Unleash the remarkable potential and intrinsic mechanisms of Generative AI in the second webinar “Unlocking Potential: Addressing Barriers to Generative AI” in the Bow and Arrow Tech Talks series hosted by Kyanon Digital, Vietnam Datathon and the Expert Partner from Ton Duc Thang University.
This insightful session will explore:
- The groundbreaking potential of Generative AI (GenAI)
- Its complex algorithms
- The key challenges in its adoption.
Our expert speakers, Dr. Pham Van Huy, Dean of IT Faculty at Ton Duc Thang University, and Mr. David Lapetina, VP of Engineering & Technology at Kyanon Digital, will provide deep insights into the technology, training methodologies, and real-world applications.
2. How does Generative AI work?
2.1. Generative language models
Generative language models learn about patterns in language through training data. Then, given some text, they predict what comes next.
Input: Text
Output:
- Text (translation, summarization, question answering, grammar correction)
- Image (image generation, video generation)
- Audio (text to speech)
- Decisions (play games)
3. What are the algorithms behind Gen AI?
Gen AI operates on a foundation of sophisticated algorithms and machine learning (ML) techniques, enabling computers to learn and make predictions without explicit programming. At the core, Gen AI uses classification and object detection algorithms, alongside a robust framework of machine learning models.
The evolution of neural networks—a technology wave that gained momentum around 2012—has been critical in advancing Gen AI capabilities. Neural networks mimic the human brain’s neural connections, creating complex layers of data processing. Within the field of AI, machine learning is a key subfield, while deep learning—which drives much of Gen AI’s potential—focuses on more intricate data patterns.
Other important structures supporting Gen AI include Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) for image-related tasks, and extensive deep learning techniques. Together, these algorithms enable Gen AI to handle diverse outputs, from text and image generation to complex decision-making tasks, marking a significant leap in AI capabilities.
Key GenAI training tools include Generative Adversarial Networks (GANs), which consist of two competing components: a generator and a discriminator. The GAN model enhances the quality of generated content by refining outputs through this adversarial process. Other essential frameworks in GenAI include Autoencoders for compressing and reconstructing data, Transformers used extensively in NLP (natural language processing) tasks, and Diffusion models applied in computer vision to produce high-quality images.
Source: Register
5.7. Scalability
- Statement: As data volumes grow, GenAI models may not scale effectively, leading to decreased performance and increased latency.
- Pain point: Performance degradation might occur as data grows, which is impacting the ability to make timely decisions.
- Solution: Data curation. Model monitoring to anticipate issues. Scaling infrastructure, with a cost.
Source: CIO
5.9. Maintenance and Updates
- Statement: As new data emerges, GenAI models require regular updates and maintenance to ensure they remain effective and relevant.
- Pain point: It can be challenging to keep AI models up-to-date with the latest data, which is impacting their performance and accuracy.
- Solution: Data Governance is critical in building suitable processes that encompass the full Data strategy of Enterprises, including AI.
Source: EuroNews
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In conclusion, Kyanon Digital‘s Bow & Arrow Tech Talks series is uncovering the transformative power of Generative AI, with the second webinar, “Unlocking Potential: Addressing Barriers to Generative AI.” This session dives into GenAI’s potential, its complex algorithms, and the main challenges in adoption, offering valuable insights for those navigating today’s AI advancements.
Stay tuned to the Bow & Arrow Tech Talks series for more trends and deep dives into cutting-edge technologies shaping our world.
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