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Sıla Ermut

Sıla Ermut

Industry Analyst
70 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

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.

AIJan 26

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.

AIJan 23

Top 20 Sustainability AI Applications & Examples

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.

AIJan 23

Recommendation Systems: Applications and Examples

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.

AIJan 23

Top 9 AI Infrastructure Companies & Applications

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.

AIJan 23

Top 9 AI Providers Compared

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.

AIJan 23

17 Generative AI Healthcare Use Cases

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.