There are 3 common challenges that organizations face while transforming AI aspirations into scalable and intelligent solutions. Get an insider’s view of use case scenarios that illustrate real business and functional value through a proven framework and process toward sustainable digital transformation.

About the speaker

Vishal Kapoor, Vice President, Data and AI, Kyndryl

Vishal Kapoor is the Kyndryl Applications, Data & AI practice leader. He leads the team of applications, data, and AI experts ensuring they are developing market-leading solutions and supporting customer teams with the technology and solutions customers need most. Vishal was previously with DXC Technology where he was the senior business leader for their Data & Analytics AI, Machine Learning, and IoT business. Leading a significant pivot in the company’s strategy, he leveraged best-in-class partnerships with cloud-based next-gen platforms to implement scalable industrialized solutions for DXC customers. Vishal is a senior executive with experience in multiple geographies including Asia Pacific, India, and the Americas. He has deep experience in consulting across IT and Business Process Services with exposure to Product Management, Design Thinking, Marketing, Operations, and P&L leadership in diverse industries. Vishal has deep experience with companies such as Tech Mahindra, NTT Data (Previously Dell Services), and Wipro in driving large complex transformational initiatives for leading Fortune 500 organizations.

Related work from others:  Latest from MIT Tech Review - You need to talk to your kid about AI. Here are 6 things you should say.

Similar Posts

Latest from MIT Tech Review – Generative AI taught a robot dog to scramble around a new environment

Teaching robots to navigate new environments is tough. You can train them on physical, real-world data taken from recordings made by humans, but that’s scarce, and expensive to collect. Digital simulations are a rapid, scalable way to teach them to do new things, but the robots often fail when they’re pulled out of virtual worlds…

Latest from Google AI – The Data Cards Playbook: A Toolkit for Transparency in Dataset Documentation

Posted by Mahima Pushkarna, Senior Interaction Designer, and Andrew Zaldivar, Senior Developer Relations Engineer, Google Research As machine learning (ML) research moves toward large-scale models capable of numerous downstream tasks, a shared understanding of a dataset’s origin, development, intent, and evolution becomes increasingly important for the responsible and informed development of ML models. However, knowledge…

Latest from Google AI – Pre-training generalist agents using offline reinforcement learning

Posted by Aviral Kumar, Student Researcher, and Sergey Levine, Research Scientist, Google Research Reinforcement learning (RL) algorithms can learn skills to solve decision-making tasks like playing games, enabling robots to pick up objects, or even optimizing microchip designs. However, running RL algorithms in the real world requires expensive active data collection. Pre-training on diverse datasets…

Latest from Google AI – Pic2Word: Mapping pictures to words for zero-shot composed image retrieval

Posted by Kuniaki Saito, Student Researcher, Google Research, Cloud AI Team, and Kihyuk Sohn, Research Scientist, Google Research Image retrieval plays a crucial role in search engines. Typically, their users rely on either image or text as a query to retrieve a desired target image. However, text-based retrieval has its limitations, as describing the target…

Latest from Google AI – Re-weighted gradient descent via distributionally robust optimization

Ramnath Kumar, Pre-Doctoral Researcher, and Arun Sai Suggala, Research Scientist, Google Research Deep neural networks (DNNs) have become essential for solving a wide range of tasks, from standard supervised learning (image classification using ViT) to meta-learning. The most commonly-used paradigm for learning DNNs is empirical risk minimization (ERM), which aims to identify a network that…

Latest from MIT : Helping companies deploy AI models more responsibly

Companies today are incorporating artificial intelligence into every corner of their business. The trend is expected to continue until machine-learning models are incorporated into most of the products and services we interact with every day. As those models become a bigger part of our lives, ensuring their integrity becomes more important. That’s the mission of…