Ford’s internal combustion engine car beat Edison’s EV to the market and as a result we are on our current fossil fuel dependent path. What if things were different. Few Shot Learning is an alternative to data guzzling artificial intelligence models that allows us to not be dependent on large datasets.
The argument that data localization will boost India’s AI competence is flawed. Simply storing data in-country doesn’t translate to AI development, as data structures are company-specific and insights are often non-transferable. Instead, focusing on building AI infrastructure, incentivizing researchers, and encouraging homegrown AI development with existing data is more effective for fostering AI prowess.
Many prominent figures have warned of the dangers of uncontrolled AI development. Even so, skeptics argue that humans will always control machines. Modern AI lacks the ability to reason with “what if?” questions and counterfactual imagination, which are essential for human-like intelligence. Though machines are not yet at this level, I would urge caution in advancing AI towards these capabilities.
It is important to incorporate human oversight into automated systems. Despite the efficiency of these systems, there is a need to balance human judgment with machine precision in critical decision-making processes.
The rapid advancement of smart home devices, with their increasing conversational intelligence, is leading to a future where touch-based inputs may become obsolete. These devices offer significant benefits, such as aiding the elderly and entertaining children, but also raise complex ethical and legal challenges. Issues like privacy, psychological impacts, especially on the young and elderly, and the handling of sensitive information, such as potential abuse reports, require careful consideration. The evolving nature of these interactions necessitates a new framework to address the multifaceted implications of conversational AI in our daily lives.
Algorithmic sentencing, using machine learning to assess recidivism risk, has demonstrated consistent outcomes. But is not without flaws, sometimes reflecting human biases. Despite imperfections, I believe algorithms can introduce objectivity and be fine-tuned to reduce biases, making them more reliable than human judgment.
Despite its initial promise, AI solutions often fall short in the Indian legal context due to training on non-local data. A hybrid human-AI approach could build more responsive and effective systems.
We should not create specific laws for new technologies when general legal principles will suffice. Recently, a government task force recommended applying existing legal provisions to AI, but this approach may not address AI’s unique aspects, such as personhood and liability in autonomous systems. The complexity of AI decisions, especially in impactful areas like criminal sentencing, necessitates a tailored regulatory framework that balances accuracy with explainability, challenging the notion of applying traditional legal principles to AI regulation.
DeepMind has developed the world’s first tabula rasa algorithm, AlphaGo Zero, which learns from scratch without relying on human expertise or existing data. Unlike previous AI models, it learns through self-play, achieving mastery in the game of Go and uncovering novel strategies. This approach could revolutionize areas like genomic research and law, reducing concerns about privacy and human bias in algorithmic decision-making, and possibly leading to true artificial general intelligence.
Law firms struggle with partner compensation models, balancing profit and collaboration. The “eat-what-you-kill” model, based on individual revenue generation, can lead to competition and reduced cooperation. In contrast, the lockstep system, rewarding tenure over performance, may not fully incentivize productivity. Similar challenges exist in finance, where hedge funds guard proprietary data. Numerai, an AI firm, addresses this by using homomorphic encryption and a public platform, allowing data scientists to contribute to a meta-model, democratizing data without compromising confidentiality, and rewarding contributions with bitcoin. This innovative approach could inspire similar solutions in the legal industry.
