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
eCommerce AI Image Editing: GPT Images & Nano Banana
AI image editing tools analyze and automatically adjust product photos, allowing eCommerce businesses to enhance quality, remove backgrounds, or modify details with minimal effort. We tested the top 7 AI image editing tools on 20 images and 20 prompts across five dimensions, including prompt adaptability, realism, shadows, color rendering, and image quality.
Supervised Fine-Tuning vs Reinforcement Learning
Can large language models internalize decision rules that are never stated explicitly? To examine this, we designed an experiment in which a 14B parameter model was trained on a hidden “VIP override” rule within a credit decisioning task, without any prompt-level description of the rule itself.
Text-to-Image Generators: Nano Banana Pro & GPT Image 1.5
We compared the top 6 text-to-image models across 15 prompts to evaluate visual generation capabilities in terms of temporal consistency, physical realism, text and symbol recognition, human activity understanding, and complex multi-object scene coherence: Text-to-image generators benchmark results Review our benchmark methodology to understand how these results are calculated and see output examples.
AI Energy Consumption Statistics
A recent forecast predicts AI will use over half of data center electricity by 2028.As compute-intensive workloads such as generative AI expand, total electricity demand is also expected to rise. Explore the key statistics on AI energy consumption and best practices derived from leading AI researchers and agencies.
57 Datasets for ML & AI Models
Data is required to leverage or build generative AI or conversational AI solutions. You can use existing datasets available on the market or hire a data collection service. We identified 57 datasets to train and evaluate machine learning and AI models.
LLM Scaling Laws: Analysis from AI Researchers
Large language models predict the next token based on patterns learned from text data. The term LLM scaling laws refers to empirical regularities that link model performance to the amount of compute, training data, and model parameters used during training.
AI in Sales: 15 Use Cases & Examples
Artificial intelligence can enhance sales processes from lead generation to sales forecasting, helping businesses overcome low conversion rates and long sales cycles.
AI Ethics Dilemmas with Real Life Examples
Though artificial intelligence is changing how businesses work, there are concerns about how it may influence our lives. This is not just an academic or societal problem, but a reputational risk for companies; no company wants to be undermined by data or AI ethics scandals that damage its reputation.
Compare Large Vision Models: GPT-4o vs YOLOv8n
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
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.
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