Uber was Built in Silicon Valley of India

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TAUSIF ALAM & AMIT RAJA NAIK Wednesday, Jan 3, 2024 | Was this email forwarded to you? Sign up here ___________________________________________________________ When the world was completely locked down during the COVID-19 pandemic, there was some major development happening in the Silicon Valley of India. While witnessing a huge surge in global food delivery business (Uber Eats), the Uber tech team in Bangalore discovered themselves managing two equally sizable businesses, Uber Ride and Uber Eats, each with its distinct tech stacks. They observed that the resources were wastefully distributed to manage two tech stacks instead of one.    There were, however, various overlaps between the two tech systems. For instance, functionalities such as reservation and upfront driver assignment effortlessly transcend both domains, requiring only minimal adjustments for optimal performance. Similarly, the implementation of technology to extend job offers to multiple drivers for improved acceptance rates was seamlessly applied to delivery services as well. The tech team realised the problem and decided to merge the two tech systems into one. This development drastically reduced tech investments to reduce the cost of servers, database resources, and data storage resources. However, the merger of technology of two different platforms was not an easy task and required to be achieved in a three-fold path.  Firstly, organisational alignment brought delivery and rides teams onto the same page, addressing challenges in earners, fulfilment, fares, pricing, and matching tech.  Next, the tech strategy involved a complete rewrite of the fulfilment stack, introducing Java and innovative in-house frameworks for seamless business logic integration.  Lastly, a multi-site execution plan was executed, with a central team rewriting 80% of the code and decentralised product teams contributing 20%. Collaboration challenges were overcome through a multi-site approach involving teams across the US, Canada, and Bangalore, ensuring successful completion of significant rewrites. Uber's tech overhaul has transformed its user experience, moving from a single-trip model to one that accommodates multiple trips within a single order. This design evolution allows the tech team to seamlessly integrate features. The shift marks a significant enhancement in the platform's flexibility and functionality, offering users more diverse and tailored transportation options. Read the full story here. Can ‘approximate retrieval’ save OpenAI?   Last month, The New York Times filed a lawsuit against OpenAI claiming that the AI company  used millions of its copyrighted articles without permission to train ChatGPT models. This new lawsuit has opened a Pandora's box of ‘plagiarism’ — the accusation that generative AI platforms are facing. However, this is not the end of the case for OpenAI. The GenAI company can still win the case if it proves that ChatGPT is producing the same result because of ‘approximate retrieval.’  LLMs, operating as n-gram models, deviate from traditional databases, prioritising context-based token generation over precision. Prompts function as cues, introducing unpredictability to the retrieval process. Read the full story here. The Real Use Case of AI is Here DeepTek.ai, a Pune-based medical imaging AI startup, has launched Augmento X-Ray—an FDA-cleared AI tool designed for chest X-rays. The solution utilises deep learning algorithms to automatically detect, categorise, and highlight abnormalities in chest X-rays, aiming to reduce radiologists' workload by up to 50% while improving reporting quality.  With a team of 200 and FDA clearance, DeepTek plans to expand globally, especially in the US market. The company's Genki solution, a public health screening tool, has been successfully employed in India, and collaborations with state governments align with efforts to combat tuberculosis and enhance public health. Read the full story here. Riding the Gen AI Race In 2023, India saw a surge in generative AI adoption among key consumer companies, signalling a shift toward personalised experiences. Zomato introduced an intelligent foodie companion, Paytm embraced AI-driven automation, and Swiggy experimented with custom food images.  Myntra has collaborated with Microsoft for MyFashionGPT, while Flipkart used Flippi for personalised recommendations. Ola incorporated AI for dynamic ride pricing, and Hotstar focused on content recommendations. Byju’s advanced education delivery with the BYJU’S WIZ suite, and MakeMyTrip partnered with Microsoft for voice-assisted bookings and personalised travel recommendations. These initiatives highlighted a transformative trend in leveraging generative AI for enhanced consumer engagement. Read the full story here.   DOWNLOAD OUR MOBILE APP Stay Connected info@analyticsindiamag.com © 2023 Analytics India Magazine

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