Aleyna Daldal
She focuses on AI foundation models, AI reliability, and AI memory topics. Her past work included developing deep learning algorithms for materials informatics and particle physics fields.
Latest Articles from Aleyna
AI Memory: Most Popular AI Models with the Best Memory
AI models can remember earlier parts of a conversation, but their memory capacity varies wildly. Interestingly, smarter models often have worse memory. We tested 23 popular large language models to see which ones actually remember information during long conversations.
10+ Large Language Model Examples & Benchmark
We have used open-source benchmarks to compare top proprietary and open-source large language model examples. You can choose your use case to find the right model. Comparison of the most popular large language models We have developed a model scoring system based on three key metrics: user preference, coding, and reliability.
Compare Best AI Agents in Customer Service
AI agents powered by large language models (LLMs) can respond to customer queries in natural language, interpret context, and generate human-like responses. These agents can process and synthesize large volumes of information from sources such as knowledge bases. We compiled a list of the best use cases for the top 4 customer service AI agents.
AI Hallucination: Comparison of the Popular LLMs
AI models sometimes generate data that seems plausible but is incorrect or misleading; known as AI hallucinations. 77% of businesses concerned about AI hallucinations. We benchmarked 29 different LLMs with 60 questions to measure their hallucination rates: AI hallucination benchmark results Our benchmark revealed that Anthropic Claude 3.7 has the lowest hallucination rate (i.e.
AI Agent Performance: Success Rates & ROI
The AI agent market reached $5.4 billion in 2024 and is projected to grow at 45.8% annually through 2030. Therefore, companies that master AI agent deployment will see significantly greater investment returns.
Multi Agent Systems: Applications & Comparison of Tools
Multi-agent systems(MAS) enable distinct AI agents to work together to achieve complex objectives. Every AI agent in the system possesses its specific characteristics and responsibilities that contribute to a greater goal. MAS provides a distinctive approach to managing multi-step tasks and enhancing efficiency.
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