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
Top 125 Generative AI Applications
Based on our analysis of 30+ case studies and 10 benchmarks, where we tested and compared over 40 products, we identified 125 generative AI use cases across the following categories: For other applications of AI for requests where there is a single correct answer (e.g., prediction or classification), check out AI applications.
Text-to-Video Generator Benchmark
A text-to-video generator is an AI system that turns written prompts into short videos by generating visuals, motion, and sometimes audio directly from natural language.
Compare Remote Control Software: NinjaOne & Acronis
We tested the top 3 remote control software (also known as remote access software) to evaluate the general UI and remote control experience, their remote control quality, protocols, and unique capabilities: Strengths and weaknesses based on our observations An agent needs to be installed for each tool we tested in this benchmark.
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.
AI Hallucination Detection Tools: W&B Weave & Comet
We benchmarked three hallucination detection tools: Weights & Biases (W&B) Weave HallucinationFree Scorer, Arize Phoenix HallucinationEvaluator, and Comet Opik Hallucination Metric, across 100 test cases. Each tool was evaluated on accuracy, precision, recall, and latency to provide a fair comparison of their real-world performance.
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.
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.
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