PODCAST

The Lindahl Letter

Dr. Nels Lindahl

Thoughts about technology (AI/ML) in newsletter form every Friday www.nelsx.com

technology management

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5:03

We have a National Artificial Intelligence Advisory...

The Lindahl Letter ·

2022/10/28

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 myriad of research institutes or think tanks that are jumping into this area of consideration. This is an area

5:12

What are ensemble ML models?

The Lindahl Letter ·

2022/10/21

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 learning stand between you and the completion of that penultimate tasking. To that end I’ll be working

3:51

What is probabilistic machine learning?

The Lindahl Letter ·

2022/10/14

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 learning from Kevin Patrick Murphy has been downloaded 168 thousand times [1]. That is pretty darn good for

6:47

That ML model is not an AGI

The Lindahl Letter ·

2022/10/07

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 learning. Artificial general intelligence or more commonly abbreviated as AGI represents an interesting

5:59

The future of academic publishing

The Lindahl Letter ·

2022/09/30

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 aside is now complete. Some congratulations are in order, you made it to the post where I am going to write

7:28

MLOps (ML syllabus edition 8/8)

The Lindahl Letter ·

2022/09/23

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. Second, I’ll provide you my insights on the topic of MLOps which I have been presenting for the last few

7:12

Ethics, fairness, bias, and privacy (ML syllabus...

The Lindahl Letter ·

2022/09/16

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 to any journey within the machine learning space. This technology in terms of machine learning and

3:53

Neuroscience (ML syllabus edition 6/8)

The Lindahl Letter ·

2022/09/09

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 neuroscience, it will make sense here to focus on 5 scholarly articles that could help provide a solid

13:00

Neural networks (ML syllabus edition 5/8)

The Lindahl Letter ·

2022/09/02

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 include that wonderful graphic are from researchers at the Asimov institute that had papers published in 2016

9:24

Machine learning approaches (ML syllabus edition 4/8)

The Lindahl Letter ·

2022/08/26

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 learning. Like the model for consideration used in the last lecture I’ll cover the topics in general and

12:57

ML algorithms (ML syllabus edition 3/8)

The Lindahl Letter ·

2022/08/19

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 this very real scenario. Within the general scientific community getting different results every time you

13:12

A machine learning literature review (ML syllabus...

The Lindahl Letter ·

2022/08/12

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 rather high quality textbooks and manuscripts exist within the field of machine learning. You can even find

9:44

Bayesian optimization (ML syllabus edition 1/8)

The Lindahl Letter ·

2022/08/05

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 learning. Please do consider that machine learning is widely considered just a small slice of the totality of

4:58

Why is diffusion so popular?

The Lindahl Letter ·

2022/07/29

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 Synthesis with Latent Diffusion Models,” and it has over 2,000 stars and has been forked 242 times [1]

8:01

Trust and the future of digital photography

The Lindahl Letter ·

2022/07/22

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 images. This missive is more about expressing and recognizing concern than delivering information

5:41

Is quantum machine learning gaining momentum?

The Lindahl Letter ·

2022/07/15

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, Texas, and Florida [1]. I’m a little surprised that my research efforts within the last 12 months did not

4:52

What is post theory science?

The Lindahl Letter ·

2022/07/08

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 of the ML things that are bubbling up right now in terms of ethical conundrums and rapidly changing ML

Is ML destroying engineering colleges?

The Lindahl Letter ·

2022/07/01

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 back on January 22, 2022 on LinkedIn of all places was a post titled, “How a False Love for AI/ML is

5:24

ML content automation; Am I the prompt?

The Lindahl Letter ·

2022/06/24

Thank you for tuning in to this audio only podcast presentation. This is week 74 of The Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for The Lindahl Letter is, “ML content automation.”At this point, I started to ask myself if I’m the prompt of an exceedingly large language model. Perhaps a better trained and curated model than any foundational model. Prompt engineering is a wonderfully interesting part of ML content automation in terms of

5:38

Symbolic machine learning

The Lindahl Letter ·

2022/06/17

Within my staging Google document for Substack posts I reached the end of the originally planned out posts for this series of content. Earlier this morning I expanded the staging shell post outlines to week 104 which as you can imagine is a significant point in the publication lifecycle. 2 years of writing Substack posts will be here before you know it. I have enough content in the backlog for this Substack series to get to week 120. At the two year mark I’m planning on moving away from machine

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