Annual Tech Symposium

Artificial Intelligence & the Future of Engineering

18 January 2025 Department of Computer Science

Artificial Intelligence & the Future of Engineering

18 January 2025, Saturday

18 January 2025, Saturday

11:00 AM

01:00 PM

Department of Computer Science

CSE Student Association (CSESA)

Dr. Meera Krishnan, Professor & Head

Main Auditorium, Block A, Ground Floor

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Top Insights

The symposium opened with a compelling keynote on machine learning integration in real-world engineering pipelines. Experts highlighted that over 70% of engineering problems can now be modelled, predicted, and solved using AI-driven frameworks, reducing prototype cycles significantly.

A recurring theme was the importance of responsible AI development — ensuring that automated decision-making systems remain transparent, auditable, and aligned with ethical principles. Panelists stressed that universities must embed AI ethics into core curricula.

Participants also discovered that edge AI — running inference directly on devices — is rapidly replacing cloud-dependant architectures in IoT and manufacturing, opening massive opportunities for engineers who master embedded systems and model optimisation.

Speaker Quote

The engineers of tomorrow will not merely write code — they will architect intelligent systems that think alongside humans. Your job is not to fear AI, but to be fluent in it. The question is no longer whether AI will transform industries; it already has. The question is whether you will be the one designing that transformation
D
Dr. Rajesh Kumar
Director of AI Research, IIT Delhi — Keynote Speaker
The engineers of tomorrow will not merely write code — they will architect intelligent systems that think alongside humans. Your job is not to fear AI, but to be fluent in it.
D
Dr. Alok Kumar
Director of AI Research, IIT Delhi — Keynote Speaker

Student Takeaways

Students left the symposium equipped with practical knowledge and a clear roadmap for entering the AI-driven engineering landscape. Key takeaways included:

  • Understanding how to use Python-based ML libraries (TensorFlow, PyTorch) to prototype AI models for domain-specific engineering problems.
  • Insight into career pathways in AI research, MLOps, and AI product management — fields growing at 35% annually in India.
  • Hands-on exposure to prompt engineering and fine-tuning large language models for specialised industry datasets.
  • Awareness of national initiatives like the IndiaAI Mission and how students can access government-backed AI infrastructure.
  • Networking opportunities with industry professionals and guidance on internship applications at AI-first companies.
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