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
Time Series Foundation Models: Use Cases & Benefits
Time series foundation models (TSFMs) build on advances in foundation models from natural language processing and vision. Using transformer-based architectures and large-scale training data, they achieve zero-shot performance and adapt across sectors such as finance, retail, energy, and healthcare.
E-Commerce AI Video Maker Benchmark: Veo 3 vs Sora 2
Product visualization plays a crucial role in e-commerce success, yet creating high-quality product videos remains a significant challenge. Recent advancements in AI video generation technology offer promising solutions.
25 Healthcare AI Use Cases with Examples
Healthcare systems are under growing pressure from rising patient data volumes and increasing demand for personalized care. Healthcare AI applications have emerged as a powerful solution to these problems by optimizing processes, enhancing diagnostic accuracy, and improving patient outcomes.
Compare Relational Foundation Models
We benchmarked SAP-RPT-1-OSS against gradient boosting (LightGBM, CatBoost) on 17 tabular datasets spanning the semantic-numeral spectrum, small/high-semantic tables, mixed business datasets, and large low-semantic numerical datasets. Our goal is to measure where a relational LLM’s pretrained semantic priors may provide advantages over traditional tree models and where they face challenges under scale or low-semantic structure.
Large World Models: Use Cases & Examples
Despite advances in large language models, artificial intelligence remains limited in its ability to understand and interact with the physical world due to the constraints of text-based representations. Large world models address this gap by integrating multimodal data to reason about actions, model real-world dynamics, and predict environmental changes.
Top 5 AI Guardrails: Weights and Biases & NVIDIA NeMo
As AI becomes more integrated into business operations, the impact of security failures increases. Nearly all AI-related breaches occurred in environments without proper access controls, underscoring the risks of poorly governed AI deployments. AI guardrails address this gap by defining clear boundaries for AI use, supporting regulatory compliance and accountability, and enabling responsible long-term adoption.
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.
LLM Quantization: BF16 vs FP8 vs INT4
Quantization reduces LLM inference cost by running models at lower numerical precision. We benchmarked 4 precision formats of Qwen3-32B on a single H100 GPU. We ran over 2,000 inference runs and 12,000+ MMLU-Pro questions to measure the real-world trade-offs between speed, memory, and accuracy.
Top 5 AI Services to Enhance Business Efficiency
AI adoption is rapidly increasing. Around 98% of companies are experimenting with AI, reflecting its growing accessibility and potential to improve operations. Yet only 26% have advanced beyond trials to achieve measurable business value, showing that many are still building the capabilities needed to scale AI effectively.
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.
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