Generative AI vs. Conventional AI: Introduction For Operational Risk Professionals

Summary

In this informative video, Manos Kulal, co-founder and chief risk officer at Risk Spotlight, introduces generative AI and contrasts it with conventional narrow AI technologies. He explains how generative AI, capable of producing various outputs like text, videos, and images, can enhance risk management in the financial services industry. Kulal highlights the accessibility and affordability of generative AI models, such as OpenAI's chat GPT, which has revolutionized the field by offering advanced AI to businesses of all sizes. The video also emphasizes the importance of prompt engineering to effectively harness AI's expertise and concludes with an offer of a specialized training course on applying generative AI in operational risk management.

Takeaways

• 🧠 Generative AI vs. Narrow AI: The video introduces generative AI as a technology that can produce various formats of output, contrasting it with narrow AI, which is designed for specific use cases.
• 🛡️ Narrow AI Applications: Narrow AI is widely used in financial services for tasks such as credit card fraud detection, cyber risk management, and money laundering detection.
• 🚀 Generative AI Emergence: Generative AI gained prominence with the launch of OpenAI's chat GPT in November 2022, which quickly reached 100 million users.
• 💡 Generative AI Capabilities: This technology can generate outputs in text, videos, images, and audio, offering a wide range of applications in operational risk management and beyond.
• 💼 Business Benefits: Generative AI can enhance productivity and quality of risk management activities in the financial services industry.
• 📈 Adoption and Accessibility: OpenAI's mission to democratize AI has made advanced generative AI models available for free or at a low cost, allowing even small and medium-sized firms to adopt these technologies.
• 📚 Expertise Access: Generative AI models, trained on large internet data, can provide expertise on a wide range of topics, from fitness and nutrition to business strategy and risk management.
• 🔍 Use Cases: The video highlights over 100 operational risk use cases where generative AI models can significantly improve productivity and risk management quality.
• 🛠️ Prompt Engineering: Operational risk professionals are encouraged to learn prompt engineering to effectively utilize AI models and maximize benefits.
• 🎓 Training Opportunities: Risk Spotlight offers a 15-hour training course focused on prompt engineering for operational risk management, available in online or classroom formats.
• 📧 Contact and Resources: Interested parties can reach out to Risk Spotlight for more information on prompt engineering courses and AI services via email or their website.

Q & A

What is the main focus of the video by Manos Kulal?

The video focuses on introducing generative AI, contrasting it with conventional AI technologies, and discussing how generative AI can enhance risk management activities in the financial services industry.

What is the term 'narrow AI' referring to in the context of the video?

Narrow AI refers to AI technologies built for specific use cases and is what is commonly referred to as AI in business discussions, as these technologies have been widely implemented across many financial services firms for around 10 to 15 years.

Can you provide examples of narrow AI implementation in operational risk management?

Examples include credit card fraud detection, detecting malicious transactions on IT networks to manage cyber risks, and detecting money laundering transactions.

What are some first-line use cases of narrow AI technologies mentioned in the video?

First-line use cases include marketing teams predicting customer churn, AI-based algorithmic trading, and credit scoring models based on unstructured data such as social media content.

Why are generative AI technologies labeled as 'generative'?

Generative AI technologies are labeled as such because they can generate outputs in various formats such as text, videos, images, and audio.

What significant event in November 2022 brought the term 'generative AI' into widespread recognition?

The term 'generative AI' became widely known when OpenAI launched ChatGBT, which signed up 100 million users in the first two months, making it the fastest adopted technology of all time.

How does generative AI differ from narrow AI in terms of accessibility and cost?

Generative AI models, such as those provided by OpenAI, are available for free or at a low cost of $20 to $30 per user per month, making them accessible to even small and medium-sized firms, unlike narrow AI technologies which are resource-intensive.

What is the key benefit of generative AI technologies mentioned in the video?

A key benefit of generative AI technologies is that they can provide access to expertise on a wide range of topics due to their training on large amounts of data available on the internet.

Can you provide examples of how generative AI can be utilized in operational risk management?

Examples include identifying risks related to a business process, identifying controls to mitigate operational risks, and building detailed cyber risk scenarios.

What is 'prompt engineering' and why is it important for operational risk professionals?

Prompt engineering is the skill of writing effective prompts to maximize the benefits gained from AI models. It is important for operational risk professionals to learn this skill to utilize AI models for real operational risk management cases effectively.

What training does Risk Spotlight offer to help professionals learn prompt engineering?

Risk Spotlight offers a 15-hour training course focused on generative AI and operational risk management, which includes learning how to apply generative AI in the context of operational risk management and writing effective prompts.