The buzz around the Department for Promotion of Industry and Internal Trade (DPIIT)'s draft plan for AI training on copyrighted works has certainly caught my attention. It suggests a future where, by default, all publicly available online content could be fair game for AI models, unless a creator actively "locks it away" through an opt-out mechanism. This "one nation, one licence" approach, as detailed in articles like AI Copyright: One Nation, One Licence and India's draft AI copyright model may treat everything online as training data unless one locks it away, raises profound questions about the value of human creation in an increasingly AI-driven world.
I've often pondered the shifting landscape of intelligence. Years ago, I wrote about "A Case of AI Substituting NI?" [http://mylinkedinposting.blogspot.com/2019/01/a-case-of-ai-substituting-ni.html], where I speculated on how AI would render certain jobs, those requiring "Natural Intelligence," obsolete. While that discussion focused on professions like lawyers, the current DPIIT plan brings this idea to the forefront for all creators. It's striking to see how the very output of human intellect – art, writing, code – is now becoming the raw material for advanced AI, prompting a re-evaluation of its ownership and compensation.
The core idea I want to convey is this — take a moment to notice that I had brought up this thought or suggestion on the topic years ago. I had already predicted this outcome or challenge, and I had even proposed a solution at the time. Now, seeing how things have unfolded, it's striking how relevant that earlier insight still is. Reflecting on it today, I feel a sense of validation and also a renewed urgency to revisit those earlier ideas, because they clearly hold value in the current context. My earlier thoughts on AI's pervasive impact, from its ability to substitute human intelligence to its role in transforming how we access information, set the stage for this very discussion about data rights.
This proposed policy attempts to balance the rapid pace of AI innovation with the crucial need to protect creator royalties, as explored in AI LLM training under scanner as DPIIT weighs creator royalties. On one hand, unrestricted access to data fuels the development of powerful models, pushing technological boundaries. On the other, it risks devaluing the original human effort.
We are seeing AI agents and large action models (LAMs) becoming increasingly sophisticated, capable of making choices and executing complex tasks. In my blog "Invasion of AIgents" [http://mylinkedinposting.blogspot.com/2024/08/invasion-of-aigents.html], I reflected on the advancements, citing experts like Andrew Ng, founder of Deeplearning.ai (andrew@aifund.ai), who predicted that "AI agent workflows will drive massive AI progress this year." We've seen how figures like Microsoft Chairman and CEO Satya Nadella (satyan@microsoft.com) were impressed by devices like Rabbit's R1, which leverages LAMs to provide an "intuitive app-free experience." Puneet Chandok, Microsoft India president, also spoke about copilots gaining agency, while Silvio Savarese, executive vice president and chief scientist at Salesforce (ssavarese@salesforce.com), highlighted LAMs as the next phase of AI. Paramdeep Singh, co-founder of Shorthills AI (paramdeep@shorthillstech.com), emphasized their potential ROI, and Jesse Lyu, founder of Rabbit (j@rabbit.tech), detailed their training on human interactions. Rajnil Mallik, partner and Gen AI go-to-market leader, PwC India (rajnil.mallik@pwc.com), noted how LAMs challenge existing paradigms. All these advancements fundamentally rely on vast amounts of data – the very "content" that DPIIT is now discussing in terms of copyright.
Even in the realm of information retrieval, AI is reshaping the game. My earlier post, "Time for Google to Re-invent Itself?" [http://myblogepage.blogspot.com/2024/10/time-for-google-to-re-invent-itself.html], touched upon OpenAI's ChatGPT search engine directly competing with Google, and the ongoing race among tech giants like Microsoft and Baidu in integrating AI. This competition itself is a testament to the immense value placed on training data.
The question then becomes: how do we foster innovation without inadvertently undermining the very foundation of creative work? The challenge lies in creating a framework that respects intellectual property while allowing AI to learn and evolve. It’s a delicate balance, and the "opt-out" model proposed by DPIIT places the onus on creators to protect their work, rather than on AI developers to seek permission. This shift in responsibility is significant and will undoubtedly shape the future of creative industries and the digital economy.
Regards, Hemen Parekh
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