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
10 Computer Vision Agriculture Use Cases & Examples
Labor shortages, resource inefficiencies, and environmental pressures increasingly challenge agriculture. Climate change adds to this burden through extreme weather, water scarcity, and rising pest threats, further straining productivity and sustainability. Computer vision offers targeted solutions by enabling automation and data-driven insights across critical farming operations.
AI Ad Generator: Compare Icon, AdGen & AdCreative
Creating high-converting digital ads remains a challenge for businesses aiming to reach diverse audiences across platforms like Google, Facebook, and LinkedIn. AI ad generators offer a solution by automating ad creation, enabling faster production, broader customization, and data-driven content optimization.
How to Create Ordinal Inscriptions: Step by Step Guide
Creating NFTs on the Bitcoin blockchain can be complex due to limited wallet support, technical steps, and high transaction fees. As ordinal inscriptions, also known as Bitcoin NFTs, gain popularity, understanding how to create and manage them has become essential.
How to Measure AI Performance: Key Metrics & Best Practices
Measuring AI performance is crucial to ensuring that AI systems deliver accurate, reliable, and fair outcomes that align with business objectives. It helps organizations validate the effectiveness of their AI investments, detect issues like bias or model drift early, and continuously optimize for better decision-making, operational efficiency, and user satisfaction.
Top 8 Computer Vision Construction Use Cases & Examples
Adopting AI technologies, such as computer vision, is accelerating in the construction industry, offering solutions for safety, efficiency, and quality control. However, challenges like an aging workforce and high-risk tasks continue to hinder widespread implementation. Explore the top 8 use cases with real-life examples where computer vision construction technologies already impact construction sites.
AI Fail: 4 Root Causes & Real-life Examples
Whether it’s a self-driving car crash, a biased algorithm, or a breakdown in a customer service chatbot, failures in deployed AI systems can have serious consequences and raise important ethical and societal questions.
AI Data Collection: Risks, Challenges & Tools
AI builders need fresh, high quality data: However, data collection comes with its risks. For example, enterprises need to avoid unethical data collection practices and ensure that data is collected ethically to minimize reputational risk.
Artificial Superintelligence: Opinions, Benefits & Challenges
The prospect of artificial superintelligence (ASI), a form of intelligence that would exceed human capabilities across all domains, presents both opportunities and significant challenges. Unlike current narrow AI systems, ASI could independently enhance its capabilities, potentially outpacing human oversight and control. This development raises concerns regarding governance, safety, and the distribution of power in society.
Top 5 RLHF Platforms: Guide & Features Comparison
As AI adoption grows, with 65% of organizations now regularly using generative AI, selecting the right tools for optimizing AI models has become more crucial than ever. Reinforcement learning from human feedback (RLHF) platforms have emerged as key players in this process.
AI for Mental Health: 7 Use Cases with Real-Life Examples
Mental health challenges are a worldwide concern, especially after the COVID-19 pandemic, which saw an estimated 76 million additional cases of anxiety disorders.This heightened stress strained healthcare systems and increased demand for mental health support. Yet, traditional care faces barriers like professional shortages, high costs, and social stigma.
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