The Lindahl Letter

Perhaps you were looking for a bit more of a deep dive about deep generative models than will be contained in this relatively short missive. You could go check out Stanford University’s CS236 from Fall 2021 course [ 1 ]. That would help you begin to figure out just how unsupervised learning could be used to figure out the data distribution well enough to generate predicted other data elements. The...

We are going to break out of the planned programming and go where everybody else involved in the machine learning space is going to go this week. Something new arrived this week and has captured the attention of the public mind. Seriously, I thought stable diffusion would be the big thing for 2022, but something else arrived in the intellectual space that might be more influential in the long run....

Recently, I started spending a bit more time writing about quantum machine learning and quantum computing in general. One of the things I became curious about was related to a thread of thoughts about how they code something for a quantum computer. One of the first things that I came across while trying to learn more about how people were coding with Twist was an article in IEEE Spectrum called, “...

Within the broader generative AI space, the part I tend to focus on is related to the written word. Right now, all of the visual generation parts of generative AI in terms of images and videos are wholesale living in the public mind [ 1 ]. Creative people are generating thumbnails and playing with all sorts of plausible image generation technology. A few teams are rapidly working on how to make vi...

We are living on the edge of meeting the weekly Friday publishing deadline at this point. As we quickly approach the 104th post and the major two-year milestone I’m still working on the same Saturday and Sunday schedule of early morning writing. I’m just having to be extra mindful of making sure I don’t get sidetracked into working on other things. This week’s topic could be an entire book full of...

I must have missed it when Samuel K. Moore covered this topic back on February 24, 2022 in an article titled, “RISC-V AI Chips Will Be Everywhere Esperanto Technology’s chip heralds new era in open-source architecture; Intel set to cash in,” [ 1 ]. I’m going to admit that I did fix the spelling on the word technology when I cut and pasted that title over to this Substack post. Fast forward just a...

We are going to get to the 104th Substack post before you know it here for The Lindahl Letter publication. Things are moving along, and we are on the very tail end of that journey. Don’t panic about this post not being full of links. (Spoiler alert) You will have plenty of perspectives to read this week that are linked to for your reading pleasure. This is one of the topics that deserves a lot of...

It seems like having a national artificial intelligence initiative is popular these days. Back on February 18, 2022, I shared my week 56 Substack post, “ Comparative analysis of national AI strategies .” That missive continues to get a good bit of traffic so I thought now would be a good time to go ahead and revisit national AI strategies, advisory committees, institutes, legislation, and the myri...

For those of you that keep track of these types of things we are now in real time based on my publishing schedule. Over the course of the next few weeks no backlog exists as we make the run to 104 consecutive Substack posts spanning 2 years of content creation on this platform. It’s week 91 right now in the publishing schedule which means that only 13 blocks of super exciting writing about machine...

The post this week is going to be on the shorter side of things. I think that is in part due to the very straightforward nature of the topic under consideration. It really could have just been a link to a single book on the subject with a polite note that reading it would help you understand pretty much everything you need to know. To that end, it looks like the book on probabilistic machine learn...

A lot of people talk about deploying AI in the business world and almost all that conjecture is entirely based on deploying a machine learning model into a production environment or some interesting POC. When those same people deploy an actual AI product into production, they will hopefully see the difference. They are not the same. A lot of the AI hype is underpinned by advances in machine learni...

Brief aside: A bunch of shuffling has occurred in the forward-looking topics as we approach two years of The Lindahl Letter. Reworking the content for weeks 89 to 104 had to happen after the syllabus project. My focus and interest shifted a bit and due to that it made sense to go ahead and rework the pathing toward that extra special two-year anniversary of writing posts on Substack. That brief as...

Research Note: You made it all the way to week 8 of 8 for the ML syllabus. You can find the files from the syllabus being built on GitHub . The latest version of the draft in PDF form can be found here . This lecture is going to be provided in two parts. First, I’m going to provide you with a few scholarly articles that dig into what MLOps involves and how researchers are addressing the topic. Sec...

This set of topics was either going to be the foundation to start this series or it was going to be collected as a set of thoughts at the end. You can tell that obviously I demurred from starting with ethics, fairness, bias, and privacy in machine learning until the full foundation was set for the topics under consideration. These topics are not assembled as an afterthought and are very important...

Neuroscience is a complex topic to dig into in general. Studying the nervous system is a complex thing to do before you add in the concept of machine learning or artificial intelligence. Within the context of machine learning it gets even more interesting for academic researchers, practitioners, and anybody building neural networks. Understanding that context of complexity within any inquiry into...

You may find in the literature that this topic of neural networks is sometimes called the zoo or more specifically, “the neural network zoo.” Corresponding to the articles that make this reference is a wonderful included graphic that shows a ton of different neural networks and can really give you a sense of how they work at the most fundamental level. Two papers that make this reference and inclu...

During the last lecture we jumped in and looked at 10 machine learning algorithms. This week the content contained within this lecture will cover from a machine learning perspective reinforcement learning and 3 types of supervised learning. Those types of supervised learning will include the general use case of supervised learning, unsupervised learning, and the super interesting semi-supervised l...

Welcome to the lecture on ML algorithms. This topic was held until the 3rd installment of this series to allow a foundation for the concept of machine learning to develop. At some point, you are going to want to operationalize your knowledge of machine learning to do some things. For the vast majority of you one of these ML algorithms will be that something. Please take a step back and consider th...

You can find a lot of quality explanations of the differences between the various flavors of machine learning [ 1 ]. This second lecture in the introduction to ML syllabus series should open with a series of the best literature reviews I could find and pull together to share. That will be the second part of this lecture. The third part will cover the intersection of programming languages. Some rat...

You might remember the Substack post from week 57 titled, “How would I compose an ML syllabus?” We have now reached the point in the program where you are going to receive 8 straight Substack posts that would combine together to compose what I would provide somebody as an introduction to machine learning syllabus. We are going to begin to address the breadth and depth of the field of machine learn...

Transformers were the thing. They were a big thing in the machine learning field. It was glorious. People talked about them a lot and papers were published. Oh so many papers were published. Now it feels like diffusion might be the thing. You will find that the thing of the moment in the field of machine learning shifts rapidly. I was looking at a GitHub repository based on, “high-Resolution Image...

This week based on the backlog, I should be covering the topic of Bayesian optimization. During the course of sitting down to write this week something different happened. Apparently, I was a highly misbehaven backlog prompt this morning. Instead of digging into that topic I’m going to spend some time talking about a more pressing philosophical question related to the future of trust and digital i...

One of the things I have seen trending around the internet places I visit is related to quantum machine learning. I went over to Google Trends and took a look at the last 12 months and could see a decent volume of people generating related queries. If you were to categorize interest by state, then the top 8 would look like: New Hampshire, Washington, California, Pennsylvania, New York, Indiana, Te...

Last week some difficult questions were asked about overcrowding within the field of machine learning and the effect of that on engineering colleges [ 0 ]. This week things are getting even deeper into the hard philosophical questions we are starting to face. Working to answer the question, “What is post theory science?” will require a lot of consideration and a good bit of digging around. A lot o...

Welcome to a more investigative journalism based issue of The Lindahl Letter this week. This one really made me think. It's a provocative question for sure. Emotional reactions to the premise of destruction aside the question of overcrowding within the field of ML has really caught my attention this week. One piece of prose stood out on this topic and I’m not the only one to react to it. Published...