In January 2023, OpenAI CEO Sam Altman told a large audience in India that the country was not capable of building a ChatGPT. Eight months later, India’s Chandrayaan 3 made a perfect landing on the largely unexplored dark side of the Moon, putting India alongside the United States and China in the exclusive club of nations with working lunar rovers.
Altman later recanted his comments and said they were taken out of context–but his Freudian slip did illustrate the ignorance of Silicon Valley. Altman and his Silicon Valley peers are the wrong ones to lead us into the AI future because they are largely driven by profit, disconnected from the world’s realities, and often suffer from a serious god complex.
And it’s not only space technology. India has also already built the world’s most advanced cancer care infrastructure and is rolling it out on an unprecedented scale, leading the White House to announce a partnership with India to combine efforts on curing cancer.
India is also the only major country that has designed and fostered an intelligent regulation strategy to maintain open and free markets in key aspects of technology such as e-commerce and finance. Its Interface system is leveling the playing field and preventing market abuse by large technology giants.
We can do the same with AI. We can take Silicon Valley’s open-source technology and build something that benefits the masses. Altman surely believes that his company is doing good for the world and is uniquely positioned to deliver advanced AI, but even one of OpenAI’s top funders, Elon Musk, has distanced himself from this project due to concerns over its profit-seeking motives.
Research shows the market dysfunction created by Google, Amazon, Facebook, and other large players that dominate e-commerce, advertising, and online information-sharing. Big Tech monopolists are already positioning themselves to dominate AI. The shortage of GPUs and massive lobbying dollars spent requesting expensive regulation that would lock out startups are just two examples of this troubling trend.
Altman’s comments in India made him sound like he is unaware that Indian scientists have played a massive role in AI’s recent progress. They have published important research papers and have been key players inside large technology giants that have driven the development of foundational Large Language Models like GPT-4 (which powers ChatGPT) and Google’s Bard. Needless to say, the CEOs of U.S. technology companies building AI technologies, including Google, Microsoft, and IBM, are Indian.
To start, India can build, train, and finetune a massive foundational LLM that uses data already in the public domain or that is legally aggregated with full permission from its creators or owners. Notably, social media data should be underweighted as large chunks are toxic and unhelpful. This will also make a homegrown LLM safer than current models. As data scientists always say: Garbage in, garbage out.
India’s LLM would be trained on data representing diverse world views and situations, something OpenAI and StableDiffusion neglected. The cardinal sin of much AI and algorithms today is discrimination baked into their fabric through poor data design. This leads to the next key component of India’s LLM gift to the world: complete transparency and traceability. Unlike a few years ago, it is now largely possible to shine a light on the inner workings of deep neural networks used to train and create LLMs. This requires innovation and advances, but India is up to the task. Transparent AI would radically change the game.
Sharing such a system with the world will give us greater safety by allowing the brightest minds to work on the best technology and counter-balance dominant technology companies and evil-doers as rogue states and criminal groups seek to leverage AI. The AI cat is already out of the bag. LLM code and weights for large models like Meta’s Lllama are in the wild. Restrictions on transmission are no longer useful and would only cripple innovation.
Building, sharing, and maintaining a truly open AI LLM is not sufficient. Half of the challenge with LLMs is training, which is extraordinarily expensive and compute-intensive, costing tens of millions of dollars for each model version. Costs will fall, and researchers and AI companies are already figuring out how to train effectively with less data, less computing, and more specificity, often yielding superior results.
AI is a public good, and the best way to support its creation is to ensure all resources required are publicly accessible. Part of why Google and Meta have leaped to the forefront of AI is their brute access to massive technology infrastructure, something few university researchers could dream of.
To cement AI’s future development as an open resource, India should build a massive training cloud for AI and offer it up at cost to Indian startups and researchers–and to the entire world. A good model for this is massive telescopes that scan the skies and offer usage time to astronomers everywhere, with a small slice dedicated to the operating institution.
India can reap enormous benefits by fostering AI innovation in its own economy and helping its own AI community leap forward with the resources required to do the work. Even better, India and Silicon Valley can join forces in this quest to foster truly open and publicly available AI. LLMs are foundational technology components that can benefit all and develop faster with a motivated community. The Linux operating system, which now dominates global enterprise computing, is a great example of this dynamic. A public, community-driven vision for AI will accelerate innovation, reduce bias, ensure greater transparency, and provide a better outcome for all.
AI will fundamentally change society and billions of lives. Its development is too important to be left to the hubris of Silicon Valley’s elites. India is well positioned to break their dominance and level the AI playing field, accelerating innovation and benefiting all of humankind.
Vivek Wadhwa is an academic, entrepreneur, and author. His book, From Incremental to Exponential, explains how large companies can see the future and rethink innovation.
Vinita Gupta was the first woman of Indian origin to take her company public in the United States. She is an accomplished entrepreneur, author, and bridge champion.
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