Researchers have discovered that some of the elements of AI neural networks that contribute to data-privacy vulnerabilities are also key to the performance of those models. The researchers used this new information to develop a technique that better balances performance and privacy protection in these models.
If you look at the architecture diagram of almost any AI startup today, you will see a large language model (LLM) connected to a vector store.
Memory is one of the most overlooked parts of agentic system design.
In the modern AI landscape, an agent loop is a cyclic, repeatable, and continuous process whereby an entity called an AI agent — with a certain degree of autonomy — works toward a goal.
Unlike fully structured tabular data, preparing text data for machine learning models typically entails tasks like tokenization, embeddings, or sentiment analysis.
Everyone’s
Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems …
An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before …
A new tomato-picking robot is learning to think before it acts. Instead of simply identifying ripe fruit, it predicts how easy each tomato will be to harvest and adjusts its approach accordingly. This smarter strategy boosted success rates to 81%, with the robot even switching angles when needed. The breakthrough could pave the way for …
Read more “AI-powered robot learns how to harvest tomatoes more efficiently”
Large Language Models (LLMs) often lack meaningful confidence estimates for their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether they can assess confidence in the actual meaning of their responses beyond the token level. We find that, when using a certain sampling-based notion of semantic calibration, base LLMs are …
Read more “Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs”