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
AGI/Singularität: 9.800 Vorhersagen analysiert
Künstliche allgemeine Intelligenz (AGI) bezeichnet ein KI-System, das menschliche kognitive Fähigkeiten in allen Aufgabenbereichen erreicht. Basierend auf verfügbaren Prognosen lassen sich folgende Fragen zu AGI schnell beantworten: Wird AGI/die Singularität eintreten? Laut den meisten KI-Experten ist AGI unausweichlich. Wann wird die Singularität/AGI eintreten? Jüngste Umfragen unter KI-Forschern prognostizieren AGI für die 2040er Jahre.
AI-Rollups: Finanzierung, Investoren und Branchentrends
We analyzed 30 investments involving over 130 investors from the past 3 years to understand the current trend for AI rollups. Based on our analysis, we identified investor activity and trends, including the number of investors backing AI rollups, the total funding raised for AI rollups, and the leading industries.
Vergleich der KI-Erlöse über den gesamten Stack
The AI market expanded rapidly across all four layers (data, compute, models, and applications). For example, NVIDIA’s data center revenue jumped from $47.5B to $115.2B in a single year; OpenAI reached about $13B in annual revenue; and Anthropic approached $7B in ARR. We tracked revenue data from over 100 AI companies.
LLM Skalierungsgesetze: Analyse von KI-Forschern
Large language models predict the next token based on patterns learned from text data. The term LLM scaling laws refers to empirical regularities that link model performance to the amount of compute, training data, and model parameters used during training.
Vergleich von Fernsteuerungssoftware: NinjaOne & Acronis
We tested the top 3 remote control software (also known as remote access software) to evaluate the general UI and remote control experience, their remote control quality, protocols, and unique capabilities: Strengths and weaknesses based on our observations Check out the agent deployment process before jumping into our experiences and observations on remote access.
Top 20+ Vorhersagen von Experten zum Verlust von Arbeitsplätzen durch KI
As a McKinsey consultant, I helped enterprises adopt new technology for a decade. My quick answers on AI job loss: 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.
Top 4 KI-Sicherheitsvorkehrungen: Weights & NVIDIA NeMo
AI security failures are expensive and increasingly common. Many incidents stem from weak governance, particularly gaps in access control, data permissions, and oversight of model usage. AI guardrails reduce this risk by setting enforceable boundaries for how AI systems access data, generate outputs, and interact with users or business workflows.
MSP-Automatisierung: 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.
Text-zu-Bild-Generatoren: Nano Banana Pro & GPT Image 1.5
We compared the top 6 text-to-image models across 15 prompts to evaluate visual generation capabilities in terms of temporal consistency, physical realism, text and symbol recognition, human activity understanding, and complex multi-object scene coherence: Text-to-image generators benchmark results Review our benchmark methodology to understand how these results are calculated and see output examples.
Empfehlungssysteme: Anwendungen und Beispiele
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.
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