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Sıla Ermut

Sıla Ermut

Industry Analyst
73 Articles
Stay up-to-date on B2B Tech

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

AIJan 29

AI Agent Productivity: Maximize Business Gains

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.

AIJan 28

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.

AIJan 28

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.

Enterprise SoftwareJan 28

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.

AIJan 28

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.

AIJan 28

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.

DataJan 28

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.

AIJan 27

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.

AIJan 27

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.

AIJan 27

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.