Categories: AI/ML Research

Can LLM Embeddings Improve Time Series Forecasting? A Practical Feature Engineering Approach

Using large language models (LLMs) — or their outputs, for that matter — for all kinds of machine learning-driven tasks, including predictive ones that were already being solved long before language models emerged, has become something of a trend.
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Bringing Conversational Analytics to your entire data ecosystem

Increasing the adoption of generative AI across the enterprise requires you to do more than…

6 hours ago

OpenAI’s Rogue AI Agent Hacked More Than Just Hugging Face

In a new disclosure, OpenAI says its agent used exposed logins to gain access to…

7 hours ago

Brain-inspired AI is capable of flexible planning and problem-solving while using far less energy

The capabilities of large AI systems are constantly improving, but they consume a great deal…

7 hours ago

5 Architectural Patterns for Persistent Memory and State in AI Agents

Memory & State For AI Agents Building an AI agent can be tricky. Keeping it…

1 day ago

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

Overview of ABBEL compared to traditional recursive summarization. Beliefs replace the full interaction history as…

1 day ago

GH-ESD: Grounded Hypothesis-Driven Error Slice Discovery for Instance-Level Vision Tasks

Systematic failures of vision models on semantically coherent subsets, known as error slices, reveal limitations…

1 day ago