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
MSP Automation: Acronis, ConnectWise Automate & Rewst
Managed service providers (MSPs) handle a constant operational load, including ticket management, patch management, onboarding, alert monitoring, billing reconciliation, and documentation updates. These are necessary but time-intensive tasks.
Time Series Foundation Models: Use Cases & Benefits
Time series foundation models (TSFMs) are pre-trained models that forecast, classify, impute, and detect anomalies in time series data without requiring a separate model for every dataset or industry. TSFMs use transformer-based architectures and large-scale time-series datasets to generalize across domains such as finance, retail, energy, and healthcare.
AGI/Singularity: 9,800 Predictions Analyzed
Artificial general intelligence (AGI) is when an AI system matches human cognitive abilities across all tasks. We analyzed 9,800 AI researchers‘, leading entrepreneurs‘, and community predictions about the AGI timeline: Will AGI/singularity happen? AGI is inevitable according to most AI experts. When will we reach AGI? Between late 2020s and early 2030s.
Federated Learning: 7 Use Cases & Examples
Federated learning (FL) enables models to learn from decentralized data while keeping sensitive information private and ensuring compliance with data localization and privacy laws. Explore what federated learning is, how it works, common use cases with real-life examples, potential challenges, and its alternatives.
Top 20+ Predictions from Experts on AI Job Loss
As a McKinsey consultant, I helped enterprises adopt new technologies for a decade. My quick answers: AI job loss predictions Note: The size of the plots is correlated with the size of the job loss prediction. The percentages referenced in our analysis are derived from assumptions about overall job displacement.
Recommendation Systems: Applications and Examples
We examined the main types of recommendation systems, key concepts, and real-world applications, and benchmarked LightFM, Cornac BPR, and TensorFlow Recommenders using AUC, Precision@10, and Recall@10. Best Python libraries for recommendation systems These libraries implement machine learning algorithms to process training data and generate personalized recommendations using collaborative or content-based filtering techniques.
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 15 leading multimodal AI models on visual reasoning using 200 visual-based questions. The evaluation consisted of two tracks: 100 chart understanding questions testing data visualization interpretation, and 100 visual logic questions assessing pattern recognition and spatial reasoning. Each question was run 5 times to ensure consistent and reliable results.
Compare Large Vision Models: GPT-4o vs YOLOv8n
Large vision models (LVMs) can automate and improve visual tasks such as defect detection, medical diagnosis, and environmental monitoring. We benchmarked three object detection models: YOLOv8n, DETR, and GPT-4o Vision, across 1,000 images each, measuring metrics such as mAP@0.5, inference speed, FLOPs, and parameter count.
Top 20 Sustainability AI Applications & Examples
By applying generative AI to logistics optimization, demand forecasting, and waste reduction, companies can reduce emissions across their operations beyond the AI systems themselves. Discover sustainability AI applications with real-world examples that leverage AI to build a smarter, more efficient, and more sustainable future.
+100 Datasets for ML & AI Models
Data is required to leverage or build generative AI or conversational AI solutions. You can use existing datasets available on the market or hire a data collection service. We identified over 100 datasets to train and evaluate machine learning and AI models.
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