João Bocas - The #Wearables Expert Interviews Prashant Natarajan, VP at H2O.ai | Best Selling Author | Reviewer and Speaker at Stanford University
The Topic is: Business Transformation, #GenerativeAI and Data Analytics in Health & Life Sciences
We have covered:
- How can we demystify AI in #Healthcare
- How can we implement effective digital transformation in business with data analytics
- How can we empower patients and health professionals with Generative AI
How can we make Healthcare Uncomplicated?
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Generative AI refers to a category of artificial intelligence (AI) techniques that are used to generate new content or data based on patterns and examples from existing data. These techniques involve training a model on a large dataset and then using that model to create new, original content.
Generative AI models can be used in various domains, such as image generation, text generation, music composition, and even video synthesis. They are capable of producing content that resembles human-generated content and can often exhibit creativity and originality.
Generative AI involves using machine learning techniques to create new content based on patterns and examples from existing data. These models are trained on large datasets and learn the underlying structure and characteristics of the data, enabling them to generate new and original content.
Generative AI has been applied in various domains to create realistic and creative outputs. For example, in image generation, generative adversarial networks (GANs) can generate new images that resemble real photographs. In text generation, recurrent neural networks (RNNs) or transformer models can generate coherent and contextually relevant text. Similarly, in music composition, generative models can create new melodies or even entire compositions based on existing music samples.
Video synthesis is another area where generative AI has made advancements. Models can be trained on large video datasets and then used to generate new video sequences by predicting and generating frames that follow the learned patterns. This can have applications in video editing, special effects, and even generating entirely new video content.
Generative AI techniques have shown great potential in enabling machines to exhibit creativity and produce content that can be difficult to distinguish from human-generated content. They have opened up new possibilities in various creative fields and have the potential to assist and inspire human creators in their work.
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