Sıla Ermut
Sıla ist Branchenanalystin bei AIMultiple und spezialisiert auf E-Mail-Marketing und Vertriebsvideos.
Forschungsschwerpunkte
Sılas Forschungsschwerpunkte umfassen E-Mail-Marketing, E-Commerce-Marketingkampagnen und Marketingautomatisierung. Sie ist außerdem Teil des AIMultiple-Projekts zur E-Mail-Zustellbarkeits-Benchmark-Analyse. In Zusammenarbeit mit dem Technologie-Team von AIMultiple entwickelt und implementiert sie Benchmarks zur E-Mail-Zustellbarkeit.Berufserfahrung
Sıla arbeitete zuvor als Personalvermittlerin und war in Projektmanagement- und Beratungsunternehmen tätig.Ausbildung
Sie hält:- Bachelor of Arts-Abschluss in Internationalen Beziehungen von der Bilkent-Universität.
- Master of Science-Abschluss in Sozialpsychologie von der Başkent-Universität.
Neueste Artikel von Sıla
Vergleich großer visueller Modelle: 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.
Agentic AI in ITSM: 10 Anwendungsfälle & Beispiele
Agentic AI in ITSM marks a practical shift in how organizations manage IT operations and service delivery. Instead of relying on static automation or predefined workflows, agentic AI enables contextual reasoning, allowing AI agents to act autonomously within IT environments.
Generative KI-Ethik: Wie man sie verwaltet
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. 1.
Zielgruppen-Simulation: Können LLMs menschliches Verhalten vorhersagen?
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.
Die 125 besten Anwendungen für generative KI
Basierend auf unserer Analyse von über 30 Fallstudien und 10 Benchmarks, in denen wir über 40 Produkte getestet und verglichen haben, identifizierten wir 125 Anwendungsfälle für generative KI in den folgenden Kategorien: Weitere KI-Anwendungen für Anfragen mit einer einzigen richtigen Antwort (z. B. Vorhersage oder Klassifizierung) finden Sie unter KI-Anwendungen.
LLM Marktanteil: Nutzung & Adoption vergleichen
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: LLM market share comparison by country Read the methodology to see how we measured and calculated these results.
15 KI-Agenten in Marketing-Tools & Beispiele
Research shows that 50% of organizations using generative AI plan to launch agentic AI pilot programs in 2025.AI agents in marketing represent a significant shift in the industry, introducing systems that can reason, make decisions, and act with minimal human oversight.
Vergleich relationaler Fundamentaler Modelle
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.
LLM Quantisierung: BF16 vs FP8 vs INT4
We benchmarked Qwen3-32B at 4 precision levels (BF16, FP8, GPTQ-Int8, GPTQ-Int4) on a single NVIDIA H100 80GB GPU. Each configuration was evaluated on 2 benchmarks (~12.2K questions) covering knowledge and code generation, plus 2,000+ inference runs to measure throughput. Int4 is 2.
Top 10 Preisüberwachungstools
Price monitoring tools track competitors’ prices to enable pricing strategies that respond to market changes and stay competitive as conditions shift. Explore the top 10 price monitoring tools, review their key features and pricing models, and learn how they can enable pricing strategies that respond to market changes.
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