By Ivan Vlaevski
A Journey Through AI Evolution
Back in 2018, Swami Chandrasekaran, then Head of AI & Data Labs at KPMG Delivery Network, created an insightful infographic breaking down the components of artificial intelligence. Kudos to him for the clarity and structure! However, AI has come a long way since then, and the landscape has dramatically evolved. What was once a relatively structured domain with distinct borders between model algorithms, architectures, and learning processes has now become a blurred and intertwined ecosystem.
As I revisited the topic, I realized there’s no longer a way to separate model algorithms from AI system architectures, nor learning processes from data gathering strategies. The complexity of modern AI systems has grown to a point where functionalities mix, and features emerge from interactions rather than from predefined structures.
What’s New in AI?
In my 2025 version of the Demystifying AI infographic, I aimed to capture these shifts, focusing on key developments:
- Indoctrination
One of the most intriguing recent findings is how reinforcement models can unintentionally build bias values through adversarial communication with users. This “error” has significant implications, raising new ethical and security challenges for AI alignment. - Expanding Learning Architectures
We’ve seen the rise of hybrid models, federated learning, and edge AI integration, making centralized processing less dominant. - The Shift Away from Rule-Based AI
I deliberately chose not to cover rule-based AI and Google’s Symbolic AI engine, as their impact has become more niche in the broader AI evolution.
AI Meetup & The Future
I shared this infographic at the AI Meetup conference, sparking an engaging discussion with the community. The key question? Where is AI headed next? Will we continue refining human-AI collaboration, or are we inching closer to Artificial General Intelligence (AGI)?
This infographic is my attempt to summarize AI’s latest trends and challenges in a single snapshot. The landscape of AI is ever-changing, and keeping up with these advancements requires constant learning and adaptation.
I’d love to hear your thoughts – what do you think is the most significant change in AI over the last five years?
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