AI beyond the buzzword: Do it well or do it twice!
Who is this presentation for?
- C-level decision makers and account managers
Over the last four years, the amount of enterprises that have deployed AI has gone up from 10% to 37%. As confidence in the technology continues to grow, decision makers are accelerating adoption to stay ahead of the competition, innovate, and grow businesses.
But how fast is too fast when it comes to adopting AI? What are the priorities? Is there a point where the rush for deployment turns into a treat for development? And what are the ethical implications of this nascent tech?
As machine learning teaches us, these questions can be answered by looking at the past.
Walter Riviera goes beyond the buzzwords to dive deep into the practical, real-world implementations of AI, from analyzing recruitment in education to predicting delays and traffic on complex railway networks to figuring out why self-driving cars can’t see kangaroos. Join in to learn the five steps that characterize AI project engagement along with best practices for deploying an AI leading solution.
- A basic understanding of AI
- Familiarity with the difference between deep learning and (classic) machine learning
What you'll learn
- Understand how data drives decisions
- Learn why AI is not a shopping list of turnkey solutions but a way for uniqueness
- Explore a pipeline/approach to avoid the buzzword effect
Walter Riviera is an AI technical solution specialist (TSS) covering EMEA at Intel, playing an active role on most of the AI project engagements within the data centers business in Europe. He’s responsible for increasing business awareness regarding the Intel AI offer, enabling and providing technical support to end user customers, independent software vendors (ISVs), original equipment manufacturers (OEMs), partners in implementing high-performance computing (HPC) and/or cloud solutions for AI based on Intel’s products and technologies. Previously, Walter collected research experience working on adopting ML techniques to enhance image-retrieval algorithms for robotic applications, conducting sensitive data analysis in a startup environment, and developing software for text-to-speech applications.
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