As the capabilities of Large Language Models (LLMs) evolve, their applications in fields beyond natural language processing are expanding rapidly. One of the emerging areas where LLMs show promise is time series forecasting, where they can enhance predictions in various industries, including finance and energy management. I add my perspective onto previous research and explore how LLMs can be leveraged for time forecasting, outlining both the strengths and limitations of this cutting-edge technology.
Kamran Darugar
ML/AI Engineer
I discuss my time in Seoul studying Applied Artificial Intelligence at Sungkyunkwan University, one of the top universities in South Korea. I go into some of my favorite memories, the challenges I faced, and some of the lessons I learned.
Kamran Darugar
ML/AI Engineer
Large Language Models (LLMs) are often seen as a game-changing solution for optimizing workflows, but their limitations are frequently overlooked. While LLMs can appear toassist with tasks like contract management and review, without a clear understanding of how they operate, companies risk relying on inaccurate outputs that still require extensive manual review.
Kamran Darugar
ML/AI Engineer
I co-authored in a research project within my time at Sungkyunkwan University. The research investigates the integration of emotional intelligence into artificial intelligence (AI) systems, with a focus on AI's ability to interpret and respond to human affective responses. By utilizing advanced neuroimaging techniques, the research explores how AI-generated content compares to human-created marketing stimuli, shedding light on the challenges AI faces in emulating authentic emotional intelligence and the implications for future AI development in digital marketing.
Kamran Darugar
ML/AI Engineer
