Mostly Harmless AI

🖖 Welcome to the first issue of Mostly Harmless AI! Every Sunday, I’ll send you a curated list of interesting bits about AI all around the spectrum, from cool new research and tools to news and discussions, and everything in-between.

❤️ Thanks for beta-testing our first issue! Please let me know what do you think about the newsletter, and how we can improve it to make it more useful for you.

Probably the biggest news of the last couple of weeks was the flat-out war between retail traders at Reddit, and hedge funds at Wall Street. If you haven’t caught up yet, here is some general analysis by Reuters. An interesting bit at The Next Web hints on how Wall Street is doubling-back by investing in AI technology used for fake news detection to (in the near future) monitor social media and detect these types of behaviors earlier.

Google continues to struggle with its handling of diversity issues within and outside the company’s headquarters. After the very public incident with Timnit Gebru, a top researcher in AI Ethics, this week they continue to receive criticism over the resignment of another two engineers.

The tech startup Weights & Biases, which produces services for running large-scale machine learning experiments on the cloud with built-in tools for monitoring and evaluation, has raised 45M in another investment round, which speaks of the rapid adoption of the MLOps paradigms in both large and small teams.

In a piece of news that echoes some of the creepiest Black Mirror-esque stories, Microsoft has filed a patent to, supposedly, create chatbots that resemble dead relatives. While we’re still far from being able to replicate seemingly human-like conversation, these developments (which other companies are also working on) hint at a near-future market on our memories.

This is hands down the best introduction to deep learning for anyone with a bit of coding background, especially if you don’t want to spend years studying the math fundamentals first. The course is based on Pytorch and fast.ai‘s own library, both of which are very mature frameworks.

This 3-course specialization on Generative Adversarial Networks from deeplearning.ai will take from zero to GAN expert. It’s based on Pytorch and requires no previous familiarity with advanced math or machine learning concepts, just a basic level of Python.

The author of the masterfully written “Machine Learning: A Probabilistic Perspective” is back with an updated version, which you can get for free. This is a perfect book for all math savvy out there who want to get a more in-depth introduction to machine learning.

This week’s tool of the trade is the spaCy Python library. It’s an industry-grade framework for natural language processing. It comes packaged with pre-trained models for a bunch of tasks, from entity recognition to sentiment analysis. They just launched v3.0 which updates their models to state-of-the-art transformer architectures, and puts them at the frontier in terms of accuracy. They provide a free beginner course that’s a very smooth entry to their ecosystem.

Reddit is definitely the place where long and interesting conversations happen. In the last couple of days, one topic has stirred the r/MachineLearning subreddit, regarding the difference between how pop media talks about AI, and what’s really happening in science. Most people agree that social media has been overhyping the term AI for a few years, and most popular news articles do not reflect the realistic limitations and challenges we are facing in current systems. Check out the thread for some witty comments, some funny jokes, and some deep insights into this issue.

If you’re interested in learning about machine learning, you should follow Santiago (@svpino). He’s always sharing very practical advice on machine learning engineering and a bunch of other topics. Plus, he’s a super nice person.

Madeline (@madeline_pc) is a musician and mom, turned data science apprentice. She’s learning and documenting her journey on Twitter and on her Dev.to blog. Plus, her kids are always around to make you smile.

Jose (@josejorgexl) is a recently graduated computer scientist, who shares what he knows about algorithms, math, data structures, AI, history, and whatnot. And he knows a lot about those topics.

A couple of weeks ago I started a series on foundational concepts of machine learning. Here is the first thread, where you’ll find some initial definitions, and links to the next three threads that go deeper into the basic concepts.

This week’s thematic thread was about the philosophical question of identity. Here I explore what it means to be you, what is consciousness, and how your beliefs about these topics relate to your beliefs about the possibility of strong AI.

👋 That’s it for now. Please let me know what do you think of this issue, what would you like to see more or less of, and any feedback you want to share. If you liked this newsletter, consider subscribing (in case you’re not) and forwarding it to those you love. It’s 💯 free!