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
AI Hallucination Detection Tools: W&B Weave & Comet ['26]
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.
LLM Scaling Laws: Analysis from AI Researchers in 2026
Large language models are usually trained as neural language models that predict the next token in natural language. The term LLM scaling laws refers to empirical regularities that link model performance to the amount of compute, training data, and model parameters used when training models.
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.
AI Agent Productivity: Maximize Business Gains in 2026
AI agent productivity is emerging as a measurable driver of business output. Studies report up to 30% productivity gains, indicating that agents can handle procedural steps, retrieve information, and interact with enterprise systems with consistent accuracy.
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.
Supervised Fine-Tuning vs Reinforcement Learning in 2026
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.
AI Ethics Dilemmas with Real Life Examples in 2026
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 marred by data or AI ethics scandals that damage its reputation.
Text-to-Speech Software: Hume, ElevenLabs & Resemble
As AI capabilities evolve, text-to-speech (TTS) software is becoming more adept at producing natural, human-like speech. We evaluated and compared the performance of five different TTS and sentiment analysis tools (Resemble, ElevenLabs, Hume, Azure, and Cartasia) across seven core emotion categories to determine which could most accurately, consistently, and comprehensively recognize emotional tones.
No-Code AI: Benefits, Industries & Key Differences in 2026
No-code AI tools allow users to build, train, or deploy AI applications without writing code. These platforms typically rely on drag-and-drop interfaces, natural language prompts, guided setup wizards, or visual workflow builders. This approach lowers the barrier to entry and makes AI development accessible to users without a programming background.
Meta Learning: 7 Techniques & Use Cases in 2026
Training and fine-tuning a typical machine learning (ML) model can take weeks and cost thousands of dollars. Meta learning helps cut this down by leveraging prior learning experiences to accelerate training, reduce costs, and improve generalization. Explore the key meta-learning techniques and use cases in fields such as healthcare and online learning.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.