Sıla Ermut
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
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.
Meta Learning: 7 Techniques & Use Cases
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.
Top 18 ITSM Case Studies
Leveraging IT Service Management (ITSM) tools is essential for businesses aiming to increase the efficiency of their IT operations and enhance service delivery.
Top 10 AI in ITSM Use Cases & Examples
Leveraging AI for IT service management (ITSM) tools supports organizations in terms of: See the top 10 use cases of AI in ITSM, examples, and benefits of leveraging AI in ITSM.
Top 10 Delivery Management Software: Tookan & Routific
Many businesses struggle with inefficient routes, limited visibility, and manual coordination, leading to delays, higher costs, and poor customer satisfaction. Delivery management tools help address these issues by automating route planning, enabling real-time tracking, and optimizing dispatch operations.
When Will AGI/Singularity Happen? 8,590 Predictions Analyzed
We analyzed 8,590 scientists’, leading entrepreneurs’, and the community’s predictions for quick answers on Artificial General Intelligence (AGI) / singularity timeline: Explore key predictions on AGI from experts like Sam Altman and Demis Hassabis, insights from major AI surveys on AGI timelines, and arguments for and against the feasibility of AGI: Artificial General Intelligence timeline
Generative AI Ethics: Concerns and How to Manage Them
Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, filed a lawsuit against Perplexity, alleging that the company illegally and knowingly copied Britannica’s human-verified content and misused its trademarks without permission. Explore what generative AI ethics concerns are and best practices for managing them. 1.
20 Strategies for AI Improvement & Examples
AI models require continuous improvement as data, user behavior, and real-world conditions evolve. Even well-performing models can drift over time when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions.
Top 11 AI in Fashion Use Cases & Examples
Faced with creative bottlenecks, inefficient supply chains, and rising consumer expectations, fashion brands are seeking smarter solutions. McKinsey estimates that generative AI could boost operating profits in the fashion, apparel, and luxury sectors by up to $275 billion by 2028.
LLM Observability Tools: Weights & Biases, Langsmith
LLM-based applications are becoming more capable and increasingly complex, making their behavior harder to interpret. Each model output results from prompts, tool interactions, retrieval steps, and probabilistic reasoning that cannot be directly inspected. LLM observability addresses this challenge by providing continuous visibility into how models operate in real-world conditions.
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