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
Blockchain Case Studies Across Key Industries
We collected the top 20 real-world blockchain case studies to help executives identify mature and high-impact investment opportunities. Business challenge: The pharmaceutical supply chain needed to comply with the U.S. Drug Supply Chain Security Act, which requires interoperable tracking of prescription drugs through changes of ownership. The industry also faced risks from counterfeit drugs, fragmented…
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…
AGI/Singularity: 10,000 Predictions Analyzed
Artificial general intelligence (AGI) is when an AI system matches human cognitive abilities across all tasks. We analyzed 10,000 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. AGI timeline shortened…
Top 11 AI in Fashion Use Cases & Examples
Faced with creative bottlenecks, inefficient supply chains, and rising consumer expectations, fashion brands are seeking smarter solutions. McKinsey estimates that generative AI could boost operating profits in the fashion, apparel, and luxury sectors by up to $275 billion by 2028.55 Explore the top 11 use cases of AI in fashion to help fashion brands cut…
20 Strategies for AI Improvement & Examples
AI models require continuous improvement as data, user behavior, and real-world conditions evolve. Even well-performing models can drift when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions. Changes in regulations, product requirements, or customer expectations can also introduce new constraints that existing models were not designed to…
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 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…
AI Energy Consumption Statistics
A recent forecast predicts AI will use over half of data center electricity by 2028.143As 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…
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: 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. In specific scenarios, these assumptions included potential job gains…
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