Unlock the Power of GenAI and Address The Barriers

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.

Let’s discover how Generative AI can revolutionize industries and how to overcome the barriers standing in its way.

1. What is Generative AI?

Generative AI is a type of Artificial Intelligence that creates new content based on what it has learned from existing content.

The process of learning from existing content is called training and results in the creation of a statistical model.

When given a prompt, GenAI uses this statistical model to predict what an expected response might be-and this generates new content.

Popular GenAI interfaces:

• ChatGPT,
• Dall-E
• Gemini (formerly Bard).

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)

2.2. Generative image models

Generative image models produce new images using techniques like diffusion. Then, given a prompt or related imagery, they transform random noise into images or generate images from prompts.

Input: Image

Output:

• Text (image captioning, visual question answering, image search)
• Image (super resolution, image completion)
• Video (animation)

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.

Some of the most prominent models in generative language include LaMDA, PaLM, and GPT. These generative language models are designed to predict text patterns, creating natural and contextually relevant responses.

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.

4. How to train a model?

Training a GenAI model requires sophisticated techniques in machine learning, tailored to handle complex data patterns and generate new content. Supervised learning is one primary approach, where models learn from labeled data to make accurate predictions. This classical method is often paired with unsupervised learning, which allows models to uncover patterns in unstructured data without labeled guidance.

In the context of GenAI, discriminative models differentiate between classes of data, while generative models like those in GenAI create new content based on patterns they learn. A predictive ML model typically focuses on relationships between specific data inputs and labels, while a GenAI model learns broader patterns in unstructured content to generate realistic outputs.

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.

These varied approaches collectively empower GenAI to learn complex structures within data, enabling it to generate human-like text, images, and other forms of content.

5. 10 key pain points when applying GenAI

5.1. Ambiguity of Language

• Statement: GenAI models struggle with nuanced language understanding, leading to misinterpretation of context, intent, and meaning. This can result in inaccurate or irrelevant responses.
• Pain point: AI can misunderstand tone or intent, which could lead to frustrating interactions.
• Solution: To achieve effective results you need high quality prompts to reinforce the statements you want to make. We are no longer in a conversational interaction.

“The human-to-machine interface is critical in making AI accessible and useful for a wide range of applications. LLMs have significantly advanced these interfaces, but the inherent subjectivity and ambiguity of language still present unique challenges, from contextual understanding to feedback mechanisms and much more. Life is not engineered—it just happens.” (Forbes)

5.2. Lack of Understanding of the World

• Statement: GenAI models are trained on limited datasets and may not have a comprehensive understanding of the world beyond their training data. This can lead to inaccuracies or hallucinations in generating content that is relevant to real-world scenarios.
• Pain point: AI-generated content might not be grounded in reality, which could impact its usefulness or credibility.
• Solution: LLM training data reveals more about how humans see the world than the world itself. So Data needs to be carefully curated from various data sources.

“You can know the name of that bird in all the languages of the world, but when you’re finished, you’ll know absolutely nothing about the bird. You’ll only know about humans in different places, and what they call the bird. So let’s look at the bird and see what it’s doing-that’s what counts.”

– Richard Feynman

5.3. Cost

• Statement: Developing and deploying GenAI models can be expensive, requiring significant investments of time, money, and resources.
• Pain point: The cost of developing and maintaining AI is prohibitively high, making it challenging to integrate into our business operations.
• Solution: Develop more ad hoc solutions: SLM vs LLM for instance.

Source:

• Fortune
• The Information

5.4. Performance Variance

• Statement: GenAI models can exhibit performance variability depending on the specific use case, data quality, and training settings. This can make it difficult to predict and ensure consistent results.
• Pain point: GenAI is struggling with inconsistent performance and outputs across different scenarios, which is affecting the ability to rely on it for critical tasks.
• Solution: Again good prompts can help as well as having specific models and not all purposes models.

“Variability: GenAI can give different outputs for the same input. This is because it generates responses based on probabilities and patterns learned from vast amounts of data. Contextual Understanding: The responses depend on the context and vary so as to provide a more human-like interaction.” (LinkedIn)

5.5. Explainability

• Statement: GenAI models lack transparency in their decision-making processes, making it challenging to understand how they arrive at certain conclusions or recommendations.
• Pain point: It is often mandatory to be able to explain AI decisions to stakeholders, but the black box nature of these models makes that difficult or impossible.
• Solution: Explainable AI is a field in development and is already giving interesting results. Meanwhile human expertise is still required to validate outputs.

“With financial services, regulations and compliance are always key concerns. Those concerns might be leading to a more measured pace of adoption of gen AI. Maufe said that many gen AI deployments in financial services are for internal use cases where organizations are using a human in the loop as a control point. He does however see a near-term future where gen AI is even more widespread and prominent in financial services.” (VentureBeat)

5.6. Security

• Statement: GenAI models can be vulnerable to security threats such as prompt injection attacks, where an attacker manipulates input data to deceive the model and elicit a specific response.
• Pain point: Gen AI can be used for malicious purposes, such as generating fake content or manipulating public opinion or access to sensitive data.
• Solution: Filter input and output. Some AI agents can be used for extra validation … at the cost of the performances.

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: Constellation

5.8. Ethical Considerations

• Statement: GenAI models can perpetuate biases and stereotypes present in the training data, potentially leading to discriminatory outcomes or perpetuating social inequalities.
• Pain point: AI might amplify existing biases, which could have serious consequences for customer based services (Marketing, Recommendation, etc).
• Solution: Data curation. Additional filters.

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: A&O Shearman

5.10. Regulatory Compliance

• Statement: GenAI models may not comply with existing regulations or laws, particularly in industries like healthcare, finance, or education, where data privacy and security are critical.
• Pain point: It is challenging to ensure our AI solutions meet regulatory requirements, which could result in fines, penalties, or reputational damage.
• Solution: Adapt the business strategy and deployment to each geographic zone.

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.