The Lindahl Letter

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]. I started reading this Tweet from Sebastian Raschka back on January 30, 2022 that asked the question, “Has anyone tried diffusion-based models, yet? Heard that they produce better results than GA” [2]. That Tweet linked out to a paper on ArXiv called, “Diffusion Models Beat GANs on Image Synthesis” [3]. It was published back during May of 2021 by OpenAI researchers and has seen 4 revisions so far. The paper loaded very slowly for me which was surprising. Rarely do I ever watch an update bar slowly creep across the screen waiting for a file to load up. It was 44 pages and 38 megabytes of data. That file should have arrived a lot faster. I took a look at another GitHub repository on guided diffusion from OpenAI that had 1,700 stars [4]. The audience for these diffusion code sets seems to be about 2,000 people which is interesting. Machine learning in general gets roughly 10,000 people focusing on things making this a subset within that slightly larger universe of attention. 

Given that we have moved into the 2nd paragraph it might be a good time to talk about what exactly diffusion might be in the context of machine learning. Over in the field of thermodynamics you could study gas molecules. Maybe you want to learn about how those gas molecules would diffuse from a high density to a low density area and you would also want to know how those gas molecules would reverse course. That is the basic theoretical part of the equation you need to absorb at the moment. Within the field of machine learning people have been building models that learn how based on degree of noise to diffuse the data and then reverse that process. That is basically the diffusion process in a nutshell. You can imagine that the cost to do this is computationally expensive. Let’s jump from OpenAI over to the Google AI team who wrote about, “High Fidelity Image Generation Using Diffusion Models” [5]. If you get to read that last link from the Google AI team then you will get to see a bunch of examples of how this works in practice. Imagine a lower quality image that is smaller, being increased in both quality and size. Now you need to imagine that happening again which is what makes the model they are using seem really novel and exciting. I ended up going back to a 2015 paper, “Deep Unsupervised Learning using Nonequilibrium Thermodynamics,” and trying to get a little bit more detail on how the noise process works to create and reverse diffusion [6]. 

Links and thoughts:

Top 5 Tweets of the week:

Footnotes:

[1] https://github.com/CompVis/latent-diffusion 

[2]

[3] https://arxiv.org/abs/2105.05233 

[4] https://github.com/openai/guided-diffusion

[5] https://ai.googleblog.com/2021/07/high-fidelity-image-generation-using.html

[6] https://arxiv.org/abs/1503.03585 

What’s next for The Lindahl Letter?

* Week 80: Bayesian optimization (ML syllabus edition 1/8)

* Week 81: Deep learning (ML syllabus edition 2/8)

* Week 82: ML algorithms (ML syllabus edition 3/8)

* Week 83: Neural networks (ML syllabus edition 4/8)

* Week 84: Reinforcement learning (ML syllabus edition 5/8)

* Week 85: Graph neural networks (ML syllabus edition 6/8)

* Week 86: Neuroscience (ML syllabus edition 7/8)

* Week 87: Ethics (fairness, bias, privacy) (ML syllabus edition 8/8)

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. If you are new to The Lindahl Letter, then please consider subscribing. New editions arrive every Friday. Thank you and enjoy the week ahead.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com

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. Starting in April of 2022, OpenAi shared the DALL-E 2 model which the researchers noted, “DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language” [1]. Strangely enough the word ethics does not really appear on the homepage for the DALL-E 2 system. It was apparently more important to spend time setting up an Instagram account for art created by the model [2]. You can pretty easily go see how photorealistic some of these images are. With this release from OpenAI, I never requested to join the waitlist to kick the tires on this one. I’m generally more interested in natural language processing than visual image processing. 

Let’s set the stage as clearly as possible on this one. People are used to being able to go to the photo finish. Races have been decided by photos and ultimately video for years. We trust video in replay for sports and it remains the visual record of our times. Things changed. Full stop. It used to require a lot of effort to make a deep fake or to alter photographs. It required software and spending time to accomplish that task. Right now a host of new models and other ML implementations are creating the possibility of asking a prompt for an image and within a few seconds getting a reasonable approximation. Some of these images are really high quality. They are photorealistic renderings. You can call up pictures of people who never existed doing things that never happened. 

All of this raises really interesting ethical questions about the use and propagation of such ML technology. Keep in mind that these models are just openly being used and served up online. No degree of concern for the potential social impact stopped the distribution and ultimately additional model development. Right now I’m going to say that you cannot trust the future of digital photography. Don’t believe what your eyes report at this point within the digital space. Right now these models very quickly change and make digital images. Soon enough people will develop that technology into a series of images and realistic video will be produced based on a prompt. Essentially they just have to extend the model to the context of a few frames in series and short videos will spring into existence. The evening news could galvanize popular opinion with a story and photograph. At this point, I’m not sure we can trust that type of evidence anymore. Lingering implementations for how civil society is going to change in the face of a zero trust image paradigm. I’m not sure people even understand the alternate realities that could be created and presented as fact. Somebody could bring forward the presentation of a very news forward YouTube channel powered by DALL-E 2 created images. For example, you could introduce a new continent and talk about the discovery of Atlantis and potentially go on for years presenting an alternate reality as truth. Somebody will probably make a living doing that or something equivalent to it. That is where the ethical considerations of this technology and the impacts on society as a whole took a backseat to race to share and demonstrate effectiveness. 

Take a moment and consider that just because a technology can do a thing does not mean it should be used to do those things. We make choices. You have to have ethical considerations at the forefront of that type of decision making. We are getting to a point where we have a zero trust image paradigm that will effectively make it a necessity to question everything you see in terms of digital photography and ultimately video. That realization and reality will reverberate across interactions in daily life. At this point, based on the evidence we have, I’m going to declare we have to embrace and ultimately enforce a zero trust image paradigm. How do we even label actual historical documents accurately at this point? Historians will have to be very careful going forward in the analysis of the times about to happen. This may very well be a watershed moment about how we evaluate the truth in front of us and how we verify and validate that narrative. My argument here is not intended to be hyperbolic or presented with any sarcasm whatsoever. A very real situation is developing within our ability to trust the visual world being presented to us. We have to consider the possibilities in front of us and begin to evaluate a path forward. I’m assuming that the path forward is zero trust. That should be clear within the argument being presented. You will have to decide what to do with the world being brought to live by models and systems like DALL-E by OpenAI.

Other prompt based text to image generating models exist as well: DALL-E mini, GLID-3, CLIP, RuDALL.E, and X-LXMERT. My focus here is on the DALL-E model from OpenAI as it has seemed to capture a higher degree of interest from the public mind [3]. You can go run the DALL-E mini model from Hugging Face spaces online for free [4]. However, that site is apparently migrating to craiyon and you find a link to that shared within the footnotes [5]. You can check it out for yourself and see if you share my concern. At the very least, you need to be prepared to openly question any images that are presented go forward. They could very well be synthetically generated.

Links and thoughts:

“Intel Messed Up - WAN Show June 24, 2022”

“Vergecast: M2 MacBook Pro review, Solana’s crypto phone, and this week’s tech news”https://megaphone.link/VMP9448497842 

Top 7 Tweets of the week:

Footnotes:

[1] https://openai.com/dall-e-2/ 

[2] https://www.instagram.com/openaidalle/ 

[3] https://techcrunch.com/2022/04/06/openais-new-dall-e-model-draws-anything-but-bigger-better-and-faster-than-before/ 

[4] https://huggingface.co/spaces/dalle-mini/dalle-mini 

[5] https://www.craiyon.com/

What’s next for The Lindahl Letter?

* Week 79: Why is diffusion so popular?

* Backlog: What is GPT-NeoX-20B? Bonus topic: What is XGBoost?

* Week 80: Deep learning Bonus topic: Bayesian optimization

* Week 81: Classic ML algorithms

* Week 82: Classic neural networks

* Week 83: Neuroscience

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. If you are new to The Lindahl Letter, then please consider subscribing. New editions arrive every Friday. Thank you and enjoy the week ahead.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com

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 get Colorado on that list of interest by subregion. Apparently, I’m going to need to step up my quantum computing game.

After getting a sense of where in the United States people are interested in quantum machine learning I started to consider what topics are slightly related. That effort is intended to help me understand the edges of the topic and be able to look 360 degrees around the idea being evaluated today. 

The Top 10 related queries would be: 

* quantum machine learning

* open source quantum machine learning

* quantum machine learning solutions

* quantum machine learning tools

* quantum machine learning system

* quantum machine learning software

* quantum machine learning services

* quantum machine learning applications

* free quantum machine learning solutions

* free quantum machine learning tools

Within the Google trends frameworks they offer another layer of insight beyond the Top 10 related queries. Let’s take a look at that next layer.

The Top 10 rising queries would be: 

* free cloud based quantum machine learning tools

* free cloud based quantum machine learning solutions

* free quantum machine learning tools

* free quantum machine learning applications

* free open source quantum machine learning applications

* free quantum machine learning solutions

* open source quantum machine learning software

* free cloud based quantum machine learning software

* free cloud based quantum machine learning services

* free open source quantum machine learning tools

To help drill down to some context of how often quantum machine learning really comes up as a topic I ended up comparing machine learning, artificial intelligence, and quantum machine learning [2]. Searches for artificial intelligence are an order of magnitude bigger than searches for quantum machine learning. Overall the searches for machine learning are much larger than quantum machine learning, but still like 20% of the artificial intelligence related volume. 

The interest quantified as searches for artificial intelligence have jumped up incredibly high. That is wholesale due to an employee from Google who is an engineer that works with the technology noting publicly a concern that the AI had become sentient [3]. That disclosure has caused a stir online and in the media. I’m not going to cover the nature of what sentient AI describes in this newsletter. It will certainly be a topic that gets covered in more detail after the dust settles on this one. You can check out the first video in the links and thoughts section below for 20 minutes of coverage on this one from the one and only Yannic Kilcher. I’ll share two links to the Medium platform here, but I’m not going to cover them by naming the engineer [4][5]. I think it is entirely possible that the content from those two links will get pulled down at some point in the not so distant future. Unless all of this was just some sort of weird publicity play from Google to raise awareness about LaMDA. 

Links and thoughts:

“Did Google's LaMDA chatbot just become sentient?”

From CNET, “Why I’m More Excited About the Next UPS Truck Than the Next Tesla”

“The Download: GitHub Achievements, LTT to the Rescue, and Goodbye to an Old Friend”

Top 6 Tweets of the week:

Footnotes:

[1] https://trends.google.com/trends/explore?geo=US&q=%2Fm%2F012c1btp 

[2] https://trends.google.com/trends/explore?geo=US&q=machine%20learning,%2Fm%2F0mkz,%2Fm%2F012c1btp 

[3] https://www.npr.org/2022/06/16/1105552435/google-ai-sentient 

[4] https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917 

[5] https://cajundiscordian.medium.com/what-is-lamda-and-what-does-it-want-688632134489 

What’s next for The Lindahl Letter?

* Week 77: Is quantum machine learning gaining momentum?

* Week 78: Why is diffusion so popular?

* Week 79: What is GPT-NeoX-20B? Bonus topic: What is XGBoost?

* Week 80: Deep learning Bonus topic: Bayesian optimization

* Week 81: Classic ML algorithms

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed reading this content, then please take a moment and share it with a friend.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com

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 delivery use cases are just ahead of the next wave of publications and methodology research articles, manuscripts, and textbook updates. This is another topic that I think is going to end up getting a lot of coverage at some point. Just like the potential effects of overcrowding within the machine learning space limiting other research within engineering colleges. The compounding problem from that is that the overcrowded research happens to be very shallow within the ML space resulting in papers without real and lasting contributions to the field. 

Let’s focus on the question at hand, “What is post theory science?” This is an interesting scenario to have in existence. Machine learning models can be built to seek out solutions without any theoretical methodology being applied to the potential solution. That type of possibility created a situation where Laura Spinney of The Guardian asked the question, “Are we witnessing the dawn of post-theory science?” [1]. Within that analysis Laura called back to an article from 2008 by Chris Andeson in Wired magazine titled, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete” [2]. In this scenario without using any theoretical basis to create a testable hypothesis or any classic research method a machine learning use case could derive defendable answers. You could do a similar task with big data or just data in general. Things can be observed and built into a postulate or observation that is not directly based on scientific theory. We can learn things that are seen as being objectively true, but are not derived from theory. In this case that type of effort could very well be called a post theory scientific research methodology. You could build an AI model that aims to understand elements of the universe and it could just be allowed to run and work on that effort. Within that proposition the AI model could test and work with model settings that yield results, but are not based on theory. It could just be using random configurations or working down a path that was independently derived. 

A search within Google for “post theory science” does not yield a ton of results. At the time this post was written only about 1,740 results existed within the knowledge graph. I’m truly curious when research method books will contain post theory methods. For post theory science to really pick up steam as a method of research in academic institutions it will need to start showing up in research methods textbooks. I have a couple of them on the bookshelf next to me and while they cover mixed methods and a variety of approaches, nothing within that very large book presumes the idea of post theory science. I’m going to guess at some point that will change here in the next couple of years. We are going to see findings and research coming out of the ML space at record levels and a portion of it will be delivering results, conclusions, and data that are not derived from the traditional scientific method. 

Links and thoughts:

I watched this entire video, “This Beat Up, Non-Running Omega Seamaster Has Big Potential! Vintage Watch Restoration”

 “What unions could mean for Apple with Zoe Schiffer”

I’m in and out on Lex’s content, but sometimes I just enjoy listening to a good conversation. “Jonathan Haidt: The Case Against Social Media | Lex Fridman Podcast #291”

“The Lab is a Disaster - WAN Show June 3, 2022”

Top 5 Tweets of the week:

Footnotes:

[0]

“Is ML destroying engineering colleges?”

[1] https://www.theguardian.com/technology/2022/jan/09/are-we-witnessing-the-dawn-of-post-theory-science 

[2] https://www.wired.com/2008/06/pb-theory/ 

What’s next for The Lindahl Letter?

* Week 77: What is GPT-NeoX-20B?

* Week 78: A history of machine learning acquisitions

* Week 79: Bayesian optimization

* Week 80: Deep learning

* Week 81: Classic ML algorithms

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. Thank you and enjoy the day!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com

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 Destroying our Engineering Colleges,” [1]. This post seems like an article in waiting or a research note of some kind that was intended to be widely shared by Smruti Sarangi who is the Usha Hasteer Chair Professor at IIT Delhi. It has caused a lot of discussion for sure with over 500 comments and around 10,000 interactions.

Obviously, at this point, I needed to do a bit more research on this one beyond just reading a post shared by a professor on LinkedIn. I ended up looking all over and found a few Tweets that were adjacent to the topic. Yes, I fell deeply into the rabbit hole of reading exchanges on Twitter that were long stale and otherwise left online like the remnants of previous sunrises. Sure they happened and outside of photograph evidence or in this case Tweets everyone has generally moved on to whatever is next on the agenda.

One of them was from Yaroslav Bulatov who is on the PyTorch team over at Meta/Facebook. 

That Tweet included some arguments toward trying to illustrate what was wrong with ML research by putting papers with a theorem expressed or mentioned, but no significant improvement demonstrated. That paper referenced was from Schmidt, Schneider, and Hennig back in 2021 called, “Descending through a Crowded Valley – Benchmarking Deep Learning Optimizers,” [2]. It’s about selecting an optimizer in the deep learning space and it is an interesting read. They are essentially trying to empirically test optimizers and share the results. It’s an interesting thing to go out and work on in terms of answering questions about what optimizers to actually use. Within the conclusion the authors shared a truly interesting observation that, "Perhaps the most important takeaway from our study is hidden in plain sight: the field is in danger of being drowned by noise." I don’t normally include quotes in my weekly research notes, but this one just stood out and needed to be read aloud for the true impact to be appreciated. My interest here really is about a deeper question about if overcrowding within the ML space is causing negative externalities.

Generally I avoid doing research by reading Twitter posts, but I thought sharing this thread from Tom Goldstein focusing on a history of AI winters. It's from a National Science Foundation town hall talk, “ML Needs Science,” but distilled on Twitter to be easily consumed. 

A counter argument was shared by Melanie Mitchell on Twitter as well. Melanie has also written a paper about AI Winters that is easy to read called, “Why AI is Harder Than We Think” [3].

One of the things in all of that back and forth that caught my attention was a commencement address from 1974 delivered at the California Institute of Technology by Richard Feynman [4].

I could not find enough content to really complete a full post on this topic so not only did I reach out to Gary Marcus on Twitter, but also I sent a note over to Smruti Sarangi on LinkedIn to see if anything additional had been written or published. It turns out that Smruti was just sharing a few thoughts on LinkedIn as a post back on January 22, 2022 and was not preparing any academic papers on the core question of this Substack post. That question was really about, “Is ML destroying engineering colleges?” I ended up wondering about an overcrowding effect within engineering colleges where ML is sucking up all the oxygen and research focus of a generation. That could be reframed into a testable hypothesis by grabbing the last 10 years of publications in some of the top engineering journals to see if publications related to ML are crowding out other academic contributions. I spent a bit of time trying to figure out if somebody had done some academic work on ML creating overcrowding within engineering fields, but I have not found anything directly addressing the elements that caught my attention. 

My Twitter interactions this week have been interesting and lively. Normally my Tweets and other research links shared during the weeks don’t get such a high level of interaction. This is a topic that people seem to be passionate about and a lot of different points of view exist on this one. Research is certainly happening and will continue to happen. My honest guess here is that all the efforts to publish within the general scope of ML will reach a peak and people will branch off into other things. My take is that veering off into new areas of academic exploration for most researchers will be a healthy thing to happen for the academy after a bit of intellectual overcrowding occurred. 

Footnotes:

[1] https://www.linkedin.com/pulse/how-false-love-aiml-destroying-our-engineering-colleges-sarangi/ 

[2] https://arxiv.org/pdf/2007.01547.pdf 

[3] https://arxiv.org/pdf/2104.12871.pdf 

[4] https://calteches.library.caltech.edu/51/2/CargoCult.htm 

[5] https://www.barnesandnoble.com/w/surely-youre-joking-mr-feynman-richard-phillips-feynman/1112142471

What’s next for The Lindahl Letter?

* Week 76: What is post theory science?

* Week 77: What is GPT-NeoX-20B?

* Week 78: A history of machine learning acquisitions

* Week 79: Bayesian optimization

* Week 80: Deep learning

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. Thank you and enjoy the day!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com

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 generative models. You have to sort of know how to prime the pump or in this case the prompt to get the right content to start to flow from something like GPT-3 or one of the larger foundational models that are starting to be floated around. Previously I have warned about content flooding and the potential for the entire internet to just be astroturfed with nearly endless content if the wrong Web3 comes into being. 

Machine learning elements can certainly be used for content generation and extending that functionality to the practice of content automation. You can set up a workflow that just automatically generates content. It could be a bot implementation use case that generates content in response to people. That is an easy method of feeding the model prompts as the only method to interact with a chatbot is to engage in the prompt base activity of sending something from the user to the chatbot. At that point, the prompt has been opened and something is being exchanged to the model which will cause the generation of content. The use case could be extended nearly indefinitely at that point. 

Let’s go beyond just a chatbot use case and jump into the complexity of “automated journalism” or more generally the use of machine learning or artificial intelligence to generate articles for the purpose of reporting news. This is where I worry about the use case being expanded from news rooms to a general effort of flooding or astroturfing topics. Bad actors could step in and create such a flood of content that figuring out what was real and what was synthetic could become the greatest challenge facing the internet. Truth could be swept away into a totality of coverage that covers all potential prompt lines given that synthetically generated content may have no association with reality whatsoever.  My concern related to this path of course started with the advent of GPT-3 and the potential for the synthetic creation of prose that is believable like the article that was published in The Guardian in 2020 [1]. A lot of the content I run into during the course of reading news during the day could very well be synthetically created from a large language or foundational model. We are seeing things like the Microsoft corporation reducing news staff at MSN to replace them with automation [2].

We are now starting to see models like DALL-E 2 from places like OpenAI that are able to make realistic images from a prompt [3]. That takes a prose based use case for the creation of content to a much wider range of use cases. I’m sure the model will go from images to videos at some point and that type of model would be a legitimate game changer for content creation. Automating the ability for a machine learning model to create video from a prompt would be a huge advancement within the world of content automation. You could open and stage an art gallery with live prompt based installations. I think it would actually be an interesting use case for OpenAI to demonstrate the potential of the model. Bad actors within this space could also decide to create endless images and videos to flood the online world with content dedicated to a specific topic or general theme. 

I have spent a lot of time worrying about how to deal with or manage the problematic elements content flooding could create for society in general. The very fabric that binds civil society together might already have seen the breakdown of a curated common thread based on shared experiences. We may have created such a curated content bubble that any shared experience might be limited to commercials and knowledge of products. Being willing to make that assumption might explain a lot of the things happening within society in general at the moment as we face the reality of the intersection of technology and modernity. 

Links and thoughts:

The WAN Show was full of wild Linus stories about home automation today. “Story Time! - WAN Show May 27, 2022”

“Stanford Seminar - Leveraging Human Input to Enable Robust AI Systems”

“Ask the Experts: Scaling responsible MLOps with Azure Machine Learning | CATE21”

Top 5 Tweets of the week:

Footnotes:

[1] https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt-3 

[2] https://www.seattletimes.com/business/local-business/microsoft-is-cutting-dozens-of-msn-news-production-workers-and-replacing-them-with-artificial-intelligence/ 

[3] https://openai.com/dall-e-2/ 

What’s next for The Lindahl Letter?

* Week 75: Is ML destroying engineering colleges?

* Week 76: What is post theory science?

* Week 77: What is GPT-NeoX-20B?

* Week 78: A history of machine learning acquisitions

* Week 79: Bayesian optimization

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed reading this content, then please take a moment and share it with a friend.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com

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 learning posts into just generally covering artificial intelligence and producing research notes related to a planned set of academic articles. 

That means that it is possible that weeks of ongoing coverage of a topic being worked on as a future academic article could be distributed during year 3 of this Substack series. That is probably a good method to really dig deep into a few topics along the way. One of the things I have worked pretty hard to avoid is producing coverage of the same topic over and over again. One of the things I have noticed in the last few months is that I may have reached conceptual exhaustion within the machine learning topics at around one hundred different concepts. At this point, I should probably go look at all the general conceptual models of the machine learning space and see how close I am to reaching comprehensive coverage. I jumped over into Google Trends and took a look at what topics are bubbling to the surface [0].

That very meta aside about the future of The Lindahl Letter being complete; let’s jump into the topic at hand for today. Most of the time you will see people calling out Symbolic AI vs. symbolic machine learning. If you are interested in trying to build an artificial intelligence system that works similar to the human brain in terms of learning, then you are going to run into the idea of Symbolic AI. Think of things like deep learning, Bayesian networks, or evolutionary algorithms. What I was curious about this week was how many times people try to evaluate symbolic machine learning as a concept. Explicitly searching on Google Scholar for “symbolic machine learning” will yield just over 2,000 results [1]. Some of the academic coverage on this topic goes back to the 1990’s which was obviously where my reading started. Typically I try to rewind back to where articles were sparse and the content was more focused. Recently the volume of content has exploded, but a good portion of it is derivative. I ended up reading an article from Harries and Horn called, “Detecting Concept Drift in Financial Time Series Prediction using Symbolic Machine Learning,” that was published back in 1995 [2]. Sometimes I just enjoy reading about forecasting related concepts as it is grounded in a field of study that has always just made sense to me. Within that space of consideration a copy of Armstrong’s Principles of Forecasting (2001) is sitting on my bookshelf just a couple of feet away. I don’t plan on letting go of that weighty tome any time soon. Oddly enough this article seemed to focus on the potential promise of symbolic machine learning in the future. The phrase only occurs 4 times in the article and that includes the title and abstract. I’m wondering if maybe it was added after the article was written. 

I was reading a few academic articles and wondering what exactly people are doing within the practical applications of symbolic machine learning. Google Scholar indicates that 6 related searches stand out. Those searches include remote sensing, algorithms, neural networks, classifiers, reactive control systems, and European settlement maps. Obviously, I was super curious what symbolic machine learning had to do with settlement maps. I found a letter from IEEE Xplore called, “Application of the Symbolic Machine Learning to Copernicus VHR Imagery: The European Settlement Map,” [3]. It’s pretty much exactly what you might think it would be about in terms of a literal mapping of settlements. The scale of the data being processed on this one seems really interesting. This letter did mention symbolic machine learning within the body of the work related to model innovations. It’s a pretty dense publication in terms of concepts being blended together without a lot of explanation. That is probably a byproduct of the authors trying to keep this to 5 pages vs. around 20 pages where that additional commentary would be flushed out.

Links and thoughts:

“Computex 2022 laptops 💻 Elon vs Twitter bots 🤖 Apple ‘testing’ foldable E Ink display 📱”

“See Where Your Electric Car’s Battery Will Go One Day”

“My Investment Pays Off - WAN Show May 20, 2022”

Top 5 Tweets of the week:

Footnotes:

[0] https://trends.google.com/trends/explore?q=machine%20learning 

[1] https://scholar.google.com/scholar?q=%22symbolic+machine+learning%22&hl=en&as_sdt=0&as_vis=1&oi=scholart 

[2] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.5260&rep=rep1&type=pdf 

[3] https://ieeexplore.ieee.org/abstract/document/8941071 

What’s next for The Lindahl Letter?

* Week 74: ML content automation

* Week 75: Is ML destroying engineering colleges?

* Week 76: What is post theory science?

* Week 77: What is GPT-NeoX-20B?

* Week 78: A history of machine learning acquisitions

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed reading this content, then please take a moment and share it with a friend.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com

Security is the element of open source software that has to always be considered. Depending on the size of the associated developer community participating and the rate of development the number of security vulnerabilities is going to rise and fall. It will be a constant battle between those people trying to take advantage of vulnerabilities and the people who fight the good fight of software security. I have taken a serious look at this topic before. Realities of risk associated with software security are a problem for both open source and proprietary software. Arguments have been made that bringing together more and more people who are using a piece of software via the open source model will create a scenario where risk is reduced via transparency and contribution from a multitude of sources.

Back in January Kent Walker who is the President of Global Affairs for Google shared a blog post about, “Making Open Source software safer and more secure” [1]. That missive talked about log4j, a recent open source vulnerability that was impactful to a variety of industries. A reminder was included about a $100 million dollar donation to the Open Source Security Foundation [2]. Kent reduced the question down to figuring out the critical projects instead of trying to boil the ocean, being clear about security testing baselines, and figuring out methods for increased support from both public and private sources. 

Let’s zoom out for a second and look at public policy and regulation related to open source software security. Back on May 12, 2021 Executive Order 14028 was issued about “Improving the Nation’s Cybersecurity” [3]. The whole order is 15 pages long and may take you about 20 minutes to read. You can pivot from that to the update from May 11, 2022 to the National Institute of Standards and Technology (NIST) guidance on “Software Security in Supply Chains” [4]. That collection of online pages will take you a lot longer to read. It has a pretty high density of content. A lot of supply chains are now using machine learning and a mix of open source software elements to make things work along the path from production to delivery. As you can imagine a lot of policy makers are legitimately concerned about risks to supply chains. 

Now that we have considered concerns related to the developers, companies, and governments looking at open source software security you can see the scope of risk involved. I’m not sure I see any easy solutions on the horizon for this one. It is going to be something that has to be mitigated in real time and a lot of people are going to have to work together to make that happen on an ongoing basis. 

Does this week include some bonus edition content? Yes, it does. We are about to cover a bonus topic related to, “Machine learning and surveillance.”

Welcome to the bonus topic this week. My backlog of topics has grown a bit out of control. This is week 72 for example and the backlog has 120 topics. Moving forward I’m going to grab a few of the topics and work on making a few double issues of The Lindahl Letter. 

Making sense of and working with mind boggling amounts of data is something that machine learning can help with based on anomaly detection and computer vision elements. You can quickly work through hours of security video footage from cameras at a building and only work with the footage where motion or some type of change occurs. In terms of overnight security and monitoring this means that a large portion of the effort can be almost immediately cleared away. No review is required. You can then move from anomaly detection to the more complicated elements of computer vision to tag elements in the video and flag things for manual review or intervention by alarming or notification. I jumped into a quick Google Scholar search for all of the academic papers that might include or be related to, “computer vision machine learning surveillance” [5]. This is an area where you can find some really solid and well understood use cases. 

Back during week 37 coverage one of the links referenced out to the CLIP technology from OpenAI [6]. You can grab an implementation of that from Johan Modin over on GitHub that will help you do contrastive language to image searches [7]. When you see people in movies just searching hours and hours of video for the needle in the haystack and coming back with a quick response of all the examples of “The Man with One Red Shoe” it would be based on a technology like this making that magic happen. If you have not seen the 1985 Tom Hanks comedy thriller by the same name, then you might be missing out on the rich comedic depth of that reference. With the right amount of investment and computing power you can do amazing things in the surveillance space with machine learning. Some of them are shockingly advanced compared to where we were before.

The part of this topic that I really want to cover, but is again a deeper topic for conversation involves the various methods people stitch data together for internet tracking. Some of these tracking methods make the surveillance methods mentioned above seem primitive. I’ll try to figure out a solid way to explain how machine learning is being used within internet tracking frameworks and work that content into a weekly post in the not so distant future.  

Links and thoughts:

“[ML News] DeepMind's Flamingo Image-Text model | Locked-Image Tuning | Jurassic X & MRKL”

“UiPath CEO Daniel Dines thinks automation can fight the great resignation”

“Vergecast: Google CEO Sundar Pichai on Google I/O 2022”

“The Download: Markdoc, VS Code Updates, Optimus Prime LEGO and More!”

Top 5 Tweets of the week:

Footnotes:

[1] https://blog.google/technology/safety-security/making-open-source-software-safer-and-more-secure/ 

[2] https://openssf.org/

[3] https://www.federalregister.gov/documents/2021/05/17/2021-10460/improving-the-nations-cybersecurity or in PDF here https://www.govinfo.gov/content/pkg/FR-2021-05-17/pdf/2021-10460.pdf 

[4] https://www.nist.gov/itl/executive-order-14028-improving-nations-cybersecurity/software-security-supply-chains 

[5] https://scholar.google.com/scholar?q=computer+vision+machine+learning+surveillance&hl=en&as_sdt=0&as_vis=1&oi=scholart 

What’s next for The Lindahl Letter?

* Week 73: Symbolic machine learning

* Week 74: ML content automation

* Week 75: Is ML destroying engineering colleges?

* Week 76: What is post theory science?

* Week 77: What is GPT-NeoX-20B?

I’ll try to keep the what’s next list for The Lindahl Letter forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. Thank you and enjoy the week ahead.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com

This week we are going to dig into content about ML newsletters. I’ll do my best to bring that content to life via the spoken word, but you may want to check out the actual written text this week if you are looking for hyperlinks to the actual newsletter locations. Generally, if you want to live dangerously, then you can find these newsletters with a quick Google search or you can visit this post for a more direct approach.

Probably the newsletter that I have subscribed to the longest would be the Inside AI newsletter that has featured weekend commentary from Rob May [1]. Recently the entire Inside newsletter ecosystem has changed it up a little bit with a new platform where you can login and interact with posts. The Inside newsletter ecosystem includes tons of different topics thanks to the founder and CEO Jason Calacanis mixing things up. Way back on July 22, 2018 I did get to write a weekend commentary for that newsletter. I’ll leave that missive below at the end of this post.

A lot of smaller newsletters exist and a few of them are wrapped around brands or larger institutions. Let’s talk about a few Substack newsletters: Last Week in AI which provides a weekly free edition or you can subscribe for more issues [2]. Subscribing to that Substack newsletter will suggest you subscribe to 3 other newsletters. I always think it is interesting to see what other people are suggesting in terms of subscriptions. 

1. The AI Ethics Brief: Democratizing AI ethics literacy [3]

2. The Gradient: Overviews on cutting-edge AI research and perspectives on the direction of the field [4]

3. AI Weirdness: The weirdness of artificial intelligence [5]. Interestingly enough when I subscribed to the Substack of this one I got an email from the author advising me the newsletter had moved to Ghost and I should shift my subscription. 

I have read a lot of content from The Next Web (TNW) including the Neural newsletter [6]. You can find newsletters from O’Reilly and the MIT Technology review and a slew of other businesses that share AI/ML related content primarily associated with their interest. Over the years I have gotten subscribed to a ton of these and some of them I’m not entirely clear how those subscriptions occurred. A lot of them came about around the time I was working on my AIOps/MLOps research which makes sense. 

Here is my weekend commentary post from back in 2018, “Everyday AI: From open source tools with a growing library of free training to being accessible in the business world.”

Sitting down to write a commentary on AI that will match Rob’s high standards made me pause and think deeply. These are amazing times and anybody that is actively seeking to take training on artificial intelligence or use the multitude of tools and languages being deployed within the open source community can open the door to truly interesting possibilities. That is the part of all of this that really caught my attention.

Translating the newly accessible tools and training into action has been the big challenge facing practitioners of artificial intelligence, machine learning, and deep learning. People throughout business are trying to gain insights from data to make decisions. Some of those efforts help build out highly complex dashboards and compendiums of KPIs that help provide insights and drive decisions. Some of the AI use cases are more plumbing related vs. being a scoreboard by replacing a step in a workflow that drives productivity or introducing something new that brings tremendous value to a product. Between informing strategic decisions and driving value, people are hearing about AI and they want to use it in the workplace. They want to use it to be successful. Nobody wants to miss out on this wave of opportunity to describe how things are happening and make predictions about the future. 

A new host of tools and techniques related to deep learning exist to really perform complex analysis. The increase in data availability for training models has also risen exponentially. Within our everyday lives models have been deployed to improve the pictures we take and handle other tasks at the edge. Very few of those new tools have started to really be deployed within the day to day workflow of business. That is where I see the greatest opportunity for practitioners. My gut tells me that 2019 will be the year that AI becomes accessible to common workplace reporting, tasking, and IT deployments. 

During the demo of Google’s Duplex at Google IO a technology was delivered that will change more than a few games. Both call centers and personal digital assistants come to mind. Both of those advancements will help push engagements with consultant companies and business partners delivering new technology implementations. Just about everybody that has called a major corporation over the years has felt the frustration with poorly setup phone systems. Some of them are interactive and some of them were just a mix of old technologies that made it hard to reach customer service. The technology demoed as part of Google Duplex at IO will provide something different, “...the Assistant can understand complex sentences, fast speech, and long remarks, so it can respond naturally in a phone conversation.” 

Part of what makes advances in assistive technology that incorporate artificial intelligence into the solution they deliver so interesting is how accessible they are becoming. A host of free, low cost, or professional training resources are making the technology more accessible within the workplace. Anybody that works on reports or a definable and repeatable tasking could see artificial intelligence, machine learning, or deep learning as a possible door to pushing things forward. Tools like Microsoft Excel that have helped people complete data analysis for years are starting to get backward linkages to very powerful artificial intelligence plumbing. Those are the seeds that are going to grow the use of artificial intelligence from the ground up in business. Beyond major initiatives with consultants and key business partners the building blocks of artificial intelligence techniques will begin to simply be a part of normal business routines. 

A growing combination of open source software and free training are helping to grow the number of advocates and enthusiasts in the workplace. That enthusiasm mixed with an increasing level of accessibility are the basis of what I expect to be a banner year in 2019 for the adoption and implementation of complex machine learning and deep learning models in the business world. A lot of that change will be driven from the bottom up based on enthusiasts using technology that has now become accessible. 

Links and thoughts:

“How the mechanical keyboard went mainstream again”

“A Billionaire Hedge Fund Manager Predicts the Future — and What He Sees Is Concerning”

“[ML News] Meta's OPT 175B language model | DALL-E Mega is training | TorToiSe TTS fakes my voice”

Top 5 Tweets of the week:

Footnotes:

[1] https://inside.com/ai 

[2]

[3]

[4]

[5] https://www.aiweirdness.com/

[6] https://thenextweb.com/neural 

What’s next for The Lindahl Letter?

* Week 72: Open source machine learning security (Machine learning and surveillance bonus issue)

* Week 73: Symbolic machine learning

* Week 74: ML content automation

* Week 75: Is ML destroying engineering colleges?

* Week 76: What is post theory science?

I’ll try to keep the what’s next list for The Lindahl Letter forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. Thank you and enjoy the week ahead.  

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com

A lot of enthusiasm and money are flowing into cryptocurrency these days. That wave of 25 billion in venture backed financing started back in 2021 [0]. Most of that technology is based on blockchains. The co-founder of Ethereum Gavin Wood as far back as 2014 started talking about a blockchain technology being used to fundamentally change the internet [1]. Gavin is a big proponent of fixing the modern internet and very proud of the idea of coining the term “Web 3.0” certainly before the term became a part of the general lexicon of technology terms. Certainly fixing the modern internet is a noble quest to undertake. At this point, the fundamental technology allowing a free and open internet still exists, but we are seeing islands and speed traps popping up at a much larger pace. It is still an open question about if the metaverse will be pay to play or free and open with financial consequences after you enter the front door. 

At this point, it might be a good time to talk about what each of the different versions of the webs really mean and why that is important. You can learn about it from various cryptocurrency forms including Etherum [2]. They are very loud and eager to sell you on a vision of the future. A lot of people are betting venture capital on this version of the future. One of the writers over at Geeks for Geeks broke it down in a pretty decent way back on January 27, 2022 [3].

We need to go back to the 1990’s and consider how Web1 came to be and was used and consumed by a lot of people. People started to stand up servers and have web pages with rather static content. In a lot of ways it was the billboards of the internet built for the online highway. It was amazing and this method of communication was based on servers with content being delivered in a mostly one way direction. Some people called this the mostly “read-only web” [4]. That description was and still remains a pretty solid way of describing Web1.

Fast forward to 2000’s and you start to see Web2 begin to emerge. This is where the “read-only web” shifted to a way more collaborative web. You can think back to examples like Flickr for photo sharing, digg which mostly gave way to Reddit for links and things, Twitter, Facebook, and MySpace. I had to drop a MySpace reference into this post for those of you who miss your first online friend Tom. This is where network platforms started to gain steam and walled ecosystems began to flourish from collecting data and selling advertisements. Sometimes people call this the surveillance web and reference the immense infrastructure deployed to track people online using a combination of cookies and stitching data together. That is a topic that could end up being a future post. Nothing really stops me from weaving in bonus topics for future editions.

While Gavin Wood might be very vocal about coining the term Web3 you can rewind back to San Francisco, California on November 12, 2006 and see that term shared by John Markoff in the New York Times [5]. That article however does not talk about or have any context for blockchain or Bitcoin. Given those considerations I can see how Gavin makes the argument to have coined the version of Web3 that Etherum or other blockchain based technologies would power. Leave it to John Bogna of PC Magazine to dig into “What Is Web3 and How Will it Work?” [6]. It basically comes down to having a decentralized digital infrastructure where your personal information is not the foundation of the interaction. I would describe it as a version of the internet powered in some way by a blockchain based technology. I’m very curious to see an implementation working in practice. 

You can go out to the Web3 foundation and learn about what they are doing [7]. On their about page it clearly talks about the Web 3.0 technology stack. Entire communities have developed to track and learn about Web3 jobs including one that tracks the job counts at the top 100 Web3 companies [8]. The top five with several hundred jobs each are Binance, Crypto.com, Coinbase, Ripple, and Consensys. I scrolled past the rest of the list and some of them were names I know something about, but the vast majority of them don’t have any meaning to me. One day they might occupy space in the public mind. It is also possible that most of them will never have the name recognition of Google, Facebook, Microsoft, or Apple. 

Before we wrap up this post I will acknowledge that machine learning and artificial intelligence are mentioned all over the Web3 landscape in a variety of ways. However, primarily the technology core of how the stacks are being built seems to be around some type of blockchain based method to be decentralized. Generally speaking both machine learning and artificial intelligence are centralizing technologies. Certainly distributed machine learning model delivery or decentralized artificial intelligence would be interesting topics to consider, but neither of those things are commonly in the lexicon of discussion currently ongoing.

Links and thoughts:

1. This week I’m sharing a video from Dr. Rachel Thomas who is a researcher in residence at the Queensland AI Hub. The video is titled “You CAN and SHOULD get involved with AI” which I thought was an interesting pitch about getting involved with AI.

Top 5 Tweets of the week:

Footnotes:

[0] https://www.nytimes.com/2021/12/01/business/dealbook/crypto-venture-capital.html 

[1] https://gavofyork.medium.com/why-we-need-web-3-0-5da4f2bf95ab 

[2] https://ethereum.org/en/developers/docs/web2-vs-web3/ 

[3] https://www.geeksforgeeks.org/web-1-0-web-2-0-and-web-3-0-with-their-difference/ 

[4] https://www.web3.lu/divide-the-web-timeline-in-nine-epochs/ 

[5] https://www.nytimes.com/2006/11/12/business/12web.html 

[6] https://www.pcmag.com/how-to/what-is-web3-and-how-will-it-work#:~:text=Proponents%20envision%20Web3%20as%20an,public%20ledger%20of%20the%20blockchain

[7] https://web3.foundation/ or https://web3.foundation/about/ 

[8] https://web3.career/web3-companies/ 

What’s next for The Lindahl Letter?

* Week 71: What are the best ML newsletters?

* Week 72: Open source machine learning security

* Week 73: Symbolic machine learning

* Week 74: ML content automation

* Week 75: Is ML destroying engineering colleges?

I’ll try to keep the what’s next list for The Lindahl Letter forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. Thank you and enjoy the week ahead.  

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com