Sıla Ermut
Sıla is an industry analyst at AIMultiple covering AI models, AI infrastructure, AI governance, and enterprise applications of AI.
Research interests
Sıla’s research focuses on the use of AI in marketing, healthcare, supply chains, and sustainability. She also covers AI evaluation, governance, ethics, and adoption.Her work includes researching and comparing AI technologies, analyzing their business applications, and contributing to AIMultiple’s technology benchmarks.
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
Compare GPT-5 Models: Reasoning, Speed & Cost
We explore the differences between OpenAI’s GPT-5 family (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) by gathering benchmark performance and the costs to run the benchmarks. We used the GPT-5 family in our analysis. We used six benchmarks across various areas, including reasoning, coding, instruction-following, and math. Our analysis revealed: The three models differ primarily in reasoning…
Top 9 AS2 Software
We compared the top 9 tools that deliver AS2 support based on their pricing, key features and deployment options: The table is sorted based on the number of reviews. Our subscribers are listed at the top. JSCAPE MFT Server’s AS2 implementation has been Drummond-certified since 2015. It supports synchronous or asynchronous MDN, and OFTP2 runs…
Top 12 AI Avatar Generation Tools
When choosing the right AI avatar generation tool, businesses can take into account the following components: We tested 7 AI avatar generation tools and compared their visual (resolution and export capabilities) and voice (number of languages supported and voice cloning availability) features, as well as their pricing plans. We signed up for the free trial…
LLM Market Share: Compare Usage & Adoption
We analyzed LLM market share by combining usage-based data and web visit estimates to show how demand for large language models is distributed across AI labs and AI applications. Read the methodology to see how we measured and calculated these results. The United States dominated web visits across all four months, consistently accounting for 90–95%.…
Generative AI Ethics: How to Manage Them
Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, filed a lawsuit against Perplexity, alleging that the company illegally and knowingly copied Britannica’s human-verified content and misused its trademarks without permission. Explore what generative AI ethics concerns are and best practices for managing them. AI models learn patterns from…
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. These libraries implement machine learning algorithms to process training data and generate personalized recommendations using collaborative or content-based filtering techniques. Additionally, these libraries implement machine learning models to analyze…
Top 13 GAN Use Cases
While GANs pioneered many early generative AI applications, particularly in image synthesis and style transfer, most consumer-facing generative AI tools today rely on diffusion-based architectures or related approaches such as flow matching and diffusion transformers (DiT). However, GANs remain important in specific domains, such as super-resolution, face restoration, the generation of synthetic tabular or healthcare…
AI Fail: 10 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. By identifying and addressing the underlying issues, companies can mitigate the risks associated with AI and ensure that it is used safely…
AI Ethics Dilemmas with Real Life Examples
Though artificial intelligence is changing how businesses work, there are concerns about how it may influence our lives. This is both an academic/societal problem and a reputational risk for companies; no company wants to be undermined by data or AI ethics scandals that damage its reputation. Explore insights into ethical issues that arise with the…
The Future of Large Language Models
See the future of large language models by comparing approaches, such as self-training, fact-checking, and sparse expertise, that could address LLM limitations. LLMs access external sources during conversations instead of relying on training data. The model queries external databases, retrieves current information, and provides citations. Limitation: Still makes errors. Citations don’t guarantee accuracy; models sometimes…
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