Digests » 59

ai

Using artificial intelligence to better predict severe weather

When forecasting weather, meteorologists use a number of models and data sources to track shapes and movements of clouds that could indicate severe storms. However, with increasingly expanding weather data sets and looming deadlines, it is nearly impossible for them to monitor all storm formations -- especially smaller-scale ones -- in real time.

AI, Truth, and Society: Deepfakes at the front of the Technological Cold War

This is the first part of our special feature series on Deepfakes, exploring the latest developments and implications in this nascent field of AI. We will be covering detailed implementations on generation and countering strategies in future parts, stay tuned to GradientCrescent to learn more.

Hidden Technical Debt in Machine Learning Systems

Machine learning offers a fantastically powerful toolkit for building useful complexprediction systems quickly. This paper argues it is dangerous to think ofthese quick wins as coming for free. Using the software engineering frameworkof technical debt, we find it is common to incur massive ongoing maintenancecosts in real-world ML systems. We explore several ML-specific risk factors toaccount for in system design. These include boundary erosion, entanglement,hidden feedback loops, undeclared consumers, data dependencies, configurationissues, changes in the external world, and a variety of system-level anti-patterns.

Weights & Biases - Image to Latex

In order to reduce the amount of time a scientist takes to write a LaTeX equation, we created an automated process which translates images of formulas into LaTeX code for the user. We hope by utilizing this application, our users can free themselves from learning and spending time creating correct LaTeX, but rather focus on what’s really important - their work. A scientist could capture an equation that already exists in a paper or on the internet and instantly get the LaTeX code to modify it to fit their purpose. By leveraging deep learning, we managed to train a model that performs better than the public state of the art for this task.

The German Tank Problem

Early in the war, the Allies started to record the serial numbers on tanks captured from the Germans. The statisticians looking at this data believed that the Germans, being Germans, had numbered their parts in the order they rolled off the production line. Amazingly, using the serial numbers from a relatively small numbers of captured tanks, the statisticians were able to accurately estimate the total number of tanks that were produced! So, how did they do it?