How Libraries Are Quietly Redefining AI

Posted on 05/10/2025 by Sukhdev Singh

Over the last few months, I’ve been watching with curiosity how libraries are quietly—but decisively—reshaping their relationship with artificial intelligence. It’s no longer just about adopting a new tool; it’s about redefining our professional DNA.

It began, perhaps fittingly, with the books themselves. Leading libraries—Harvard, Boston Public, and even Oxford’s Bodleian—have started opening up massive digitized collections for AI training [1]. These aren’t copyrighted bestsellers but millions of public domain works spanning hundreds of languages. The idea is simple yet profound: let AI learn from humanity’s collective memory, curated and preserved by libraries. This act of sharing feels like librarianship at its noblest—quietly empowering innovation while protecting cultural integrity. And yet, there’s always a thin line between use and misuse; once data leaves the stacks, who ensures its ethical handling?

At the same time, a strange irony has surfaced. Librarians are now being asked to find AI-hallucinated books—titles that exist only in the imagination of chatbots [2]. It’s almost poetic: AI depends on libraries for truth, yet it also invents illusions that send people back to those very libraries for verification. Many of my colleagues describe it as part detective work, part myth-busting. No wonder librarianship today demands as much digital literacy as human empathy.

Meanwhile, in the name of efficiency and inclusivity, many libraries are turning to automated diversity audit tools to evaluate their collections [3]. But new research warns that these systems can flatten identities and miss local nuances. It’s a reminder that algorithms, no matter how elegant, cannot replace community understanding. By the way, I find this debate refreshing—it forces us to revisit what “representation” truly means beyond checkboxes and metadata.

Encouragingly, the profession isn’t shying away from these complexities. Across institutions, librarians are enrolling in AI literacy programs, attending workshops, and even taking up newly created roles such as Director of AI or AI Librarian [4]. I find this deeply symbolic: librarians stepping out of the reactive corner into leadership positions. From Stony Brook to San José, they’re proving that AI is not an external force to be feared but a field to be shaped—ethically, critically, and confidently.

All of this feeds into a growing scholarly and professional conversation about aligning AI with the library’s enduring mission—access, equity, and trust. New frameworks from IFLA, Frontiers, and several universities emphasize that libraries must be partners in AI development, not mere users [5]. The message is clear: technology must bend toward human values, not the other way around.

So yes, the AI wave has reached the library world—but it’s not a tidal surge of disruption. It’s more like a steady current of reinvention. From digitized archives feeding neural networks to librarians decoding machine-made myths, the profession is finding its rhythm again.

And as I see it, this is the dawn of a new librarianship—one that reads, writes, and reasons alongside the machines, but always, always in service of humanity.

Hello AI World!!!

#AIinLibraries #Librarianship #DigitalTransformation #ArtificialIntelligence #LibraryInnovation #EthicalAI

References:
[1] https://apnews.com/article/e096a81a4fceb2951f232a33ac767f53
[2] https://www.404media.co/librarians-are-being-asked-to-find-ai-hallucinated-books/
[3] https://arxiv.org/abs/2505.14890
[4] https://about.proquest.com/en/blog/2025/bridging-the-ai-skills-gap-a-new-literacy-program-for-academic-libraries/
[5] https://www.ifla.org/news/just-published-new-horizons-in-artificial-intelligence-in-libraries/

Generative AI in Academic Libraries: Ethical, Pedagogical, Labour, and Equity Challenges

Posted on 28/09/2025 by Sukhdev Singh

Generative Artificial Intelligence (AI) has emerged as a disruptive technology with transformative potential for academic libraries. The *Library Trends* two-part series (Vol. 73, Issues 3 & 4, 2025) provides a foundational exploration of AI’s impact on libraries from multiple perspectives, including ethics, pedagogy, labour, and decolonial approaches.

Ethical Challenges and Bias in Generative AI

Generative AI systems pose significant ethical challenges that academic libraries must navigate carefully. One key concern is algorithmic bias, where AI models trained on historical data amplify existing societal inequities, leading to unfair or inaccurate information retrieval outcomes. A 2025 scoping review by Igbinovia highlights how AI biases affect Information Retrieval Systems (IRS) and calls upon LIS professionals to engage in ethical data curation, algorithmic auditing, and policy advocacy to mitigate harm [1].

Beyond bias, reliable and trustworthy output remains a challenge. Generative AI is prone to “hallucinations,” producing factually incorrect or fabricated information, which can impair academic integrity [2]. Georgetown University’s guidance emphasises that AI-generated text must be critically evaluated and transparently attributed to avoid plagiarism and misinformation [3].

Ethical AI practice mandates human accountability, transparency, data privacy, and fairness [2][4]. Stahl et al. (2022) link these principles to European regulations, emphasising protection of fundamental rights in AI governance [5]. Researchers advocate integrating moral values into AI systems through frameworks such as utilitarianism, deontology, virtue ethics, and care ethics to promote equitable AI designs [6]. Virtue ethics, in particular, offers nuanced guidance focusing on moral character in decision-making, echoing the calls in *Library Trends* for character-based ethical frameworks around AI use [7][5].

AI Literacy: Skills and Pedagogy in Academic Libraries

Effective AI literacy emerges as a critical response to ethical and practical challenges. Leo S. Lo’s framework for AI literacy in academic libraries underscores the need for broad technical knowledge, ethical awareness, critical thinking, and practical skills to empower users and librarians alike [8]. The widespread recognition of AI’s impact has driven many academic libraries to develop literacy programs; Clarivate and ACRL Choice launched a free eight-week micro-course on AI literacy essentials addressing this urgent need [9].

Studies consistently reveal gaps in preparedness among LIS professionals to teach AI literacy, with softer ethical competencies sometimes outperforming harder technical skills [10]. Pedagogical research stresses incorporating critical information literacy, enabling users to evaluate biases and misinformation in AI-generated content [7][11]. Workshop case studies demonstrate successful models for teaching responsible AI use grounded in theoretical frameworks such as post-phenomenology and critical pedagogy [12].

Impacts on Library Labour and Professional Practice

Generative AI is reshaping library workflows and professional roles, presenting both opportunities and disruptions. Research shows growing adoption of AI tools to improve productivity in cataloguing, classification, reference, and research services [13]. However, concerns persist about job displacement, skill obsolescence, and ethical use of automations [7][14].

Luo’s survey highlights varied librarian experiences using AI in daily tasks, emphasising the need for ongoing training and support [14]. The impact of labour extends to how libraries organise instruction and reference service labour—areas analysed in *Library Trends* through the lens of material conditions of instruction and professional identity shifts [7]. Scholars call for thoughtful policy development to balance AI efficiency gains with humane labour practices that preserve professional autonomy [15].

Addressing Algorithmic Bias in Information Retrieval

Algorithmic bias is widely acknowledged as a serious risk in library AI applications. Workshops like the BIAS 2025 at SIGIR concentrate on developing strategies for fairer search and recommendation systems [16]. These initiatives complement academic calls for algorithmic audits and inclusion of diverse datasets to improve AI fairness and transparency [1]. LIS professionals’ role is pivotal in advocating for ethical AI in information retrieval, ensuring algorithms do not perpetuate discriminatory outcomes. Training in algorithmic literacy allows librarians to audit AI tools critically and promote equitable access to information [1]

Decolonial and Equity-Oriented AI Perspectives

Decolonial approaches to AI demand centring Indigenous knowledge systems and challenging Western epistemologies embedded in AI designs. Works like those by Cox and Jimenez in *Library Trends* highlight the necessity of decolonising digital libraries through ethical AI frameworks [7]. Such perspectives align with broader global calls to recognise AI’s sociocultural impacts and counteract systemic biases [7].

These approaches intersect with data privacy and user equity concerns, emphasising transparency, inclusiveness, and community engagement as core principles for responsible AI governance in libraries [17].

Future Directions and Recommendations

The converging research points to several actionable recommendations for academic libraries integrating generative AI:
Develop comprehensive AI literacy programs_ that include ethics, critical thinking, and technical training for librarians and patrons [8][9].
Engage in ongoing algorithmic auditing and bias mitigation efforts, leveraging multi-disciplinary partnerships to ensure fair and transparent systems [1][16].
Adopt ethical frameworks, including virtue ethics, to guide AI policy, design, and usage decisions, emphasising accountability and human flourishing [7][5][6].
Support library labour through upskilling and redefining roles to optimise human-AI collaboration rather than simple automation-driven displacement [7][14].
Incorporate decolonial methodologies in AI development and deployment to elevate marginalised perspectives and knowledge systems [7].
Maintain vigilant attention to data privacy and user consent within AI systems, upholding trust and ethical standards [2].

Selected References

1. Igbinovia, M. O. (2025). Artificial intelligence algorithm bias in information retrieval: Implications for LIS professionals. Journal of Information Science, 51(4). https://doi.org/10.1080/07317131.2025.2512282
2. Dilmegani, C., & Ermut, S. (2025). Generative AI Ethics: Concerns and How to Manage Them? AI Multiple. https://research.aimultiple.com/generative-ai-ethics/
3. Lo, L. S. (2025). AI Literacy: A Guide for Academic Libraries. College & Research Libraries News, 86(3). https://digitalrepository.unm.edu/ulls_fsp/210/
4. Georgetown University Libraries. (2023). Ethics & AI. https://guides.library.georgetown.edu/ai/ethics
5. Gmiterek, G. (2025). Generative artificial intelligence in the activities of librarians. Journal of Academic Librarianship. https://www.sciencedirect.com/science/article/abs/pii/S0099133325000394
6. Mwantimwa, K. (2025). Application of generative artificial intelligence in library operations. Library Hi Tech News. https://www.tandfonline.com/doi/full/10.1080/07317131.2025.2467574
7. Stahl, B. C., et al. (2022). AI ethics and governance in Europe. Ethics and Information Technology, 24(1). https://link.springer.com/article/10.1007/s10676-021-09598-z
8. Education and Library Trends on AI, 2025. Library Trends Vol. 73(3) & (4). https://ischool.illinois.edu/news-events/news/2025/09/library-trends-completes-two-part-series-ai-and-libraries
9. BIAS Workshop @ SIGIR 2025. (2025). International Workshop on Algorithmic Bias. https://biasinrecsys.github.io/sigir2025/

Sources:

[1] Artificial intelligence algorithm bias in information retrieval: Implications for LIS professionals.. https://www.tandfonline.com/doi/full/10.1080/07317131.2025.2512282
[2] Generative AI Ethics: Concerns and How to Manage Them? https://research.aimultiple.com/generative-ai-ethics/
[3] Ethics & AI – Artificial Intelligence (Generative) Resources https://guides.library.georgetown.edu/ai/ethics
[4] AI Ethical Guidelines. https://library.educause.edu/resources/2025/6/ai-ethical-guidelines
[5] Philosophy and Ethics in the Age of Artificial Intelligence https://jisem-journal.com/index.php/journal/article/download/9232/4266/15377
[6] Integrating Moral Values in AI: Addressing Ethical … https://journals.mmupress.com/index.php/jiwe/article/view/1255
[7] Library Trends completes two-part series on AI and libraries https://ischool.illinois.edu/news-events/news/2025/09/library-trends-completes-two-part-series-ai-and-libraries
[8] AI Literacy: A Guide for Academic Libraries by Leo S. Lo https://digitalrepository.unm.edu/ulls_fsp/210/
[9] Bridging the AI skills gap: Literacy program academic … https://about.proquest.com/en/blog/2025/bridging-the-ai-skills-gap-a-new-literacy-program-for-academic-libraries/
[10] AILIS 1.0: A new framework to measure AI literacy in library … AILIS 1.0: A new framework to measure AI literacy in library and information science (LIS) https://www.sciencedirect.com/science/article/abs/pii/S0099133325001144
[11] Information Literacy for Generative AI https://edtechbooks.org/ai_in_education/information_literacy_for_generative_ai?tab=images
[12] Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study https://digitalcommons.lmu.edu/cgi/viewcontent.cgi?article=1178&context=librarian_pubs
[13] Application of generative artificial intelligence in library operations and service delivery: A scoping review. https://www.tandfonline.com/doi/full/10.1080/07317131.2025.2467574
[14] Library Trends examines generative AI in libraries http://ischool.illinois.edu/news-events/news/2025/06/library-trends-examines-generative-ai-libraries
[15] Leo Lo – libraries #generativeai #openaccess #innovation https://www.linkedin.com/posts/leoslo_libraries-generativeai-openaccess-activity-7269345269811408896-jWcM
[16] International Workshop on Algorithmic Bias in Search and Recommendation (BIAS 2025) https://dl.acm.org/doi/10.1145/3726302.3730357
[17] Exploring the integration of artificial intelligence in libraries https://ijlsit.org/archive/volume/9/issue/1/article/3116
[18] Generative artificial intelligence in the activities of academic libraries of public universities in Poland. https://www.sciencedirect.com/science/article/abs/pii/S0099133325000394
[19] Practical Considerations for Adopting Generative AI Tools in Academic Libraries https://www.tandfonline.com/doi/full/10.1080/01930826.2025.2506151?src=exp-la
[20] The transformative potential of Generative AI in academic library access services: Opportunities and challenges. https://journals.sagepub.com/doi/10.1177/18758789251332800
[21] How National Libraries Are Embracing AI for Digital Transformation. https://librarin.eu/how-national-libraries-are-embracing-ai-for-digital-transformation/
[22] International Workshop on Algorithmic Bias in Search and Recommendation https://biasinrecsys.github.io/sigir2025/
[23] Generative Artificial Intelligence and Its Implications … https://www.rfppl.co.in/subscription/upload_pdf/single-pdf(19-25)-1746421080.pdf
[24] Investigating the “Feeling Rules” of Generative AI and Imagining Alternative Futures. https://www.inthelibrarywiththeleadpipe.org/2025/ai-feeling-rules/

Bridging Stacks and Circuits: Rethinking Library Science Curriculum for the AI Era

Posted on 22/09/2025 by Sukhdev Singh

When I imagine redesigning the Library and Information Science curriculum for the age of AI, I see it semester by semester, like walking through the library stacks, each level taking me closer to new knowledge, but always with a familiar fragrance of books and values.

Semester 1 – The Roots
Here I would begin with Foundations of Library Science, Information Sources & Services, and alongside them introduce Introduction to AI and Data Literacy. Students should learn what algorithms are, how language models work, and why data matters. Just remember, this is not to turn them into computer scientists, but into informed professionals who can converse with both technology and community.

Semester 2 – The Tools
This stage could focus on Knowledge Organization, Cataloguing and Metadata, but reframed to show how AI assists in subject indexing, semantic search, and linked data. Alongside, a course on Digital Libraries and Discovery Systems will let them experiment with AI-powered platforms. By the way, assignments could include building small datasets and watching how AI classifies them — both the brilliance and the flaws.

Semester 3 – The Questions
Here ethics must enter the room strongly. A full course on AI, Ethics, and Information Policy is essential: patron privacy, copyright, algorithmic bias, transparency. At the same time, practical subjects like Digital Curation and Preservation should demonstrate how AI restores manuscripts, enhances images, or predicts file degradation. No wonder, students will begin to see AI as both a tool and a responsibility.

Semester 4 – The Bridge
I see this as a turning point: courses on Human–AI Interaction in Libraries, Information Literacy Instruction in the AI Era, and Data Visualization for Librarians. Students would learn to teach communities about AI tools, to verify machine answers, and to advocate for responsible use. A lab-based course could even simulate AI chatbots for reference desks, showing how humans must stay in the loop.

Semester 5 – The Expansion
By now, students are ready for deeper exploration. They could take electives like AI in Scholarly Communication (covering plagiarism detection, trend forecasting, citation networks) or AI for Community Engagement (local language NLP, accessibility, inclusive design). At the same time, collaboration with computer science or digital humanities departments could be formalized as joint workshops.

Semester 6 – The Future
The final stage should be open-ended: a Capstone Project in AI and Libraries, where each student selects a challenge — say, AI in cataloguing, or a chatbot for local history archives — and builds a small prototype or research study. Supplement this with an Internship or Residency in a library, tech lab, or cultural institution. Just imagine the confidence this gives: they graduate not as passive observers of AI but as active participants in shaping it.

And beyond…
I must not forget lifelong learning. The curriculum should be porous, allowing micro-credentials, short courses, and professional updates, because AI won’t stop evolving. In fact, it will keep testing us — and so our readiness must be continuous.

Looking back at this imagined curriculum, I feel it keeps the spirit of librarianship alive — service, access, ethics — while opening the doors to AI-driven realities. It is like adding a new wing to the old library: modern, glowing, full of machines perhaps, but still part of the same house of knowledge where the librarian remains a human guide.

From Stacks to Algorithms: Librarianship’s New Chapter

Posted on 21/09/2025 by Sukhdev Singh

I remember when the word artificial intelligence first started appearing in library journals, it felt distant, almost experimental, as if it belonged more to labs than to our reading rooms. But just yesterday I came across a note from the American Library Association — they have now published guidance for school librarians on how to use AI in their everyday work [1]. No wonder, because librarians today are juggling so many roles: teachers, mentors, administrators, sometimes even technologists. The ALA’s advice is not about replacing them, but about helping them — streamlining tasks, improving communication, and yes, teaching students how to use AI ethically (plagiarism, citations, authorship, all those tricky parts).

By the way, it is not just policy notes. At Illinois, the journal Library Trends has just completed a two-part special issue on generative AI and libraries [2]. I skimmed through some of the abstracts: studies on how students use ChatGPT, how faculty perceive these tools, case studies of AI literacy instruction. This is serious scholarship, freely available, meant to guide practice. It reminds me of my early days in the profession, when such research gave us the language to argue for budgets and staff — and sometimes, just the courage to try new things.

And then, in Prague, librarians and researchers gathered under the banner of an “AI Knowledge Café,” more than 650 participants thinking together about the place of libraries in national AI strategies [3]. Imagine that: librarians not just adopting AI tools quietly, but sitting at the policy table, influencing how society will treat knowledge, ethics, and inclusion in the age of algorithms.

When I read all this, I feel both hopeful and cautious. Hopeful, because libraries are no longer seen as passive — we are active shapers of how AI unfolds. Cautious, because guidance and journals and cafés will mean little without real resources, training, and recognition, especially in countries like ours where libraries carry such a heavy heritage burden across many languages.

Still, I like to think that this is the beginning of a new chapter. Librarianship in the AI age is not a threat to our role, but a chance to re-articulate it. And in my heart, I feel grateful to be part of this transition — from catalog cards to chatbots, from dusty stacks to digital literacy.

References

[1] American Library Association. AI Guidance for School Librarians. Published September 2025. https://www.ala.org/news/2025/09/ai-guidance-school-librarians

[2] iSchool at Illinois. Library Trends Completes Two-Part Series on AI and Libraries. Published September 2025. https://ischool.illinois.edu/news-events/news/2025/09/library-trends-completes-two-part-series-ai-and-libraries

[3] ALA / IFLA. Libraries Towards a National AI Strategy (AI Knowledge Café). September 2025. https://connect.ala.org/acrl/discussion/libraries-towards-a-national-ai-strategy

Why India Needs Libraries at the Heart of Its National AI Strategy

Posted on 09/09/2025 by Sukhdev Singh

Artificial Intelligence (AI) is rapidly reshaping how societies learn, work, and connect. As India builds its national AI strategy, there is an urgent need to ask: who will ensure that AI development remains ethical, inclusive, and accessible to every citizen? One powerful answer lies in our libraries.

Think about it. For decades, libraries have been safe spaces where anyone, regardless of background, could walk in and learn. Whether it was a student preparing for exams, a farmer checking market information, or a job seeker updating their resume, libraries have been bridges to opportunity. In the age of AI, they can once again be the guiding hand that helps people navigate complexity and change.

Guardians of Ethics and Accountability
Libraries can champion transparency, fairness, and human oversight in AI systems adopted by public institutions.
Protectors of Privacy and Intellectual Freedom
Library principles of confidentiality and equitable access align perfectly with India’s need for citizen-centric AI governance.
AI and Digital Literacy Hubs
Just as libraries once taught computer literacy, they can now lead community workshops, training, and resources on AI literacy.
Upskilling the Workforce
Librarians must be trained to use AI in cataloguing, research support, and community services—ensuring the profession adapts and thrives.
Bridging the Digital Divide
Rural and underserved communities can access AI tools through public libraries, preventing exclusion from India’s digital transformation.
Policy Participation
Libraries should have a seat at the table in national AI governance—bringing the voices of ordinary citizens into policy-making.

A Call to Action for Librarians in India

Librarians must step forward to:

Advocate for their role in national AI consultations.
Develop pilot projects that showcase responsible AI use in library services.
Build partnerships with universities, civil society, and government bodies to amplify their impact.

A Call to Action for the Government of India

To truly build an AI for All strategy, the Government of India should:

Recognise libraries as strategic partners in AI education and governance.
Fund training and digital infrastructure for libraries.
Mandate representation of library associations in AI policy consultations.

Final Word

AI is like electricity—it will power every sector of life in the coming years. Libraries are the transformers that can make this power safe, reliable, and accessible to all. If India wants an inclusive AI future, it must weave libraries into its national AI strategy.

Librarians: this is your moment to lead.

Government: this is your chance to listen.

Why India Needs to Develop Its Own GPU to Lead in AI

Posted on 29/07/2025 by Sukhdev Singh

Artificial Intelligence (AI) is transforming the world, reshaping industries, economies, and societies at an unprecedented pace. For India, a nation with a burgeoning tech ecosystem and ambitions to become a global AI powerhouse, the path to leadership in AI hinges on addressing a critical bottleneck: access to high-performance computing infrastructure, particularly Graphics Processing Units (GPUs). While India has made strides in AI research, software development, and talent cultivation, its reliance on foreign GPUs poses a significant challenge. Developing indigenous GPUs is not just a matter of technological self-reliance but a strategic necessity for India to unlock its AI potential and secure its place in the global tech race.

The Central Role of GPUs in AI

GPUs are the backbone of modern AI systems. Unlike traditional Central Processing Units (CPUs), GPUs are designed for parallel processing, making them exceptionally efficient for the computationally intensive tasks that underpin AI, such as training deep learning models, running simulations, and processing vast datasets. From natural language processing models like those powering chatbots to computer vision systems enabling autonomous vehicles, GPUs are indispensable.

However, the global GPU market is dominated by a handful of players, primarily NVIDIA, AMD, and Intel, all based in the United States. These companies control the supply chain, set pricing, and dictate the pace of innovation. For a country like India, which is heavily investing in AI to address challenges in healthcare, agriculture, education, and governance, dependence on imported GPUs creates vulnerabilities in terms of cost, accessibility, and strategic autonomy.

The Case for Indigenous GPU Development

Reducing Dependency on Foreign Technology
India’s AI ambitions are constrained by its reliance on foreign GPUs. Supply chain disruptions, geopolitical tensions, or export restrictions could limit access to these critical components, hampering AI development. For instance, recent global chip shortages exposed the fragility of depending on foreign semiconductor supply chains. By developing its own GPUs, India can achieve technological sovereignty, ensuring that its AI ecosystem is not at the mercy of external forces.
Cost Efficiency for Scalability
GPUs are expensive, and their costs can be prohibitive for startups, research institutions, and small enterprises in India. Importing high-end GPUs involves significant expenses, including taxes and logistics, which drive up the cost of AI development. Indigenous GPUs, tailored to India’s needs and produced locally, could be more cost-effective, enabling broader access to high-performance computing for academia, startups, and government initiatives. This democratization of access would foster innovation and accelerate AI adoption across sectors.
Customization for India-Specific Use Cases
India’s AI challenges are unique. From multilingual natural language processing for its diverse linguistic landscape to AI-driven solutions for agriculture in resource-constrained environments, India’s needs differ from those of Western markets. Foreign GPUs are designed for generalized, high-end applications, often with a one-size-fits-all approach. Developing homegrown GPUs allows India to create hardware optimized for its specific AI use cases, such as low-power chips for edge computing in rural areas or specialized architectures for processing Indian language datasets.
Boosting the Semiconductor Ecosystem
Building GPUs would catalyze the growth of India’s semiconductor industry, which is still in its nascent stages. It would require investment in chip design, fabrication, and testing, creating a ripple effect across the tech ecosystem. This would not only create high-skill jobs but also position India as a player in the global semiconductor market. Programs like the India Semiconductor Mission (ISM) and partnerships with global foundries could be leveraged to support GPU development, fostering innovation and reducing reliance on foreign manufacturing.
National Security and Strategic Autonomy
AI is increasingly a matter of national security, with applications in defense, cybersecurity, and intelligence. Relying on foreign hardware raises concerns about potential vulnerabilities, such as backdoors or supply chain manipulations. Indigenous GPUs would give India greater control over its AI infrastructure, ensuring that sensitive applications are built on trusted hardware. This is particularly critical as India expands its use of AI in defense systems, smart cities, and critical infrastructure.

Challenges in Developing Indigenous GPUs

While the case for India developing its own GPUs is compelling, the path is fraught with challenges. Designing and manufacturing GPUs requires significant investment in research and development (R&D), access to advanced fabrication facilities, and a skilled workforce. The global semiconductor industry is highly competitive, with established players benefiting from decades of expertise and economies of scale.

India also faces a talent gap in chip design and fabrication. While the country produces millions of engineering graduates annually, specialized skills in semiconductor design are limited. Bridging this gap will require targeted education and training programs, as well as collaboration with global leaders in the field.

Moreover, building a GPU is not just about hardware. It requires an ecosystem of software, including drivers, frameworks, and developer tools, to make the hardware usable for AI applications. NVIDIA’s dominance, for example, stems not only from its hardware but also from its CUDA platform, which has become a de facto standard for AI development. India would need to invest in a robust software ecosystem to complement its GPUs, ensuring seamless integration with popular AI frameworks like TensorFlow and PyTorch.

Steps Toward Indigenous GPU Development

Government Support and Investment
The government should prioritize GPU development under initiatives like the India Semiconductor Mission. Subsidies, grants, and tax incentives for R&D in chip design and manufacturing can attract private investment and foster innovation. Public-private partnerships, like those with companies such as Tata and Reliance, could accelerate progress.
Collaboration with Global Players
While the goal is self-reliance, India can benefit from partnerships with global semiconductor leaders. Technology transfer agreements, joint ventures, and collaborations with companies like TSMC or Intel could provide access to cutting-edge fabrication processes and expertise.
Building a Skilled Workforce
India must invest in education and training programs focused on semiconductor design, AI hardware, and related fields. Partnerships with institutions like IITs and IISc, as well as international universities, can help develop a pipeline of talent. Initiatives like the Chips to Startup (C2S) program can be expanded to include GPU-specific training.
Fostering an Ecosystem for Innovation
India should create a supportive environment for GPU development by building a robust software ecosystem, encouraging open-source contributions, and supporting startups working on AI hardware. Hackathons, innovation challenges, and incubators focused on semiconductor design can spur grassroots innovation.
Leveraging Existing Strengths
India’s strength in software development and IT services can be a foundation for building GPU-compatible software stacks. Companies like Wipro, Infosys, and startups in the AI space can contribute to developing frameworks and tools that make indigenous GPUs viable for AI applications.

The Road Ahead

Developing indigenous GPUs is a bold but necessary step for India to achieve its AI ambitions. It aligns with the broader vision of “Atmanirbhar Bharat” (Self-Reliant India) and positions the country as a global leader in technology. While the journey will be challenging, the rewards are immense: reduced dependency, cost efficiency, customized solutions, and enhanced national security.

India has already shown its ability to leapfrog in technology, from UPI in digital payments to Aadhaar in biometric identification. By investing in GPU development, India can take a similar leap in AI, creating a future where its technological innovations are not just powered by India but also made in India. The time to act is now—India’s AI revolution depends on it.

How AI Tools Revolutionize Academic Research: Top 10 Free Tools to Boost Your Workflow

Posted on 25/07/2025 by Sukhdev Singh

Artificial Intelligence (AI) is transforming academic research by streamlining repetitive tasks, uncovering insights, and enhancing productivity across every stage of the research process. From conducting literature reviews to analyzing data and polishing manuscripts, AI tools save time and improve efficiency. In this blog post, we explore how AI tools can elevate your research and highlight 10 free AI tools (with free plans) that support various research stages, complete with descriptions and links to get you started.

How AI Tools Enhance Academic Research

AI tools empower researchers by automating and optimizing key research tasks. Here’s how they help at different stages:

Literature Review: AI tools search vast academic databases, summarize papers, and identify connections between studies, making it easier to stay updated and find relevant sources.
Data Collection: Extract data from PDFs, texts, or online sources quickly, reducing manual effort.
Data Analysis: Analyze large datasets, identify patterns, and create visualizations with minimal coding.
Academic Writing: Improve clarity, grammar, and academic tone while generating outlines or paraphrasing content.
Citation Management: Automate citation formatting and reference organization across styles like APA or MLA.
Collaboration: Organize research materials, visualize citation networks, and share findings with teams.
Translation: Break language barriers by translating papers in real-time for global accessibility.

Now, let’s dive into the top 10 AI tools with free plans that can supercharge your academic research.

Top 10 Free AI Tools for Academic Research

1. Semantic Scholar

What It Does: A powerful AI-driven search engine for accessing over 200 million academic papers. It generates concise summaries, recommends related studies, and highlights connections between papers, perfect for literature reviews.
Free Plan: Completely free with unlimited searches and access to open-access papers (paywalled papers depend on your subscriptions).
Best For: Finding and summarizing relevant research quickly.
Website: semanticscholar.org

2. Elicit

What It Does: An AI research assistant that searches over 125 million papers, automates literature reviews, summarizes findings, and extracts data. It’s ideal for empirical research but less suited for theoretical studies.
Free Plan: Free access to search, summarization, and data extraction with no strict limits (verify results due to ~90% accuracy).
Best For: Streamlining literature reviews and data extraction.
Website: elicit.com

3. Research Rabbit

What It Does: A free tool that creates visual citation networks, suggests related papers, and organizes research collections. It’s great for exploring research connections and collaborating with peers.
Free Plan: Fully free with unlimited collections and paper additions (note: the interface may take some getting used to).
Best For: Organizing research and discovering related studies.
Website: researchrabbit.ai

4. Zotero

What It Does: A reference management tool that uses AI to suggest papers, organize citations, and generate bibliographies in various formats. It integrates seamlessly with word processors.
Free Plan: Free with unlimited reference storage; cloud syncing limited to 300 MB (expandable with paid plans).
Best For: Managing citations and references effortlessly.
Website: zotero.org

5. Scholarcy

What It Does: Summarizes research papers, articles, and book chapters into flashcards, highlighting key findings, limitations, and comparisons. It cuts screening time by up to 70%.
Free Plan: Summarize up to three documents per day; includes a browser extension for open-access and subscription-based papers.
Best For: Quickly digesting complex papers.
Website: scholarcy.com

6. ChatPDF

What It Does: Upload PDFs and interact with them via a chatbot to extract information or summarize content. It’s a time-saver for understanding dense research papers.
Free Plan: Upload two PDFs per day (up to 120 pages each) and ask 20 questions daily.
Best For: Extracting specific data from PDFs.
Website: chatpdf.com

7. Paperpal

What It Does: An AI writing assistant tailored for academia, offering grammar checks, paraphrasing, citation generation, and journal submission checks. It also supports literature searches and PDF analysis.
Free Plan: Basic grammar and style suggestions, 10 AI generations daily, and limited research features.
Best For: Polishing academic writing and translation.
Website: paperpal.com

8. NotebookLM

What It Does: A Google-powered tool that lets you upload up to 50 documents per notebook and generates summaries, audio overviews, or study guides. It’s perfect for organizing research materials.
Free Plan: Free with up to 100 notebooks, 50 sources per notebook, and daily limits on queries and audio summaries.
Best For: Summarizing and organizing research notes.
Website: notebooklm.google

9. AI2 Paperfinder

What It Does: Developed by the Allen Institute, this tool provides access to 8 million full-text papers and 108 million abstracts. It ranks search results by relevancy and exports citations in BibTeX or other formats.
Free Plan: Fully free with no limits on searches or citation exports.
Best For: Comprehensive literature searches and citation exports.
Website: paperfinder.allenai.org

10. DeepSeek

What It Does: A free large language model that answers research queries and synthesizes information. While not as advanced as premium models, it’s a solid option for general research assistance.
Free Plan: Fully free with no specific query limits (performance may vary for complex tasks).
Best For: General research queries on a budget.
Website: deepseek.com

Tips for Using AI Tools in Research

Verify Outputs: Tools like Elicit and ChatPDF may have errors (~90% accuracy for Elicit). Always cross-check results with original sources.
Combine Tools: Free plans have limitations (e.g., Scholarcy’s three-document cap). Use multiple tools to cover all research needs.
Maintain Integrity: AI should enhance, not replace, your critical thinking. Use these tools to boost productivity while ensuring originality.
Explore Paid Plans: If you hit free plan limits, consider paid upgrades for heavy use or advanced features.

Conclusion

AI tools are game-changers for academic research, helping you save time, uncover insights, and produce high-quality work. The 10 free tools listed above cover everything from literature reviews to citation management, making them accessible for students, researchers, and academics on a budget. Start exploring these tools today to streamline your research process and focus on what matters most—advancing knowledge.

Have a favorite AI research tool or need help with a specific research task? Share your thoughts in the comments below!

What Would S. R. Ranganathan Do in the Age of Generative AI if He Were Alive?

Posted on 24/07/2025 by Sukhdev Singh

S.R. Ranganathan, the pioneering Indian librarian and mathematician, is best known for his Five Laws of Library Science and the development of the Colon Classification system. His work emphasised organising knowledge for accessibility, relevance, and user-centricity. If he were alive today, his approach to generative AI would likely be shaped by his knowledge organisation principles, focus on serving users, and innovative mindset. While it’s impossible to know exactly what he would have done, we can make informed speculations based on his philosophy and contributions.

Applying the Five Laws to Generative AI
Ranganathan’s Five Laws of Library Science (1931)—”Books are for use,” “Every reader his/her book,” “Every book its reader,” “Save the time of the reader,” and “The library is a growing organism“—could be adapted to generative AI systems, which are increasingly used to organize and generate knowledge. Here’s how he might have approached generative AI:
– Books are for use: Ranganathan would likely advocate for generative AI to be designed with practical utility in mind, ensuring it serves real-world needs, such as answering queries, generating content, or solving problems efficiently. He might push for AI interfaces that are intuitive and accessible to all users, much like a library’s catalog.
– Every reader his/her book: He would likely emphasise personalisation in AI systems, ensuring that generative AI delivers tailored responses to diverse users. For example, he might explore how AI could adapt outputs to different languages, cultural contexts, or knowledge levels, aligning with his goal of meeting individual user needs.
– Every book its reader: Ranganathan might focus on making AI-generated content discoverable and relevant, developing classification systems or metadata frameworks to organise AI outputs so users can easily find what they need. He could propose taxonomies for AI-generated text, images, or code to enhance retrieval.
– Save the time of the reader: He would likely prioritise efficiency, advocating for AI systems that provide accurate, concise, and relevant outputs quickly. He might critique models that produce verbose or irrelevant responses and push for prompt engineering techniques to streamline interactions.
– The library is a growing organism: Ranganathan would recognise generative AI as a dynamic, evolving system. He might encourage continuous updates to AI models, integrating new data and user feedback to keep them relevant, much like a library evolves with new books and technologies.
Developing Classification Systems for AI Outputs
Ranganathan’s Colon Classification system was a faceted, flexible approach to organising knowledge, allowing for complex relationships between subjects. He might apply this to generative AI by:
– Creating a taxonomy for AI-generated content: He could develop a faceted classification system to categorize outputs like text, images, or code based on attributes such as topic, format, intent, or audience. For example, a generated article could be tagged with facets like “subject: science,” “tone: formal,” or “purpose: education.”
– Improving information retrieval: Ranganathan might work on algorithms to enhance the discoverability of AI-generated content, ensuring users can navigate vast outputs efficiently. He could integrate his classification principles into AI search systems, making them more precise and context-aware.
– Addressing ethical concerns: He would likely consider the ethical implications of AI-generated content, such as misinformation or bias, and propose frameworks to tag or filter outputs for reliability and fairness, aligning with his user-centric philosophy.
Advancing AI for Libraries and Knowledge Management
As a librarian, Ranganathan would likely focus on how generative AI could enhance library services and knowledge management:
– AI-powered library assistants: He might advocate for AI chatbots to assist patrons in finding resources, answering queries, or recommending materials, saving librarians’ time and improving user experience. For example, an AI could use natural language processing to interpret complex research queries and suggest relevant books or articles.
– Automating cataloguing: Ranganathan could explore generative AI for automating metadata creation or cataloguing, using models to summarise texts, extract keywords, or classify resources according to his Colon Classification system. This would align with his goal of saving time and improving access.
– Preserving cultural knowledge: Given his work in India, he might use AI to digitise and generate summaries of regional texts, manuscripts, or oral traditions, making them accessible globally while preserving cultural context.
Ethical and Social Considerations
Ranganathan’s user-focused philosophy suggests he would be concerned with the ethical and societal impacts of generative AI, as noted in sources discussing AI’s risks like misinformation and job displacement. He might:
– Promote equitable access: He would likely advocate for open-source AI models or affordable tools to ensure generative AI benefits diverse populations, not just affluent institutions or countries.
– Address misinformation: Ranganathan might develop guidelines for libraries to educate users about AI-generated content, helping them distinguish reliable outputs from “hallucinations” or deepfakes.
– Mitigate job displacement: While recognising AI’s potential to automate tasks, he might propose training programs for librarians to adapt to AI-driven workflows, ensuring human expertise remains central.
Innovating with Generative AI
Ranganathan was an innovator, so he might experiment with generative AI to push boundaries in knowledge organisation:
– AI for creative knowledge synthesis: He could use AI to generate new insights by synthesising existing literature, creating summaries or interdisciplinary connections that human researchers might overlook.
– AI in education: Drawing from his focus on accessibility, he might develop AI tools to generate educational content tailored to different learning styles, supporting students and educators.
– Collaborative AI systems: He might propose collaborative platforms where AI and librarians work together, with AI handling data-intensive tasks and humans providing critical judgment, aligning with his belief in human-centric systems.
Critiquing and Shaping AI Development
Ranganathan’s analytical mindset suggests he would critically examine generative AI’s limitations, such as data dependence, bias, and lack of true creativity. He might:
– Push for transparency: Advocate for clear documentation of AI training data and processes, ensuring users understand how outputs are generated.
– Enhance AI explainability: Develop frameworks to make AI decisions more interpretable, helping users trust and verify generated content.
– Focus on sustainability: Given the environmental impact of AI training, he might explore energy-efficient models or advocate for sustainable practices in AI development.

Conclusion
If S.R. Ranganathan were alive today, he would likely embrace generative AI as a tool to enhance knowledge organisation and accessibility while critically addressing its ethical and practical challenges. He would adapt his Five Laws to AI, develop classification systems for AI outputs, and leverage AI to improve library services and education. His focus would remain on serving users, ensuring equity, and advancing knowledge management in an AI-driven world. His innovative spirit and user-centric philosophy would make him a key figure in shaping generative AI’s role in libraries and beyond.

Chat with PDF files: AI Tools to Ask Questions to PDFs for Summaries and Insights

Posted on 03/10/2024 by Sukhdev Singh

In today’s digital world, we are inundated with information, much of it locked away in PDF documents. Whether you are a student combing through research papers, a professional analysing detailed reports, or someone simply trying to extract crucial information from a large PDF, you’ve likely felt overwhelmed. But what if I told you that you could actually chat with those PDFs? Thanks to recent advancements in AI, this once far-fetched idea is now a reality.

The Power of AI in Document Analysis

AI-powered tools are transforming how we engage with PDFs, allowing us to swiftly access information, summarise content, and even query documents directly. These tools combine several cutting-edge technologies:

Text Extraction: Utilising Optical Character Recognition (OCR) for scanned documents and PDF parsing libraries for digital PDFs.
Natural Language Processing (NLP): AI analyses the extracted text to grasp content, structure, and context.
Entity Recognition: Identifies specific entities such as names, dates, and organisations.
Chat Integration: AI generates responses based on user queries and the document’s content. Top AI Tools for PDF Interaction

Let’s explore some of the leading tools in this field:

ChatPDF

ChatPDF allows you to upload any PDF and ask questions about its content. Ideal for textbooks, research papers, or business documents, it quickly generates answers based on the data within the PDF. It’s also available as a plugin within ChatGPT, making it even more accessible.

PDF.ai

PDF.ai specialises in multi-language PDF interaction, making it perfect for users working across different languages. It enables dynamic conversations with documents, breaking down language barriers in document analysis.

GPT-PDF by Humata

Built on GPT technology, this tool offers deep interaction with complex files like reports or whitepapers. It’s particularly useful for users needing to analyse and generate insights from technical documents.

Ask Your PDF

Ask Your PDF stands out with its powerful semantic search capability, excelling at analysing multiple documents simultaneously. This makes it an excellent choice for comprehensive research projects that require synthesising information from various sources.

Adobe Acrobat AI Assistant

Integrated into the widely used Adobe Acrobat, this AI assistant enhances document interaction while retaining Acrobat’s traditional editing capabilities. It’s a great option for users already familiar with the Adobe ecosystem.

PDFgear (Open-Source Option)

For those who prefer open-source solutions, PDFgear offers notable advantages:

Its open-source framework ensures transparency and customisation.
It supports interactions with multiple PDF files in a single session.
It is compatible with various AI backends like OpenAI and Anthropic.
Local deployment options provide greater privacy and security.
Available through both a web interface and command-line option. The Future of Document Interaction

These AI-powered PDF tools are just the beginning. As natural language processing and machine learning technologies continue to evolve, we can expect even more advanced document interaction capabilities. Imagine AI assistants that not only answer questions but also provide personalised insights, generate summaries tailored to your needs, or even create new documents based on the information contained within your PDFs.

Conclusion

The days of tediously scrolling through lengthy PDFs or relying solely on basic search functions are behind us. With these AI tools, we are entering an era where documents become interactive, responsive resources. Whether you’re a student, researcher, professional, or anyone who frequently works with PDFs, these tools can significantly streamline your workflow, making it easier than ever to extract and analyse information.

Have you tried any of these PDF tools? What’s been your experience? The world of AI-assisted document analysis is rapidly evolving, and it’s an exciting time to explore these new capabilities. As AI continues to push the boundaries of document interaction, the future promises even more innovative and powerful tools.

AI Tools in Education: Empowering Learning and Creativity

Posted on 28/09/2024 by Sukhdev Singh

In recent years, artificial intelligence (AI) has made significant strides in various fields, and education is no exception. The integration of AI tools in education is revolutionising how we learn, teach, and collaborate. This blog post explores the exciting world of AI in education, focusing on different types of AI tools and their applications, as well as discussing the responsible use of this powerful technology.

Understanding Generative AI

Generative AI is a branch of artificial intelligence that focuses on creating new content such as text, images, audio, and video by learning from existing data. Unlike traditional AI, which primarily analyses and predicts outcomes based on input data, generative AI models can produce original outputs that mimic the characteristics of their training data.

This capability has led to significant interest and investment across various sectors, with tools like ChatGPT, DALL-E, and Midjourney demonstrating practical uses in text, image, audio, and video generation.

AI Tools for Various Educational Purposes

1. Chatbots and Text Generation

Several AI-powered chatbots and text-generation tools are available to assist students and educators:

ChatGPT: A versatile conversational AI for writing, coding, and tutoring.
Claude: Designed for various tasks with a focus on safety and ethical AI behaviour.
Google’s Gemini: A multimodal AI capable of understanding and generating text, images, videos, and audio.
Microsoft Copilot: Integrates into the Microsoft ecosystem for context-aware assistance.
Perplexity: An AI-powered search and answer engine.
Pi: An AI assistant designed for open-ended conversations and emotional support.
Grok: Unique AI with real-time access to X (formerly Twitter) for current events analysis.

For more specific text generation tasks, tools like HyperWrite, Smart Copy AI, Simplified AI Writer, Quillbot, and Copy.AI offer various features to improve writing efficiency and quality.

2. Research Assistance

AI tools can significantly enhance the research process:

Consensus AI: Scans millions of scientific papers to find relevant ones based on your query.
Connected Papers and Litmaps: Visualize research areas and discover related papers.
Research Rabbit: Assists with literature mapping and paper recommendations.
Scite: Analyses and compares citations across research papers.
Open Knowledge Maps: Emphasizes open access content and provides research topic overviews.
Paper Digest: Helps in writing literature reviews by extracting essential information from papers.
PDFgear: Offers AI-powered PDF manipulation and information extraction.
Paperpal and Jenni: Provide specialized AI-powered writing assistance for academic and scientific writing.

3. Writing Improvement

Grammarly: A free AI writing assistant that provides personalized suggestions to enhance your text across various platforms.
Trinka: Designed specifically for academic and technical writing, focusing on clarity and precision.

4. Learning and Teaching

Summarize.tech: Uses AI to summarize lengthy YouTube videos, condensing hours of content into key points.
Quizlet: An AI-powered learning platform offering interactive flashcards, practice tests, and study activities.
Curipod: Helps teachers create engaging lessons with interactive activities.
ClassPoint: An all-in-one teaching and student engagement tool that works within PowerPoint.
Yippity: Converts information into various types of questions for learning and assessment.
Coursebox: An AI-powered platform for creating and managing online courses.
Goodgrade AI: Assists in writing essays, summarizing documents, and generating citations.

5. Collaboration Tools

Otter.ai: Transcribes speech in real-time and offers collaboration features for document sharing and management.
Notion: A versatile digital workspace with AI capabilities for organizing research materials, managing projects, and facilitating collaboration.

Responsible Use of AI in Education

While AI tools offer tremendous benefits, it is crucial to use them responsibly. Here are some key considerations:

1. Avoid Plagiarism: Always review AI-generated content carefully, rephrase ideas in your own words, and cite AI-generated content when necessary.

2. Maintain Academic Integrity: Use AI as a brainstorming tool, not a shortcut for entire projects. Be transparent about AI usage in your work.

3. Protect Privacy: Read terms of service, avoid sharing sensitive information, and use AI tools that prioritize user privacy.

4. Apply Human Oversight: AI is not always accurate and may lack context or nuance. Verify its output, especially in critical fields like law, medicine, or academia.

5. Set Boundaries: Find a balance where AI enhances your creativity but does not replace your effort. The goal is to learn and develop your own skills.

6. Follow Institutional Guidelines: Adhere to your institution’s policies on AI use to maintain integrity and trust.

Conclusion

Generative AI is transforming education by offering powerful tools for learning, research, writing, and collaboration. By using these tools responsibly and ethically, students and educators can unlock new levels of creativity and efficiency in their academic pursuits. As AI continues to evolve, it is exciting to imagine the future possibilities in education and beyond.

Remember, while AI can be an invaluable assistant, it is your unique human perspective, critical thinking, and creativity that will truly set your work apart. Embrace AI as a tool to enhance your abilities, not replace them, and you will be well-equipped to thrive in the AI-augmented future of education.