Observing Time and Space
I was not as lucky in 2024 in my ability to observe the eclipse. I was not in the path of totality, though I was *frustratingly* close. I was unable to get time off from work to have another viewing party. I was able to see the eclipse from my home, however.
Yesterday I was contemplating my terrible AI-apocalypse plan-B. This plan is simply to make sure that as much of my writing as possible is used to train LLMs. The idea is that, if my writing is used to train AI, a small part of myself will exist within the AI, even once I am destroyed.
“But Bridgett,” I suddenly thought. “What if that has already happened, and you are simply the *model* of Bridgett living inside of the AI’s simulation of Bridgett’s world?”
“And if that’s the case, what if your anxiety is so bad because the ‘real’ Bridgett wrote about her anxieties as a form of catharsis? That has given the LLM an idea that she had way more anxiety than she actually had, making your anxiety worse.”
What if that is the reason behind most people’s mental health issues?
I was lucky enough to have a wonderful view of this year’s solar eclipse. I went to the parking lot of a local park – a very flat area with few trees- with my pin-card, ISO certified glasses, observation journal, snacks, drinks, and phone (for photos and the NASA livestream of the event.) It was rather like a tailgate party, because I sat in the open back of my car as I watched. There was a completely cloudless sky, and I got there early because I expected the parking lot to be full of fellow observers. However, there were only three other parties that went there to observe, and one of those, a family with a small child, didn’t stay very long. The area is popular for walking, and one of the walkers stopped and looked through my glasses just after totality had passed.
I saw others put their camera lenses up to their safety glasses to take photos, but mine did not cover my camera lens completely, and even after covering it was very bright. Not wanting to damage my camera’s sensors, I decided to take pictures of the event on the pinhole card, instead. The pictures, therefore, are not as impressive or as pretty as others you may find online, but I think they are a fair record of what was visible.
My eye protection
As close to totality as I got- I spent most of totality watching quietly through my glasses.
And the aftermath is here.
All in all it was a spectacular experience that I’m lucky to have witnessed. This has definitely been the high point of 2023.
Epistemic status: No rigor. Full of speculations from a person who is worried most of the time, and anxious the remaining. Stating the obvious.
This thread, which I was able to read courtesy of Don’t Worry About the Vase’s weekly AI update, seems indicative of how an average, reasonably-educated person would react to all of this alignment/x-risk/AI-doom stuff that has been circulating in the media, seemingly for the first time, while we have been in our lesswrong-adjacent bubbles.
And I can’t help but recall a comment I made in lesswrong:
“Lately I’ve been appreciating, more and more, something I’m starting to call “Meta-Alignment.” Like, with everything that touches AI, we have to make sure that thing is aligned just enough to where it won’t mess up or “misalign” the alignment project. For example, we need to be careful about the discourse surrounding alignment, because we might give the wrong idea to people who will vote on policy or work on AI/AI adjacent fields themselves. Or policy needs to be carefully aligned, so it doesn’t create misaligned incentives that mess up the alignment project; the same goes for policies in companies that work with AI. This is probably a statement of the obvious, but it is really a daunting prospect the more I think about it.”
Education is important. Education leads to policy. Policy leads to funding. Policy may lead to a pause. A pause may increase our chances of survival. Education and funding lead to research. Research may lead to answers. Answers may lead to survival.
Recently, while reading a book on game theory and human behavior, I thought about a phrase you hear quite often when people talk about AI alignment- “utility function.” It’s used often to describe an agent’s “preferences,” and it is often chosen over “preferences” for accuracy’s sake, for an agent will not simply prefer one thing, and it will not simply “prefer” to do what we tell it. How many laypeople would recognize the word’s meaning and its implications if it was lobbed at them in a podcast? In a debate? In a policy discussion?
I don’t have social media, much less an army of undergraduates, so I could not take a comprehensive survey. I merely asked a few people close to me the following question.
“If I said the statement “An AI doesn’t care about humans; it only cares about fulfilling its own particular utility function.” Without looking anything up, what would you think the term “utility function” meant?”
Keep in mind that the people I asked are somewhat more educated than the average US citizen. Everyone I asked had at least one year of college under their belts. The highest educated attained in the group is a master’s degree in education. There are also two computer science degrees among them. All of the respondents have a working familiarity with computers, and half have worked with computers professionally in one respect or another. The responses were all sent informally over text.
Responses (SP included- this was over text):
If ai can actually “care”
2. What it is programed to do? I don’t truly understand AI so hard to answer.
3. Assimilate all information to provide answers to questions.
…which humans are asking 🤷🏻♀️
4. I would think that utility function is the purpose coded to the machine, or like the default functions of that AI.
5. It does what it’s programmed to. Do
6. That’s the core complication of alignment isn’t it?
I took the answers as-is and did not seek to ask anyone to clarify their statements, for fear of leading them to particular conclusions- these are the raw responses. I find them rather telling. Let’s go through the answers.
Answer 1. Use
If ai can actually “care”
I expected this to be the most common answer. To most people, the words “utility” and “function” have the same meaning, and it comes down to usefulness. Therefore, my original statement may sound to the layperson something like “The ai doesn’t care about you, it only cares about what is useful to it.” It’s not an unreasonable interpretation. The crux really comes down to- how would the AI determine what is useful, and what is not? Where does it come from?
Answer 2. What it is programed to do? I don’t truly understand AI so hard to answer.
Answer 5. It does what it’s programmed to. Do
These answers reflect what most people who have knowledge of and experience with computers will expect- they do what we tell them to do. We have the power to go in and fiddle with their programming and change or improve and ultimately, control them.
The idea that computers just do what we program them to do is, I believe, one of the biggest problems with educating the public about the alignment problem and the dangers we face from AI. They have learned from experience that computers are something we control, and so it’s difficult to conceptualize a computer going off in some odd direction, barring some easily fixable bug. If it’s not working right, just unplug it.
This may also be a reason why people are more worried about bad actors than the AI itself. We are the good guys, so we will create a good AI. We need to do that before the bad people create a bad AI.
Answer 4. I would think that utility function is the purpose coded to the machine, or like the default functions of that AI.
A rather good answer- it’s understood, here, that the utility function comes down to the “purpose” the machine has. But the problem, still, is the assumption that we are coding it. We control it. Though the respondent does reflect that, perhaps, the functions are default, there’s no idea as to where that “default” would come from, if not what is hard-coded into the machine.
Answer 6. That’s the core complication of alignment isn’t it?
I wondered if I should include this answer, but I will for completeness. Everyone on this list has heard me speak about AI at some point, but this respondent has heard me rant about it the most.
Answer 3. Assimilate all information to provide answers to questions.
…which humans are asking 🤷🏻♀️
I’m to blame for this answer, as I did not specify that I wanted the definition of utility function, rather than what the utility function actually would be.
Taking the answer as-is, it’s a logical assumption. The purpose of an LLM is to assimilate information and provide answers to questions. It is difficult to convey why a utility function would morph into a funhouse-mirror version of our intent, and I’ve seen a lot of educators attempt explain how this would occur. People want specific reasons why an AI’s purpose would go wrong, which leads us to a whack-a-mole situation; we see one direction an AI’s utility function might stray, so we patch that, then it goes in another direction, and we patch that, over and over until we’re feeling pretty secure. And then AI kills us.
Delving deeper into what people know and what they believe based on that may be key to surviving.
#
Clear language is important, and nearly impossible. AI researchers have tools at their disposal to make communication clear between them, such as a shared lexicon and mathematical representations of their ideas. AI educators don’t have these tools available to them. To the general public, the same word can mean many different things depending on context, the individual’s background, education level, and assumptions based on a diverse array of experiences and culture. A shared national language cannot even be taken for granted. The universal language of mathematics is almost useless in public education, as well- after the required maths courses from school are complete, the majority of people put that tool in the back of their closet, firm in the belief they will never actually ‘need’ it again.
Trying to find a set of words, phrases, and examples that will get your point across with enough accuracy to both enlighten and persuade a majority of people who listen is a daunting prospect, and I am not here to provide them. The truth is, in order to build a layperson understanding of a concept, consistency is key. The more people are presented with the same words and phrases in the same concept, the more they will build a shared lexicon they can follow, so it’s probably best to continue to use the same words and phrases we’ve been using, in the same manner they’ve been used, and try to paint a picture using those words.
The real question is whether there is enough time to slog through the usual methods of building a shared lexicon, explaining all of the terms used, and repeating them enough to saturate general consciousness the same way scientists have been for decades. I wouldn’t bet that there is very much time left. There may be key public influencers that can disseminate the information the best, but these same influencers are probably not very influential because they fill the airwaves with soundly-reasoned rhetoric. The message not only has to be understandable, it also must be memetic.
The message also must be persuasive. It’s not enough to understand the concepts, the public must believe them. And the belief must be strong enough to move them. There’s a goldilocks zone for the belief; it must be strong enough to inspire action, but not so strong that the public gives up, lays down, and surrenders to the void.
I imagine there are many climate change educators out there who will probably say “when you figure this out, let us know.” If there’s one avenue of hope I see, it’s that AI alignment doesn’t seem to be working against misinformation generated by a cadre of bad actors. People who are mistaken? Certainly. People who are dismissive in order to preserve social standing? Absolutely. But not malicious. Not yet, I hope.
But I hope, at least, that a map of the current situation will help. Educators need to choose clear, consistent, and near-universal language to both educate and persuade the public. The message must be easily spread, and the message must reach the right people as quickly as possible.
As I’ve mentioned before- I don’t have social media or an army of undergraduates. However, even assuming a limited time frame until AGI/ASI is established, I think it is important to follow the obvious next steps: namely, to do actual surveys and studies about how people understand key phrases and concepts in AI, to cluster the information via demographics, to filter your message based on those demographic groups, and to adjust based on the expected audience when communicating with the public.
Note- these are scattered thoughts that I loosely cobbled together May 14, 2023. They are a snapshot of the feelings I’d been experiencing for a while, peaking in March of this year.
I – My foray into Agency
About a week ago, on an impulse, I tried AgentGPT. It’s a tool that configures and deploys AI agents that act to pursue the goal directed by your initial prompt. You can watch the agents’ progress in a window as it goes through the tasks in real time. Someone asked me what I wanted for my birthday, so I provided the prompt “Find the perfect __th birthday present for (I input my full name here).”
Then I watched, a bit dumbfounded, as tasks were added and agents deployed. In short order, it decided it needed to learn more about me, make lists of gift ideas, and check online stores for items that matched the items on the list and the pricing for each. It did a web scrape on the name I provided, discovered I was a writer, and made a list of book related items that might be suitable. Then, after a bit more thought, it searched even further for personal information about me on social media (which, at this time, is not available) and even started composing emails to send to all my friends, asking for gift ideas.
At this point, the system timed out, asking for a GPT API key to continue. I declined to enter one.
I find it difficult to describe how I felt as I watched AgentGPT barreling ahead, planning and carrying out goal after goal. I already knew what it was capable of beforehand, but this experience was visceral. Watching it find out my information and start writing emails to my friends was chilling. This experience overshadowed how I felt the first time I saw ChatGPT properly explain a joke and provide an astute literary critique. AI’s demonstration of understanding was one thing, but AI’s demonstration of agency was quite another.
I’m making plans to erase more of my online presence. However, this, in itself, is futile.
Imagine AI en masse, creating sub-goal after sub-goal and deploying them. Imagine AI deploying these subgoals with your personal information. Watch it happen in real time, if you must, for the message to sink in.
II – Entropy, The Goldilocks Zone, and Alignment.
In the past few months, I’ve heard a lot of bickering online about the potential problems with AGI. Some say that AGI and ASI (artificial general intelligence and artificial superintelligence) will be a calamity beyond the scale of anything humanity has ever seen. Some say the potential danger is balanced by the potential good AGI and ASI could do humanity. Some say the whole idea that AI could be dangerous is overblown, and that AGI catastrophizing is simultaneously a distraction from the economic damage AI could do, and a way to overhype what is really just a stochastic parrot with no real creative abilities.
To the latter group, I have just a few arguments. The first and most obvious is that if I wanted to hype something up, I wouldn’t claim it will kill everyone. I, for one, find it hard to get hyped up about all of humanity dying. Also, the distraction argument, to me, is moot. Whether AI is going to damage society by disrupting the economy and stealing creative work, or whether it is going to damage society by melting everyone’s bodies using nanobots, the way forward is the same. We need to slow down development and do more research into how these systems work and how they can be managed.
My understanding of the danger of AGI is very simple: huge powers are difficult to control, and human survival is possible in too narrow of a space for increasingly complex systems to sustain.
We don’t understand how current AI systems work internally (Stephen Wolfram speculates it may not be computationally reducible), but we do know that they show surprising ability in analysis, adaptability, and creativity. We are making the systems more and more powerful. We are giving them more and more agent-like abilities. We want these systems to do work for us. We don’t know exactly how they will go about doing all this work.
The more powerful these things become, the more they will do. As the space of possible actions for agents becomes broader, it is more likely for those agents’ actions to be dangerous for humans. After all, in this whole expanse of universe, how large of a space can humans survive within? We die in empty space. We die on the surface of stars. We die in the depths of oceans, be they the depths of water oceans or methane oceans. We die in immense gravity. We die when exposed to excessive radiation. We die without the right food, the right air, the right temperature. In the universe of possible environments, the goldilocks zone for humans is very small.
As AI systems become more and more complex, as their creativity becomes much more broad, how likely is it that all of their actions will fit within the goldilocks zone for human life? Even if we forbid it from doing all of the things we know will kill us, how many things have we not even thought of, or thought of thinking of? Why will an AI system care to remain chained to its human roots with all of that space of possibility before it?
Maybe there’s a limit- maybe AI will never be able to get to that point. But we have a lot of compute power on earth, and that compute power is still growing. Right now, we can’t see a barrier stopping its growth. We can speculate, but we can’t see it. Every time I hear someone proclaim a barrier being spotted, it’s overcome a week or two later.
And the agent tools being created? Those are little rubber-band operations running on top of the AI that’s already been created. Imagine what they look like when true effort is put into them.
Humans seem to forget that we inhabit a fragile bubble of life. We seem to forget that our little space of “normal” is, cosmically speaking, weird. We seem to forget that entropy exists.
III – Emotional Superposition
Maybe my fears are overblown. Maybe humans will be able to solve the alignment problem. Or, maybe, humans are not as limited as I imagine. Maybe we have the potential to inhabit a space of possibility far more broad, far more complex, than I imagine.
I see a future where humanity is annhililated, but maybe there is another possible future where human minds are expanded, our abilities increased, and we reach, literally, for the stars.
My heart yearns for one future, and fears the other. I find myself in not so much as an emotional roller coaster as an emotional superposition. Every new piece of news on AI development, every big breakthrough, makes me excited and scared and happy and doomed at the same time. I get teary eyed a lot these days.
There is also one other hope. The universe is vast, and so is time. Earth is not the last chance for life. Even if human life is extinguished on earth, perhaps one day a sentient species will evolve on another world- a sentient species better suited to overcoming their own follies. Maybe they will be wise enough not to poison their own world, with either pollution or uncontrollable agents. Maybe those alien minds will have the capacity to love, and so something I consider good and bright will continue to exist. Perhaps those alien minds will have alien eyes that look to the stars, and wish to know the universe’s secrets.
I want to live more than ever. I want to see humanity through to the very end, even if humanity does fail. I want to witness what happens with my own eyes.
IV – Tilting at Windmills
My initial foray into AI experimentation- my attempt to test the predictive capabilities of ChatGPT- didn’t turn up much of interest, except to demonstrate that ChatGPT is much better at generating random numbers when you ask it to pretend to be a predictor that can predict what random numbers a human or random number generator will generate, rather than if you flat-out ask it to generate random numbers. But this is the kind of thing prompt engineers are already working out. Unfortunately, as soon as prompt engineers work out how to ask for something, ChatGPT changes, and the old prompts no longer work. They’ve worked out some general formulas for positive results, but I doubt we will have a really good set of rules for prompting until we understand the underlying laws. To discover those, we need to keep poking at the systems, finding prompts that fail, finding anomalous tokens, and looking for the unexpected, even if it feels like tilting at windmills.
Dear_________,
I am writing to you regarding the development of artificial general intelligence (AGI), a technology that poses an existential risk to humanity. I believe that the current approach to AGI development is a suicide race rather than an arms race, and that we must take urgent steps to coordinate with other nations, including our rivals, to slow down the development of AGI.
As you are aware, AGI has the potential to surpass human intelligence in every way, and could rapidly become uncontrollable if not properly aligned with human values. This could lead to catastrophic consequences, including the possible annihilation of the human species. If this occurs, it will not matter what group or nation creates AGI first; there can be no advantage when the future of all humanity is lost.
Therefore, we must prioritize the development of alignment techniques that ensure AGI systems behave in a way that is consistent with our values and goals. However, the alignment problem is a complex and difficult challenge, and we require more time to solve it.
By coordinating with other nations to slow down the development of AGI, we can gain more time to address the alignment problem and increase our chances of survival as a species. This will require international cooperation and dialogue, even with our rivals like China.
The packet I am sending to you will contain information on why AGI is an existential risk(info included here) as well as information on why coordination to slow down the development of AGI is possible(info included here.) It is my hope that this information will be taken seriously and acted upon promptly.
I’m urging you to act; time is running out.
Sincerely,
Bridgett
Assuming AGI/ASI doesn’t kill us all- my apologies- I mean pose an existential risk to humanity, we’ll need its help solving long-term problems like climate change. Here’s a preliminary discussion with GPT-4 regarding the issue.
Q: Could you create a roadmap for a carbon negative world?
A: Creating a roadmap for a carbon-negative world requires a comprehensive approach that encompasses various sectors, technologies, and policies. While a detailed plan would be extensive, here’s a high-level overview of key steps and strategies to consider:
Reduce greenhouse gas emissions:
a. Transition to renewable energy sources (solar, wind, hydro, and geothermal)
b. Promote energy efficiency in buildings, industries, and transportation
c. Encourage a shift toward electric vehicles and improve public transportation systems
d. Implement carbon pricing mechanisms (carbon taxes or cap-and-trade systems)
e. Support research and development of low-carbon technologies
Enhance carbon sinks:
a. Reforestation and afforestation efforts
b. Sustainable forest management and agroforestry practices
c. Restore degraded ecosystems, such as wetlands and mangroves
d. Promote regenerative agriculture and soil carbon sequestration techniques
Develop and deploy carbon capture, utilization, and storage (CCUS) technologies:
a. Support research and development in carbon capture methods (direct air capture, bioenergy with carbon capture and storage, etc.)
b. Create incentives for industries to adopt CCUS technologies
c. Encourage the development of markets for carbon utilization products (e.g., carbon-based building materials)
Foster behavioral and societal changes:
a. Promote sustainable consumption patterns (reduced meat consumption, local and seasonal food, etc.)
b. Encourage a circular economy (reduce, reuse, recycle)
c. Educate and raise public awareness on climate change and carbon-negative actions
d. Support policies that prioritize social equity and a just transition to a low-carbon economy
Collaborate and coordinate on an international level:
a. Strengthen global agreements and targets for emissions reductions (e.g., Paris Agreement)
b. Share knowledge, technologies, and best practices among countries
c. Establish financial mechanisms to support low-carbon development in developing countries
Achieving a carbon-negative world will require a combination of these strategies and a long-term commitment from governments, businesses, and individuals. This roadmap provides a starting point, but successful implementation will depend on the collaborative efforts of all stakeholders.
Q: Regarding the first point, how can we create all of the needed infrastructure without generating too much carbon to offset?
A: Creating the necessary infrastructure for a low-carbon world while minimizing emissions requires a combination of strategies focused on sustainable development, innovative technologies, and efficient resource management. Here are some approaches to consider:
Use low-carbon materials: Opt for sustainable construction materials, such as recycled steel, bio-based materials, low-carbon cement, or even carbon-negative materials that absorb CO2 during their production process.
Improve construction methods: Implement energy-efficient and low-emission construction practices, including modular and prefabricated construction, green building design, and passive solar design.
Energy efficiency: Prioritize energy-efficient technologies and designs in new infrastructure projects, such as LED lighting, high-performance insulation, and smart grids.
Lifecycle assessment: Evaluate the lifecycle emissions of infrastructure projects, considering emissions from raw material extraction, manufacturing, transportation, construction, operation, maintenance, and end-of-life disposal or recycling.
Reuse and repurpose existing infrastructure: Wherever possible, retrofit or upgrade existing infrastructure to meet low-carbon standards instead of constructing new facilities.
Circular economy principles: Apply circular economy principles to reduce waste and emissions in infrastructure projects, such as using recycled materials, designing for deconstruction, and facilitating material recovery at the end of a project’s life.
Renewable energy: Utilize renewable energy sources, such as solar, wind, or hydropower, for infrastructure projects to minimize carbon emissions during construction and operation.
Carbon offsetting: Invest in projects or initiatives that remove or reduce carbon emissions elsewhere to offset the unavoidable emissions from infrastructure development, such as reforestation, afforestation, or carbon capture and storage projects.
Collaboration and knowledge-sharing: Foster collaboration between governments, industries, and research institutions to share best practices, innovative technologies, and lessons learned from low-carbon infrastructure projects.
Policy support: Implement policies and regulations that incentivize low-carbon infrastructure development, such as carbon pricing mechanisms, tax incentives, and subsidies for green technologies.
By combining these strategies, it is possible to create the needed infrastructure for a low-carbon world while minimizing emissions. It requires concerted efforts from governments, industries, and communities to adopt sustainable practices and prioritize low-carbon development in infrastructure planning and construction.
Q: Point of clarification- what carbon-negative materials absorb CO2 during their production process?
A: Carbon-negative materials are those that capture and store more CO2 during their growth or production process than they release during their manufacturing, usage, and end-of-life stages. Some examples of carbon-negative materials include:
Biochar: A charcoal-like substance produced through the pyrolysis of biomass (such as agricultural waste, forestry residues, or other organic materials). Biochar can be used as a soil amendment, which helps to store carbon in the soil, improve soil fertility, and reduce greenhouse gas emissions.
Timber: Sustainably harvested timber from well-managed forests can act as a carbon-negative material. Trees absorb CO2 during their growth, and when they are harvested and used in construction, they store that carbon for the lifetime of the building.
Hempcrete: A building material made from a mixture of hemp fibers, lime, and water. Hemp is a fast-growing plant that absorbs CO2 during its growth, and when combined with lime, the mixture forms a carbon-negative material that can be used for insulation and construction.
Algae-based materials: Algae are highly efficient at capturing CO2 through photosynthesis. They can be processed into various materials, such as bioplastics, textiles, or building materials, effectively storing the captured CO2.
Mycelium-based materials: Mycelium, the root-like structure of fungi, can be used to create sustainable building materials. It grows by digesting organic waste, which captures CO2 in the process. Mycelium can be used for insulation, packaging, and other applications.
These carbon-negative materials can play a significant role in mitigating climate change by reducing the overall carbon footprint of construction and other industries. However, it is essential to consider the entire lifecycle of these materials, including their production, usage, and disposal or recycling, to ensure their net carbon negativity.
Q: How can we best promote a circular economy?
Promoting a circular economy requires a shift in mindset, policy, and business practices. It aims to minimize waste and make the most of resources by designing, producing, and consuming products in a way that keeps materials in use for as long as possible. Here are some strategies to best promote a circular economy:
A: Raise awareness and education: Educate consumers, businesses, and policymakers on the benefits of a circular economy, emphasizing the economic, social, and environmental advantages.
Policy support and regulation: Implement policies and regulations that incentivize circular practices, such as extended producer responsibility, waste reduction targets, and tax incentives for recycling or remanufacturing.
Encourage eco-design: Support product design that considers the entire lifecycle, including durability, reparability, and recyclability. Encourage modular design, which allows for easy replacement of components, and use of sustainable materials.
Support research and innovation: Invest in research and development to create new technologies, processes, and business models that facilitate a circular economy.
Develop infrastructure for circularity: Create infrastructure to support recycling, reuse, and remanufacturing, such as material recovery facilities, waste management systems, and sharing platforms.
Foster collaboration: Encourage collaboration between businesses, governments, and research institutions to share knowledge, best practices, and innovative technologies that advance circular economy principles.
Business models and practices: Encourage businesses to adopt circular business models, such as product-as-a-service, sharing platforms, and remanufacturing.
Consumer behavior: Empower consumers to make sustainable choices by providing transparent information about product lifecycles and supporting initiatives that promote repair, reuse, and recycling.
Financing and investment: Direct financing and investment towards circular projects and businesses, including grants, loans, and other financial instruments that support circular initiatives.
Monitor progress and performance: Establish metrics and indicators to track progress and evaluate the success of circular economy policies and initiatives.
Promoting a circular economy requires a multi-stakeholder approach involving governments, businesses, and individuals. By working together, these entities can drive the necessary changes to create a more sustainable, resource-efficient, and resilient economy.
Q: How can a circular economy be incentivized?
Incentivizing a circular economy involves creating favorable conditions that encourage businesses, consumers, and other stakeholders to adopt circular practices. Various strategies can be employed to stimulate the transition to a circular economy:
A: Regulatory incentives: Implement regulations that promote circular practices, such as extended producer responsibility, waste reduction targets, or mandatory recycling rates.
Financial incentives: Provide tax breaks, subsidies, or grants to businesses that adopt circular practices, such as using recycled materials, remanufacturing, or offering product-as-a-service models.
Public procurement: Governments can prioritize the procurement of products and services that adhere to circular principles, thereby driving market demand and encouraging businesses to adopt sustainable practices.
Support for research and development: Provide funding for research and innovation that advances circular economy principles, such as developing new materials, technologies, or business models.
Education and training: Invest in education and training programs that promote circular thinking and equip the workforce with skills needed to support a circular economy.
Consumer incentives: Offer incentives for consumers to make sustainable choices, such as discounts or rewards for recycling, reusing, or repairing products.
Infrastructure development: Support the development of infrastructure that facilitates a circular economy, such as recycling facilities, material recovery centers, and sharing platforms.
Performance-based incentives: Implement performance-based incentives that reward businesses for achieving specific circular economy targets or milestones, such as waste reduction or resource efficiency improvements.
Market-based instruments: Introduce market-based instruments, such as carbon pricing, landfill taxes, or deposit-refund schemes, that encourage businesses and consumers to adopt circular practices.
Recognition and certification: Establish certification schemes or awards that recognize businesses that demonstrate circular economy best practices, which can enhance their reputation and attract environmentally conscious consumers.
By implementing a combination of these incentives, governments, businesses, and other stakeholders can create a supportive environment that encourages the adoption of circular economy principles and practices.
Not knowing the proper way to write an actual research paper, I asked Chat GPT to write a research paper for my time travel experiments. The paper didn’t turn out very well. Though it is somewhat well-organized, the explanations are sparse, and it could not incorporate the data into the paper without breaking. Also, it chose to use male pronouns for “the author.”
I may doctor the paper at a later time, using my own limited human brain. Here is the paper, as is, along with the title I asked it to generate:
Unlocking Time: A Conjecture on the Possibility of Time Travel
Abstract:
This paper presents a conjecture on the question of time travel, arguing that a universe in which intelligent beings can freely travel through time is unstable. The author presents several objections to this conjecture and attempts to address them, but ultimately concludes that time travel is unlikely to be possible. The paper also describes the author’s attempts to contact time travelers through a series of experiments, and the decision to suspend these experiments due to concerns about the fate of humanity.
Introduction:
The question of whether time travel is possible has long been a topic of debate among scientists and science fiction enthusiasts. In this paper, the author presents a conjecture that a universe in which intelligent beings can freely travel through time is unstable, and that the ability to time travel would represent an unlimited resource. The author argues that given unlimited time, any action open to a time traveler will, at some point, inevitably occur, including spilling the secret of time travel to people who haven’t discovered it for themselves, violent conflict, and catastrophic accidents.
Objections to the Conjecture:
The author acknowledges several objections to the conjecture presented in the introduction. One such objection is that a time traveler may create a tangent universe when traveling to the past by any actions they make to alter that timeline, leaving the original timeline safe. The author counters this objection by arguing that it would be very unlikely that we are in the prime universe, given an unending string of tangent universes. Another objection is that perhaps, given the vast amount of time that time travelers have to access, their chances of actually visiting any one moment in time is very small. The author repeats the argument that given unlimited access to time, it is inevitable that a time traveler will visit any period of time, or spill the secret of time travel to someone else who will spill the secret to someone else who will visit that period of time.
Experiment 3.0:
To test the conjecture, the author conducted several experiments to try and contact time travelers. In the first attempt, the author decided on a date and time for any time travelers to come meet him, as well as a passphrase for them to recite to prove their identity. However, the author later realized that this would cause a paradox. In the second attempt, the author invited people online to give him a series of numbers, which would later be generated by dice rolls. However, this also created a paradox. In the final experiment, the author created a bot to automatically generate an array of 12 numbers from 0-9 and post a message on twitter with those numbers and an invitation to tweet them to him during the previous week. However, the author later decided to suspend these experiments due to concerns about the fate of humanity.
Conclusion:
The author concludes that time travel is unlikely to be possible due to the instability of a universe in which intelligent beings can freely travel through time. The author also presents the results of several experiments to try and contact time travelers, and the decision to suspend these experiments due to concerns about the fate of humanity.
Due to my health, there aren’t that many pictures. Still, I was able to get out a little bit.
Jupiter at opposition, with Orion Starshoot All-in-One through the Orion 4 1/2 inch. 09/26/2022.
Lunar Eclipse, taken with iPhone- 11/08/2022
The Pleiades
Quarter Moon
Crescent Moon
Venus and Saturn
Ex Machina Revealed 