Webinar
Challenging Common Misconceptions in Artificial Intelligence
Clarifying AI myths through real-life use cases
In recent months, we’ve shared insights about AI assistants and how they can simplify everyday tasks. However, there’s more than Generative AI. l it's time to take a step back and explore the bigger picture. We’re excited to invite you to our upcoming webinar, where we’ll dive into some common misconceptions in AI that we encounter in our day-to-day work. The session will provide a balanced view of what’s fact and what’s misunderstood.
Join our experts as they tackle prevalent myths and provide real-world examples to showcase AI’s current capabilities and limitations, including:
Data Requirements in AI: It’s commonly believed that AI needs massive amounts of labeled data. We’ll explain how AI can thrive with smaller datasets or even minimal annotation, using techniques like transfer learning and self-supervised learning to achieve impressive results.
AI Implementation Challenges: Many believe AI requires a complete technology overhaul. We’ll highlight how organizations can start small, gradually integrating AI into existing systems without overwhelming costs or disruptions.
AI and human oversight: There’s a misconception that AI can run autonomously without human intervention. We’ll demonstrate how ongoing human oversight is essential to ensure ethical, accurate, and reliable outcomes.
Throughout the webinar, we’ll share use cases that bring nuance to these misconceptions and demonstrate how AI can drive real value when applied for the right purpose in your organization.
Agenda
Misconceptions addressed:
Data requirements in AI
AI implementation challenges
More data = better AI
AI does not require human oversight once it is deployed
More complex AI models are always better
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