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We've already talked about neural nets in some detail (links below), and in particular we've been blown away by the way that image recognition from convolutional neural nets can be fed into recurrent neural nets that generate descriptions and c
We often hear from folks wondering what advice we can give them as they search for their first job in data science. What does a hiring manager look for? Should someone focus on taking classes online, doing a bootcamp, reading books, something e
This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows. If you've worked in software before, you're probably familiar with the idea of technical debt, which are inefficiencies
If you’re like most software engineers and, especially, data scientists, you find it really hard to make accurate estimates of how long a project will take to complete. Don’t feel bad: statistics is most likely actively working against your bes
53.5 million light-years away, there’s a gigantic galaxy called M87 with something interesting going on inside it. Between Einstein’s theory of relativity and the motion of a group of stars in the galaxy (the motion is characteristic of there b
As artificial intelligence algorithms get applied to more and more domains, a question that often arises is whether to somehow build structure into the algorithm itself to mimic the structure of the problem. There’s usually some amount of knowl
It’s not news that data scientists are expected to be capable in many different areas (writing software, designing experiments, analyzing data, talking to non-technical stakeholders). One thing that has been changing, though, as the field becom
If you work in data science, you’re well aware of the sheer volume of high-risk, high-reward projects that are hypothetically possible. The fact that they’re high-reward means they’re exciting to think about, and the payoff would be huge if the
When you are running an AB test, one of the most important questions is how much data to collect. Collect too little, and you can end up drawing the wrong conclusion from your experiment. But in a world where experimenting is generally not free
OpenAI recently created a cutting-edge new natural language processing model, but unlike all their other projects so far, they have not released it to the public. Why? It seems to be a little too good. It can answer reading comprehension questi
Imagine you have two choices of how to build something: top-down and controlled, with a few people playing a master designer role, or bottom-up and free-for-all, with nobody playing an explicit architect role. Which one do you think would make
It’s time for our latest installation in the series on artificial intelligence agents beating humans at games that we thought were safe from the robots. In this case, the game is StarCraft, and the AI agent is AlphaStar, from the same team that
For many data scientists, maintaining models and workflows in production is both a huge part of their job and not something they necessarily trained for if their background is more in statistics or machine learning methodology. Productionizing
You’d be hard-pressed to find a field with bigger, richer, and more scientifically valuable data than particle physics. Years before “data scientist” was even a term, particle physicists were inventing technologies like the world wide web and c
K Nearest Neighbors is an algorithm with secrets. On one hand, the algorithm itself is as straightforward as possible: find the labeled points nearest the point that you need to predict, and make a prediction that’s the average of their answers
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