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
Applying RLHF: Techniques, use cases, and challenges
Training AI systems to align with human values can be a challenge in machine learning. To mitigate this, developers are advancing AI through reinforcement learning (RL), allowing systems to learn from their actions. A notable trend in RL is Reinforcement Learning from Human Feedback (RLHF), which combines human insights with algorithms for efficient AI training.
eCommerce Data Collection: Best Practices & Examples
As online shopping grows and customer expectations shift, eCommerce businesses face increasing pressure to stay competitive. Real-world data is key to making faster, smarter decisions. Failing to collect and utilize data properly can result in missed sales, inefficient operations, and poor customer retention.
Top 5 Price Monitoring Tools
A key challenge for businesses is maintaining competitive pricing while adapting to market fluctuations. Price monitoring tools help solve this challenge by tracking competitors’ prices and providing insights for more dynamic pricing decisions.
Conversational AI for Sales: Applications & Real-Life Examples
By combining natural language processing, machine learning, and integration with customer data systems, conversational AI tools enable sales teams to handle routine tasks, qualify leads, and engage in personalized conversations with prospects and customers.
Generative AI for Sales Use Cases
Unlike traditional AI in sales applications, which typically focuses on data analysis and pattern recognition, generative AI actively contributes to the sales process by creating content, drafting communications, and improving customer engagement. Explore the future of generative AI for sales, including use cases with examples that align with the steps of a typical selling process.
Image Recognition vs Classification: Applications with Examples
Businesses increasingly leverage AI-powered visual data solutions, but confusion between image recognition and classification leads to inefficiencies. Understanding the key differences helps businesses optimize AI deployment in the security, healthcare, and retail fields. Explore image recognition vs classification, their key differences, and applications with real-life examples.
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.
Generative AI in Marketing: AdsGency AI, Creatify & Jasper
A survey of 5,000 marketers worldwide revealed that their top priority was “implementing or leveraging AI”, showing a growing recognition of its potential in marketing. In particular, generative AI is becoming a critical component of marketing operations, offering capabilities that support more efficient content production, data-driven decision-making, and enhanced content scalability, personalization, and campaign optimization.
Generative AI Ethics: Concerns and How to Manage Them?
Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, has accused Perplexity of reusing its content without consent and even attaching its name to inaccurate answers, showing how these technologies can blur the lines between reliable sources and AI-generated text.
Generative AI Healthcare: 15 Use Cases with Examples
As healthcare systems face rising data volumes, workforce shortages, and increasing demands for personalized care, generative AI is emerging as a critical solution. By generating insights from complex medical data, generative AI healthcare applications offer hospital administrators, clinicians, and researchers new ways to improve decision-making and patient outcomes.
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