Sıla Ermut
Sıla es analista del sector en AIMultiple, especializada en marketing por correo electrónico y vídeos de ventas.
Intereses de investigación
Las áreas de investigación de Sıla incluyen el marketing por correo electrónico, las campañas de marketing de comercio electrónico y la automatización del marketing. También forma parte del equipo de evaluación comparativa de entregabilidad de correo electrónico de AIMultiple, donde diseña y ejecuta dichas evaluaciones en colaboración con el equipo de tecnología.Experiencia profesional
Anteriormente, Sıla trabajó como reclutadora y en empresas de gestión de proyectos y consultoría.Educación
Ella sostiene:- Licenciatura en Relaciones Internacionales por la Universidad de Bilkent.
- Máster en Psicología Social por la Universidad Başkent.
Últimos artículos de Sıla
AGI/Singularidad: 9.800 predicciones analizadas
Artificial general intelligence (AGI) is when an AI system matches human cognitive abilities across all tasks. Based on available predictions, quick answers on AGI: Will AGI/singularity happen? AGI is inevitable according to most AI experts. When will the singularity/AGI happen? Recent surveys of AI researchers predict AGI in 2040s.
Consolidaciones de IA: Financiación, Inversores y Tendencias del Sector
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.
Compara los ingresos de IA en toda la pila
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 Leyes de Escalado: Análisis de Investigadores de IA
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.
Comparar software de control remoto: 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.
Las 20 principales predicciones de expertos sobre la pérdida de empleos en el sector de la IA.
Como consultor de McKinsey, ayudé a empresas a adoptar nuevas tecnologías durante una década. Mis respuestas rápidas sobre la pérdida de empleos por IA: Predicciones de pérdida de empleos por IA Nota: El tamaño de los gráficos está correlacionado con el tamaño de la predicción de pérdida de empleos. Los porcentajes a los que se hace referencia en nuestro análisis se derivan de supuestos sobre el desplazamiento laboral general.
Las 4 principales barreras de IA: Weights and Biases y 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.
Automatización MSP: Acronis, ConnectWise Automate y 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.
Generadores de texto a imagen: 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.
Sistemas de recomendación: Aplicaciones y ejemplos
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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