From "Traditional" AI to Generative AI

AI in the pandemic: Vital tool and evolution

**Artificial Intelligence** became a key tool in the early stages of the pandemic to understand and combat the virus. We used AI to analyze infection patterns, predict outbreaks, and offer real-time solutions. The app we developed to combat COVID-19, which was based on real-time data analysis to identify trends and anomalies in the virus spread, was a clear example.

**"Traditional" AI**, although excellent at identifying patterns and making predictions based on historical data, was not always able to adapt to the speed and unpredictability of the pandemic. This highlighted the need for a more dynamic and creative approach to technology: Generative Artificial Intelligence.

To understand how they relate, it is interesting to see the following figure:

Generative AI is a subfield of Deep Learning that uses deep neural networks to generate new data based on learned patterns from the training data. While AI focuses on processing and analyzing data, Generative AI focuses on creating, better adapting to constantly changing situations.

Today, Generative AIs are redefining multiple sectors. In this article we are going to talk about how we are doing it in call centers, universities, and hospitals that are already benefiting from the advantages of IAG:

Call centers

It is transforming the way call centers provide support to operators in customer service.

• In public administration, facilitating access to procedure information through generative AI-based chatbots that process all corporate databases.
• In the private sector, offering generative AI systems supervised by a human.
• Using the power of RAG (Retrieval Augmented Generation) to work with dynamic information without the need to retrain the model each time, also reducing the level of AI hallucination.

Universities

It is changing the way universities interact with their students, for example, providing support to students in selecting their studies.

• Virtual assistants based on generative AI and customized with all the internal corporate information of the educational offerings of the center.
• Unsupervised support systems for students that evaluate and control the impact and likelihood of generating incorrect responses or hallucinations.
• Managing privacy issues inherent to AI by applying advanced safeguard mechanisms.

Hospitals

In the healthcare sector, this technology is modernizing the way of supporting patients, offering contextualized medical information and facilitating appointment management, which represents a great advance in communication and efficiency in the healthcare sector.

• Customized access to medical information and hospital knowledge databases.
• Integrated into the patient portal. Without the need for human supervision, taking into account the particularities of being generated by generative AI responses.
• Evaluating the effect of different techniques on reducing the number of hallucinations or incorrect responses (fine tuning vs RAG, few shot learning, zero shot prompt…).

Despite its benefits, GAN presents challenges, advantages, and disadvantages as discussed in the article "The Ultimate Guide to Generative Artificial Intelligence" that you can check out here.

Conclusions

The IAG is redefining technological interaction in various sectors, marking a leap towards a future where AI not only responds, but anticipates and actively contributes to a better world.