LQM

Since the initial release of ChatGPT-3.5 in November 2022, Large Language Models (LLMs) have received huge attention as the generative AI model.  Subsequent LLMs such as OpenAI GPT-4o, Meta Llama 3.1, or Anthropic Claude 3 Sonnet continue to generate high interest.

There’s another trend that the next AI wave will include Large Quantitative Models (LQMs), which are designed to handle complex data sets and capture the intricacy of quantitative relationship that LLMs can’t.  You would use LLMs which focus on language tasks to summarize documents, but to discover the next cancer drug you would need LQMs.  LQMs utilize advanced computational techniques and data analysis to make predictions, identify trends, and optimize outputs.  The impact of LQMs will be enormous because these AI models will accelerate new discovery in pharmaceutical drugs, chemical compounds, financial models, weather patterns etc.

Advantages of LQMs:

LQMs have many advantages over LLMs and conventional predictive AI models:

1)  Precision:  LQMs excel in tasks that require numerical precision and the ability to analyze vast quantitative data and to model complex mathematical relationships.

2)  Interpretability:  LQMs address the “black box” nature of AI models by offering enhanced interpretability.

3)  Flexibility:  LQMs can be fine-tuned for specific quantitative tasks, providing powerful tools for data-driven decision-making.

4)  Robustness:  LQMs can understand hidden patterns in data and create new synthetic data to add to the limited historical data, making forecasts and predictions more reliable and accurate.

Overall, LQMs promise to usher in the next wave of deep knowledge exploration and discovery.  LQMs will be critical in fields such as finance, healthcare, energy, supply chain management, climate modeling, manufacturing, and retail.  Some examples of LQMs being used in the industry include Flatiron Health, GE Predix, or Paige.  While LQMs also have challenges, their potential benefits in improving decision-making and optimizing outcomes are substantial.