The Role Of Artificial Intelligence In DATA SCIENCE: Shifting Paradigms
With the advent of artificial intelligence in our lives, many people are pondering questions like: " Are our talents redundant anymore? Is a skill that was valuable last year meaningless today?"
AI expert Lance Eliot draws a parallel with the words of Charles Dickens on the French Revolution in "A Tale of Two Cities”,
“It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of light, it was the season of darkness, it was the spring of hope, it was the winter of despair.”
The advancement of AI technology is enhancing efficiency by automating business processes and creating new job opportunities. However, behind this bright face of AI lies the reality of certain job roles transforming or disappearing entirely. Concrete examples of this paradox are seen in major companies like IBM, which reduces its workforce using AI technologies. While layoffs occur on one hand, these companies also offer courses like "Generative AI: Elevate Your Data Science Career" to encourage employees to adapt to the AI revolution. The stance on AI's dual role depends entirely on perspective.
AI Innovation and Data Maestros
We are transitioning from a world where traditional AI excelled in analyzing existing data to generative AI capable of producing new data such as text, images, or code. This capability automates tasks typically undertaken by data scientists, like data cleaning, pattern recognition, and model development. For instance, tools like ChatGPT Code Interpreter make generative AI capable of producing code for analytical tasks, broadening the accessibility of data science. This accessibility could revolutionarily change decision-making processes at various organizational levels. Data scientists are integrating AI into workflows using tools like Einblick's Prompt. This integration simplifies tasks from data preprocessing to model creation into just natural language prompts, making the process much more straightforward. Does this eliminate the need for skilled data scientists? No, but the required skills and competencies are evolving. Let's explain this with a favorite comic book character of mine:
Tony Stark (Iron Man) and J.A.R.V.I.S.
Artificial Intelligence (AI) is to a Data Scientist what J.A.R.V.I.S. is to Tony Stark. Just as J.A.R.V.I.S. assists Stark by providing information and interacting with him, AI aids Data Scientists in data analysis, forecasting, and decision-making processes. AI lightens the Data Scientist's workload by processing data, making predictions, and automating complex tasks. However, complete reliance on current versions of J.A.R.V.I.S. isn’t feasible, particularly due to generative AI's tendency to produce fictitious data and information, making its use risky for professional purposes. To address this issue, data scientists still need their technical expertise. Experts recommend asking various questions to overcome these problems, including:
How did you perform data cleaning and/or preprocessing? How did you generate synthetic data?
Was data quality evaluated?
Was there model architecture and hyperparameter tuning?
Are training data compatible with expected uses?
How was the dataset divided for model training and validation?
How was the model evaluated using metrics and stress tests?
Do you have mechanisms to address data drifts?
Have you integrated tools to increase model confidence?
The rise of AI could transform the roles and requirements of data scientists. For example, AI can automate routine tasks like data cleaning and preprocessing. However, the quality and accuracy of these processes cannot be guaranteed without the supervision and intervention of data scientists. Synthetic data production and data quality assessment remain within the expertise of data scientists. Deeper technical knowledge and strategic thinking of data scientists play a critical role in more complex processes like model architecture and hyperparameter tuning. Tasks that were once carried out by data scientists are now swiftly executed with the assistance of AI. While AI automates routine tasks, data scientists should focus on more complex problem-solving and strategic thinking. Artificial intelligence should be seen not as a threat to data scientists, but as an opportunity for evolution and innovation.
As Benjamin Franklin said: “Look before, or you’ll find yourself behind.”