TOPIC
Foundation models, multimodal LLMs (Gemini, Gemma, Mistral, MM1), prompt engineering, and the open-source releases that shape what banks can build inside their own data perimeter.
The seeds published with the NIST P-192, P-224, P-256, P-384 and P-521 curves were never hash outputs. They came from the C library's lrand48 generator, seeded by Unix timestamp. Claude worked...
An AI assistant worked on two problems about progression-free sets: an argument for the reciprocal-sum problem, and a quasipolynomial bound for every fixed progression length. It used Bloom and...
Foundations of Machine Learning (second edition, MIT Press, 2018) by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar treats machine learning as the design of efficient and accurate...
An internal OpenAI model produced a collection of results across mathematics and theoretical computer science. The headline is a determination of the exact exponential decay rate of the...
Jev, a model trained with reinforcement learning for calibrated decisions (RLCD), had never been measured on alignment-failure detection. RLCDAlignBench now does that across 44 benchmarks, ten...
Explore Apple's MM1 paper on Multimodal Large Language Models (MLLMs). Learn about their architecture, pre-training strategies, and AI potentials.
Meet Mistral AI's new multilingual Assistant. An advanced AI that can understand and respond in multiple languages, all in one conversation and in real-time.
Explore Google's Gemma AI Model: An open-source project offering ethical AI solutions for both personal and enterprise use.
Gemini 1.5 from Google scales context windows past 1M tokens. What that unlocks for retrieval-augmented finance and the trade-offs worth knowing.
Prompt engineering controls LLM behaviour at inference time. This article covers zero-shot and few-shot prompting, chain-of-thought reasoning, self-consistency sampling, ReAct tool-use architecture, indirect prompt injection risks, and applied patterns from financial services deployments.
AI trends for 2024: generative AI in finance, multimodal models, on-device LLMs and the shifts that will reshape banking and product engineering.
Explore Generative AI in 2023: how it works, where it lands first in financial services, and the ethical and architectural questions worth asking.