Fang Liu
Bellevue, Washington, United States
33 followers
29 connections
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Shield AI
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In an exclusive conversation with Debanagana Ghosh, moneycontrol.com, Oracle's #AI engineer Saurabh Netravalkar highlights the growing impact of #dataanalytics in sports, aiding teams in #performance optimization and #strategicplanning and how well he is balancing his love for #cricket and #technology Oracle. #AI #Dataanalytics #worklifebalance #CloudWorld https://lnkd.in/ggRMiYvg
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Prasanth Tarikoppad
In an exclusive conversation with Debanagana Ghosh, moneycontrol.com, Oracle's #AI engineer Saurabh Netravalkar highlights the growing impact of #dataanalytics in sports, aiding teams in #performance optimization and #strategicplanning and how well he is balancing his love for #cricket and #technology Oracle. #AI #Dataanalytics #worklifebalance #CloudWorld https://lnkd.in/gqj7Q-8z
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Shivani Matta
Work tidbits - Living on the edge! Over a year back, I received my patent in the Knowledge Annotation space after 5 years of wait - it triggered me to join this new team. The space was/still is AI Accelerators, within Cloud Compute. My team specifically was the ‘last mile’, productionize and land the AI accelerators for the top cloud customers. The work would have been fine in the last 2+ decades of my career - but these times were different. Everyone and everything was in the AI/ML race. Before we(or others) could plan a new stack - something better was out there - sometimes open sourced! Before anyone could make the perf/$ cheaper - There was an update on new numbers available. There were negotiations on software lifecycles, in favor of velocity. Customers were eager more than any other time. They were feeling the same stress - best LLM, best Stable diffusion, best MLPerf, best ML stack, best chips, best networking, best price! We did very well overall and that part will continue - despite living on the edge. Recently things shifted and I have gotten time to breathe(joined a sister team), also write my thoughts/tidbits - 1.) A friend, a mentor once said - Go far, not fast! Life is not a race and you have a long way to go! I do. 2.) Build resilience. You will see a lot of ups and downs. You don’t always have time to feel sorry for the down, or cheer for the ups! Keep your emotions in the middle spectrum. 3.) Find the true allies, and keep them close. They will tell you when you are wrong, or not - but most importantly they will stick with you, for long. I just ran into my peer from 9 years ago, 2 companies back in the hallway today! 4.) Recognize that some folks are indeed for the fast, maybe also far.. but mostly fast. Let them be! Take 2 steps back. Build resilience. 5.) Invest in your team - My reports, people are/were the team's biggest strength, and the primary reason to keep us going. We certainly don’t end here. The jobs will always be demanding, and that is by design. But the 'how' is on you.. My Amazon manager (now a VP@Microsoft) wrote me a handwritten note that said - Shivani, slow down and take the time to smell the flowers - I have been practicing something like this lately.. I am taking my learnings and my tribe along - and certainly aiming to go far. Please take care of yourself, no matter what stage! ##resilience #livingontheedge #farnotfast #machinelearning #google #cloud
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Amritpal Singh
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Vatsal Singh
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Alex Acero
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Tapomoy Adhikari
At Corgnit Research Lab, India, we are excited to share our latest findings on the convergence of Reinforcement Learning and Federated Learning and their implications for advancing AGI. Key Insights: - 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: The distributed nature of FL enables scalable and efficient training of RL models, significantly reducing computational overhead and accelerating convergence. - 𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐏𝐫𝐞𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: By ensuring that sensitive data remains local, FL facilitates the development of AGI in privacy-sensitve domains, such as healthcare, without compromising data security. - 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Multiple RL agents can share knowledge through FL, enhancing overall learning efficiency and robustness without the need to share raw data. - 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐝𝐚𝐩𝐭𝐚𝐭𝐢𝐨𝐧: The integration of FL allows AGI models to be tailored to individual users or environments, combining global intelligence with local customization. - 𝐑𝐨𝐛𝐮𝐬𝐭𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞: Aggregating diverse experiences through FL results in more generalized and resilient AGI models, mitigating the risk of overfitting. Benefits for Researchers: - 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Leveraging distributed computational resources lowers costs and enhances the feasibility of large-scale AGI experiments. - 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Model updates can be shared instead of raw data, promoting cross-institutional and interdisciplinary research. - 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞𝐝 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Faster training times and rapid iteration lead to quicker advancements in AGI. - 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐀𝐈 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Prioritizing user privacy and data security aligns with ethical standards and regulations, fostering responsible AI research. - 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Immediate feedback and improvements from real-world data enable practical deployment in sectors like smart cities, healthcare, and IoT. Our research at Corgnit underscores the transformative potential of merging RL and FL in the pursuit of AGI. We believe these insights will catalyze further innovations and collaborations in the AI community. #ArtificialGeneralIntelligence #ReinforcementLearning #FederatedLearning #AIResearch #CorgnitResearchLab #PrivacyPreservingAI #ScalableAI #EthicalAI Corgnit, Corgnit Research India, Reinforcement Learning, Federated Learning Consortium
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