Day 1
August 19,2021
The recent advances in artificial intelligence are mainly attributed to deep learning models, which are intrinsically data-hungry and compute-hungry. Moreover, such deep learning models also suffer from black-box-ness, vulnerable understanding and reasoning ability, and intrinsic bias. All these factors limit its domain application. Explicit knowledge represented in knowledge graphs has a lot to offer. It could potentially drive the next surge in AI by addressing all of the above concerns. Let us brainstorm “Role of Knowledge Graphs in AI” with the education domain case study researched and developed at Embibe.
The ever-evolving economic scenarios, regulatory framework, changing consumer behaviour, data availability are additional factors besides the analytical consideration for a credit score. Therefore, a credit score using the robust analytical technique while addressing these factors is key for its adoption.
Learn how the world’s second-largest home improvement retailer helps customers love where they live. This talk will be focused on a couple of case studies where Lowe’s has been able to improve customer engagement and, thereby, revenue through the use of ML and personalisation in the context of offline retail. This will also delve into challenges faced and the key learnings.
Unravel Data is an AI-enabled product to simplify the observability of modern data platforms and is used today by the largest brand names in Fortune 500. This talk covers the hard lessons I learned over the years in developing dozens of Data+AI products from idea to production — things that can go wrong across ML problem definition, dataset selection, data preparation, model design, training, and operationalization in production. I wrap up the talk with key patterns you can follow to build your AI/ML products successfully.
Routing diagnostics show traffic is the biggest cause of ETA errors. To improve Routing, ETAs, and Navigation, we need to improve our Traffic prediction accuracy. In this session, we will talk about i) how does traffic work? ii) How is traffic used in Routing? iii) why does traffic matter?
Explainable AI (XAI) tends to refer to the movement, initiatives, and efforts made in response to AI transparency and trust concerns, more than to a formal technical concept. Interpretable ML is one of the most talked-about topics in the AI domain, to convert the black-box algorithm into easy to comprehend solutions to improve adoption for real-life business problems. Interpretable ML is the degree to which a human can understand the cause of a decision. We plan to talk about some of the cutting edge algorithms in the world of Explainability. Interpretability/ during the conference, explaining the importance of this topic along with real business applications and examples.
A glimpse of what it takes to enable unparalleled convenience to Swiggy’s customers and how AI powers decisions at each stage of the customer journey.
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