teacher vs. chatbot?

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This article is about an English teacher trying to figure out how to cope with AI in and out of the classroom. The main ideas seem to be just in-class writing assignments and discussions. This is high school (I think), and one thing that might surprise an older reader is the idea that the teacher spends a significant amount of time reading out loud to students, and also giving them time to read themselves in class. I am not surprised because I see the teachers of my own children in middle school doing this.

I’ve thought about trying my hand at teaching, at some point. I may or may not ever do that, but if so I will have to think about how to use AI in an engineering classroom. I don’t have all the answers of course, but this is a bold new frontier so neither does anyone else. Here is my first attempt at a brainstorming list:

  • In-class writing assignments,
  • Paper and pencil tests (or portions of a test – maybe if there is a 2-3 hour chunk of time to take a test, you have the first hour to do the paper and pencil part, hand that in, and get the second part where you are allowed to at least use a spreadsheet program. Or if the class period is only an hour, you could give a test over two days)
  • Journaling – I’ve always thought that reading and writing needs to be a part of what students do, even in STEM fields. In my own classes, teachers have tended to spend almost all the time focused on doing math problems. The problems are critical and I plan to come back to them. But I learn by reading and writing first and foremost, and there are others like me. So my idea is to have short reading assignments and ask students to summarize key points in a few bullets or sentences to show that they did the reading. Then, crucially, I would ask them to relate the reading to a relevant outside reference – kudos for a peer reviewed article, but could also be a relevant news or magazine article, or even a Wikipedia page. Now, obviously students are going to use AI for this, and that is okay. There would have to be classroom discussion related to it, and points given for being able to discuss what they wrote intelligently.
  • Okay, now to those problems, homework assignments, and/or take-home exams. These have always been the beating heart of engineering education. This is the struggle and the hard work of relating theory to problem solving, resulting in deep understanding. This is also where students benefit from individualized feedback from teachers and collaboration with peers. They are going to interact with AI here, and I see no real alternative other than classifying AI as not cheating. In fact, this is where you may want to actually encourage them to experiment and openly share what effective techniques they found that they think enhanced their learning. You have to give some points for the homework, otherwise all but the most-disciplined students will not make the significant effort needed for learning. But most of the points should probably be given for doing supervised in-class assignments that are similar to the homework.
  • There’s another problem here though – distance learning has become pretty common, particularly in graduate school. And that throws a monkey wrench into the whole in-person thing. It is potentially unfair if some students are in-person and some are virtual – are you going to surveil students on-camera the whole time and try to ascertain if they are cheating? This is a tough one and I don’t have all the answers.
  • Then there is the traditional “term paper” or “research paper”. This is a traditional cornerstone, but students are just going to use AI for it. Asking them to present their results and asking them personalized questions is part of the answer here, but that doesn’t really help them with feedback on their writing. The in-class writing assignments have to be part of the answer here. But again, in terms of out-of-class-writing, I think you have to classify the use of AI as not cheating and actually encourage them to experiment and share what they thought was effective.

Well, that was just some stream-of-consciousness drivel, wasn’t it? For me, writing and thinking are inseparable. I had some thoughts on this topic in the back of my mind, but now that I have tried to articulate them (without the use of AI), they are more fully formed than they were before I did that. If I were going to think about this more right now, I might ask AI to help me find some more articles and blogs on the subject.

What can we do about garage bioweapons?

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Ever since I read Why the Future Doesn’t Need Us, probably some time in the early 2000s, I’ve been concerned about the idea that biotechnology will eventually be available to amateurs. And a few amateurs will be psychopaths or suicide terrorists willing to take down civilization along with themselves. My only real idea for preventing this has been heavy duty government surveillance, somehow tempered by political institutions that keep the government itself from being evil. And of course, we don’t think we want this surveillance, and I don’t really want it, but I haven’t seen clear alternatives.

Here are some suggestions by others. I am not sure if I am convinced, but these are ideas worth thinking about. First, Amodei from Anthropic in his recent TLDR essay The Adolescense of Technology (where he tries to pull a Bill Joy and doesn’t quite pull it off, in my view.) You have to keep in mind that although he seems to be motivated by ethical views, heavy government surveillance is not in the interest of private companies, so his suggestions tend toward self-regulation by industry. Anyway, his suggestions are:

  • “AI companies can put guardrails on their models to prevent them from helping to produce bioweapons.”
  • “transparency requirements,27 which help society measure, monitor, and collectively defend against risks without disrupting economic activity in a heavy-handed way” – this would be government regulation, but it would constrain all competitors equally
  • “try to develop defenses against biological attacks themselves. This could include monitoring and tracking for early detection, investments in air purification R&D (such as far-UVC disinfection), rapid vaccine development that can respond and adapt to an attack, better personal protective equipment (PPE),28 and treatments or vaccinations for some of the most likely biological agents.” – One imagines scientists using AI extensively to develop these defenses, or possibly “AI scientists” developing them with some human mediation.

Now, here is an article from Big Think called How to deter biothreats in the age of gene synthesis. What they come up with is to screen all orders of biological materials (like DNA) from the companies that make and sell them to researchers. Screen the orders, screen the customers themselves, and screen for particularly dangerous DNA sequences.

This has to be done internationally, which adds to the difficulty. Even if it is done effectively by most countries most of the time, you will have enterprising countries and criminal organizations out there setting up a black market, just like you do with drugs and nuclear materials. Because it will be profitable to do so. The more effective the clamp down, the higher the risk and potential profit becomes (which is why the war on drugs has not and probably cannot be won, other than by massive and intrusive surveillance of individuals which most people in most countries agree they don’t want. Drugs are a tragedy for addicts and for people caught up in all the violence fueled by the black market created by their prohibition. But they aren’t going to spread like fires through uninvolved and unprepared populations and cause the extinction of our species. Which is why I wonder if accepting some level of intrusive surveillance on individuals is the least evil option as the technology gets more accessible and more dangerous all the time.

Nonetheless, it is probably good to take an “all of the above” approach. If I were feeling optimistic I might say perhaps the existential nature of the threat will prompt some international cooperation and formation of some more functional institutions for cooperation than we have seen over the past decade or so. But maybe it will take some sort of near miss or partially contained disaster for this to happen. (Covid-19 was not bad enough, apparently.)

Let’s close with a few quotes from Why the Future Doesn’t Need Us, which anyone who has not read must drop what they are doing and read now:

The 21st-century technologies – genetics, nanotechnology, and robotics (GNR) – are so powerful that they can spawn whole new classes of accidents and abuses. Most dangerously, for the first time, these accidents and abuses are widely within the reach of individuals or small groups. They will not require large facilities or rare raw materials. Knowledge alone will enable the use of them…

I think it is no exaggeration to say we are on the cusp of the further perfection of extreme evil, an evil whose possibility spreads well beyond that which weapons of mass destruction bequeathed to the nation-states, on to a surprising and terrible empowerment of extreme individuals.

Joy’s proposed solutions included “relinquishment” of certain technologies we all agree are too dangerous (as we have, to some extent up until now, with nuclear, chemical, and biological weapons at the nation-state level) and strong protections for whistleblowers in the scientific and technical communities.

what is engineering anyway?

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Engineering is both a professional practice and an academic field (but really, an overlapping set of sub-categories within both of these). And none of these categories agree on what it even is. This article suggests it is something like “implementing things that work by following previously successful models”. And I think that is close to how many practicing engineers and the public, and even some academics such as the one quoted here, think of it. To be fair, the person quoted is talking about how historical engineering methods often predated the scientific theories that could explain why they worked.

The article actually suggests that engineering is somewhat disconnected from science. I happen to think this is wrong in the modern context, or at best a small part of what the relationship between modern science and modern engineering is or should be. The definition I prefer is something like “applying science to understand systems, how their component parts determine their behavior, and how they interact with conditions at their boundaries in time and space.”

Decision Making Under Deep Uncertainty

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I like the idea of applying decision theory to real-world problems. The ideal concept is that decision makers and stakeholders have a body of highly relevant objective information available to them at the time they are also applying judgment and values (and politics) to make those decisions. There is a huge academic endeavor, but the hard part in my work experience is pulling together relevant information in a timely manner. Today’s schedule and budget expectations just don’t allow complex analyses in many cases, so we default to relatively simplistic approaches to complex problems. Maybe AI can help apply these methods more quickly, alongside more traditional methods like benefit-cost analysis. I like the focus on robust decisions in this paper, and I like the collection of practical tools including some R packages.

A review of tools and resources to support Decision-Making Under Deep Uncertainty

Decision-making under Deep Uncertainty (DMDU) offers approaches to support robust, adaptive strategies for complex decision-making. However, practical uptake of DMDU remains limited, partly due to fragmented access to resources and a lack of an inventory of available tools. This study introduces a comprehensive catalogue of tools and resources. Through a structured survey and expert elicitation, we identify 28 resources and 16 tools that support DMDU research and practice and classify them using an established DMDU taxonomy. Our analysis reveals a focus on introductory guidance regarding theory and methods of DMDU application, with some bias toward water-related applications. Technical, method-specific resources on how to implement existing frameworks remain limited. Our results identify tools supporting all core DMDU components, though they highlight persistent scalability challenges. The resulting online catalogue provides a foundation for expanding the use of DMDU in practice and is intended as a living, community-driven platform.

Waymo coming to Philadelphia

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Waymo test-cars are being spotted pretty frequently now in Philadelphia. I have been paying attention, and at first they had a human driver with hands on the wheel. Recently, they have had a human sitting in the passenger seat with no hands on the wheel. Recognizing that this technology has been out for awhile on the U.S. west coast and abroad, especially in China, the lag time and the public amazement here (expressed in positive and negative ways) is partly a metric of how far behind our city and state are.

Just the other day, I watched a Waymo pull over quickly and come to a complete stop as a fire truck overtook and passed it from the back. This is mostly great – you can imagine if all or even the majority of vehicles did this, fire trucks could get where they need to go a lot faster. This is NOT what human drivers do in Philadelphia currently – people either choose to ignore traffic laws or they may actually not be aware that there are traffic laws, and there are virtually no consequences. So I can imagine human drivers being startled and possibly causing a few crashes when a Waymo does the right thing, rationally and efficiently. And I see a metaphor here – automation/AI will overtake some industries quickly that are mostly unaffected by human institutions and quirky, irrational, inefficient, suboptimal human behavior. But it will have a harder time penetrating other industries and institutions, and this might be a good thing if the technology and institutions take some time to adapt to each other, at a speed that buffers some of the potential disruption. And then there is government, which might be like a brick wall. Just look at DOGE – I think a charitable interpretation of DOGE is that it was an attempt to automate and streamline the federal government using AI. AI probably identified a million ways the government is irrational and inefficient, and it was able to fix none of them in a short period of time, and in fact caused massive disruption and human suffering by trying to fix them, consequences we continue to live with.

The article had a couple interesting statistics:

  • ‘ Waymo recently published a journal article analyzing 127 million miles of trips taken by fully autonomous robotaxis, comparing those results to human benchmarks. “The Waymo Driver,” says Teicher, referring to the name of their driverless-car system, “achieved a tenfold reduction in serious injury or worse crashes, and a twelvefold reduction in injury crashes with pedestrians.” ‘
  • “Between 2014 and 2017, the value of a taxi medallion — the source of family-sustaining wealth for many taxi drivers — plummeted, from a high of more than $500,000 to $10,000. Uber and Lyft eventually grew the total amount of jobs for drivers, but shaped them in the mold of the gig economy.” [major pain for a small group of workers, eventual expansion of jobs but arguably lower quality]
  • In a city like San Francisco, where Waymo has eclipsed 10 percent of the ride-hailing market share, the average fare remains 12 percent higher than Uber and 17 percent higher than Lyft. [okay, so there will be competition between Waymo and ride sharing, and eventually Waymo and other robo-taxi companies.]

good bye ISS

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NASA plans to deorbit the International Space Station “around 2030” and crash it into the ocean. According to Longreads (linking to a Wired article):

The [Deorbit Vehicle] will then move the ISS into the lower atmosphere. Most of it will burn, but some of it will fall into the sea. 

It sounds somewhat spectacular, and possibly risky. I’ll give NASA the benefit of the doubt and assume they know what they’re doing until/unless they show us that they don’t (case studies: FEMA after Hurricane Katrina and CDC during Covid-19). There is no ISS2 in the works – instead, individual countries and private companies seem to be talking about their own space stations and moon bases. Maybe this is partly because technology has improved and costs have come down, so cooperation is no longer necessary just to share the cost. But to me, it is also sad because it represents the end of the post-Cold War era of relative optimism and international cooperation in space.

The AI-energy demand feedback loop

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I’ve been thinking about the idea that the massive data center expansion needed to drive the current AI boom is increasing energy demand, and in the near to medium term may be limited by energy supply. It is also apparently competing for the energy supply we have currently, driving up prices for consumers and businesses across the board, along with toxic air pollution and greenhouse gas pollution. The big technology companies are trying to tell a story that this short term pain will go away when AI “solves climate change” with its superior intelligence. That seems like a longer-term thing that will not solve our short term issues, although the tech-bro-cracy will tell us we are just not smart enough to think exponentially like them, and what we think is long term is in fact just around the corner.

I’ve been thinking about the Kardashev Scale. This is the idea that the level of sophistication of a civilization can be linked to the amount of energy it uses – just the energy striking the surface of the planet, all the energy of its star, all the energy of all the stars in its galaxy, etc. (we are just off the left end, trying to get onto this scale). This might seem not to square with the idea that more energy use means more pollution, negative health and environmental impacts for us at our current level of technology. The way you square that logically is to say that we have to get over the hump from dirty to clean energy technology before we can take the next step to a higher order of civilization. If AI can increase our rate of scientific and technology advancement, maybe it can help us get there sooner. Currently we are at the stage where we are turning decommissioned coal plants back on to fuel the AI, without seeing any measurable benefits so far. Realists/cynics (don’t cynics always consider themselves realists?) might also point out that there is a lot of friction built into human institutions and sociopolitical systems, and that this tends to set a speed limit on the rate we can progress, even if the state of science and technology could allow us to progress faster.

One thing I always wondered was what came first – the coal chicken or the steam engine egg? It turns out, this is a true chicken and egg story that went something like this (citation: Gemini for the background, not fact checked – the words are from my human brain):

  • The ancient Romans, Chinese, and other cultures were aware of coal and burned it to heat buildings. There isn’t really a record of when it was “discovered”, you can imagine deposits of it were first found just lying around on or very near the surface, and people found it burned hotter and longer than wood.
  • By the 1700-1800s, London grew to the point that England started to run out of accessible coal to heat all the buildings. They were digging deeper and deeper to get at the coal, and starting to hit the water table. So the first steam engine was invented in 1712 to power pumps to lower the groundwater table in coal mines. This was a dirty, inefficient machine but they didn’t care because it was literally at the mine.
  • From there, the steam engine technology was refined (famously by James Watt in the 1760s) and gave rise to railroads and steam ships (both of which could move coal), and coal-powered factories. From there, the feedback loop of economic growth and energy demand (and localized toxic air pollution and global greenhouse gas emissions, not to mention labor exploitation and industrial war) took off.

So an analogous process today, we might want to think, is that the sharp increase in energy demand for the data centers will incentivize innovation around clean energy, or at least cheaper energy than what fossil fuels are currently providing. Fossil fuels seem to be pretty close to the limit of how much power they can reasonably supply our civilization, both economically and in terms of pollution we are willing to tolerate. It is almost an iron law of economics that this will fuel innovation. So AI is on the energy demand side of the equation – there is no requirement that it also has to be on the innovation side of the equation because humans are doing pretty well advancing clean energy technology (contrary to the fossil fuel industry-funded propaganda we are being force-fed in the U.S.). But humans will figure out ways to use the AI technology to innovate, and at some point maybe AI will be able to do some innovation on its own with high-level human oversight. It will make a big difference whether this process takes years or decades to unfold (I am not buying the “months” time frame being pushed by the tech-bro-cracy). If it’s decades, the ecological impacts and human suffering are going to be enormous.

What kinds of technologies could possibly be boosted by AI? This is just me brain storming:

  • Somewhat obviously, we can continue refining/scaling up the solar, wind, and battery technology we have today. Electrification of buildings, industry, and transportation are important here too, and a smarter/more resilient electric grid is also a big part of this equation. Scaling these involves the high-friction human institution-mediated processes where AI can probably point out how we can do things more rationally and efficiently, but our human inefficiency and irrationality are going to limit the pace for the foreseeable future. AI can probably design much better institutions for us, but we would have to come to some consensus and then successfully implement them. This may happen very slowly – but I see this taking decades for sure, and not much AI will be able to do about it.
  • Fusion – Let’s try to pick up the pace of basic research leading to safe commercialization.
  • Fission – I don’t think we should give up on it. The small modular reactors seem to hold out some promise even if there is also a certain amount of industry exaggeration. There is also a big push-pull between industry, government regulation, and public acceptance, and I am not sure this is something we want to speed up. It may need to just take its course to find solutions we agree are both effective and safe enough – again, decades. If we actually get fusion figured out, maybe this technology will be obsolete before it can be scaled.
  • Space-based solar technology – The energy from just our one Sun is unlimited in any practical near-term sense. There must be lots of technical problems to be worked out here – straightforward mechanical/electrical/chemical engineering stuff. Let’s get to it! The private sector may actually take care of this one without much prompting. No, I don’t want to make Elon Musk even richer, but he is a leader on this so how about some real competition. Even if a few people get unreasonably rich, there are plenty of policy options to share the wealth. AI can easily point these out for us, but again this is one of those human socio-political institutional friction areas where any progress at all is going to be painfully slow, even if we can reverse current trends and get it moving back in the right direction.
  • Applied research on energy storage, materials science, etc – there are plenty of ideas for storing energy beyond our current battery technologies. You can simply pump water up a hill while the sun is out and let it flow back down, turning a turbine when the sun goes in. You can take advantage of pressure/temperature/salinity gradients in the ocean, or electrolyze water to charge hydrogen fuel cells, just to name a few. Intelligent AIs should be able to work on problems involving materials and chemicals, first using numerical (differential equation based) simulations, and later bringing robots in real laboratories into the mix to test out the most promising ideas.
  • Fundamental research to unlock the things we can’t even think of yet – Physicists are forever searching for the holy grail of understanding gravity, relativity, quantum physics, dark matter, and probably other things I have not heard of. I have heard some of them say that new branches of mathematics may need to be invented (discovered? a philosophical question) before the right questions can even be asked. Then new instruments to collect new forms of data may need to be invented before experiments can even be designed to begin answering the new questions. This seems like an area where if AIs are eventually as capable as a leading human physicist, you can have a team of a million or billion or trillion leading physicists working on a problem 24 hours a day, without ever getting tired. One tiny little detail – we need ethical frameworks and nation-state regulations and enforceable international agreements to control the dangerous stuff that could result.

February 2026 in Review

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In fast-moving current events as I write on March 1, 2026, the United States (executive branch, which is unconstrained in this moment by the other supposedly co-equal branches or public opinion) has launched an unprovoked military attack on Iran, in crystal clear violation of the UN Charter and domestic law. Theoretically, there are mechanisms both international (International Criminal Court) and domestic (impeachment – which can apply to cabinet members, agency heads, and federal judges in addition to the President and Vice President; and court martial which applies to military officers who follow illegal orders) that could eventually hold the criminals involved accountable for their crimes. Lots of people have lots to say and we will see how this unfolds. I am just documenting that I am present at this particularly sad moment in history.

Most frightening and/or depressing story: I hadn’t heard of mirror life, technology we apparently have right now which can destroy all life on Earth. This new, shocking, theoretically existential threat narrowly edged out the usual stream of depressing climate disaster news, the existential threat known to be currently unfolding, but which I suppose I am somewhat desensitized to.

Most hopeful story: Falling consumer prices in China might represent a new industrial revolution analogous to the age of railroads and electricity in the west in the late 1800s, rather than a textbook financial recession which seems to be the (propaganda-tainted?) conventional wisdom. I put this in the win column because if it is true, I am hopeful we will see it spread peacefully to the rest of the world rather than representing a threat.

Most interesting story, that was not particularly frightening or hopeful, or perhaps was a mixture of both: Ray Kurzweil predicts broad consensus that Artificial General Intelligence has arrived by 2029 (defined as AI equal to the leading experts in all fields), “longevity escape velocity” in 2032 (which would reverse the US slipping in recent decades), universal basic income in the U.S. sometime in the 2030s, and the Singularity in 2045 (defined as 1000X human intelligence – always pronounced TIMES according to me), but most importantly and the only thing that truly matters, robots doing my dishes in a couple years.

what’s going on with smart glasses?

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Real augmented reality is one of those technologies that always seems around the corner but never seems to quite arrive, at least in a widespread commercial way. It just seems silly that we are still walking around looking at these tiny screens as they give us data about our surrounding environment. I thought this would be built into car windshields by now, and at some point glasses/goggles/visors of some sort.

This article in futurism.com about Meta’s entry is negative, focusing on potential privacy risks. And of course those do exist. But the technology itself is neutral in my view. Sure, ICE can use it to track me down and beat me if they want to. I can also use it to follow directions without tripping and falling in a hole or wandering in front of a bus. It could be really useful for tourism, understanding the past history or possible future of whatever place you happen to be at this point in time. If it is eventually deployed on all or most human-operated vehicles, I really think it could control intersections much better than the antiquated stoplights and pedestrian signals that haven’t improved much over the last century.

The article suggests that Meta’s smart glasses are widely commercially deployed and that people like them. If this is the case, I simply hadn’t noticed. If it is being marketed to me at all, I hadn’t noticed. Maybe it is just deployed in California or geographic pockets other than where I am.

lies, lies, and more lies to the U.S. public about greenhouse gas emissions

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The “endangerment finding” through the Clean Air Act may not have been the ideal way to incentivize clean energy technology in our country. But it was one dial we had to turn, and now it has been turned back. This is just a temporary giveaway to the short-term interests of corporate donors in the automobile and fossil fuel industries. In the case of the auto industry, it is not in their long term interests to subsidize inefficient outdated technology, then use propaganda to swindle the public. The lie about “affordability” is particularly egregious.

Affordable vehicle ownership is essential to the American Dream and a primary driver of economic mobility out of poverty in the United States. The Endangerment Finding led to vehicle and engine regulations with an aggregate cost of more than $1 trillion and played a significant role in EPA’s justification of regulations of other sources beyond cars and trucks, resulting in additional costly burdens on American families and businesses. Americans rely on vehicles to reach jobs, education, health care, and essential services. This is especially true in rural areas and regions without robust public transit. The costs imposed by these climate policies have placed new cars out of reach for many American families and harmed Americans’ ability to climb out of poverty or reach essential services. The Trump EPA is expected to deliver Americans over $1.3 trillion in cost savings, which includes reduced costs for new vehicles and avoided costs of purchasing equipment related to EVs. This action will result in an average cost savings of over $2,400 per vehicle. By lowering vehicle and regulatory compliance costs, EPA is improving affordability and expanding consumer choice and ultimately advancing the American Dream by making it easier to reach jobs, grow small businesses, and participate fully in the transportation and logistics systems that power the U.S. economy.

Here is what Gemini has to say about this. And the analysis below is true in the United States. In China, most new vehicles being sold are electric and you can buy one for $10,000. We are being lied to, and as we have withdrawn even more from the world, we are even less aware what is going on elsewhere, but still U.S. consumers are intelligent enough that we will catch on even if there is some delay. Our legacy auto companies will fail again and again, and eventually need to be bailed out again and again, until eventually the economics of electrification is just too obvious to lie about and get away with it.

While the sticker price of an EV is typically higher, the savings in fuel and maintenance usually “pay back” that difference within 3 to 7 years. By the time a car reaches the end of its life, an EV owner in the U.S. has typically saved between $6,000 and $11,000 compared to a gas-car owner. [generated by Gemini]

There’s another sleight of hand. There is rock solid scientific and economic work showing the costs of air pollution, and here I am talking about good old fashioned toxic smoke from factories and tail pipes. There is also solid (but controversial) economic work over decades quantifying the economic value that people place on a year of worker life. For example, a construction worker on a roof might get paid more than one doing the same job on the ground, because there is a greater statistical risk of death on the roof. Aggregate these numbers over many workers, jobs, and time, and you can say the “value of a statistical life” is a certain number of dollars. It seems cold, but it provides a sound data point when a new regulation with some cost is being considered. Put these two things together and you have the scientific and economic basis to compare the costs and benefits of a policy decision. This is old school environmental economics and really a basis for some pretty conservative policy because you are acknowledging it may be rational to sacrifice some human health and life for economic production. And we are not really assigning the environment any intrinsic value in this equation, unless knowing the environment is a little better brings people some pleasure which some value can be placed on, which economists sometimes try. Of course, real world policy decisions are some combination of science, economics, and politics, as they probably should be in a democracy. But what the EPA has recently suggested it is prepared to do is set the value of a statistical life to zero when analyzing costs and benefits of air pollution. [See pp. 214-217 of the document I link to above. I acknowledge this not a crystal clear policy directive, then again, it may be intentionally buried to try to avoid scrutiny, when in fact if you dig deeper EPA is departing from official directives of the federal government, including the President’s own Office of Management and Budget.] In other words, they propose to assume the cost of pollution is zero. This is wrong, fake, naked propaganda! Their policies are killing us AND stealing our money. It is time to get rid of these immoral people claiming to lead us.