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Dive in to ChatGPT and Large Language Models | Briefing

Event Overview

The incredible popularity of generative AI and ChatGPT is forcing technology leaders to take notice and understand its role in enterprise IT. What is it? How is it made? What are its use cases? And what are its limitations?   Join WWT data scientists Ankur Gupta, Achal Sharma and Aditya Prabhakaron as they go under the hood of ChatGPT and talk about the technology behind the scenes of Chat GPT and how large language models are poised to make an impact on how we do business.

What to expect

WWT Briefings are easily consumable overviews of key technologies and issues facing enterprise IT leaders today. Briefings are available for free exclusively for wwt.com registered users. Registration also grants users free access to our WWT Research reports, 24/7 access to our virtual lab and training environments and vast library of technical and business-oriented content. Use our platform to:
  • Get hands on, on demand experience
  • Capture real-world insights and research
  • Leverage practical and actionable guidance
  • Compare, contrast and validate multi-vendor solutions
  • Think creatively about strategy
  • Tap into our industry-leading expertise and partnerships

Goals and Objectives

Gain an understanding of large learning models, dissect ChatGPT to understand what’s behind the technology and learn about what is driving demand. 

Who should attend?

IT and business leaders; anyone interested in learning more about ChatGPT 

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How Business Leaders Should Think About ChatGPT | WWT Experts

The remarkable debut and adoption of ChatGPT has rapidly pushed generative AI front and center amongst business leaders. While disruptive use cases are being thrown out right and left, the utilization of this technology and the implications it will have on the business are still very much up for discussion.   Join WWT Managing Director of AI and Machine Learning Tim Brooks as talks about the ways generative AI and large language models can impact your business right now and how you should be thinking about it over the next three to five years. Tim also talks about the practicality of training your own AI model and potential pitfalls of going all in blindly.  
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