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Artificial intelligence (AI) is being integrated into our society at a breakneck pace, fueled by its incredible abilities in a wide range of human-like tasks, from facial recognition to playing Go. In particular, the emergence of deep learning models, a family of models based around neural networks, has driven the recent success of AI at different problems previously believed to be intractable for computers to solve.

Traditional debate around the role of AI in our society has focused on model bias and discrimination. Such concern and discussion are certainly necessary and well-warranted. …

This essay was inspired by Jeffrey Ding’s reflections on gatekeeping in his ChinAI Substack newsletter, which covers China-US tech policy.

COVID-19: A Catalyst for Experimentation

“There are decades where nothing happens, and there are weeks where decades happen.” …

AI is becoming the transformational technology that will define the next wave of a broad swath of industries, similar to the massive online e-commerce shift that began in the late 2000’s. Oftentimes with new technological shifts, one of the most important resulting innovations are new business models. As I’ve been discussing throughout this newsletter, the intersection of AI and science has particular nuances that make its adoption and development subtly different from the development of AI as a whole — the same is true of its business models. …

What happens when a $100B tech company takes on basic science and AI problems?

I recently started a free email newsletter called ML4Sci which highlights applications of Machine Learning for Scientific applications, a topic which I’ve written about here on Medium as well. Interested in reading more? You can find more issues here and if you like what you see, feel free to sign up!

Hi, I’m Charles Yang and I’m sharing (roughly) monthly issues about applications of artificial intelligence and machine learning to problems of interest to scientists and engineers.

This newsletter is still in its early days and I’m still figuring out the format and topical coverage that I want to pursue. Right now, I’m leaning towards having several short article discussions, with one long-form, in-depth description and analysis. If you have any feedback or suggestions, including interesting articles, new ideas for how to format these newsletters, length considerations, scope or coverage, or anything else, feel free to reach out at …

What does GPT-2 have to say about data science?

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Human-written Content

Using the 1558MB version of OpenAI’s GPT-2 model (the largest version) and Max Woolf’s gpt2-simple package on github powered by Google colaboratory, I generated the following text with data science focused prompts. The github repo for this project is located here. All text in bold is human written. Scroll down for a meta-analysis about the implications of this technology and some more about how I created this.

GPT-2 Generated Content

Data Science is a set of techniques for conducting complex, multidimensional analysis of large data sets. …

A survey of opportunities and trends

1 Corinthians 10:23

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“The AI revolution in science”, Science Mag, 2017


In this post, I’ll survey the opportunities for applying Deep Learning (DL) to scientific and engineering applications. I’ll begin wtith a perspective on the emergence of empirical models and then briefly cover some of the salient characteristics of neural networks. The main focus will be covering emerging general trends and problem types for applying DL across a variety of scientific domains.

The Rise of Empirical Models

Humans have always been building things. But only recently did we start to engineer extremely complex things: skyscrapers, commercial jets, etc. In order to make this leap from rocks to steel I-beams, we constructed models: simplified, succinct, representations of the world. In this way, we could build complex things, confident that they would work the way we wanted them to, because we had already modeled them beforehand. …

A reflection on learning math

“…knowledge puffs up, but love builds up.” — 1 Corinthians 8:1

“Mathematical maturity” is one of those catch-phrases in higher education that is often used but rarely defined. Even more obscure is how exactly to reach “mathematical maturity”. In this post, I want to share some insights I’ve gained along my math-filled journey.

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The Journey

Disclaimer: If you’re in a class and you don’t know what’s going on, none of the below will help. Basic understanding is a hard prerequisite for mastery. Read the textbook, go to lecture (and pay attention). Start with the minimum, then strive for more.

Practice required beyond this point

It might seem obvious, but you really do need lots of practice. Repeated exposure to the material helps ingrain the mechanical operations into your muscle memory. Practice also helps to eliminate simple algebraic mistakes that plague many students in middle and high school. Eliminating these mistakes is an early indicator of progress on the path to mathematical maturity. But simple practice is not a sufficient condition to deep conceptual understanding. The rest of these tips improve off of “brute-force” practicing to encourage mindful practice that is more efficient and goes beyond the ability to replicate a series of mechanical steps. …

All linked articles are open-access!

Metal Organic Frameworks (MOF) are one of the most exciting classes of chemical structures to be discovered in the past decade. …


Charles Yang

UC Berkeley student interested in clean tech and machine learning

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