Sıla Ermut
Sıla is an industry analyst at AIMultiple focused on email marketing and sales videos.
Research interests
Sıla's research areas include email marketing, eCommerce marketing campaigns and marketing automation.
She is also part of AIMultiple's email deliverability benchmark. She is designing and running email deliverability benchmarks while collaborating with the AIMultiple technology team.
Professional experience
Sıla previously worked as a recruiter and worked in project management and consulting firms.
Education
She holds:
- Bachelor of Arts degree in International Relations from Bilkent University.
- Master of Science degree in Social Psychology from Başkent University.
Her Master's thesis was focused on ethical and psychological concerns about AI. Her thesis examined the relationship between AI exposure, attitudes towards AI, and existential anxieties across different levels of AI usage.
Latest Articles from Sıla
LLM Parameters: GPT-5 High, Medium, Low and Minimal
New LLMs, such as OpenAI’s GPT-5 family, come with different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and various parameters, including high, medium, low, and minimal. Below, we explore the differences between these versions of the models by gathering their benchmark performances and the costs to run these benchmarks. Price vs.
Audience Simulation: Can LLMs Predict Human Behavior?
In marketing, evaluating how accurately LLMs predict human behavior is crucial for assessing their effectiveness in anticipating audience needs and recognizing the risks of misalignment, ineffective communication, or unintended influence.
Large Quantitative Models: Applications & Challenges
Modern systems are becoming too complex for traditional statistical analysis, as institutions now handle massive datasets, including patient data, weather data, and financial market data. Large quantitative models (LQMs) help by processing these datasets, integrating structured and unstructured data, and applying predictive modeling to uncover patterns and provide data-driven insights that traditional methods cannot deliver.
Large World Models: Use Cases & Real-Life Examples
Artificial intelligence has advanced significantly with the development of large language models; however, these systems continue to struggle to comprehend and interact with the physical world. Text alone cannot capture spatial relationships, dynamic environments, or the causal impact of actions, thereby limiting progress in fields such as robotics, healthcare, and autonomous systems.
Hyper-Personalization in Marketing: Use Cases & Examples
Gen Z is reshaping retail with unique consumer behaviors. With their projected $12 trillion spending power by 2030, they will heavily influence what brands sell.They see marketing as valuable only when it is authentic, values-driven, and empathetic, rejecting anything that feels fake, jargon-heavy, or disconnected from real people and causes.
Multimodal AI in Healthcare: Use Cases with Examples
Healthcare systems face challenges in delivering accurate diagnoses, timely interventions, and personalized treatments, often because critical patient information is scattered across different data sources. Multimodal AI offers a solution by combining medical images, clinical notes, lab results, and other data into a unified framework that mirrors how clinicians think and reason.
Time Series Foundation Models: Use Cases & Benefits
Time series foundation models (TSFMs) build on advances in foundation models from natural language processing and vision. Using transformer-based architectures and large-scale training data, they achieve zero-shot performance and adapt across sectors such as finance, retail, energy, and healthcare.
AI Ad Generator: Compare Icon, AdGen & AdCreative
Creating high-converting digital ads remains a challenge for businesses aiming to reach diverse audiences across platforms like Google, Facebook, and LinkedIn. AI ad generators offer a solution by automating ad creation, enabling faster production, broader customization, and data-driven content optimization.
Artificial Superintelligence: Opinions, Benefits & Challenges
The prospect of artificial superintelligence (ASI), a form of intelligence that would exceed human capabilities across all domains, presents both opportunities and significant challenges. Unlike current narrow AI systems, ASI could independently enhance its capabilities, potentially outpacing human oversight and control. This development raises concerns regarding governance, safety, and the distribution of power in society.
Inverse Reinforcement Learning: Use Cases & Examples
Inverse reinforcement learning is an approach in machine learning where machines infer the goals or reward structures that guide an expert’s behavior by observing their actions rather than receiving explicit instructions. Discover what inverse reinforcement learning is, how it works, and the top industry use cases with examples.
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