Topic 1 : The PM KISAN model
Introduction: The Narendra Modi government is undertaking a “saturation drive” to take the total number of farmer-beneficiaries under the Pradhan Mantri Kisan Samman Nidhi (PM-Kisan) to about 8.75 crore, from the current 8.12 crore or so.
The PM Kisan Scheme
- It was launched on 24th February, 2019 to supplement financial needs of land holding farmers.
- Financial benefit of Rs 6000/- per year in three equal installments, every four month is transferred into the bank accounts of farmers’ families across the country through Direct Benefit Transfer (DBT) mode.
- The scheme was initially meant for Small and Marginal Farmers (SMFs) having landholding upto 2 hectares but scope of the scheme was extended to cover all landholding farmers.
It is a Central Sector Scheme with 100% funding from the Government of India.
- It is being implemented by the Ministry of Agriculture and Farmers Welfare.
What is the objective of the scheme?
- To supplement the financial needs of the Small and Marginal Farmers in procuring various inputs to ensure proper crop health and appropriate yields, commensurate with the anticipated farm income at the end of each crop cycle.
- To protect them from falling in the clutches of moneylenders for meeting such expenses and ensure their continuance in the farming activities.
Advantages of PM KISAN scheme
- The Rs 6,000-amount is also indifferent to inputs used, whether chemical fertilisers and insecticides or organic manure and biological control of pests and diseases.
- In other words, it’s a subsidy that is not market-distorting or privileging chemicals-based agriculture over so-called natural farming.
- So long as every farmer who actually farms get it, one cannot find fault with PM-Kisan.
A scope of improvement
- But even within this overall framework, refinements are possible.
- For instance, can Direct Income Support be given on a per-acre, rather than per-farmer, basis?
- The Telangana government’s Rythu Bandhu scheme provides farmers up to Rs 12,000 per acre per year.
- This, it is alleged by some, benefits rich farmers over smallholders.
- That’s perhaps an unfair criticism. Those farming larger holdings or growing more crops, after all, also incur higher expenditures.
- Such farmers, who are probably more dependent on income from agriculture than marginal holders, deserve extra support.
- The Rs 6,000 annual payment under PM-Kisan can be made on a per-acre basis up to a limit of, say, 10 acres.
- That would, to a great extent, address concerns over rich-versus-poor farmers.
- Nobody should grudge the “middle farmer” — who is responsible for much of India’s agricultural production and, indeed, national food security — getting something extra.
- In fact, state governments can top up the Rs 6,000 amount under PM-Kisan with an equivalent DIS, again as a per-acre transfer.
The mobilization of extra funds for refined PM KISAN
- By ending all market-distorting subsidies, whether on farm inputs (fertiliser, electricity and water) or output (government procurement of grain at high support price beyond necessary stocking requirements).
- The savings from that can be redirected towards PM-Kisan and state government-financed DIS schemes.
- For illustrative purposes, consider the Centre’s fertiliser subsidy alone, budgeted at Rs 1,75,100 crore.
- If this humongous amount were simply distributed among the projected 8.75 crore PM-Kisan beneficiaries, it would work out to over Rs 20,000 per farmer.
- Add savings from similar inefficient and environmentally disastrous subsidies by states, and money shouldn’t be a constraint as much as political will.
- That’s a challenge the government that takes charge after the April-May 2024 elections must take head-on.
Conclusion: Direct income support, not market-distorting subsidy, is the way forward. Within this overall framework, refinements are possible.
Topic 2 : First, don’t panic
Introduction: Regulation and policy are at least a step behind innovation. This has indeed been the case historically, from the printing press to the rise of social media. But it need not be so with AI.
The disruptive inventions in the field of AI
- The public launch of Large Language Model (LLM) ChatGPT by OpenAI in November 2022 has disrupted the technology space.
- 2023 was witness to the democratization of LLMs — never before has AI this sophisticated, with such human-like interfaces and vast knowledge, been so easily available.
- ChatGPT also brought into the open the race between the biggest companies in the world including Google, and between China and the US, to develop Artificial General Intelligence (AGI).
- This year saw a watershed moment in technology, as well as questions around its regulation.
The advantages and disadvantages of AI
- There is a curious dichotomy between the so-called dangers of AI and its purported advantages.
- The latter are specific: From advanced gene sequencing to tutors for students, from writing code to a competent virtual assistant.
- The dangers are generally apocalyptic, ranging from science fiction-inspired doomsday scenarios to the effects LLMs and AI will have on creativity, work and the economy, and society as a whole.
- Two possible reasons account for this.
- First, those who develop the technologies have a fair idea of its capabilities (although the more advanced AI becomes, in terms of computing power and the data sets it is trained on, the more unpredictable it becomes) but perhaps less so about its impacts.
- Second, the jobs and ways-of-being threatened by LLMs and possible AGIs differ in a fundamental aspect from earlier disruptions. This time, it is white-collar jobs based on intellectual and cognitive abilities that are under threat. That could explain why there was less panic over mechanisation replacing manufacturing jobs than, say, machines replacing lawyers, doctors, artists and writers.
How Large Language Model (LLM) works?
- Training:
- LLMs are first fed massive amounts of text data. This data is then processed and broken down into smaller chunks, like sentences or paragraphs.
- The LLM then tries to predict the next word in each chunk. It does this by analyzing the patterns it has learned from the data.
- If the LLM's prediction is wrong, it adjusts its internal parameters and tries again. This process is repeated billions of times over, slowly but surely improving the LLM's accuracy.
- Using the LLM:
- Once trained, one can give the LLM a piece of text as input.
- The LLM will then analyze the input and try to understand its meaning. It will consider the context of the surrounding words, the overall structure of the sentence, and even the broader topic of the text.
- Based on its understanding, the LLM will then generate an output. This could be anything from completing a sentence to writing a whole new paragraph, poem, or even code.
What the current panic around AI would result into?
- The panic around AI holds twin dangers.
- First, it could invite a regulatory response that stifles the potential of a tool that can aid innovation and make knowledge and skills more accessible.
- Second, it may obfuscate the issues that incrementally end up making AI more harmful than beneficial.
The way forward
- The counter to plagiarism needs to evolve.
- Questions of authorship and copyright are already being litigated in some jurisdictions — for example, with The New York Times case against OpenAI.
- The use of machine learning software for surveillance, facial recognition, and predictive policing has major implications for privacy and human rights.
- Addressing these concerns requires more than the expertise of the engineer and the technologist.
Conclusion: In 2023, ChatGPT and other LLMs sparked existential anxieties. In the new year, society must drive technology, not vice versa.