By Naveen MR, Head of Technology for Generative AI Business Services, Happiest Minds Technologies
My dear, here we must run as fast as we can just to stay in place. And if you wish to go anywhere, you must run twice as fast as that.” – Alice in Wonderland.
The concept of neural networks traces back to the early 1940s when Warren McCulloch and Walter Pitts designed a computational model for neural networks based on algorithms. Fast forward to 2022, ChatGPT was released by OpenAI, capturing the public imagination and dominating media and political discussions about the future of AI. This was the first easily accessible demonstration of the power of Large Language Models. It took some time. But ever since ChatGPT came onto the scene, the pace at which Gen AI technology has been evolving is insane. And if you are someone who wants to get onto this bandwagon, it’s like getting onto a high-speed train journey.
The pace at which GenAI technologies, LLM, and the underlying hardware that enables such large-scale processing are growing almost certainly seems to overtake any of the Moore, Metcalf, or any other (law) predictions that we know. While Moore’s Law provided the foundation for ever-increasing processing power, Gen AI advancements go beyond raw computational muscle.
Here’s how:
⦁ Data surge: The explosion of digital data in recent year’s fuels AI development. Vast amounts of text, images, and code train AI models, allowing them to learn and improve at an accelerated rate.
⦁ Algorithmic breakthroughs: Advancements in machine learning algorithms, particularly deep learning techniques, enable AI to extract complex patterns from data and perform tasks once thought to be exclusively human.
⦁ Collaborative research: The field of AI research is becoming increasingly collaborative. Open-source platforms and shared datasets allow researchers around the world to build upon each other’s work, accelerating progress.
Similar to Metcalfe’s Law, a network effect exists in AI. As AI models become more sophisticated, they can be used to create even better training data and algorithms. This continuous cycle of improvement fuels further breakthroughs, pushing the boundaries of what’s possible.
Some business executives have taken a slow approach to Generative AI adoption, and some even outright banned it in their organisations, mentioning the hallucination errors of GPT 3.5, viewing the technology through a narrow lens of text generation only. However, in doing so, executives risk missing out on productivity and creativity gains for their organisation. But at the pace at which the technological advancement of Gen AI is happening, it predicts well for the productivity promise. While the consumer side of the technology is looking at applications that will enhance their business workforce productivity, providers (IT companies) are also looking at Gen AI to boost developer productivity, enabling faster turnaround times for their customers.
There’s no longer much doubt about the impact that Gen AI is expected to make on businesses of all sizes. The path to reach that level of impact and the pace at which it will be achieved is accelerating rapidly. Organisations may find discrepancies in Gen AI maturity across different units, highlighting potential gaps in areas like people & culture, strategy & governance, and data & tools. These gaps present opportunities for collaboration and learning. Efforts to educate about Gen AI’s evolution, including clearer messaging, are crucial. While the journey may be challenging, it’s also thrilling. Embrace it, even if it means a few battle scars along the way!
