Vlad Kolesnikov, an AI/ML expert from Google, provides a clear explanation of the distinction between AI and machine learning and explores exciting developments shaping the future of AI. These include multimodal interaction, which allows AI systems to process various forms of input like text, images, and audio, and the rise of AI agents—intelligent systems designed to perform specific tasks. Vlad also emphasizes the critical role of well-annotated data models in ensuring the effectiveness of these advanced AI systems and offers guidance for developers seeking to enter the AI/ML field.

Bryan DeBois, Director of Industrial AI at Rovisys, is sharing the real-world applications of AI in manufacturing. Learn more about the limitations of generative AI like ChatGPT on the plant floor, the potential of autonomous AI for decision-making, and the importance of data readiness for successful AI implementation. Plus, Bryan discusses the future of work and how AI can actually enhance human capabilities.

Watch now to hear Bryan share:

Generative AI like ChatGPT is powerful but not yet suitable for the complex, real-time decision-making required in manufacturing.

Autonomous AI, similar to self-driving cars, is the key to unlocking AI's potential on the plant floor.

Manufacturers face significant challenges with data silos and lack of integration, hindering AI adoption.

AI is not about replacing humans but empowering them to focus on higher-value tasks.

The future of AI lies in enhancing human capabilities and improving our quality of life.

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Vivek Agrawal, Strategic Product Manager at Scale AI, is discussing all things enterprise AI adoption. Vivek shares his insider information on the AI ecosystem, the challenges companies face in implementing AI solutions, and the importance of identifying the right use cases. He also gets into the rise of multimodal AI and its potential to transform various industries.

Watch now to hear Vivek share:

-The AI value chain consists of four key layers: infrastructure, foundational models, tools/platforms, and applications.

-Thought leadership and understanding the capabilities and limitations of AI are critical for successful enterprise adoption.

-Data readiness and the ability to identify the right use cases are major bottlenecks for many companies.

-Multimodal AI, which goes beyond text-based models, holds immense potential for various industries.

-The future of AI is bright, with increasing enterprise adoption and focus on revenue optimization and innovation.

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