IEC Tech
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How IEC Standards are helping India to become an AI
powerhouse and jump ahead of the queue.
At the start of the year, the Indian government gave its
electronics and tech sector a mission to develop the country’s first large
language model (LLM). The artificial intelligence (AI) model, expected to
be ready as soon as this winter, is part of a broader vision for the country to
become a global AI powerhouse by 2047.
Key planks of the programme include the IndiaAI Mission,
which launched in 2024, promising a budget of over ₹ 10,000 crore (USD 1,5
billion) a year to develop India's AI ecosystem through public-private
partnerships. This builds on an earlier National Strategy for AI published
by India’s public policy think tank to guide R&D in the emerging technology
across healthcare, agriculture, education, smart cities and infrastructure.
The overall aim is to build standalone or sovereign
capability in a technology which is seen as transformational across many
sectors. “Unlike in the past, AI in India is no longer confined to a privileged
few or dominated by global tech giants,” it argues. “[The] government is
empowering students, startups and innovators with world-class AI
infrastructure, fostering a truly level playing field, paving the way for
innovation and self-reliance in this critical sector.”
Core to the strategy is for LLMs to be developed and hosted
in India, tailored to work for the specific social, cultural and business
diversity of its 1,46 billion people, who converse in 22 officially recognized
languages. This approach is supported by Sovereign AI, a concept which
refers to a nation’s capacity to be self-reliant by using its own
infrastructure, data, workforce and business networks. In India’s case, the
objective is to be competitive with China and the USA, which are currently
leading the AI race.
Growing adoption of AI in India
India’s AI market is growing at a rate of 25-35% per year
and is projected to reach USD 17 billion by 2027, according to a
trade association for software and services. In a separate report, the
association says it expects the broader technology industry in India to deliver
USD 300 billion in 2026, driven in part by steady advances in AI, as
enterprises expand their tech initiatives at scale.
Gnani.ai is one of
hundreds of AI-focussed start-ups in India. “People are excited about AI and
impatient to adopt it,” says founder and CEO Ganesh Gopalan. “Every board of
every organization is putting pressure on their employees to use more AI in the
workplace. It could be for cost reasons, to increase revenue or to improve
customer satisfaction. Whatever the end goals are, I see a lot of interest in
using AI in India.”
Anecdotally, there appears to be a lot less fear in adopting
the tech than in western economies, which, Gopalan suggests, is partly because
of the country’s rapid growth. “The question of AI replacing jobs comes less
into the equation in developing markets like China and India compared to
developed markets. In India, the discussions are always around return on
investment (ROI). We are a nation culturally obsessed with ROI and that’s not
necessarily the case in developed markets.”
Handling multi-linguistic complexity
Gopalan believes that Indian sovereign LLMs are a huge
opportunity. He says global models like GPT (developed by OpenAI), Claude
(Anthropic), Gemini (Google) and Meta (Llama), trained on largely English
language and Western cultural databases, work less effectively in a country as
diverse as India. “The world is not just one in which English is spoken. There
are complexities within languages. So, when you translate global LLMs into
other languages, they often fail because there isn't enough training data. The
same is true for countries in Latin America or Africa. Even the basic things
that global LLMs do for the US or the UK just cannot be done for many markets
around the world.”
His company develops AI models from an India-first
perspective. He explains, “The number of tokens [base units of text that LLMs
use to understand language] that you would use for a typical LLM is very
different when it comes to India. An AI model in Tamil or Telugu local language
requires very different inputs and parameters.”
Gnani.ai has launched a speech-to-speech LLM that it claims
can handle over 30 million voice interactions daily and supports 14 languages
for businesses across India and the US. Speech-to-speech (or voice-to-voice) AI
technology enables humans to converse with digital chatbots and other AI
systems. As a subset of its LLM, Gnani.ai has developed small language models
(SLMs) targeted at specific verticals, including the banking industry. It is in
the process of launching another for insurance, with a retail model in the
works.
“Every industry has a different vocabulary, phraseology,
pitch and tone so in the context of customer management, for example, each will
be different,” Gopalan says. Beyond addressing a specific audience with local
and sector specific languages, the development of India-first AI models is also
deemed important to counteract potential cultural bias, or lack of nuance, in
global LLMs.
Addressing bias, a key concern
“It is incredibly important to take bias into account,”
Gopalan adds. “Even without getting into the ethics of copyright, the first and
most important component in your model is bias. It is a huge issue since you
can’t ever be sure of the exact weight [or bias] on which a global model has
been trained. You don't even know if it's biased until you use it. You can make
an assumption, but basically, it's an unknown factor.”
A practical use case his company is trying to solve is to
automate the underwriting of loans or insurance at a bank. “What if your
underwriting process actually gave people loans they could not repay or
unfairly denied people access to finance because the AI model had a bias? It’s
not only highly unethical on your part, but it’s going to negatively affect
your business.”
He advocates training models for corporations using the data
they own and control. “These SLMs are not trained on trillion-parameter models
[like LLMs] but on a few billion-parameter models that the [corporation] owns
to make sure that biases are under control. That's why the concept of Sovereign
AI is important. You can’t just write an application programming interface
(API) for a global LLM.”
Gopalan says private industry, government and education are
in lock-step in developing the country’s AI capability. “Educational
institutions will adapt their course curriculum to include more AI components
to feed demand from the private sector for jobs,” he explains. “Since AI is
fundamentally software and coding, things at which Indians tend to be broadly
good at, the future is looking good.”
Indian investment in education for AI
The government is building homegrown expertise to fuel
research, innovation and the workforce in concert with multinationals.
Mumbai’s Universal AI University, founded in 2023, was the first in the
country to integrate AI across its curriculum, with the backing of a
leading Indian IT company. A Fortune 500 company funded Mumbai’s Jio
Institute (named after the multinational’s telecom division), including
the Centre of AI for All, which advances “India-centric AI capabilities”.
The Thapar Institute of Engineering and Technology has its AI
programmes supported by an American computer processing giant. Another
multinational is backing a training scheme to boost the AI skills of
over 2 million Indians, and one of its leading researchers is
working with the Bill & Melinda Gates Foundation to build
“gender-intentional” datasets in five Indian languages that together are spoken
by over 1 billion people.
Standards for responsible AI
As Gopalan made clear, an AI model is only as good as the
data it is trained on, and bias can creep in, creating all sorts of problems,
depending on the application area. International benchmarks which set
guidelines to check the data quality and avoid any form of bias are a way of
ensuring systems work efficiently and ethically. The joint ISO/IEC
committee, established to develop standards in the area of AI, has
published ISO/IEC 5259-5, which provides a data quality governance
framework for analytics and machine learning to enable organizations and
companies to direct and oversee the implementation and operation of data
quality.
To identify and prevent bias in outcomes, the committee has
published ISO/IEC TS 12791, which provides mitigation techniques that can
be applied throughout the AI system life cycle in order to address unwanted
bias. This document is applicable to organizations of all types and sizes .
AI is making huge strides in India. By favouring homegrown
systems to compete with worldwide tech giants, India is rapidly becoming an AI
force to be reckoned with. Standards can help it avoid some of the mistakes
made by companies based in the USA or China, which started earlier in the race
but initially lacked internationally recognized benchmarks to guide them.
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