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
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…
World Foundation Models: 10 Use Cases
Training robots and autonomous vehicles (AVs) in the physical world can be costly, time-consuming and risky. World Foundation Models offer a scalable alternative by enabling realistic simulations of real-world environments. These models accelerate development and deployment in robotics, AVs, and other domains by reducing reliance on physical testing. Explore how World Foundation Models work, their…
AI Hallucination Detection Tools: W&B Weave & Comet
We benchmarked three hallucination detection tools: Weights & Biases (W&B) Weave HallucinationFree Scorer, Arize Phoenix HallucinationEvaluator, and Comet Opik Hallucination Metric, across 100 test cases. Each tool was evaluated on accuracy, precision, recall, and latency. We tested 100 responses (50 correct, 50 hallucinated) from factual Q&A scenarios against their source context. See the benchmark methodology.…
AI Energy Consumption Statistics
A recent forecast predicts AI will use over half of data center electricity by 2028.29As compute-intensive workloads such as generative AI expand, total electricity demand is also expected to rise. We covered data from the IEA, MIT, and major cloud providers to identify AI energy consumption efficiency trends and policy responses and best practices. We…
Compare AI Revenues Across the Stack
The AI market expanded rapidly across all four layers (data, compute, models, and applications). For example, NVIDIA’s data center revenue increased from $47.5B to $115.2B in a single fiscal year (FY2024 to FY2025, ending January 2024 and January 2025). We tracked revenue data from over 80 AI companies. Explore how revenues shifted across compute, data,…
Compare Relational Foundation Models
We benchmarked SAP-RPT-1-OSS against gradient boosting (LightGBM, CatBoost) on 17 tabular datasets spanning the semantic-numeral spectrum, small/high-semantic tables, mixed business datasets, and large low-semantic numerical datasets. Our goal is to measure where a relational LLM’s pretrained semantic priors may provide advantages over traditional tree models and where they face challenges under scale or low-semantic structure.…
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. See our benchmark methodology…
Audience Simulation: Can LLMs Predict Human Behavior?
In marketing, evaluating how accurately LLMs predict human behavior is crucial for assessing their effectiveness in anticipating audience needs and recognizing the risks of misalignment, ineffective communication, or unintended influence. Audience simulation with LLMs enables the modeling of virtual audiences, helping organizations anticipate reactions to content or products without relying on costly surveys or focus…
AutoSys: Key Features and User Insights
Interest in Broadcom’s AutoSys is declining (Source: Google Trends) and it has a lower average rating on review platforms compared to most other workload automation tools. This may be partially explained by: Dive into AutoSys’s capabilities, user evaluations, and outlook to learn more: AutoSys was originally developed by CA Technologies and acquired by Broadcom in…
15 AI Agents in Marketing Tools & Examples
Research shows that 50% of organizations using generative AI plan to launch agentic AI pilot programs.74AI agents in marketing introduce systems that can reason, make decisions, and act with minimal human oversight. These intelligent agents analyze customer data, generate actionable insights, and coordinate campaigns across multiple platforms in real-time. We evaluated the top 15 AI…
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