A Deeper Look into Generative AI and LLMs

By Sarah Yifei Wang

Reflections on Dr. Iro Tasitsiomi's Insightful Talk

Artificial Intelligence (AI) has evolved rapidly in recent years, revolutionizing how businesses operate and impacting every aspect of our lives. One exciting domain within this field is Generative AI, including Large Language Models (LLMs).

Dr. Iro Tasitsiomi, Head of Investments Data Science at T. Rowe Price, gave an engaging talk on this subject at the Data Science Salon NYC event earlier this month, drawing attention to some critical risk and cost considerations associated with these technologies.

The Magic Behind Generative AI and LLMs

Chat GPT, according to Tasitsiomi, is a conversational AI built on top of a generative pre-trained transformer. The generative aspect of GPT refers to the creation of novel content, while the pre-trained part denotes its pre-existing understanding of language. The transformer, an architecture mechanism, enables the model to comprehend relationships between words and concepts within sentences. Lastly, the "Chat" in Chat GPT signifies its training for conversational AI.

Tasitsiomi also delved into the subject of Generative AI, a term that began to gain recognition around 2020. She related this to the concepts of generative and discriminative models in data science. Generative models try to learn the joint probability and understand data's distribution in space. In contrast, discriminative models focus on conditional probability, determining the probability of something belonging to a class given its features.

Generative AI has also been associated with Generative Adversarial Networks (GANs), where a generator produces a range of possibilities and a discriminator provides feedback to improve the generated output. 

In her talk, Tasitsiomi championed the undeniable potential of Generative AI and LLMs, exciting technologies that have the power to generate human-like text, making them invaluable assets in various fields. These models are capable of everything from drafting emails and writing articles to creating engaging narratives for video games and even generating film scripts.



In the financial sector, where Tasitsiomi has her roots, these models can analyze vast amounts of textual data, generate insightful reports, and respond to customer queries in real time. The positive implications of these technologies extend to healthcare, law, entertainment, and virtually any industry requiring human-like text generation.

The Challenges and Risks

Tasitsiomi also underscored the need for caution. With such formidable capabilities come equally significant risks. The generated content might perpetuate the biases present in the training data, inadvertently leading to skewed, discriminatory, or harmful outcomes. LLMs can also generate plausible-sounding misinformation, posing a risk to fact-based discourse, a concern that is increasingly significant in our 'post-truth' era. Additionally, privacy issues may arise if models inadvertently generate sensitive information inferred from their training data.

The Tools and Their Purpose

Taking a pragmatic approach, Tasitsiomi emphasized that Generative AI and LLMs are tools, each with specific intended uses and limitations. They can yield substantial benefits when used correctly and responsibly. Conversely, misuse or misunderstanding of these tools can lead to damaging outcomes. This sentiment resonates in our increasingly digitized world, where understanding a technology's limitations is as crucial as understanding its capabilities.

Creating a Culture of Responsible AI

Looking ahead, Tasitsiomi called for strategic and deliberate actions to mitigate the associated risks. She advocated for thorough audits of training data sets to identify and eliminate inherent biases and continuous monitoring of model outputs to ensure they meet ethical and quality standards. By maintaining a robust feedback loop, these models can be fine-tuned over time, improving the reliability and fairness of their responses.

Tasitsiomi's vision for a culture of responsible AI was a powerful call to action. Transparency, fairness, privacy, and accountability should not be afterthoughts but integral to the development and deployment processes. The responsibility is shared among all stakeholders, including developers, users, and regulators, to ensure these powerful tools are used ethically and effectively.

Future Paths for Generative AI/LLMs

As we consider Tasitsiomi's insights, the future for Generative AI and LLMs appears both exciting and challenging. With due diligence in understanding their risks and costs, businesses can strategically harness the power of these technologies. While these tools can revolutionize industries, their transformative potential must be tempered with an unwavering commitment to ethical use, transparency, and ongoing evaluation.

The Economic Aspect

From her unique position in the investment industry, Tasitsiomi provided insights into the economic implications of these technologies. As with any technological advancement, the use of Generative AI and LLMs comes with financial costs. These can range from the significant investment in computing power and data storage required for model training and operation to the potentially substantial costs of mitigating risks and addressing any negative outcomes. 

However, these costs need to be balanced against the potential economic benefits. LLMs can provide cost savings by automating tasks that previously required human intervention and generate new opportunities for innovation and growth. As Tasitsiomi emphasized, a clear-eyed view of the economic trade-offs is necessary for making informed decisions about adopting these technologies.


Tasitsiomi's enlightening talk brought a nuanced perspective to the exciting field of Generative AI and LLMs. While highlighting their transformative potential, she encouraged the audience to approach these technologies with a keen awareness of their risks, costs, and ethical implications. 

It's clear that the road to harnessing the power of Generative AI and LLMs comes with challenges. Yet, with responsible use, thorough understanding, and proactive management of these tools, the journey is likely to be worthwhile. Inspired by these insights, we are better equipped to navigate the evolving landscape of AI and its transformative impact on our world.

In the spirit of Tasitsiomi's call to action, let us move forward with both caution and optimism, leveraging the potential of Generative AI and LLMs responsibly, ethically, and effectively.

Article written by Sarah Yifei Wang, CFA, Senior Machine Learning Engineer at Meta and Data Science Salon contributor.

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