Education is evolving in the age of technology and AI. While modern education is moving away from rote learning to skills like critical thinking, students should also be trained to use AI as a research tool. However, the rise of AI-generated content poses challenges in distinguishing genuine research from fabricated material, necessitating the teaching of healthy skepticism and cross-referencing skills to students.
AI is being increasingly used in journalism and law. OpenAI’s GPT-3, can produce content nearly indistinguishable from human-written work. But we need to remember that AI is a tool, not a substitute for human creativity.
There are parallels between the myth of the Tower of Babel and the modern linguistic challenge of the internet. In this India has a unique need for translation technology, given its linguistic diversity. Bhashini may be the answer.
The South African patent office has granted a patent to an artificial intelligence program for an invention it has made. India granted a copyright to an AI application along similar lines. It is not clear how an artificial intelligence can exercise the IP granted by prosecuting a breach or negotiating commercial arrangements for its license. All these actions will have to be taken by humans on behalf of the AI in which case what is the point in calling the algorithm an inventor.
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.
