AI and Data Science: Two Sides of the Same Coin

AI and Data Science are the known trending and transformative topics in the field of Computer Science and Engineering. The demand of AI and Data Science experts is continuously growing in healthcare, finance, marketing, education and other interdisciplinary areas.

Artificial Intelligence (AI) and Data Science are two rapidly growing fields that are transforming various industries. Both fields involve working with large amounts of data, but their goals and approaches differ. In this article, we will explore the basics of AI and Data Science, highlighting their similarities, differences, and interrelations from the perspective of expertise and job profiles.

The term "AI" was coined in 1956, and it encompasses a broad range of techniques aimed at simulating human intelligence in machines. Building AI systems requires identifying, acquiring, storing, and processing vast amounts of knowledge. On the other hand, the term "Data Science" emerged in the late 20th century and involves building models and techniques for extracting valuable information from large datasets. Data Scientists leverage statistical analysis, hypothesis testing, and pattern identification to understand the science behind complex systems before applying statistical and machine learning tools.

Statisticians have long employed machine learning algorithms like regression, classification, and clustering for predictive analysis in fields such as weather, market, health, and business. However, the limited data and computing power available at the time restricted the scope of these analyses. In recent years, the surge in digital data and high-performance computing capabilities (e.g., multi-core CPUs, GPUs, and vast RAM) has enabled data scientists to build faster, more reliable, and more accurate predictive and decisive systems using advanced machine learning models.

The availability of digital data, high computing power, and the impressive performance of machine learning models on big data have inspired AI developers to create learning models where they don't need to explicitly identify patterns, form rules, or handle ambiguities and contextual knowledge. Instead, the system learns on its own through machine learning models trained on large datasets. This self-learning analogy is often compared to how young children learn by observing and listening to repeated patterns.

In contrast, Data Scientists formulate hypotheses, collect, organize, and structure data for analysis, and develop algorithms and models to answer queries from upper management and assist the organization in making informed decisions. Both AI system developers and Data Scientists contribute to building intelligent systems or extracting information embedded in large datasets, bridging the gap between the two fields.

From my perspective, the tools and techniques used in Data Science support the development of AI systems, while these AI systems, in turn, aid Data Science in decision-making. However, human involvement remains crucial in both fields due to our critical thinking abilities, innovation, and passion for achieving desired goals.

The demand for skilled AI and Data Science professionals is evident in the job market. All major companies, such as Microsoft, Google, Amazon, Apple, Nvidia, Uber, and Cruise, as well as new companies like Numerator, Databricks, Unified, Teradata, Algorithmia, etc., have either large or small Data Science teams depending on the size of the organization. Most of the big companies also have AI jobs, including AI Product Manager, AI Ethicist, Robotics Engineer, AI Consultant, and so on. In many companies, these teams work together closely to build a complete system.

For instance, companies developing driverless cars employ a team of AI experts who design and implement artificial intelligence systems that allow the cars to perceive, understand, and navigate their surroundings independently. Additionally, they have a team of Data Scientists who refine and analyze the data collected from sensors using machine learning models, ensuring the safety and reliability of the entire system.

Throughout my career, I have had the opportunity to work on several AI projects, including a machine translation system. Back in 1994 at IIT Kanpur, we worked on a project called the AnglaBharti system, which aimed to translate from English to Hindi. This Rule-Based system involved creating an English-Hindi dictionary from scratch, forming rules for parsing English sentences into phrases (e.g., noun-phrase, verb-phrase, prep-phrase), converting the parsed structure according to Hindi language, and then generating the Hindi version of the sentence.

Um all diese Module zu entwickeln, hatten wir ein Team bestehend aus Hindi- und Englisch-Sprachexperten, Dateneingabeoperatoren, KI-Experten, Programmierern mit Kenntnissen in KI-Sprachen, erfahrenen Akademikern und KI-Forschern. Das ursprüngliche Ziel bestand darin, ein allgemeines Übersetzungssystem vom Englischen ins Hindi zu entwickeln, aber letztendlich konnten wir nur ein funktionsfähiges System für den medizinischen Bereich erstellen.

Aber nach 2010 konnten Forscher mithilfe von LLMs hochwertige Übersetzungssysteme entwickeln, ohne die Feinheiten der Sprachen oder den Übersetzungsprozess im Detail verstehen zu müssen.

Da sich KI auf das Lernen konzentriert und Data Science darauf abzielt, Wissen aus vorhandenen Daten zu extrahieren, können wir daraus schließen, dass KI und Data Science zwei unterschiedliche Zweige sind, dennoch scheinen sie zwei Seiten derselben Medaille zu sein.

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