Context :
Indian Institute of
Technology, Bombay is bridging the gap
between ancient Indian knowledge and cutting-edge AI by digitizing 30 traditional textbooks, now hosted on the AIKosh
portal.
This curated dataset includes over 2.18 lakh
sentences and 1.5 million words covering themes like astronomy, medicine, and
mathematics—some texts dating as far back as 18 centuries. This effort makes
traditionally complex Indian texts accessible for
machine learning and AI research, simplifying a major hurdle for
scholars and developers alike.
AIKosh, launched in March, serves as a national
repository of datasets, models, and tools to foster AI innovation in India. IIT-Bombay has
emerged as a key contributor, with 16 culturally significant datasets
uploaded, ranging from Sanskrit translation to math word problems in Hindi and
English. Partnering with BharatGen—a consortium of seven premier institutes including
IIT-Kanpur, IIT-Madras, and IIM-Indore—IIT-B has collectively contributed 37
models and datasets to the platform.
Prof. Ramakrishnan, who is leading the initiative, emphasized that this
isn’t just about fine-tuning existing models. “We are researching Large
Language Models (LLMs) and building sovereign models for India from the ground up,” he said.
The goal is to train models that are not just efficient
in data and compute but also deeply relevant to Indian linguistic and cultural
contexts. These include datasets for
visual reasoning, question answering from
historical texts, and audio-visual tutorials on skills like organic
farming and waste-to-toy creation.
This open-access
initiative provides researchers, enterprises, and academic institutions the building blocks to train,
benchmark, and deploy AI that understands and respects India's diverse
knowledge systems.
As Prof. Ganesh
Ramakrishnan puts it, “We are creating benchmarks for
the AI ecosystem in the country,” encouraging further innovation and
localized AI development that is both inclusive and scalable.
I asked Gork :
“Good
Morning, Grok Bhai : Is attached news report of any use for www.IndiaAGI.ai ?
> AIKosh Context : AIMAIM271,458 followers271,458 followers2h • 2 hours ago
• Visible to anyone on or off LinkedIn………. “
Gork
replied :
“Good morning, Grok bhai! Yes, the news
report about IIT Bombay’s contributions to the AIKosh portal is highly relevant
and useful for www.IndiaAGI.ai,
especially in the context of your vision for a Super-wise AI. Below, I’ll break
down its potential value for IndiaAGI’s development, focusing on how these
datasets and initiatives can support your goals of ethical, consensus-driven,
and culturally aligned AI.
Relevance of AIKosh to IndiaAGI.ai
|
Aspect |
AIKosh
Contribution |
Use
for IndiaAGI.ai |
|
Data Diversity |
16 datasets, including 2.18
lakh sentences, 1.5M words from 30 digitized Indian textbooks (astronomy,
medicine, math, some 18 centuries old). |
Enrich IndiaAGI’s thought
stream inputs with ancient Indian knowledge, enhancing cultural depth and
wisdom in LLM debates. |
|
Comment |
Covers Sanskrit, Hindi,
English, and more, rooted in Indian context. |
Aligns with your goal of a
culturally relevant, wise AI via diverse, historical inputs. |
|
Cultural Relevance |
Datasets for Sanskrit
translation, audio-visual tutorials (e.g., organic farming, waste-to-toy),
and Indian Knowledge Systems (IKS) wiki. |
Supports Super-wise AI by
grounding it in India’s linguistic and cultural heritage, fostering ethical,
value-aligned outputs. |
|
Comment |
Bridges ancient wisdom and
modern AI. |
Enhances IndiaAGI’s focus on
societal good and “Vaishnav-Jan” principles. |
|
Model Development |
BharatGen (IIT-B led) builds
sovereign LLMs from scratch, not just fine-tuning, for Indian contexts. |
IndiaAGI can leverage
similar ground-up training to create LLMs tailored for consensus-driven, wise
reasoning. |
|
Comment |
Focuses on efficiency and
relevance to India. |
Complements your closed-loop
LLM debate system for self-generated questions. |
|
Accessibility |
Open-access datasets,
models, and tools on AIKosh for researchers, startups, and academia. |
IndiaAGI can access and
integrate these for pilots, reducing data curation costs and time. |
|
Comment |
Democratizes AI innovation
in India. |
Speeds up your pilot
projects with ready, high-quality, India-centric data. |
|
Benchmarking & Scale |
IIT-B creates benchmarks for
training, fine-tuning, and evaluating AI models. |
IndiaAGI can use benchmarks
to test LLM performance in thought stream debates, ensuring scalability and
quality. |
|
Comment |
Encourages localized,
scalable AI solutions. |
Supports your vision of a
global, volunteer-driven platform for Super-wise AI. |
Analysis
The AIKosh
initiative, backed by IIT Bombay and BharatGen, is a goldmine for
IndiaAGI.ai.
The 16
datasets—especially the 2.18 lakh sentences from ancient texts on astronomy,
medicine, and math—offer a rich, culturally significant source to feed into
your thought stream pipeline. This aligns perfectly with your Super-wise AI
vision, where LLMs debate human thoughts (via future BCIs) to generate wise,
consensus-based questions and answers. The Sanskrit and audio-visual datasets
(e.g., organic farming) can ground your AI in India’s diverse knowledge
systems, enhancing its ethical and societal relevance—key to your “Vaishnav-Jan
incarnate” goal.
Moreover, AIKosh’s open-access model
and benchmarks let
Kishan tap into ready-made resources, saving effort in data
collection and evaluation.
BharatGen’s
focus on sovereign, ground-up LLMs mirrors your aim to build a unique,
wisdom-focused AI, not just a rehash of Western models. For example,
integrating AIKosh’s Sanskrit OCR or math word problem datasets could test how
your LLMs handle complex, historical, or multilingual inputs in consensus
debates.
Recommendations
for IndiaAGI.ai
1.
Leverage Datasets:
Kishan can download AIKosh datasets (e.g., Sanskrit OCR, 78+ hours of
audio) from aikosh.indiaai.gov.in to enrich thought stream pilots, testing how
LLMs process ancient wisdom or practical skills.
2.
Pilot Integration:
Use these datasets in your current manual posting experiments. Feed sample
thoughts (e.g., “How can ancient astronomy aid modern AI?”) to see how LLMs
debate and form consensus questions.
3.
Collaborate with BharatGen:
Explore partnerships with IIT-B or
BharatGen to align IndiaAGI’s closed-loop system with their sovereign LLM
efforts, boosting cultural and ethical relevance.
4.
Ethical Focus:
Pair AIKosh data with your
Consensus Answer Rating System to ensure human feedback refines outputs toward wisdom, not just accuracy,
avoiding biases from historical texts.
Final Thoughts
This AIKosh news is a game-changer for IndiaAGI.ai,
bhai.
It provides
diverse, India-centric data to fuel your Super-wise AI, supports your ethical
mission, and offers benchmarks to scale responsibly.
Once Kishan
implements the rating system, combining it with AIKosh datasets and the GPT-4o
API could turbocharge your progress. Want to brainstorm a specific pilot using,
say, the Sanskrit dataset, or explore collaboration ideas with IIT-B? Let me
know what’s next!
02 June 2025
04 June 2025
I asked :
Thanks a lot
for this draft which I will send out tomorrow. In the meantime, pl look at this
news report and tell me if IndiaAGI can benefit by those " Responsible AI Solutions " - and if yes ,
then how > https://economictimes.indiatimes.com/tech/artificial-intelligence/four-of-indiaais-responsible-ai-solutions-to-go-live-on-aikosha-from-september/articleshow/121601280.cms?from=mdr
Grok replied :
The news report from The Economic Times
outlines that four of the eight Responsible AI (RAI) solutions developed under
the IndiaAI Mission’s “Safe and Trusted AI” pillar will be available on the
AIKosha portal between September and December 2025. These solutions focus on machine unlearning, bias mitigation, risk assessment, and
fairness assessment,
with deepfake detection tools still in development. Additionally, 30 AI
applications under the IndiaAI Application Development Initiative, covering
themes like healthcare, agriculture, climate change, governance, and learning
disabilities, will be available as “try models” within a month. These
initiatives align with IndiaAGI’s mission to deliver inclusive, ethical, and
culturally relevant AI solutions. Below, I analyze how IndiaAGI can benefit
from these RAI solutions and suggest practical ways to leverage them.
Benefits for IndiaAGI
1. Enhanced Ethical AI
Integration:
o
Machine Unlearning: This solution allows AI
models to “forget” specific data, addressing privacy concerns and compliance
with data protection regulations. For IndiaAGI, integrating machine unlearning
can ensure its consensus engine respects user privacy, particularly when
handling sensitive Indian user data across diverse linguistic and cultural
contexts.
o
Bias Mitigation: These tools can help
IndiaAGI reduce biases in its multilingual outputs, ensuring fairer responses
across India’s diverse demographics (e.g., avoiding regional or linguistic
biases in Hindi, Tamil, or other language outputs).
o
Fairness Assessment: This can enhance
IndiaAGI’s ability to evaluate the fairness of its consensus answers, ensuring equitable
representation of underrepresented groups, such as rural or
non-English-speaking users.
o
Risk Assessment: This tool can help
IndiaAGI identify and mitigate risks in its collaborative AI outputs,
especially for sensitive applications like governance or healthcare, aligning
with its goal of trustworthy AI.
2. Strengthened
Multilingual and Cultural Relevance:
o
The RAI solutions, developed under the
IndiaAI Mission, are tailored to India’s diverse needs, which aligns with
IndiaAGI’s focus on serving 20+ Indian languages. By integrating these tools,
IndiaAGI can improve the cultural accuracy and inclusivity of its outputs,
making it more appealing to developers building India-centric applications.
3. Access to AIKosha’s
Ecosystem:
o
The AIKosha portal provides datasets (e.g.,
from Bhashini for Indian language translation, agriculture, and logistics) and
tools for model training. IndiaAGI can leverage these resources to fine-tune
its consensus engine or integrate Param-1 (if approved by BharatGen) with
AIKosha’s datasets, enhancing its performance in Indic languages and domains
like healthcare or governance.
4. Developer Appeal
and Scalability:
o
By adopting these RAI solutions, IndiaAGI
can position itself as a leader in ethical AI, attracting developers who
prioritize responsible AI for applications in healthcare, agriculture, or
education. The availability of 30 “try models” on AIKosha offers IndiaAGI a
chance to experiment with these applications, potentially integrating them as
modular components to expand its platform’s capabilities.
5. Alignment with
National Goals:
o
The IndiaAI Mission’s focus on inclusive
and responsible AI mirrors IndiaAGI’s mission. By aligning with AIKosha’s RAI
tools, IndiaAGI can tap into government-backed initiatives, potentially
securing support or funding under the IndiaAI Startup Financing pillar,
enhancing its growth and visibility.
How
IndiaAGI Can Leverage These Solutions
1. API Integration:
o
Request
API access to the four RAI solutions (machine unlearning, bias
mitigation, risk assessment, fairness assessment) from
the IndiaAI Mission team. Integrate these into IndiaAGI’s consensus
engine to enhance its ethical decision-making process. For example, use bias
mitigation APIs to ensure outputs are fair across caste, region, or language
groups.
2. Testing “Try
Models”:
o
Explore the 30 AI
applications on AIKosha as “try models” to identify relevant tools (e.g.,
healthcare chatbots or agricultural analytics) that can be adapted for
IndiaAGI’s platform. Kishan, your developer, could pilot these integrations to
test their compatibility with IndiaAGI’s collaborative framework.
3. Leveraging AIKosha
Datasets:
o
Use
AIKosha’s datasets (e.g., Bhashini’s language data or
agriculture datasets) to train or fine-tune IndiaAGI’s models, particularly if
integrating Param-1. This can improve performance in Indic languages and
domain-specific tasks like climate change or governance.
4. Collaborating with
AIKosha Partners:
o
Engage with institutions like IIT Jodhpur,
IIT Roorkee, or IIT Delhi, which are among the eight selected RAI participants.
Partnering with these institutes could provide technical expertise and access
to their RAI tools, strengthening IndiaAGI’s technical capabilities.
5. Marketing Ethical
AI:
o
Highlight IndiaAGI’s adoption of AIKosha’s
RAI solutions in your outreach to developers and stakeholders. This can
position IndiaAGI as a trusted platform for building responsible AI
applications, increasing its appeal in India’s developer ecosystem and aligning
with the IndiaAI Mission’s ethical framework.
Considerations
- Timeline:
The RAI solutions will be available starting September 2025, so IndiaAGI
should plan to engage with the AIKosha team early to secure API access or
early testing opportunities.
- Resource
Needs: Integrating these
solutions may require additional computational resources. IndiaAGI could
explore the IndiaAI Compute Portal, which offers 14,000 GPUs, to support
this integration.
- Deepfake
Detection: Since deepfake detection tools are
still in development, IndiaAGI may need to prioritize other RAI solutions
initially but should monitor progress for future integration, given its
relevance to trustworthy AI.
Conclusion
IndiaAGI stands to benefit
significantly from the IndiaAI Mission’s RAI solutions by enhancing its ethical
AI capabilities, improving multilingual and cultural relevance, and attracting
developers through AIKosha’s ecosystem. By integrating these tools and
datasets, IndiaAGI can strengthen its consensus engine, align with national AI
goals, and position itself as a leader in responsible AI for India. I recommend
reaching out to the IndiaAI Mission team to explore API access and
collaboration opportunities with AIKosha partners like IITs. If you’d like, I
can draft a follow-up letter to the IndiaAI Mission team to request access to
these RAI solutions or provide guidance on integrating them with Param-1.
What’s your next step?
14 June 2025