The Risk Reward Matrix helps you to balance Risk and Reward when choosing among options.
WRKSHP.tools | Uncertainty Matrix
The Uncertainty Matrix helps you find threats and opportunities that are uncertain but can have a big impact on your business
Risk of bias tools - Current version of RoB 2
Current version Download the 22 August 2019 version: The full guidance document. The cribsheet summarizing the tool. A template for completing the assessment. An Excel tool to implement RoB 2 (contains macros; download to your computer before using; some text is slightly out of date). We have
WRKSHP.tools | Riskiest Assumption Canvas
How do you know you’re making the right bet with your idea? Which bets does the success of your idea hinge on? These are your riskiest assumptions; they need to be tested.
Reward is not the optimization target — LessWrong
TurnTrout discusses a common misconception in reinforcement learning: that reward is the optimization target of trained agents. He argues reward is b…
Cognitive Bias Lab | Learn to Make Better Decisions
Explore cognitive biases with interactive tests, simulations, and real-world examples. Free platform to sharpen decision-making and critical thinking — no sign-up needed.
WRKSHP.tools | Product Box
The Product Box exercise helps you when you need to convert an abstract concept into something tangible that you can evaluate with people that were not intimately part of the design process.
Reinforcement Learning from Human Feedback
The authoritative guide for Reinforcement learning from human feedback, alignment, and post-training LLMs. Aligning AI models to human preferences helps them become safer, smarter, easier to use, and tuned to the exact style the creator desires. Reinforcement Learning From Human Feedback (RHLF) is the process for using human responses to a model’s output to shape its alignment, and therefore its behavior. In Reinforcement Learning from Human Feedback, author Nathan Lambert blends diverse perspectives from fields like philosophy and economics with the core mathematics and computer science of RLHF to provide a practical guide you can use to apply RLHF to your models. In Reinforcement Learning from Human Feedback you’ll discover: How today’s most advanced AI models are taught from human feedback How large-scale preference data is collected and how to improve your data pipelines A comprehensive overview with derivations and implementations for the core policy-gradient methods used to train AI models with reinforcement learning (RL) Direct Preference Optimization (DPO), direct alignment algorithms, and simpler methods for preference finetuning How RLHF methods led to the current reinforcement learning from verifiable rewards (RLVR) renaissance Tricks used in industry to round out models, from product, character or personality training, AI feedback, and more How to approach evaluation and how evaluation has changed over the years Standard recipes for post-training combining more methods like instruction tuning with RLHF Behind-the-scenes stories from building open models like Llama-Instruct, Zephyr, Olmo, and Tülu After ChatGPT used RLHF to become production-ready, this foundational technique exploded in popularity. In Reinforcement Learning from Human Feedback, AI expert Nathan Lambert gives a true industry insider's perspective on modern RLHF training pipelines, and their trade-offs. Using hands-on experiments and mini-implementations, Nathan clearly and concisely introduces the alignment techniques that can transform a generic base model into a human-friendly tool.
#Exploration: A Study of Count-Based Exploration for Deep...
Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision...
Weighted DVF: A Simple Model for Scoring and Prioritizing Ideas
Discover a powerful yet straightforward approach to evaluating new ideas by balancing desirability, viability, and feasibility. Learn how…
Adult age differences in monetary decisions with real and hypothetical reward
Abstract Age differences in monetary decisions may emerge because younger and older adults perceive the value of outcomes differently. Yet, age‐differential effects of monetary rewards on decisions are not well understood. Most laboratory studies on aging and decision making have used scenarios in which rewards were merely hypothetical (decisions did not have any real consequences) or in which only small amounts of money were at stake. In the current study, we compared younger adults' (20–29 years) and older adults' (61–82 years) decisions in probabilistic choice problems with real or hypothetical rewards. Decision‐contingent rewards were in a typical range of previous studies (gains of up to ~4.25 USD) or substantially scaled up (gains of up to ~85 USD per participant). Reward type (real vs. hypothetical) affected decision quality, including value maximization, switching between options, and dominance violations (choices of an option that was inferior to another option in all respects). Decision quality was markedly better with real than hypothetical rewards in older adults and correlated with numeracy in both age groups. However, we found no evidence that reward type affected people's risk preferences. Overall, the findings portray a fairly positive picture regarding the use of hypothetical scenarios to assess preferences: With carefully prepared instructions, people from different age groups indicate preferences in hypothetical scenarios that match their decisions with real and much higher rewards. One advantage of using real rewards is that they help to reduce decision noise.
Why I work on self-improving AI despite the risks - Jeff Clune
Why I work on self-improving AI despite the risks. Jeff Clune.
WRKSHP.tools | Tool Overview
Search over 30 different innovation tools and download them for free
WRKSHP.tools | Trend Canvas
The Trend Canvas helps you map different trends in the world around your business that may impact you in the future.
The Four Big Risks | Silicon Valley Product Group
In the first edition of my book, INSPIRED, I discussed how successful products are valuable, usable and feasible, where I defined “valuable” as both valuable to customers and valuable to your business. While it’s easy to remember these three attributes, over the years I’ve come to believe that it was obscuring some pretty serious risks and...
Training AI Agents with RL | Unsloth Documentation
Learn how to train AI agents for real-world tasks using Reinforcement Learning (RL).
Matrix - Decentralised and secure communication
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