Four short links: 21 July 2020
Product Management, GPT-3, Stalk Studio, and Symbolic from Deep Learning
- 22 Principles for Great Product Managers — This list—pieced together over the past few years—reflects what I believe are some of the most important principles for product managers.
- Tempering Expectations of GPT-3 and AGI — When I was curating my generated tweets, I estimated 30-40% of the tweets were usable comedically, a massive improvement over the 5-10% usability from my GPT-2 tweet generation. However, a 30-40% success rate implies a 60-70% failure rate, which is patently unsuitable for a production application. (via Simon Willison)
- Stalk Studio — open source experimental debugger & profiler built on top of distributed tracing. Intuitive DevTools-like UI for extracting useful information from complex traces; Visualize & inspect multiple traces on the same stage; It supports Jaeger and Zipkin out of the box. Nifty!
- Discovering Symbolic Models from Deep Learning with Inductive Biases — We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The technique works as follows: we first encourage sparse latent representations when we train a GNN in a supervised setting, then we apply symbolic regression to components of the learned model to extract explicit physical relations. We find the correct known equations, including force laws and Hamiltonians, can be extracted from the neural network.