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 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.
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.
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.
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.
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.
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.
LLM Parameters: GPT-5 High, Medium, Low and Minimal
New LLMs, such as OpenAI’s GPT-5 family, come in different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and with various parameter settings, including high, medium, low, and minimal. Below, we explore the differences between these model versions by gathering their benchmark performance and the costs to run the benchmarks. Price vs.
AI Scientist: Automating the Future of Scientific Discovery
AI scientists mark a major advance toward fully automatic scientific discovery, aiming to perform the entire research process independently. Unlike traditional tools, these automated labs can expedite research processes by generating hypotheses, designing and executing experiments, interpreting results, and communicating findings.
Blockchain Case Studies Across Key Industries
A recent forecast projects the blockchain market will reach 943 billion U.S. dollars by 2032, growing at a CAGR of 56%.While the potential is massive, executives face uncertainty due to the varying maturity of blockchain solutions across industries.
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.
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