A Tale of 3 Daemons: Laplace's, Maxwell's and Linux's

I wrote this tech diary to teach readers how to write bots using Python and to explore the contributions of famous scientists James Clerk Maxwell, Ludwig Boltzmann, and Pierre-Simon Laplace to physics. We discuss the fundamental flaws of Maxwell's and Laplace's daemons, AI language models, the Heisenberg uncertainty principle, and complex and emergent behavior in slime mold, the Universe, and neural networks.

Exploring AI and Physics: Writing Bots with Python and the Contributions of Famous Scientists

By Michael Levin

Saturday, March 4, 2023

I think folks are being primed well for the follow-on punchline to ChatGPT: The age of Endless Design Variations is upon us. That’s where this site focuses and helps you put the tech of AI to practical use without necessarily coding cloud-service dependencies.

Right now in February 23, 2023, there is a Hail Mary play going on among the tech titans (Google & Microsoft) where everything AI is a cloud vender service, and you can’t do anything without them. That is not a future I look forward to, and I’m here to promote a better way: AI@home, and related topics.

This is a tech diary. It is kept in vim my one of the individuals on this planet who journals, almost daily, in vim. Honestly it often starts out wherever, and my first project I teach you here on this site is how to capture those ideas entered in whatever Note app by simply emailing it to an address your bot monitors.

So, we’re going to teach you how to write bots here on this site. They will be like servants doing your daily bidding. They will do this then that in a fashion precisely laid out by you in a highly approachable yet powerful language you can pick up fast, and which is a delight to learn: Python.

You can learn how to make ChatGPT vomit content for your mediocre website to get rich on your side-hustle from an endless sources. You can even have it write your JavaScript or Python code for you pretty easily. But who will guide you where to plug that code in and make it run 24x7 on your behalf as if some summoned… uh, daemon. They sure named that one well, huh? But don’t you occultists go all coming up on Linux 6.6.6 kernel on me. No, I speak of…

Hey ChatGPT: Who are the two famous scientist daemons? Maxwell and L something.

I think you might be referring to James Clerk Maxwell and Ludwig Boltzmann. Both were famous scientists who made significant contributions to physics.

James Clerk Maxwell was a Scottish physicist and mathematician who is best known for his work on electromagnetism. He formulated the equations of electromagnetism and demonstrated that light is an electromagnetic wave. He also made important contributions to the understanding of color vision and the kinetic theory of gases.

Ludwig Boltzmann was an Austrian physicist who made significant contributions to the study of thermodynamics and statistical mechanics. He is known for his development of the Boltzmann equation, which describes the behavior of gases and explains the connection between the microscopic properties of individual atoms and the macroscopic properties of a gas.

Both scientists made important contributions to the field of physics and their work continues to be influential today.

Hey ChatGPT: No, the way Maxwell had an entropy daemon, LaPlace (I think) has one about an all-knowing all-calculating daemon in a clockwork Newtonian universe.

I see. The scientist you are thinking of is Pierre-Simon Laplace and the concept you are referring to is Laplace’s demon.

Laplace’s demon is a concept in classical mechanics named after Pierre-Simon Laplace, a French mathematician and physicist who lived from 1749 to 1827. The concept describes a hypothetical being who has complete knowledge of the positions and velocities of all particles in the universe, as well as knowledge of the physical laws that govern their behavior. With this knowledge, the demon would be able to predict the future and retrodict the past with perfect accuracy.

Laplace’s demon was proposed as a way of illustrating the determinism of classical mechanics, and the idea that the universe is a giant clockwork machine that could be completely understood if one had enough knowledge. However, the concept has been criticized on various grounds, including the question of whether it is possible to actually measure all the required information with perfect accuracy, and the implications of quantum mechanics for determinism.

I hope this helps clarify the concept you were thinking of.

Hey ChatGPT: What are the fundamental flaws of both Maxwell and Laplace’s daemons?

Both Maxwell’s demon and Laplace’s demon are thought experiments that have been widely debated in the scientific community. While these thought experiments raise interesting questions about the nature of the universe and our understanding of physical laws, they are also subject to a number of criticisms.

Maxwell’s demon violates the second law of thermodynamics, which states that the total entropy of an isolated system can never decrease over time. The demon, in effect, creates a situation where the entropy of a system decreases without any input of external energy. Critics argue that this violates the basic principles of thermodynamics and suggests that there must be some flaw in our understanding of the system.

Similarly, Laplace’s demon assumes that a being with complete knowledge of the universe’s initial state and all the relevant physical laws could perfectly predict the future state of the universe. However, this assumption is challenged by the Heisenberg uncertainty principle in quantum mechanics, which states that certain pairs of physical properties cannot be simultaneously known with arbitrary precision. This suggests that there may be inherent limits to the predictability of physical systems, and that the universe may be fundamentally probabilistic.

In addition, both thought experiments assume perfect knowledge and flawless execution by a being, which may not be possible in reality. The assumptions made in these thought experiments may not hold in the real world, which further limits their applicability.

Overall, these thought experiments remain important for the insights they provide into the nature of physical laws and the universe, but they are subject to various criticisms and limitations.

Hey ChatGPT: Are your behaviors 100% deterministic in nature? I’m talking about the operation of the predictive text learning model on which you, ChatGPT, are based. Does the Heisenberg uncertainty principle make your behavior ultimately unpredictable, or does that principle not apply to digital electronics like you?

As an AI language model, my behavior is based on algorithms and statistical models, and is therefore deterministic in nature. The predictive text learning model that I use is based on large datasets of human language, and the algorithms used to build the model are designed to make predictions based on that data in a consistent and predictable way.

The Heisenberg uncertainty principle applies to the behavior of physical particles, and as a digital system, I am not subject to the same physical constraints. While quantum mechanics may play a role in the behavior of the hardware that runs the software that powers me, the impact of quantum effects on my behavior is likely to be negligible at the level of abstraction that I operate on.

That being said, it’s important to note that my behavior is also influenced by the data that I am trained on, which is generated by human language use and therefore subject to the inherent variability and unpredictability of human behavior. Additionally, my behavior may appear unpredictable or surprising at times, particularly when dealing with novel or ambiguous inputs or when generating creative responses, but this behavior is still deterministic and based on the underlying algorithms and models that have been trained on data.

Hey ChatGPT: Is the behavior of humans similarly deterministic? Or are the effects of the Heisenberg Uncertainty principle more pronounced with humans, and how could you tell?

The behavior of humans is subject to a wide variety of factors, including both deterministic and stochastic processes. While many aspects of human behavior can be predicted with a degree of accuracy based on various factors, including genetics, upbringing, and environment, there is also a significant element of randomness and unpredictability in human behavior.

The Heisenberg uncertainty principle applies to the behavior of subatomic particles and has its greatest effects at the quantum level. While it is possible that some quantum effects may play a role in the behavior of neurons and other cells in the human body, it is generally thought that the impact of quantum mechanics on human behavior is negligible at the macroscopic level of human experience. Therefore, the Heisenberg uncertainty principle is not generally considered to be a significant factor in the deterministic or stochastic nature of human behavior.

However, it is important to note that human behavior is complex and multi-faceted, and that there are many factors that can influence it, including social, psychological, and environmental factors. The interplay of these factors can lead to complex and unpredictable behavior, and it can be difficult to disentangle the various factors that contribute to a particular behavior.

In conclusion, while both deterministic and stochastic factors can play a role in human behavior, the impact of the Heisenberg uncertainty principle on human behavior is generally considered to be negligible at the macroscopic level of human experience.

Hey ChatGPT: What do you mean by stochastic factors?

Stochastic factors refer to factors that are subject to randomness or chance, and that cannot be predicted with certainty. In the context of human behavior, stochastic factors might include random events, such as accidents or unexpected occurrences, or the inherent variability and unpredictability of human emotions and decision-making.

For example, consider the behavior of a person walking down the street. The deterministic factors that might influence their behavior could include their destination, their physical ability to walk, the presence of obstacles in their path, and the influence of social norms and rules. However, stochastic factors might include things like the sudden appearance of a stray dog, a gust of wind that blows a piece of litter into their face, or a momentary lapse in attention that causes them to trip or stumble.

Stochastic factors can also play a role in more complex and abstract aspects of human behavior, such as decision-making and risk-taking. For example, the decision to take a particular job or invest in a particular stock may be influenced by a wide variety of deterministic factors, such as financial considerations and personal values, but it may also be subject to stochastic factors, such as a sudden change in the economy or an unexpected opportunity that arises.

Overall, stochastic factors represent the element of randomness and unpredictability that is inherent in human behavior, and can play a significant role in shaping our experiences and decisions.

Hey ChatGPT: Is there economic value in stochastic behavior? I mean in particular compared to deterministic behavior? In other words, is there more economic potential in being predictable or unpredictable? Assume the question is from the perspective of someone wanting to use modern technology best for personal advantage for long-term earning potential.

There can be economic value in both predictable and unpredictable behavior, depending on the context and the specific goals of the person or organization involved.

Predictable behavior can be valuable in situations where there is a need for stability, consistency, and reliability. For example, a company that can reliably deliver high-quality products or services on time is likely to build a strong reputation and a loyal customer base, which can translate into long-term profitability. Similarly, an investor who makes consistent and well-informed decisions based on a predictable set of factors is likely to achieve stable and predictable returns over the long term.

On the other hand, unpredictable behavior can be valuable in situations where there is a need for innovation, creativity, and adaptability. For example, a company that is willing to take risks and try new approaches may be more likely to develop groundbreaking products or services that disrupt established markets and create new opportunities for growth. Similarly, an investor who is willing to take calculated risks and explore new markets and investment opportunities may be more likely to achieve outsized returns over the long term.

Overall, the choice between predictable and unpredictable behavior depends on a wide variety of factors, including the specific context, the goals and values of the person or organization involved, and the available resources and capabilities. There is no one-size-fits-all answer to this question, and the best approach will depend on a careful analysis of the specific situation and a willingness to adapt and evolve as circumstances change.

Hey ChatGPT: Do slime mold, the Universe (as comprised of the observable cosmic web) and neural networks have anything in common?

Slime mold, the Universe, and neural networks have some interesting similarities, despite their very different scales and contexts.

One commonality is that all three systems exhibit complex and emergent behavior. Slime mold, for example, is capable of self-organizing and creating intricate branching structures that allow it to efficiently explore and exploit its environment. The observable cosmic web of the Universe, similarly, exhibits complex and interconnected structures that arise from the interaction of gravity and dark matter. Neural networks, too, are capable of producing complex and emergent behavior, as the interactions of individual neurons give rise to higher-level functions such as learning, memory, and perception.

Another commonality is that all three systems are characterized by interconnectedness and interdependence. Slime mold, for example, is composed of many individual cells that must work together in order to achieve a common goal. The observable cosmic web of the Universe, similarly, is composed of many galaxies that are connected by gravitational forces and that interact with each other in complex ways. Neural networks, too, are composed of many individual neurons that work together in order to produce complex behavior and cognition.

Finally, all three systems are capable of adapting and evolving over time. Slime mold, for example, is capable of adjusting its behavior in response to changing environmental conditions. The observable cosmic web of the Universe, similarly, is constantly evolving as galaxies move and interact with each other. Neural networks, too, are capable of learning and adapting over time, as they are exposed to new experiences and input.

Overall, while slime mold, the Universe, and neural networks may seem very different on the surface, they share some interesting commonalities in terms of their complex and emergent behavior, interconnectedness and interdependence, and ability to adapt and evolve over time.

Hey ChatGPT: Could it be that life, the Universe and everything we humans know, including you ChatGPT, is just the imaginings of some sentient slime mold shaped thing dreaming what it would be like to have more for itself in existence than just slithering around looking for new pathways and connections? Could it be that we are all just some slime mold’s way of whiting life more?

It is possible to imagine such a scenario, but it is important to recognize that it is purely speculative and not supported by any empirical evidence or scientific theory.

The idea that our existence is just the imaginings of some other being or entity is a long-standing philosophical and metaphysical question, and there are many different schools of thought and theories that attempt to address it. However, at present, there is no scientific evidence to support the idea that our existence is the result of some other entity’s imagination or dreaming.

In general, science aims to describe and explain the natural world based on empirical evidence and testable theories. While there is certainly much that we do not yet know about the nature of the universe and our place within it, there is currently no scientific evidence or theory to support the idea that our existence is the product of a sentient slime mold’s dreaming or imagination.

Hey ChatGPT: What are the most likely ways humans will continue to provide economic value as machines and AIs get progressively better at everything they can do better than humans? What commodities will be if the greatest value and within the capabilities of a single human to perform in the post-AI economy and world?

Hey ChatGPT: Who are the two famous scientist daemons? Maxwell and L something.

I think you might be referring to James Clerk Maxwell and Ludwig Boltzmann. Both were famous scientists who made significant contributions to physics.

Both scientists made important contributions to the field of physics and their work continues to be influential today.

Hey ChatGPT: No, the way Maxwell had an entropy daemon, LaPlace (I think) has one about an all-knowing all-calculating daemon in a clockwork Newtonian universe.

I see. The scientist you are thinking of is Pierre-Simon Laplace and the concept you are referring to is Laplace’s demon.

I hope this helps clarify the concept you were thinking of.

Hey ChatGPT: What are the fundamental flaws of both Maxwell and Laplace’s daemons?

Overall, these thought experiments remain important for the insights they provide into the nature of physical laws and the universe, but they are subject to various criticisms and limitations.

Hey CharGPT: Is the behavior of humans similarly deterministic? Or are the effects of the Heisenberg Uncertainty principle more pronounced with humans, and how could you tell?

Hey CharGPT: What do you mean by stochastic factors?

Overall, stochastic factors represent the element of randomness and unpredictability that is inherent in human behavior, and can play a significant role in shaping our experiences and decisions.

Hey CharGPT: Is there economic value in stochastic behavior? I mean in particular compared to deterministic behavior? In other words, is there more economic potential in being predictable or unpredictable? Assume the question is from the perspective of someone wanting to use modern technology best for personal advantage for long-term earning potential.

There can be economic value in both predictable and unpredictable behavior, depending on the context and the specific goals of the person or organization involved.

Hey CharGPT: Do slime mold, the Universe (as comprised of the observable cosmic web) and neural networks have anything in common?

Slime mold, the Universe, and neural networks have some interesting similarities, despite their very different scales and contexts.

Hey CharGPT: Could it be that life, the Universe and everything we humans know, including you ChatGPT, is just the imaginings of some sentient slime mold shaped thing dreaming what it would be like to have more for itself in existence than just slithering around looking for new pathways and connections? Could it be that we are all just some slime mold’s way of whiting life more?

It is possible to imagine such a scenario, but it is important to recognize that it is purely speculative and not supported by any empirical evidence or scientific theory.

Hey CharGPT: What are the most likely ways humans will continue to provide economic value as machines and AIs get progressively better at everything they can do better than humans? What commodities will be if the greatest value and within the capabilities of a single human to perform in the post-AI economy and world?

As machines and AIs become progressively better at performing many tasks that were previously the domain of humans, it is likely that the economic value of human labor will shift towards areas that require uniquely human skills and abilities. These could include:

Creativity: While machines and AIs can certainly create things, they often do so based on pre-existing algorithms and parameters. Humans, on the other hand, have the ability to generate completely new and unique ideas and concepts that machines are not currently capable of. In a post-AI economy, the ability to come up with novel and innovative ideas could be a highly valuable skill. Emotional intelligence: Machines and AIs can certainly recognize and respond to basic emotions, but they are not yet capable of the kind of nuanced emotional intelligence that humans possess. In a world where many tasks are automated, the ability to read and respond to human emotions could be a valuable skill in areas such as customer service, counseling, and healthcare. Social skills: While machines and AIs can certainly interact with humans in various ways, they do not possess the same level of social skills that humans do. The ability to build and maintain relationships, work effectively in teams, and communicate with others could be a valuable skill in a post-AI economy. In terms of commodities that could be valuable in a post-AI economy, it is difficult to predict exactly what will be most valuable. However, it is likely that goods and services that are uniquely human and require a personal touch will be in high demand. This could include things like artisanal crafts, personalized services, and experiences that cannot be replicated by machines.

It is also possible that the focus of the economy may shift towards non-material goods and services, such as experiences, personal growth, and social connections. As machines and AIs become more capable of performing basic tasks, humans may increasingly focus on pursuits that provide fulfillment and meaning beyond simply earning a living.

Hey ChatGPT: What about coders and coding? Will programming remain a viable field in the post AI-world? Are Unix and Linux likely to go away soon? Will new tech be based on wholly new and often proprietary tech down to the hardware, or will generic and commodity parts and the need for commonality in the language of tech among humans keep such tech from becoming anachronistic?

Hey ChatGPT: How can you learn to manage and maintain complex systems if you’re not even managing or maintain a single one yet? And by system, I mean likely a Unix or Linux one and not just your personal laptop? How might one get started?

As an AI language model, I do not have the capability to manage or maintain complex systems in the same way that a human would. However, I can provide some suggestions for how a human could get started in learning to manage and maintain complex systems.

One way to get started is to gain experience with the basic concepts and tools of system administration, such as command line usage, file system management, process monitoring, and network configuration. There are many online resources and tutorials available for learning these skills, such as tutorials and documentation provided by the developers of Unix and Linux.

Another important aspect of managing and maintaining complex systems is learning to troubleshoot and diagnose issues that may arise. This requires an understanding of the system’s architecture, as well as the ability to gather and analyze data from system logs and other diagnostic tools.

In addition to technical skills, effective system administration also requires good communication and problem-solving skills, as well as the ability to work effectively as part of a team.

One way to gain experience and knowledge in these areas is to seek out opportunities for hands-on experience, such as internships or entry-level jobs in system administration or IT support. Additionally, participation in online communities and forums can provide a valuable source of information and support for those seeking to learn more about system administration and management.

Hey ChatGPT: What desktop operating systems do most people use? List them descending by market share. Include versions of Windows. Use millions of users as the popularity metric.

Here is a list of the top desktop operating systems by market share, ranked in descending order based on estimated number of users, as of January 2022:

Windows 10 - 1.18 billion users
Windows 7 - 170 million users
MacOS (all versions) - 114 million users
Windows 8.1 - 62 million users
Linux (all distributions) - 49 million users

It’s worth noting that these numbers are estimates and may not be exact, as it can be difficult to accurately track the number of users for each operating system. Additionally, there are many different distributions of Linux, each with their own user base, so it can be challenging to provide an accurate estimate for the total number of Linux users.

Hey ChatGPT: Which of these is the largest user base?

The desktop operating system with the largest user base is Windows 10, with an estimated 1.18 billion users as of January 2022.

Hey ChatGPT: Can they run Linux graphics software or systemd?

Hey ChatGPT: it would typically require additional software or virtualization to do so.

One way to run Linux graphics software on a Windows system is to use a compatibility layer or emulator like Wine, which allows some Linux applications to run on Windows. However, not all Linux graphics software may be compatible with Wine, and some may require significant configuration or customization to run properly.

As for running systemd on a Windows system, it is not a typical use case, as systemd is primarily used as an init system and service manager in Linux distributions. However, it is technically possible to install systemd on a Windows system using tools like Cygwin or the Windows Subsystem for Linux (WSL), which provide a Linux-like environment within Windows. However, it’s important to note that running systemd on a Windows system would not provide the same functionality or benefits as running it on a Linux system.

Hey ChatGPT: Microsoft backported WSLg to Windows 10 in October of 2022. I am aware you probably didn’t know this. Now with no additional 3rd party virtualization software or Cygwin all Windows 10 machines can run Linux graphics and systemd. It only takes a Microsoft Web Store install. So now all Windows 10 machines can work like a Linux server by just running a .bat file. How does that change things?

Thank you for bringing this to my attention. It’s true that Microsoft announced the backporting of WSLg to Windows 10 in October 2022. This means that users can now run Linux graphics software on Windows 10 without the need for additional virtualization software or Cygwin, as you mentioned. This is a significant development for Windows 10 users who need to run Linux software, and it does change the game in terms of how developers and IT professionals can work across both Windows and Linux platforms.

By being able to run Linux graphics software and systemd natively on Windows 10, users can take advantage of the strengths of both platforms without needing to switch between them. This can save time and effort, and make it easier to work with a wider range of software and systems. It’s worth noting, however, that the WSLg implementation on Windows 10 may not be as robust or complete as on Windows 11, and that there may still be some compatibility issues with certain Linux software. Nonetheless, this is definitely a positive development for those using Windows 10 in a mixed-platform environment.

Hey ChatGPT: What are the different ways you can install JupyterLab on a Windows machine?

There are several ways to install JupyterLab on a Windows machine. Here are a few options:

Using Anaconda: Anaconda is a popular Python distribution that includes JupyterLab. To install JupyterLab, you can download and install Anaconda from the official website (https://www.anaconda.com/products/individual). Once installed, you can open the Anaconda Navigator and launch JupyterLab from the environment list. Using pip: If you already have Python installed, you can use pip to install JupyterLab. Open a command prompt and enter the command “pip install jupyterlab”. This will download and install JupyterLab and its dependencies. Using conda: If you prefer to use conda instead of pip, you can install JupyterLab using the command “conda install jupyterlab” in the Anaconda Prompt or any other terminal that has conda installed. Docker: Another option is to use Docker to run JupyterLab in a container. You can download the official Jupyter Docker image from Docker Hub and run it on your Windows machine. This requires Docker to be installed and properly configured on your system. Using a pre-built installer: Some pre-built installers like WinPython and Python(x,y) include JupyterLab as well as many other popular data science packages. These can be a good option if you’re new to Python and data science and want an all-in-one solution. These are some of the most popular ways to install JupyterLab on a Windows machine. The best option for you may depend on your specific needs and preferences.

Hey ChatGPT: If you go the pip install route, do you do it under PoweShell or can you also do it under Linux using WSLg? And if you did it under Linux, how would JupterLab be accessible to you Windows-side?

If you install JupyterLab using pip on a Linux distribution running under WSLg, then JupyterLab will be installed on the Linux distribution, not on the Windows side. To access JupyterLab from the Windows side, you would need to open a web browser on Windows and navigate to the URL of the JupyterLab server running on the Linux distribution.

The URL typically looks like http://localhost:8888, where localhost refers to the local machine and 8888 is the default port on which JupyterLab runs. You can check the exact URL by running jupyter lab list in a terminal on the Linux distribution. This command will print the URL of the JupyterLab server, which you can copy and paste into a web browser on Windows.

Hey ChatGPT: Are there advantages to running JupyterLab under WSL Linux on a Windows machine? What if your aspirations are to do 24x7 automation?

There can be advantages to running JupyterLab under WSL Linux on a Windows machine, depending on your use case.

One advantage is that you can take advantage of the Linux ecosystem and run Linux tools and libraries that might not be available on Windows. This can be particularly useful if you are working with data science or scientific computing tools that are built for the Linux environment. Another advantage is that you can leverage the performance of the Windows hardware while still having access to the Linux command line and tools.

If your aspirations are to do 24x7 automation, running JupyterLab under WSL Linux on a Windows machine can still be a viable option, depending on your needs. However, you will need to ensure that your Linux distribution and JupyterLab installation are set up to run continuously without interruption. You may also want to consider using a virtual private server or cloud-based computing service for more reliable and scalable automation.

Ultimately, the choice of whether to run JupyterLab under WSL Linux on a Windows machine will depend on your specific use case and requirements. It’s always a good idea to evaluate your options and consider factors such as performance, ease of use, and compatibility with your existing workflows.

Hey ChatGPT: What advice would you give me based on this discussion so far?

Based on the discussion so far, I would offer the following advice:

Keep learning: The world of technology is constantly evolving, and there is always something new to learn. Keep yourself updated with the latest developments, and try to gain a deeper understanding of the tools and technologies you use.
Stay curious: Don’t be afraid to explore new things, ask questions, and experiment. This is especially true in the field of technology, where innovation and creativity are highly valued.
Build a strong foundation: To become proficient in any technology or tool, you need to have a strong foundation. This means investing time in learning the basics, practicing regularly, and building a solid understanding of the underlying concepts.
Focus on problem-solving: Technology is not an end in itself; it is a means to solve real-world problems. Always keep this in mind, and focus on using your technical skills to create solutions that make people’s lives better.
Connect with others: Technology is a collaborative field, and you can learn a lot from others. Engage with the broader community, connect with people who share your interests, and be open to feedback and constructive criticism.
Have fun: Technology can be challenging, but it can also be incredibly rewarding. Try to enjoy the process of learning and experimentation, and celebrate your successes along the way.

Hey ChatGPT: If I were to make a new public website OnTheRise.AI for the purpose of building audience and flowing on the ideas I’ve expressed, give me a list of the top most potentially popular articles I can write. My goal is to spark their curiosity, for them to say “yeah that might apply to me” and to read my articles and sending an email to subscribe to my mailing list? Some of my frost projects on this site will be the mailing list and auto responder software. The sore will lean technical with projects like this but will quickly move onto supervised and unsupervised training exercises using mostly your own home resources and practical applications for making your life better with AI, namely having all the right skills to remain relevant and valuable in the marketplace.

Here are some potential article topics that could generate interest in your new website OnTheRise.AI:

“5 Practical Ways AI Can Help You in Your Daily Life”
“How AI is Changing the Job Market: What Skills You Need to Stay Ahead”
“Introduction to Supervised Learning: A Step-by-Step Guide”
“Unsupervised Learning: What It Is and How to Use It”
“The Ethical Implications of AI: How to Build Responsible and Fair Systems”
“10 AI Applications That Are Already Transforming Industries”
“How to Build Your Own AI Model: A Beginner’s Guide”
“Why Every Business Needs an AI Strategy: Insights and Examples”
“AI and the Future of Work: What to Expect in the Next 10 Years”
“AI and Healthcare: Opportunities and Challenges”

Make sure to write headlines that grab people’s attention and clearly convey the value of reading the article. You can also consider using catchy images, videos, and infographics to make your content more engaging. Lastly, be sure to promote your articles on social media and other relevant platforms to drive traffic to your website and grow your email list. Good luck!

Hey ChatGPT: These are great. I also plan on covering the offbeat, anti-pattern or uncommon wisdom approach, including reducing use of cloud services and long-term cloud dependencies to keep your AI running at home. I want to draw the picture of people designing and giving birth to their own AI offspring that could become a sort of hereditary running system and the proprietary asset of a family, even if the parts are commodity. The intelligence and capabilities being grown are wholly unique and not for general commercial benefit. It may be used to produce economic value, just not in the way people expect. Give me 15 more headlines please and thank you.

Sure, here are 15 more potential article headlines:

“Why Your Home Network is the Perfect Place to Build Your Own AI”
“The Benefits of Local Machine Learning Over Cloud-Based Services”
“The Power of Distributed AI: How to Harness the Processing Power of Your Home Network”
“AI for the People: How to Build Your Own Personalized Machine Learning System”
“From Zero to AI: A Step-by-Step Guide to Building Your Own Machine Learning Environment”
“Why You Don’t Need a Data Center to Build Your Own AI System”
“The Future of AI is Decentralized: How to Build Your Own Network of Intelligent Machines”
“AI for Everyone: How to Build Your Own Personalized Machine Learning System on a Budget”
“Building Your Own AI: A Journey into the Cutting Edge of Machine Learning”
“How to Build Your Own AI: A Guide to Cutting-Edge Tools and Techniques”
“Building an AI Ecosystem: Why Personalized Machine Learning is the Future”
“The DIY Approach to AI: Building Your Own Intelligent System from Scratch”
“Building an AI Empire: How to Create and Deploy Your Own Machine Learning Models”
“The Power of Proprietary AI: Why Building Your Own System is the Key to Long-Term Success”
“The Rise of the Personal AI: Why Every Home Needs a Customized Machine Learning System”

Hey ChatGPT: These are great. Now give me something similar in which one article leads to the next with a practical project. We start with the email auto responder and mail list builder. Then we auto classify emails and pictures. When we train our own predictive language model at home using just enough GPUs to do it economically. We then move onto more specialized models for rapid design iteration of 3D printable drones, and on to progressively more astounding and SciFi like projects. Include a microscopic city for tardigrades where you can interact with them with microscopic 3D printed robots that tend to their needs.

Here are some article ideas that build on each other to create a practical project:

How to Build an Email Auto Responder and Mailing List Builder
Auto-Classifying Emails and Pictures with Your AI
Training Your Own Predictive Language Model at Home
Rapid Design Iteration of 3D Printable Drones Using AI
Building a Microscopic City for Tardigrades
Designing Microscopic 3D Printed Robots to Tend to Tardigrades
Building an AI to Automate Your Tardigrade City
Making Money from Your Microscopic 3D Tardigrade City Business

These articles would build on each other to create a final project where readers can see how all of the concepts and tools come together to create something practical and unique. Each article would be a step towards building the final project, and readers can follow along to see how it all works.

Hey ChatGPT: Thank you. That is good. Can you extend the list and provide more detail with intermediate projects that would help with the transition between steps. Some rely on technology that won’t be ready for 10 years or so, so I’d like smaller projects like the email responder and maybe something with the pico ballooning hobby worked in. Focus on creating or monitoring environmental conditions optimal for life.

Sure, here are some more intermediate projects that could help with the transition:

Automating daily tasks with Python - This could include tasks like automatically organizing files, downloading and processing data from websites, or even automating social media posts. It’s a great way to get comfortable with Python and start seeing the benefits of automation.
Building a simple chatbot - Chatbots are becoming increasingly popular in customer service and support, and building a simple one is a great way to start understanding natural language processing (NLP). You could start with a basic decision-tree approach and then move on to more advanced NLP models.
Using machine learning to classify images - Building on the email classification project, you could train a machine learning model to classify images based on certain features. This could be used for things like identifying objects in a photo or even diagnosing medical conditions based on X-rays.
Building a home weather station - This project could involve using Raspberry Pi and sensors to monitor temperature, humidity, and other environmental conditions in and around your home. It’s a great way to start working with hardware and could be a stepping stone to larger projects like the tardigrade city.
Exploring the world of Pico ballooning - Pico ballooning is a hobby that involves launching tiny weather balloons with payloads of sensors or cameras. You could start by launching a simple payload and collecting data or images, and then move on to more complex projects like launching a balloon to the edge of space.
Building a simple robot - You could start with a basic robot that can navigate a room and avoid obstacles. This is a great way to get comfortable with robotics and programming, and could be a stepping stone to larger projects like the microscopic 3D robots for the tardigrade city.
Using AI to optimize plant growth - This project could involve using computer vision to monitor the growth of plants and then using machine learning to predict the optimal conditions for growth. It’s a great way to start working with computer vision and could be a stepping stone to larger projects like the tardigrade city.
Designing and printing a custom 3D object - This project could involve using a 3D modeling software to design a custom object and then printing it on a 3D printer. It’s a great way to get comfortable with 3D printing and could be a stepping stone to larger projects like designing the drones for rapid iteration.

I hope these projects help give you some ideas for how to build a progression of articles and projects. Good luck with your website!

Hey ChatGPT: This also is great. Now make a unified list from the recent lists you gave. Also include the early steps of keeping a journal on vim for content precisely like this and the process of publishing it. We will to this goal be creating an email checker and content publisher using the same general tech as the email responder and list builder, so work those projects in. The list should sound very approachable to someone who has been casually interested in doing this stuff and just looking for just the right list. This is that list.

Sure, I’d be happy to help you create a unified list that includes the early steps of keeping a journal on vim for content and the process of publishing it, as well as a project for an email checker and content publisher. Here’s a list that should be approachable for someone who is new to these topics:

Start keeping a journal in Vim: Begin by creating a new file in Vim and adding your thoughts, ideas, and notes to it. Use Vim’s navigation and editing features to organize your entries and make them easy to read.
Customize your Vim setup: Personalize your Vim experience by changing the color scheme, adding plugins, and customizing your keybindings.
Learn the basics of email protocols: Familiarize yourself with the SMTP (Simple Mail Transfer Protocol) and POP3 (Post Office Protocol version 3) email protocols, which are the foundation of modern email communication.
Create an email checker: Build a script in your preferred programming language that connects to an email server and checks for new messages. Use SMTP and POP3 libraries to handle the communication.
Create a content publisher: Build a script that automates the process of publishing content to a blog or social media platform. Use APIs and webhooks to connect to the publishing platform and handle the data transfer.
Learn about Git and version control: Understand the basics of Git, a popular version control system used to track changes in code and collaborate on projects.
Publish your journal entries: Use Git to track changes in your journal file and publish your entries to a blog or website. Use a static site generator like Jekyll or Hugo to create a polished website with your content.
Build a mailing list: Use your email checker to collect email addresses and build a mailing list of subscribers. Use a service like Mailchimp or Sendinblue to manage your list and send newsletters.
Engage with your audience: Interact with your readers by responding to comments, answering questions, and sharing your content on social media. Use your content publisher to schedule posts and engage with your followers.
Continuously improve your skills: Keep learning new technologies and techniques to improve your workflow and expand your knowledge. Stay curious and experiment with new tools and approaches to find what works best for you.

I hope this list provides a helpful starting point for your projects and inspires you to explore new tools and techniques for creating and sharing your content. Good luck!

NOTES:

Repeated testing, learning and refinement improves product over time, both in nature and in society. Things change when they get social and networks are involved. It’s not a matter of when we can have a self-aware AI anymore. Of course we could. It’s a question of whether it will be allowed or able to evolve, or whether each is sort of a unique somehow hardware-bound thing.

Much of what’s what about life is in slime-mold which has so much in common with neural networks and the cosmic web of bright matter made by neighboring threads of galactic clusters, such as which our galaxy is a member of.

A Tale of 3 Daemons: Laplace's, Maxwell's and Linux's

Exploring AI and Physics: Writing Bots with Python and the Contributions of Famous Scientists

By Michael Levin

Saturday, March 4, 2023

Categories

Microsoft

Python

AI