The End of Classical Computer Science: A Personal Journey into the Future of AI

Are we the dinosaurs waiting for the meteor to hit? 

Growing up in the late 1990s, I was fascinated by personal computers like the Intel Pentium P1,P2,P3 along with the 8086 microprocessor and Windows DOS/95/98 programming. PC gaming was my entry point to understanding complex classical computer science at a very young age, which sparked my curiosity about computer systems.

I used to play games on my PC, which had 32 MB of RAM and a 400 MHz processor. In the early 2000s, I got dial-up internet [64kbs] at home [ thanks to my dad ] and that experience completely changed my perspective on computers & Internet. Browsing the internet back then was a game of patience—I once kept my PC running for over 48 hours to download just 100 MB of files. :).

These machines and the early Internet led me toward programming and set me on a path that would define my career. I studied Computer Science and earned a Bachelor of Engineering at Pune University.

My education focused on what I now call "classical" Computer Science which involves Programming, algorithms, data structures, systems, and programming languages. I used to code in Turbo C++, Notepad, and TextPad—the bullock carts of IDEs at that time. :).  In classical CS, the goal is to turn ideas into programs written by humans—source code in languages like Java, C++,HTML or Python.

Every concept, no matter how complex—from database algorithms to networking protocols—can be expressed as a human-readable program. I still remember spending entire nights running a Java applet animation program, which eventually led me to receive the award.

These complexities truly helped me build patience in life, something I’m especially grateful for in today’s impatient world.

But as I reflect on how much has changed since my college days, I can't help but think that the way we've been approaching Computer Science is due for a major uplift. The rise of artificial intelligence is reshaping our field, and I believe we're on the verge of a new era.

My Early Days in Computer Science

During my time at university in the early 2000s, AI was experiencing what was known as the "AI Winter." The field was dominated by classical algorithms, distributed systems and cloud computing. My coursework involved:

• Programming Projects: Building an OS task scheduler using C++ and building websites using Java Applet, Apache Tomcat etc. I used to deal with machine-level programs like Semaphore, kernel management etc
• Classical Algorithms: Studying linked lists, trees and nodes, Breadth-First Search, Depth-First Search, round-robin scheduling, and many more.
• Achievements: As a tech nerd, I started my own website SamCodes.com, and started sharing my university programs and my technology ideas through blogging. This obsession helped me win a Planet Source Code award for animation in Java—a popular platform at the time.

We didn't discuss neural networks or deep learning during my academics. These concepts were in their early stage and not part of mainstream Computer Science education.

The Changing Landscape of Computer Science

Fast forward 20 years, and while technology has advanced I can see the core of Computer Science education remains largely unchanged. We're still teaching data structures, algorithms, and programming as the foundation. However, I believe that in the next 7 to 10 years, this approach will become outdated.

Our education system required a redesign of its courses.

Programming Will Become Obsolete

I think the traditional idea of "writing a program" is headed for extinction but core principles will become more valuable.

Here's why:

• Rise of AI Systems: Most software will be replaced by AI systems that are trained rather than programmed.
• AI-Generated Code: For simpler programs, AI will generate code automatically, eliminating the need to code by hand.
• Advancements in AI: Tools like AI coding assistants are just the beginning. The rapid progress in AI-generated content, such as image creation with DALL·E ,FLUX etc, shows how quickly AI capabilities are expanding.

In the future, Computer Scientists may not need to learn how to reverse a linked list or implement sorting algorithms. These skills could become very old as using a slide rule.

The New Role of Engineers and Computer Scientists

I personally believe Engineers of the future might:

• Utilize Massive AI Models:  They will Start using large AI models that already know a lot.
• Focus on Training Over Coding: They will spend more time providing the right examples and training data to AI systems.  It's like raising a child. AI is our new little one, & we need to guide it towards good by building helpful tools.
• Teach by Example: We have to Guide AI models to perform tasks by showing them what to do, rather than programming every instruction.

This shift moves us from being programmers to becoming educators of machines.

Your SKILLS are complementary to AI systems, not replacements. So focus on getting skilled at using AI creatively.

How This Changes Computer Science

The fundamental unit of computation is changing:

• From Code to Models: We will move away from code and algorithms to working with large, adaptive AI
• Unpredictable Behavior: These AI models can do things they weren't explicitly trained to do, making their behavior less predictable.
• Need for New Skills: Understanding attention layers, tokenizers, and datasets becomes more important than knowing programming. Deep Learning & Data Science [Good Quality Data] is going to run in parallel for better outcomes.

This transformation presents both opportunities and risks. We need to adapt our thinking to embrace this new system with responsible AI implementations.

Challenges with Large AI Models

One of the biggest challenges is that:

• Complexity Leads to Uncertainty: Nobody fully understands how large AI models work internally. Yes you heard right, they are completely strangers in their behavior.
• Emergent Behaviours: AI systems can learn new behaviors that weren't anticipated by their creators.
• Difficulty in Predicting Outcomes: This makes it difficult to determine the limits & potential risks of AI systems. Following responsible AI guidelines is very essential at the beginning to avoid misuse of AI amplification.

It's crucial to approach AI development responsibly, keeping in mind the ethical implications. We will see many regulations on core AI systems in the near future.

Embracing the Future

Given these changes, it's time for us to:

• Update Educational Approaches: Incorporate AI training and model management into Computer Science curricula. Practical use cases are the foundation of learning any system so we have to consider practicality in our education.
• Adapt Professional Roles: Shift from coding to training and supervising.  I believe we will become managers of AI systems which we will fine-tune for efficiency & powerful outputs.
• Engage in Continuous Learning: Staying informed about the latest AI advancements & methodologies is very important to 10x your growth in all verticals.

Conclusion

The end of classical Computer Science doesn't mean the end of innovation. Instead, it's the beginning of a new era where AI plays a central role. As someone who has started from programming simple computers to witnessing the rise of AI, I believe it's our responsibility to adapt and grow with these changes.

We shouldn't see ourselves as dinosaurs waiting for the meteor to hit. Instead, we can be pioneers in this new landscape, guiding the development of AI in ways that benefit society.

What's Next for you?

For anyone involved in Computer Science or related fields, I strongly suggest :

• Explore AI Technologies: Get hands-on experience with AI models and tools.
• Join the Conversation: Engage with technologists to discuss the future.
• Embrace Change: Be open to new ideas and ready to adapt your skills.

By doing so, we can ensure that we're not left behind but are active participants in shaping the future of technology. I'm happy to hear your thoughts in the comments.