BEGIN:VCALENDAR VERSION:2.0 PRODID:-//HasGeek//NONSGML Funnel//EN DESCRIPTION:A kickoff meetup - for Rootconf members and participants X-WR-CALDESC:A kickoff meetup - for Rootconf members and participants NAME:The Open Source AI Community - Jumpstarting at Rootconf X-WR-CALNAME:The Open Source AI Community - Jumpstarting at Rootconf REFRESH-INTERVAL;VALUE=DURATION:PT12H SUMMARY:The Open Source AI Community - Jumpstarting at Rootconf TIMEZONE-ID:Asia/Kolkata X-PUBLISHED-TTL:PT12H X-WR-TIMEZONE:Asia/Kolkata BEGIN:VEVENT SUMMARY:The Open Source AI Community - Jumpstarting at Rootconf DTSTART:20241122T103000Z DTEND:20241122T113000Z DTSTAMP:20260417T180606Z UID:session/U34nduasNBc7bssXx5tjYe@hasgeek.com SEQUENCE:22 CREATED:20241120T162925Z DESCRIPTION:\n## Open Source AI - questions about openness and monopolies\ nAt the open-source AI community kickoff on 22 November\, two concerns wer e raised and discussed:\n\n1. What is the incentive to develop open source AI models?\n2. Is there too much monopoly by Meta\, or corporations like Meta\, on open source AI?\n\n📅 **Friday\, 22nd Nov\, from 4:00 PM to 5: 30PM** 📅 \n📍**@ [Rootconf](https://hasgeek.com/rootconf/2024)\, BIC\ , Indiranagar\, Bengaluru** 📍\n\n## 1. Challenges with AI - an Open Sou rce perspective\n- **Limited availability of fully open-source AI models:* * The lack of fully open-source AI models is a major hurdle for independen t developers. For a model to be considered truly open-source\, it must mee t several criteria\, including open data (access to the original sources)\ , open code\, and open weights/parameters. However\, very few AI models me et these standards\, hindering developers' ability to replicate\, modify\, and build upon them.\n\n- **Data issues in Indic LLMs due to common use o f multiple languages:** Speaker Akash raised the issue of the gap between the ideal and reality in supporting regional languages for AI applications in India. Many datasets are "too pure\," focusing on standardized forms o f languages like Hindi\, Tamil\, and Bengali\, while real-world data is of ten messy and noisy as people tend to use multiple languages in most inter actions. Akash has observed that “*reality is always mixed. It is never pure.*” Product development needs this kind of mixed dataset\, and not n ecessarily pure Hindi/Indic language datasets. The speakers suggested a mo re systematic approach to collecting real-world data\, potentially through volunteer or paid contributions\, to better support multi-language langua ge models in a variety of contexts and applications.\n\n- **Need for caapc ity in the community to enable more community-driven participation in crea ting truly open source AI projects:** Venkata Pingali\, speaking at the di scussion\, pointed that capacity has to built for community development an d adoption of open source AI projects. Using Sarvam’s example\, Pingali pointed out that for most open source AI projects\, neither is the need fo r compute very high\, nor do such projects need very sophisticated hardwar e to build applications on top of LLMs and AI models. Quoting Pingali\,\n\ n> *So that the hardware is not the limitation today\, the cost is not the limitation. The willingness\, the ability of the community to come togeth er and put together applications\, is the limitation\, and that requires v ision and a concerted effort.*\n\nThis point was also made by Chaitanya Ch okkareddy when explaining Swecha's work in building an open source Telugu language model. (*Watch the talk by Kiranchandray on Swecha's efforts in c ollecting data for building Telugu LLM - https://hasgeek.com/fifthelephant /2024/sub/ai-by-the-people-for-the-people-BScezALTnRdopfbczjfbD3 and the s ubsequent discussion led by Chaitanya Chokkareddy on the need for a new li censing framework for open source LLMs - https://hasgeek.com/fifthelephant /2024/sub/need-for-new-licenses-in-this-age-of-generative-ai-MJkJFvbCjd4dz sB9KhnBfQ*)\n\n- **What is the incentive to develop open source AI models? ** One benefit is clearly that open source means more eyes watching over g litches and challenges. As Unnati - speaker at the discussion - mentioned\ ,\n\n> *... mistakes have a higher chance of being caught when done with o pen source”. Closed source means fewer people watching and maintaining. * \n \nPingali's response was also useful - that unless there is more inv estment and initiative for upskilling\, there is no incentive to build for open. \n\n> *Then you have situations where companies like OpenAI will\, in future\, charge a tax on every transaction\, for every usage.* \n\nPing ali urged that companies such as Flipkart and PhonePe should invest in the community for upskilling the community because AI will become all pervasi ve in domains such as fintech. Building capacity is very important. In the absence of capacity\, an AI tax is most likely the possible scenario\, he opined. \n\n- **Is there currently too much of a monopoly by Meta and oth er corporations on open source AI?** \nMonopoly over data sources is a big ger concern to many engineers than Meta’s monopoly over LLMs themselves. Unnati pointed that even with data protection laws\, companies like Meta may be getting away with things that are not permissible by such laws.\n\n - **Need for a leader board to rate LLMs** - Akash suggested that we need to understand the differences between LLMs from different companies better \, so engineers and organizations can make better choices at large. These LLMs should be evaluated for their accuracy\, use cases\, etc. Currently\, we don’t have any such mechanisms for audits and rating\, he pointed.\n \n## Conclusion\nThis meet-up highlighted several critical challenges and opportunities for the development of AI in India\, particularly in terms o f accessibility\, inclusivity\, and the alignment of incentives and fundin g for open source AI projects. Key issues include the scarcity of fully op en-source models and the need for more diverse\, representative\, and trul y open data. Additionally\, the importance of community-driven efforts\, o pen hardware\, and building technical expertise were emphasized as essenti al to scaling AI solutions effectively. \n \n## Moderator and discussants\ nAnwesha Sen (Assistant Programme Manager at The Takshashila Institution) moderateed the kick-off discussion. Speakers included Akash Paul (Open Sou rce AI enthusiast\; former senior ML Engineer at Airtel)\, Bharat Shetty ( AI Consultant)\, Unnati (AI/ML engineer) and Dr. Venkata Pingali (Scribble Data). \n\n### Demos were presented by\n1. Bharat Shetty on ideas/project s he is building on accessibility.\n2. Dr. Venkata Pingali on Indic approa ch to agents.\n\n## About the Open Source AI Community\n**We're community of practitioners -- from startups and enterprises to builders and tinkerer s -- who are using Open Source AI in day-to-day practice and building.** \ n\nOur tribe includes:\n- **Individual builders** - developers who are pur suing hobby projects and tech/product ideas - who are experimenting with o pen source AI to create products\, and who are navigating the ecosystem fr om the point of view of regulations\, uncertainty and not knowing what the y have control over\, and how much.\n- **Individuals** - such as Bharat Sh etty and Gopi Kumar Sasi - who have ideas on how to use Open Source and AI technologies to solve for accessibility.\n- **Ecosystem builders** who ha ve ideas/projects that are running\, and they are looking for contributors and volunteers for open source AI projects.\n\n## 💬 Chat with the OSAI group 💬\nThe group is currently active at https://chat.whatsapp.com/BG f813RGrGM3t2c9yiZ8Z6 LAST-MODIFIED:20251122T105345Z LOCATION:Bangalore - https://hasgeek.com/open_source_ai/kick-off-at-rootco nf/ ORGANIZER;CN="Open Source AI Community":MAILTO:no-reply@hasgeek.com URL:https://hasgeek.com/open_source_ai/kick-off-at-rootconf/ BEGIN:VALARM ACTION:display DESCRIPTION:The Open Source AI Community - Jumpstarting at Rootconf in 5 m inutes TRIGGER:-PT5M END:VALARM END:VEVENT END:VCALENDAR