Generative vs traditional AI: Getting the fundamentals of data right - Hindustan Times

Generative vs traditional AI: Getting the fundamentals of data right

ByHindustan Times
Nov 20, 2023 11:43 AM IST

This article is auhtored by James Fisher, chief strategy officer, Qlik.

There's no denying that Generative Artificial Intelligence (GenAI) has become a mainstream technological phenomenon. Assuming a central role in the fifth industrial revolution and dispelling any notions of mere hype, GenAI's capabilities have gathered acclaim worldwide. And India is leading this trend from the forefront.

Artificial intelligence.(Thinkstock)
Artificial intelligence.(Thinkstock)

The adoption of artificial intelligence in India surpasses the global average. According to a report by IBM, approximately 57% of Indian companies confirmed actively using AI, remarkably higher than in markets like Australia (24%), the US (25%), and the UK (26%). Indian startups, too, are proactively adopting generative AI technologies, having committed over $475 million in investments in such ventures between 2021 and 2023.

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Tech giants are using generative AI to launch competitive new solutions. For example, Google has been experimenting with new AI-powered search methods, like visually searching with Lens or across modalities, using images and text with multisearch. Apple is developing the technology for a new AI system in iOS and other apps. Analysts anticipate broader usage of GenAI tools by consumers and businesses, spanning various applications, from text and image processing to audio and video, 3D modeling, code generation, and much more.

Since its popularity exploded in 2022, many organisations rushed to get on the GenAI bandwagon — whether they had a plan in place or not. A year later, 79% of respondents to the Generative AI Benchmark report were found to have invested in a GenAI tool or project. However, given the inevitability of AI's impact, it is essential to assess some fundamental aspects of how AI can significantly improve the readiness of Indian organisations for the future.

While every organisation is bound to be different, one basic formula for driving any AI outcomes is - organising data effectively. Most organisations that have created or are creating an AI strategy plan to leverage public or open-source models to some extent, with many planning to augment those models with their proprietary data. GenAI allows everyone to access quicker and improved insights from their data. However, it's crucial to remember that the quality of the insights obtained directly depends on the data quality employed. Organisations must build capable, modern data and technology infrastructure to prepare for long-term generative AI adoption. It entails developing access to high-quality, well-coordinated data, supporting scalable data architectures, and implementing governance and security measures.

Leading organisations understand that these tools must be supported by a trusted data foundation. A sound data foundation fuels the insights and advanced use cases where the power of generative AI and traditional AI come to life. A data governance framework is essential for the responsible and effective use of data, particularly in the context of large language models. Businesses can streamline this process using catalog and lineage solutions, automatically identifying and documenting relationships between datasets. Further, AI systems benefit from access to a broad spectrum of intelligence by aggregating data from diverse sources and integrating it into a centralised warehouse or data lake. This integration is instrumental in allowing AI models to uncover valuable insights, identify patterns, and make informed predictions.

Additionally, data transformation and consumable insights are crucial. After obtaining insights through data transformation, presenting these insights in a precise and real-time accessible format becomes vital for effective collaboration and decision-making.

Even with the growing market focus on generative AI, traditional AI still brings ongoing value in areas like predictive analytics. For example, AI is delivering measurable value in space exploration. The Chandrayaan-3 mission's Lander Hazard Detection and Avoidance Camera (LHDAC) harnessed the power of AI to meticulously map the lunar surface, identifying potential hazards that could jeopardise the landing. The true prowess of AI became evident as it dynamically learned and adapted during Chandrayaan-3's delicate soft landing. Another AI applicability is for climate goals. C40, a global network of nearly 100 mayors of the world's leading cities united to confront the climate crisis, is applying machine learning to climate datasets, enabling cities like Accra, Ghana, to undertake tangible measures to cut emissions while improving the health of its inhabitants.

Traditional AI remains an important value driver for organisations and generative AI can help by extending the power of AI beyond data scientists or engineers, opening up AI capabilities to a larger population. It could scale the ability to unlock deeper insights and find new, creative ways to solve problems much faster. More organisations are now looking to adopt a hybrid approach that incorporates generative AI with traditional AI to scale its impact across their organisations.

Some may be nervous about the AI wave but embracing both traditional and generative, AI represents our best path forward. Generative AI has significantly empowered end users, and it's evident that AI is increasingly becoming a fundamental requirement for businesses. It's vital to recognise that generative AI is an ineludible trend, with an increasing number of organisations in India integrating it into their AI roadmaps. Generative AI represents the future, and its possibilities are boundless.


This article is auhtored by James Fisher, chief strategy officer, Qlik.

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