Kishore Upadhyay’s Post

🌟 Day 40 of Winter Arc 🌟 Global AI Powerhouses: Top Regions Shaping Generative AI on GitHub in 2024 Today, let's dive into data from GitHub repositories revealing the global landscape of Generative AI contributions. Key Insights: 🇺🇸 United States' Leadership: The U.S. leads with nearly 80,000 contributors, far ahead of other regions. This reflects its mature AI ecosystem backed by tech giants, top research labs, and startups. The U.S. focus on innovation and talent pool cements its top position in AI. 🇮🇳 India's Rapid Growth: India ranks 2nd, driven by a surge in AI contributions. Its expanding tech workforce, AI education initiatives, and vibrant startup culture highlight its rise as an emerging AI leader, solving global challenges. 🇭🇰🇨🇳 Hong Kong SAR & China's Influence: Hong Kong SAR ranks 3rd, followed by China, driven by investments and government support. Both regions advance AI across sectors like finance and healthcare. 🇪🇺 European Hub: Europe shines with Germany 🇩🇪, UK 🇬🇧, and France 🇫🇷. Germany leads with its engineering strength, followed by the UK and France, known for strong AI research and academic collaborations. 🇯🇵 Japan's Tech Focus: Japan focuses on automation, robotics, and industrial AI applications, particularly in manufacturing and healthcare. 🇸🇬 Singapore's Strategic Vision: Singapore punches above its weight due to tech innovation and smart city projects, aiming to be a global tech hub. 🇨🇦 Canada's Ongoing Impact: Canada is a key AI player, backed by strong research institutions and policies. Canadian researchers enhance AI frameworks, boosting its global reputation. Global Trends & Future Outlook 🚀 This data underscores the globalization of AI development. GitHub's open-source collaboration accelerates Generative AI innovations, driving advancements in NLP, computer vision, and creative AI. What This Means for AI Innovation 🧠🌍 Diverse global contributions highlight a collaborative AI ecosystem where knowledge-sharing drives breakthroughs. Investment in AI education and talent across regions fosters growth in LLMs and autonomous systems. In conclusion, this data reveals how regions are positioning in the AI landscape, driving competition and collaboration. GitHub reflects a vibrant community pushing the frontiers of AI. 🌟🤖 #WinterArc #GenerativeAI #OpenSource #AIInnovation #TechTrends #GitHub #DataScience #ArtificialIntelligence #GlobalTech #AICommunity #FutureOfWork

India to become AI use case capital of the world: Nandan Nilekani https://lnkd.in/dY9-NUZW Download Economic Times App to stay updated with Business News - https://lnkd.in/eK4XZsX #ArtificialIntelligence #ArtificialIntelligenceforBusiness

https://lnkd.in/g4Kb9zNc "Meta on Friday announced a strategic collaboration with 'IndiaAI' at the Ministry of Electronics and Information Technology (MeitY) to drive advancement of open source AI innovation, R&D and skill development in India. This includes the establishment of the Center for Generative AI, Shrijan at IIT Jodhpur and the launch of the 'AI for Skilling and Capacity Building' initiative, in partnership with All India Council for Technical Education (AICTE)." Hope this initiative also follows the path of UDI platform in revolutionising governance delivery. Would be key in enabling India to leapfrog or better Western levels of governance!!!🤔

2024 will be seen as a turning point, as India’s engineers develop innovative AI applications across various industries—without requiring deep learning PhDs, says Shekhar Kirani, Partner at Accel. In this interview, Shekhar emphasises that the future is bright for Indian startups that embrace the fusion of AI and services. Key points: ✅ India’s wealth of trainable data positions engineers to build industry-specific AI applications. ✅ India will lead in developing middleware and application layers for LLMs, personalising AI for enterprises. ✅ Similar to the global telecom boom that fuelled the growth of India's services companies, we are now witnessing AI's ability to elevate Indian SaaS companies to the next level. Read more: https://lnkd.in/gcSjuUMi

Should we avoid building LLMs? Why Google's India Research Head disagrees with Nandan Nilekani... Nandan Nilekani, co-founder of Infosys, thinks we should take a different approach in the AI Space. Speaking at Meta’s Build with AI summit in Bengaluru, he emphasised that instead of racing to build another LLM, India should focus on creating practical, last-mile solutions powered by existing models. “Our goal should not be to build one more LLM. Let the big boys in the Valley do it,” he said. Let's decode the thought process behind this. Large Language Models (LLMs) like GPT-4 and Llama are at the heart of today’s AI breakthroughs. These models are trained on vast datasets to perform a variety of tasks, from generating text to understanding complex queries. However, there are two sides to a coin. Even here. Building an LLM isn’t cheap. It demands billions of dollars, massive infrastructure, power supply and a big amount of QUALITY DATA. It's a capital intensive piece of work. The gestation period is longer & the preparedness of Indian Startups investing in such projects, especially without seeking Government's support, is not there yet. So why not spend our resources and talent building the last mile solutions / applications based on these LLMs and become the Use Case Capital of AI, globally. This is actually logical. However, there are counter opinions as well. Google Research India Head politely disagrees with Nandan Nilekani on this. He says that he's not preaching what he practices, with respect to his innovation with Aadhaar (UIDAI). The Conclusion While Mr. Nilekani has a valid & logical perspective, that India should focus more on the application of AI than training LLMs, we should strive to strike a balance between both. As India develops its infrastructure and research investment landscape, training LLMs will naturally take off as a niche and cos will invest into it. But today? Let's collect the low hanging fruits. Let's make our way to become a Global Capital for AI Use Case. -------------------- What are your thoughts on this as a Tech Entrepreneur? #AI #artificialintelligence #techcontent #largelanguagemodels #aibusiness #aiinindia AI at Meta #funnelprolabs Source: https://lnkd.in/dG2JEZYT

The world of GenAI/ LLMs/ LMMs are governed by 𝐓𝐨𝐤𝐞𝐧𝐬. Tokens are the building blocks of Language as a LLM perceives it- they can be whole words, parts of words, or even punctuation. The LLM economy transacts and charges per Token-In/Out. An LLM can be imagined, (in a reductionist way) as a Lego set. Where each Lego set comes in multiple different sizes of lego blocks, which when put together in a meaningful way creates appreciable outputs. Only but, the number of unique Lego blocks (tokens) can run up to few 100,000s, which together make meaningful generations. It is to be noted that the final collection of tokens that characterizes an LLM are unique to it and are optimized. Check out - (https://lnkd.in/dqgn5HUN) to play around with different tokenizers for each different GenAI models. The token sets per GenAI model is determined using 1) the algorithm that merges characters such as BytePairEncoding (used by GPT) or Sentencpiece (used by Google & Llama) and 2) Most critically/ importantly the 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚𝐬𝐞𝐭𝐬. Most of the GenAIs models and their optimized tokens are derived from English Datasets (Mistral (french) and Jais (Arabic) are exceptions). Hence, when you try Tamil or Telugu on GPT-4 you will see that the GenAI consumes more tokens and making the usage extremely expensive in the token economy. GenAI models have set up themselves as indispensable tools towards societal growth. And it makes most sense for each cohesive societal unit that can adopt a Language Model to leverage it. It is in this is proactive thought and direction that the India AI mission is ushered in with a holistic view of establishing an Indian LLM stack - Compute Capacity, Indigenous Datasets & Models and Applications. Excited for the Brave New World Ahead ! |<EOS>| https://lnkd.in/ddYvUruf https://lnkd.in/dAwrhxvj

🚀 We're happy to share that Unbabel is one of four EU startups who have been awarded access to two of the European High Performance Computing Joint Undertaking’s (EuroHPC JU) supercomputers! 🔬 Unbabel and three other EU startups will share 8 million GPU hours and 12 months of access to these incredible resources. At the end of this period, we’ll release our developed models under an open-source license for non-commercial use or publish our research. We’re thrilled to be recognized by the EU Commission and can’t wait to continue pushing the boundaries of AI! Read more in this TechCrunch article 👉 https://hubs.li/Q02Dx-KH0 #AI #Innovation #Unbabel #TechCrunch #EUCommission

This is a monumental moment for India's AI ecosystem, and QX Lab AI is eager to contribute to this journey, driving forward the vision of ‘Making AI Work for India’. Together, we can shape a future where AI not only fuels economic growth but also drives social inclusion and impact. #MakeInIndia #BharatKaAI #GenerativeAI #AskQXLabAI #IndianAI

Recently, I attended a MetaAI conference where Nandan Nilekani delivered an inspiring talk. He shared a striking example that deeply resonated with me: "Under the PM Kisan Yojana, we receive 25 lakh calls a month from farmers seeking advice, raising concerns, or asking for support. Yet, most of these calls go unanswered—not due to lack of intent, but because we simply don’t have the resources to address them all. With advancements in AI, we can now imagine answering every single one of those calls—offering timely, accurate, and scalable support to our farmers. And this is just one example. AI has the potential to solve countless large-scale problems in India." It got me thinking: What will it take for India to truly harness the power of AI to solve more than a billion problems? Here is what I believe are the key components for AI success in India: 1. Thriving Developer Community: Open-source contributions 2. India Specific Open-Source Datasets- particularly Voice 3. Specialized, Lightweight Models: The smallest AI models possible 4. Robust Infrastructure: Lot of compute will be required especially for inferencing the models 5. Energy Efficiency: Ample and Sustainable energy to power AI systems 6. Integration, Distribution, and Adoption: To bring AI solutions to all In the coming weeks, I’ll be diving deeper into each of these areas through a series of posts. My aim is to understand the efforts already underway and spark conversations around what more needs to be done. Your input is invaluable! If you know of initiatives, projects, or resources addressing these areas, please share them in the comments.

A landmark moment for India's tech landscape! The Cabinet has approved the IndiaAI Mission with an outlay of ₹10,372 crore. This revolutionary initiative aims to bolster India's AI capabilities, emphasizing 'Making AI in India and Making AI Work for India'. The mission outlines key pillars to advance AI development in critical sectors, improve data accessibility, and ensure ethical use. It promises to catalyze AI startups, broaden computing infrastructure access, and propel India toward global AI leadership. #IndiaAI #AIforIndia #TechRevolution 🇮🇳🤖🚀 INDIAai