Single-Shot Prompts Converge Toward Mid Experiences Single-prompt LLM games flatten out. Several things compound to make this inevitable: Sycophancy. Models mirror the player’s tone. A passive player gets validated with pleasant, low-stakes scenes. An aggressive player gets escalation that never plateaus. The model accommodates rather than challenges. Autoregression. Each token is conditioned on…
Inspiration to implementation I recently came across a fascinating blog post titled “The Abundance Problem of Generative Art” that inspired me to explore the creation of a pure Python implementation of the concepts discussed. If you’re interested in the aesthetic beauty of generative art and the thought process behind it, I highly recommend reading the blog post. The blog post features a visually…
TLDR; Notebook necessary to explore this machine-learning process hands-on and generate all the graphs below is available on my github What is dropout and how can we use it to quantify uncertainty? Dropout is a method of systematically inactivating certain elements of the layers of your neural network. Its primary use is implementing regularization and there are great tutorials like this one that…
TLDR; Notebook necessary to explore this data process hands on and generate all the graphs here In this entry in our quantifying uncertainty series, we take our first look at time-series data. There are tons of really interesting questions that can be answered about time-series data with ML methods - from forecasting to causality inference -which all have room for uncertainty quantification. In…
TLDR; Notebook necessary to explore this data process hands on and generate all the graphs here In our previous episode we investigated how ensemble methods could be leveraged to quantify the uncertainty of an underlying data-set. While this approach was super easy to implement it fell short of recapitulating the exact discordance between observed and predicted values for the most part. Here we…
Uncertainly Quantification and How much we pay for lunch. Uncertainly quantification can be something of an abstract concept, but let’s try to anchor it to a simple question. Can you guess how much someone spent on lunch based on how much they make ? While the initial intuition might be “yeah it simply correlates with how much they earn” - consider this : rich people still eat the occasional fast…
Winners and Losers The 2007 financial crisis affected each of us differently, with the “winners” and “losers” falling sharply into economic lines. The graph below compares how the bottom 20% of income earners with the top 10%. Adjusted for inflation, there is an obvious difference in how the two group faired through the financial crisis (highlighted in gray). Why did this happen? Why did the poor…
For an r/dataisbeautiful challenge I took a look at data surrounding the roman emperors. Click on the image below to check it out:
Building Animations in Python While they may be over-hyped these days, animations are a key form of data visualization. Some things just look better changing over time. Also, while some languages like d3.js or processing put a focus on animations first, Python remains relatively clunky to use if you want to make smooth animations that pop. Take for instance an example animation like the one…
The Worst Airports in America What makes a bad airport? There’s a lot that can go wrong with a flight - like having to ride in a snow crawler as in Washington Dulles . But we can all agree that having your flight delayed is one of the worst things. By extension, looking at the number of delays per airport is a good way to rank the quality of those airports. The chart below shows the worst airports…