Sublime
An inspiration engine for ideas
3 papers to understand AI research in 2023:
1. https://t.co/kvb14yRZlu (scale x simplicity for reward models)
2. https://t.co/9kqgprMMxU (continuous -> discrete reps for efficient RL)
3. https://t.co/mmiBylc6jb (contrast and episodic memory for transfer learning and efficient RL)
Chris Lengerich /ddx.com
FinRL: A python package for Financial Reinforcement Learning
Let's explore: https://t.co/57MgImMYX7
NIST AI 600-1 - Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
The document provides a profile of risks unique to or exacerbated by generative artificial intelligence (GAI) and offers actions to manage these risks.
airc.nist.gov
Mixture of In-Context Learners
Uses subsets of demonstrations to train experts via in-context learning. Given a training set, a trainable weighting function is used to combine the experts' next-token predictions.
This approach applies to black-box LLMs since access to the internal parameters... See more
The main problem with image recognition is invariance:
Stanislas Dehaene • How We Learn: Why Brains Learn Better Than Any Machine . . . for Now
A matter of choice: People and possibilities in the age of AI
The 2025 Human Development Report explores the intersection of artificial intelligence and human development, emphasizing the importance of people's choices in shaping equitable outcomes amid technological advancements.
hdr.undp.orgProfessor Howard Raiffa’s maximum bid of others (or MBOO, pronounced “maboo”) analysis, which captures the fundamental trade-off in graphical form.
Guhan Subramanian • Dealmaking: The New Strategy of Negotiauctions (Second Edition)
@pikeypilled Use llms to get statistical analysis on trading data using indicators, take bets using Kelly with the ones with 70%+ probabilities. Keep compounding and leveraging.
Frazer Sebastianx.com