidate our own knowledge, the training of AI models requires us to thoroughly evaluate our hypotheses, biases and best practices. AI development is as much about advancing our own intelligence - codifying disciplines into coherent frameworks, evidence-based principles and methodologies.

Collective training of generative models advances human knowledge across domains. This requires distilling nebulous issues into structured frameworks; formalizing messy tasks into step-by-step workflows; and crystallizing weakly defined domains into rigorous first principles. Teaching AI models through examples helps us to better evaluate solutions, generalize insights and formalize ethics for humans as well.

The future of data analytics is humans and AI working together - combining the human's imaginative definition of problems, ethics and strategic judgment with AI's vast memory, exhaustive computation and high-velocity analytical workflows. Neither can match the synergistic value of the two intelligences combined. Data science augments both human and artificial intelligence.

The time has come to actively train this child prodigy: ChatGPT! It holds enormous potential to enhance human capabilities if nurtured properly. We need to nurture it carefully - teaching analytical skills while emphasizing ethics, exposing ChatGPT to a variety of data and scenarios under supervision, so that it moves from mechanical regurgitation to contextual comprehension.

So, let us therefore guide these generative models with wisdom and kindness. Let us instill analytical techniques with values and ethics, guiding them from innocence to maturity. And let us develop artificial intelligence that makes individuals responsible instead of replacing them. Models such as ChatGPT are still in their maturing stages. With the careful supervision of researchers concerned with data ethics, they could evolve into assistants, opening up new horizons of ethically guided discovery, and collaborating more fruitfully with human than they could achieve on their own.

Just as Garry Kasparov's quote states, human success arises from our ability to synthesize the creative and intuitive aspects of cognition with the computational and analytical aspects. When we combine these complementary modes of thinking and reasoning, the result is an emergent intelligence that simply exceeds the linear sum of creativity and calculation. There are synergies and amplifying effects that result from the fusion of different thinking styles, which makes us unique as human...