CONTENTS
- Small Modular Reactors and the Cost of Proliferation Resistance
- AI Needs Cultural Policies, not Just Regulation
Small Modular Reactors and the Cost of Proliferation Resistance
Context:
Nuclear energy plays a crucial role in the global energy mix as it awaits the development and advancement of other renewable energy technologies, while fossil fuel sources, particularly coal, remain relevant and more affordable. It is in this context that the Indian government plans to collaborate with the private sector to research and test small modular reactors (SMRs).
Relevance:
- GS1- Mineral and Energy Resources, Mobilization of Resources
- GS3-Nuclear Technology, Environmental Conservation
Mains Question:
What advantages do small modular reactors offer in comparison to the traditional nuclear power reactors? Also discuss the challenges associated with SMRs and highlight the way forward strategy to overcome them? (15 Marks, 250 Words).
Proliferation Resistance:
Proliferation resistance is a characteristic of a nuclear energy system that prevents the diversion or undeclared production of nuclear material, or the misuse of technology, by states to acquire nuclear weapons or other nuclear explosive devices. The International Atomic Energy Agency (IAEA) defines proliferation resistance in this way.
About Small Modular Reactors (SMRs):
- Small Modular Reactors (SMRs) are advanced nuclear reactors with a power capacity of up to 300 MW(e) per unit, which is about one-third of the generating capacity of traditional nuclear power reactors.
- SMRs can produce a significant amount of low-carbon electricity and are characterized by the following features:
- Small: They are physically much smaller than conventional nuclear power reactors.
- Modular: Their systems and components can be factory-assembled and transported as a unit to the installation site.
- Reactors: They use nuclear fission to generate heat and produce energy.
- SMRs are designed with enhanced safety features to minimize the risk of uncontrolled radioactive material release.
- They are intended to operate for 40-60 years with capacity factors exceeding 90%.
Significance of SMRs:
- Nuclear power provides a high and sustainable energy output, despite the added complexities of building safe and reliable reactors and managing spent nuclear fuel.
- Cost and time overruns, sometimes doubling from initial project estimates, are not uncommon.
- Consequently, the nuclear power tariff is higher for newer facilities, even though they fill gaps left by renewable sources.
- SMRs, ranging from 10 MWe to 300 MWe, are smaller versions of traditional reactors.
- They aim to enhance safety without sacrificing commercial viability by utilizing the higher energy content of nuclear fuel, a modular design, a smaller operational footprint, and reduced capital costs.
- Many of the benefits of Small Modular Reactors (SMRs) are inherently tied to their small and modular design.
- Their smaller footprint allows them to be located in areas unsuitable for larger nuclear power plants.
- Prefabricated SMR units can be manufactured and then transported and installed on-site, making them more cost-effective to build than large power reactors, which are often custom-designed for specific locations and can face construction delays.
- SMRs offer savings in cost and construction time and can be deployed incrementally to meet growing energy demand.
- In areas with insufficient transmission lines and grid capacity, SMRs can be integrated into an existing grid or used off-grid due to their smaller electrical output, providing low-carbon power for industry and communities.
- Compared to existing reactors, proposed SMR designs are generally simpler, with safety concepts often relying on passive systems and inherent safety features such as low power and operating pressure.
- This means no human intervention or external power is needed to shut down systems, as passive systems rely on physical phenomena like natural circulation, convection, gravity, and self-pressurization.
- These increased safety margins can significantly reduce or eliminate the risk of radioactive releases to the environment and public in the event of an accident.
- SMRs have reduced fuel requirements. SMR-based power plants may need refueling less frequently, every 3 to 7 years, compared to every 1 to 2 years for conventional plants. Some SMRs are designed to operate for up to 30 years without refueling.
Challenges Associated:
- However, the challenge is to manage the external costs associated with SMRs.
- The government’s privatization of nuclear power generation will also heighten the need for regulatory safeguards to prevent radioactive material from being diverted for military purposes.
- The first generation of SMRs is expected to use low-enriched uranium in facilities assembled on-site with factory-made parts, producing waste that can be managed with existing technologies and generating power that can be sold at economical rates.
- However, these reactors will require frequent refueling and will produce a significant amount of plutonium, both of which will challenge proliferation resistance.
- The IAEA has advocated for the use of reactor designs that can be safeguarded, but such solutions will increase capital costs.
- Future generations of SMRs may require more enriched uranium, especially if they aim for longer continuous generation periods, or more advanced systems to improve fuel-use efficiency, which would increase the operational footprint and the cost of generation.
- In fact, nuclear reactors have fixed baseline cost and safety expectations that do not change with energy output, meaning SMR-based tariffs may not automatically be lower. This is why the Department of Atomic Energy increased its reactors’ capacity from 220 MW to 700 MW.
Conclusion:
The ability of SMRs to enhance the prospects of nuclear power in India will therefore depend on their commercial viability, which in turn relies on less uncertain market conditions, stable grids, opportunities to mass-produce parts, and the cost of proliferation resistance.
AI Needs Cultural Policies, not Just Regulation
Context:
The future of Artificial Intelligence (AI) cannot be secured by regulation alone. To ensure AI is safe and trustworthy for everyone, we must complement regulation with policies that promote high-quality data as a public good. This approach is essential for fostering transparency, creating a level playing field, and building public trust. Only by providing fair and broad access to data can we fully realize AI’s potential and distribute its benefits equitably.
Relevance:
GS3- Awareness in the fields of IT, Space, Computers, Robotics, Nano-technology, Bio-technology and issues relating to Intellectual Property Rights.
Mains Question:
What role does data play in the functioning of Artificial Intelligence (AI)? How can AI help in the preservation of cultural heritage and traditional knowledge? (10 Marks, 150 Words).
Data and AI:
- Data is the lifeblood of AI. In this context, the principles of neural scaling are straightforward: the more data, the better.
- For example, the more diverse and voluminous human-generated text available for unsupervised learning, the better Large Language Models (LLMs) will perform.
- Alongside computing power and algorithmic innovations, data is arguably the most crucial driver of progress in the field.
Paucity of Continuous Data:
- However, there is a problem. Humans do not produce enough digital content to sustain these ever-growing models.
- Current training datasets are already enormous: Meta’s LLama 3, for instance, is trained on 15 trillion tokens, over 10 times the size of the British Library’s book collection.
- A recent study suggests that the demand for high-quality text is such that we might reach a ‘peak data’ scenario before 2030.
- Other studies warn about the risks of public data contamination by LLMs themselves, leading to feedback loops that amplify biases and reduce diversity.
AI winter:
- Concerns about an ‘AI winter’ highlight the relentless data race in which researchers and industry players are engaged, sometimes compromising quality and ethics.
- A notable example is ‘Books3,’ a collection of pirated books believed to be used by leading LLMs.
- Whether this practice falls under fair-use policy is a legal debate.
- More troubling is the hoarding of these books without any clear guiding principle.
- Even though progress is being made, partly due to regulation, LLMs are still primarily trained on an opaque mix of licensed content, ‘publicly available data,’ and ‘social media interactions.’
- Studies indicate that these data reflect and sometimes even worsen existing distortions in our cyberspace, creating a predominantly anglophone and present-centric world.
The Absence of Primary Sources:
- The idea that Large Language Models (LLMs) are trained on a comprehensive collection of human knowledge is a fanciful delusion. Current LLMs are far from the universal library imagined by thinkers like Leibniz and Borges.
- While repositories of stolen texts like ‘Books3’ may include some scholarly works, these are mostly secondary sources written in English—commentaries that barely scratch the surface of human culture.
- Notably absent are primary sources and their diverse languages: archival documents, oral traditions, forgotten books in public collections, and inscriptions on stone—the raw materials of our cultural heritage.
- These documents represent an untapped reservoir of linguistic data. Take Italy, for example. The State Archives of Italy alone house at least 1,500 kilometers of shelved documents (measured linearly)—not counting the vast holdings of the Vatican.
- Estimating the total volume of tokens that could be derived from this heritage is challenging.
- However, considering the hundreds of archives spread across our five continents, it’s reasonable to believe they could match or even exceed the data currently used to train LLMs.
- If harnessed, this data would not only enrich AI’s understanding of humanity’s cultural wealth but also make it more accessible to the world.
- They could revolutionize our understanding of history while safeguarding the world’s cultural heritage from neglect, war, and climate change.
- Additionally, they promise significant economic benefits. By helping neural networks scale up, their release into the public domain would allow smaller companies, startups, and the open-source AI community to use these large pools of free and transparent data to develop their own applications, leveling the playing field against Big Tech and fostering global innovation.
Examples from Italy and Canada:
- Advancements in the digital humanities, particularly through AI, have significantly reduced the cost of digitization, allowing us to extract text from printed and manuscript documents with remarkable accuracy and speed.
- Italy recognized this potential and allocated €500 million from its ‘Next Generation EU’ package for the ‘Digital Library’ project.
- Unfortunately, this ambitious initiative, aimed at making Italy’s rich heritage accessible as open data, has since been deprioritized and restructured, showing a lack of foresight.
- Canada’s Official Languages Act offers a valuable lesson here. Although initially criticized as wasteful, this policy mandating bilingual institutions eventually produced one of the most valuable datasets for training translation software.
- However, recent discussions about adopting regional languages in the Spanish Cortes and European Union institutions have overlooked this important aspect.
- Even supporters have failed to acknowledge the cultural, economic, and technological benefits of promoting the digitization of low-resource languages as complementary.
Conclusion:
As we accelerate the digital transition, we must not overlook the immense potential of our world’s cultural heritage. Digitizing it is crucial for preserving history, democratizing knowledge, and enabling truly inclusive AI innovation.
