‘We’re still at the beginning of the AI journey’
Shreyas Subramanian, a Principal Data Scientist at AWS, shared insights on the evolution and future of AI during an interview with The Hindu. The discussion highlighted key trends and advancements shaping the field of artificial intelligence. Listeners gained valuable perspectives on the current state and potential future directions of AI technology. The interview provided an informative overview of the various factors influencing the development of AI. Shreyas Subramanian's expertise offered a unique and insightful viewpoint on the subject.
The Evolution of Artificial Intelligence
Artificial Intelligence (AI) has made remarkable strides in recent years, yet according to machine learning expert Shreyas Subramanian, there is still much to uncover. To understand AI’s evolution and where it’s headed, I connected with Subramanian, a Principal Data Scientist at AWS, on The Interface podcast, to gather his insights on the development of deep neural networks, the rise of transformers, and the broader trajectory of AI.
Tracing the Roots of Neural Networks
Tracing the roots of neural networks to the early conceptualisations of artificial neurons, Subramanian explained the work of Frank Rosenblatt in the 1950s with the Perceptron model. These perceptrons are simple pattern recognition compute units," he explained.
This foundational technology laid the groundwork for neural networks, which aimed to become universal function approximators that could map any input to any output.
The Rise of Neural Networks
Interest in neural networks resurged in the early 2000s, driven by advances in hardware and access to massive datasets. Subramanian highlighted the pivotal role of AlexNet, which popularised deep convolutional neural networks (CNNs) by winning the ImageNet competition.
CNNs transformed image recognition by using convolution operations, akin to sliding a colored plastic filter over an image to highlight specific features, dramatically improving classification tasks and paving the way for modern AI applications.
While CNNs revolutionised image processing, they lacked memory capabilities, which Recurrent Neural Networks (RNNs) addressed.
Inspired by the Human Brain
AI development has often drawn inspiration from neuroscience, though Subramanian acknowledged the gap between artificial and biological intelligence. The human brain’s ability to process sensory inputs in parallel, adapt through neuroplasticity, and perform energy-efficient computations remains unmatched.
The Transformer Revolution
In 2017, AI took a leap forward with the introduction of the Transformer architecture, particularly in natural language processing. The seminal paper, ‘Attention Is All You Need,’ introduced self-attention mechanisms that outperformed previous RNN-based models.
The adaptability of Transformers made them foundational for diverse applications, from text generation to image and audio processing.
The AI Industry and Competitive Landscape
Discussing the competitive landscape, Subramanian noted how OpenAI’s strategic focus on scaling language models gave it an edge over tech giants like Google. Meanwhile, companies like Google and Amazon pursued different strategies, balancing research with business priorities.
A Journey Still Unfolding
AI’s evolution from simple perceptrons to transformative language models reflects decades of innovation, interdisciplinary research, and technological advancements. The future of AI promises exciting developments, driven by continued exploration of both artificial and biological intelligence.
As the field advances, understanding AI’s history and foundational principles will be crucial in shaping its potential and addressing the challenges ahead.