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
Large Language Models: Complete Guide in 2026
Large language models (LLMs) are now at the core of enterprise search, customer support, software development, and decision-support workflows, often replacing or augmenting traditional analytics and rule-based systems. Built on transformer architectures and trained on massive text datasets, LLMs can interpret, generate, and summarize language at a scale that was previously impractical.
Compare Large Vision Models: GPT-4o vs YOLOv8n [2026]
Large vision models (LVMs) can automate and improve visual tasks such as defect detection, medical diagnosis, and environmental monitoring. We benchmarked three object detection models: YOLOv8n, DETR, and GPT-4o Vision, across 1,000 images each, measuring metrics such as mAP@0.5, inference speed, FLOPs, and parameter count.
AI Video Pricing: Compare Synthesia & Invideo AI in 2026
AI video pricing can differ significantly across platforms, influenced by factors such as output quality, customization options, and features. As more businesses and creators turn to AI for efficient video production, understanding these pricing models becomes essential.
Top 20 Sustainability AI Applications & Examples in 2026
According to PwC, GenAI could improve operational efficiency, which might indirectly reduce carbon footprints in business processes. By applying generative AI to areas such as logistics optimization, demand forecasting, and waste reduction, companies can reduce emissions across their operations beyond the AI systems themselves.
Recommendation Systems: Applications and Examples ['26]
Recommendation systems benefit both businesses and customers by using data to personalize experiences. They help boost sales, increase customer loyalty, and reduce churn by simplifying choices and keeping users engaged. We benchmarked three Python recommendation libraries: LightFM, Cornac BPR, and TensorFlow Recommenders, using the same implicit-feedback dataset and identical preprocessing steps.
Top 9 AI Infrastructure Companies & Applications in 2026
Many organizations invest heavily in AI, yet most projects fail to scale. Only 10-20% of AI proofs of concept progress to full deployment. A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models.
Top 9 AI Providers Compared in 2026
The AI infrastructure ecosystem is growing rapidly, with providers offering diverse approaches to building, hosting, and accelerating models. While they all aim to power AI applications, each focuses on a different layer of the stack.
17 Generative AI Healthcare Use Cases in 2026
Healthcare systems are facing increased data volumes, staff shortages, and rising expectations for personalized care. Generative AI is emerging as a key solution by synthesizing unstructured medical data, such as clinical notes, imaging reports, and patient histories, into insights for clinicians and administrators.
LLM Parameters: GPT-5 High, Medium, Low and Minimal
New LLMs, such as OpenAI’s GPT-5 family, come in different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and with various parameter settings, including high, medium, low, and minimal. Below, we explore the differences between these model versions by gathering their benchmark performance and the costs to run the benchmarks. Price vs.
AI Scientist: Automating the Future of Scientific Discovery
AI scientists mark a major advance toward fully automatic scientific discovery, aiming to perform the entire research process independently. Unlike traditional tools, these automated labs can expedite research processes by generating hypotheses, designing and executing experiments, interpreting results, and communicating findings.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.