In my upcoming book, which I will now shamelessly plug – The Skeptics Guide to the Future (release date Sept 27th, but you can pre-order now) my co-authors and I spend a lot of time extrapolating cutting edge technology into the near and medium-term future. What are the technologies that are on the cusp of disrupting current technology and changing our lives? One of them is the technology to build ever-smaller and more capable machines – the technology of the very small. We can dream of having mature nanotechnology, robots at the nanoscale that can manipulate matter at the molecular level, but this is likely still centuries in the future. Between now and then there is a lot of territory, however.

In the meantime we can imagine what the most likely early applications will be. What is the low-hanging fruit? For medical purposes there are some likely early applications, even for robots that are not quite at the nanoscale, perhaps at the micro or even centimeter scale. Tiny robots can be useful as surgical aids. They can be injected through the skin (no incision necessary) where they can make precise interventions, such as removing tumors or suturing blood vessels. This could take microsurgery (which is already a thing) to the next level.

When such robots can be more autonomous or easy to control remotely, another possible early application is to have them crawl along the inside of the large and even medium-sized blood vessels, clearing up plaque and removing clots.  Or they can move along the inside of the intestines, removing polyps and scanning for cancers. In recent decades we have been transitioning from having to open up major body cavities in order to do surgery, to being able to do the same procedures through small holes using cameras and specially designed instruments. This has made many surgeries significantly less invasive, with dramatically reduced trauma and recovery time. Micro surgical robots have the potential to take this to the next level over the coming decades.

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The big science and technology news today is the Artemis I launch, an uncrewed test flight that will orbit the Moon on a six week flight. I thought I would be writing about that today, but prior to the launch I actually don’t have much to add to the extensive reporting. I’ll probably have something to say after the launch. But there is other space news, this one from the European Space Agency (ESA). The ESA is considering a proposal for space-based solar power, which also makes a nice follow up to my previous post updating solar technology.

This is one of those ideas that, when I first heard it, I thought it was a great idea. There are some obvious benefits to placing solar panels in orbit, then beaming the energy down to Earth. Above the atmosphere in geostationary orbit, the solar panels would receive sunlight 24 hours a day and without any concern for clouds or weather. Panels can also be optimally oriented to the sun without having to worry about the Earth’s rotation. How much of a factor is this? Depends on location of ground-based solar, but for example Arizona can expect to have 7.5 peak solar hours per day, while New Jersey has 4 hours. There is still some off-peak energy production, but less. Overall light exposure efficiency can vary from 20-40%. In space we can get 100% light exposure efficiency. Being based in orbit also solves the intermittency problem. Solar can become a reliable baseload source of energy.

The obvious downside is that it’s expensive to get stuff into orbit, but until you run  the numbers it may not be obvious if space-based solar can be cost effective, compared to other options like nuclear or geothermal. This is the primary reason for the ESA feasibility study. Unfortunately, when you start to run the numbers, and consider all aspects of logistics, space-based solar looks worse and worse.

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When I first started reporting about solar cell technology around 2005 the best commercially available silicon solar cells (photovoltaics) had an efficiency rating of about 11% (the amount of the sunlight hitting the panel that ultimately gets converted into electricity). They were expensive, heavy, and didn’t product that much electricity, but still it was enough for early adopters and we were at the beginning of the commercial solar industry. That was just before the inflection point when solar started to take off.

I remember reading many solar power news item, detailing some incremental advance, but still with some limitations and uncertainty. But slowly, inexorably, these potential advances added up. Every year solar panels because a little better and a little cheaper. Now silicon crystal solar panels commercially available have efficiencies of over 20%, they have a minimum lifespan of 20 years but many are rated for 35-40 years (and some report 40-50 years), and their price has plummeted, down about 90% compared to 2010. The ultimate potential efficiency of silicon solar cells is often cited as 29%, but using various techniques higher efficiencies have been reported, such as this one in 2019 reporting a 31% efficiency.

The question is – how long will this trend continue? It’s hard to say, but it’s clear that advances in silicon solar panels are not done yet. Already the cheapest form of new power, it will likely continue to get cheaper and more efficient for the next 10-20 years, producing phenomenally cheap energy by historical standards. Even without any major new technological breakthroughs, just incremental tweaks on existing solar technology, this is likely to happen.

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It is estimated that we would need 1.1 million square kilometers of solar panels in order to power the world – smaller than the state of Alaska at about 1.7 million square km. Of course, we would want to spread these solar panels out as much as possible. Rooftop solar alone would provide much of this needed area. In the US there is an estimated usable rooftop area for about 1 terrawatt of production capacity, compared to 1.2 terrawatt total current us power production capacity. Solar is also currently the cheapest form of new energy, in fact the cheapest source of electricity in human history.

Now come the well-known caveats – solar panels only produce energy when the sun is shining. So capacity is misleading as solar panels only produce electricity during the day when there isn’t too much cloud cover, and they produce less energy in the winter than the summer. At low solar power penetration this does not matter much because solar can displace more expensive and more polluting sources of energy when the sun is shining. We can also partly shift some of our energy consumption to sunny times – run the dishwasher or laundry during the day.

There are basically three ways to solve this problem. One is to have on-demand and baseload power to cover a good chunk of energy consumption, essentially limiting the percentage of intermittent sources. Another is to install overcapacity (more capacity than is needed at any one time) and share electricity over a broad grid. This works best for wind power, as the wind is always blowing somewhere, but could also help with solar. With a large enough grid solar power generated in Arizona could cover peak demand in New York. The third method is grid storage, storing up energy during producing and then releasing in when needed. We will likely need all three methods to get to net zero carbon energy production as quickly as possible.

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How people make decisions has been an intense area of study from multiple angles, including various disciplines within psychology and economics. Here is a fascinating article that provides some insight into the state of the science addressing this broad question. It is framed as a meta-question – do we have the right underlying model that properly ties together all the various aspects of human decision-making? It is not a systematic review of this question, and really just addresses one key concept, but I think it helps frame the question.

The title reflects the author’s (Jason Collins) approach – “We don’t have a hundred biases, we have the wrong model.” The article is worth a careful read or two if you are interested in this topic, but here’s my attempt at a summary with some added thoughts. As with many scientific phenomena, we can divide the approach to human decision making into at least two levels, describing what people do and an underlying theory (or model) as to why they behave that way. Collins is coming at this mostly from a behavioral economics point of view, which starts with the “rational actor” model, the notion that people generally make rational decisions in their own self-interest. This model also includes the premise the individual have the computational mental power to arrive at the optimal decision, and the willpower to carry it out. When research shows that people deviate from a pure rational actor model of behavior, those deviations are deemed “biases”. I’ve discussed many such biases in this blog, and hundreds have been identified – risk aversion, sunk cost, omission bias, left-most digit bias, and others. It’s also recognized that people do not have unlimited computational power or willpower.

Collins likens this situation to the Earth-centric model of the universe. Geocentrism was an underlying model of how the universe worked, but did not match observations of the actual universe. So astronomers introduced more and more tweaks and complexities to explain these deviations. Perhaps, Collins argues, we are still in the “geocentrism” era of behavioral psychology and we need a new underlying model that is more elegant, accurate, and has more predictive power – a heliocentrism for human decision-making. He acknowledges that human behavior it too complex and multifaceted to follow a model as simple and elegant as, say, Kepler’s laws of planetary motion, but perhaps we can do better than the rational actor model tweaked with many biases to explain each deviation.

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I have to be honest, I don’t believe it. Whenever research seems to show a phenomenon that defies the known laws of physics, that is my initial reaction. It’s a good default approach, and so far it has proven correct. I didn’t believe it when researchers claimed they found neutrinos traveling faster than light. It turns out, it was a flaw in the equipment. In fact, I have not believed the many claims over the years of faster than light phenomena, all of which have fallen away. I did not believe the countless claims of free energy or perpetual motion, all of which have failed. I did not believe claims of cold fusion, and still don’t. I did not believe it when engineers claimed to have produced propellantless acceleration (the EM drive). That one crashed and burned as well.

These claims typically have two features in common. They are based on an observed anomaly, and that anomaly is very tiny. It’s just more likely that a tiny anomaly that appears to break the laws of physics is the result of a tiny error, not that the laws of physics as we currently know them are wrong. This is especially true when talking about conservation laws, which are so well established that we can treat them as – laws.

I always acknowledge that our understanding of the laws of physics is incomplete, and there could be some phenomenon hiding in the parts we have not figured out yet (quantum gravity is a good example) that could allow for these apparent anomalies. I’m just not holding my breath. Also, the bar we set for the threshold of evidence before accepting the anomaly as real should be incredibly high. Again, history has proven countless times that this is a good approach.

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The US is about to pass into law the first real action on climate change in decades. Obviously there is a lot of politics involved, and I don’t want to get sucked into that, but rather I want to discuss the strategy of this approach to mitigating climate change. Here is a summary of the climate-related provisions in the bill. The bill provides tax incentive and grants for states, industry, and individuals to purchase electric vehicles, install green energy, make buildings energy efficient, convert cement, steel, and agricultural industries to more green methods, reduce leaks from methane pipes, and accelerate research in green technologies and manufacturing. Proponents estimate these measures will reduce US carbon emissions by 40% by 2030.

This projected reduction, however, is not compared to zero reduction, but rather what would happen without the bill:

Recent modeling by Rhodium Group highlights the substantial emissions reduction impact of these provisions. Under a business-as-usual scenario, the United States is on track to reduce greenhouse gas (GHG) emissions by between 24% to 35% by 2030 compared to 2005 levels. Should the IRA become law, this would increase to between 31% to 44% by 2030.

So it looks like the provision will produce an additional 10% reduction. Critics would also argue that this is only for the US and therefore as a percental of global GHG emissions, this is small potatoes. In a tradeoff to get support, the bill also would increase leasing for more oil and gas drilling:

“…it requires the U.S. Department of the Interior to lease 2 million acres in federal lands onshore and 60 million acres offshore each year for oil and gas development (or whatever acreage the industry requests, whichever is smaller).”

This has some environmentalists upset (aren’t we supposed to be reducing fossil fuel production). I don’t think they should be. There is a very deliberate strategy to this bill, and I think it is the correct one. At the top level, strategically there are two basic approaches to reducing GHG emissions, or specifically the burning of fossil fuels (which is the major contributor) – either we reduce supply of fossil fuels or we reduce demand. These, of course, are not mutually exclusive, we can do both, but specific measures usually fall into one or the other category.

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One of the many unintended consequences of social media is what is popularly referred to as FOMO – fear of missing out. People see all the wonderful things people are doing and buying in their social media profiles, and fear that they are missing out on the good life, or the latest trend, or perhaps some investment opportunity. This is the social media equivalent of “keeping up with the Joneses”. FOMO results from a basic human psychological tendency, to determine our own happiness by comparing ourselves to some relative standard, whether that’s our neighbors, our social group, or what we see on TV or on people’s Facebook pages.

This phenomenon also interacts with another, that we determine our happiness relative to our own current state, meaning that we habituate to our current situation. Functionally what this means is that if we want to remain happy we constantly need more – more than we have now, and more that other people have. The habituation phenomenon was humorously depicted in the video game, Portal 2 (an excellent game, highly recommended if you like video games). The main antagonist is an AI that is programmed to run the player through various testing scenarios. Each time the player completes a test the AI gets the silicon version of a dose of dopamine, but the digital Nirvana is short-lived and it has to run another test to maintain the good feeling. But it rapidly habituates to this feedback, with shorter and less intense reward meaning it has to test faster and harder.

This is essentially how humans function as well. We are never content. We cannot remain happy by standing still. We need whatever other people have, and we need more than we currently have. This lines up with research into happiness. Making more money does make people happier, up to the level where basic needs and security are met (in the US this is now about 75k per year). Some researchers frame this not as money making people happy, but rather not having enough money to meet basic needs is stressful and makes people unhappy. Beyond this basic level, increasing income does not correlate with happiness. Whether you make 75 thousand a year or 75 million a year does not matter. Further, everyone thinks that they would be happy if they just made 20% more than they currently make – regardless of how much that is. We habituate to our current situation and then think we need a little more to be happy.

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This is a great idea, and in fact is long overdue. The NIH is awarding various grants to establish educational materials and centers to teach principles of scientific rigor to researchers. This may seem redundant, but it absolutely isn’t.

At present principles of research are taught in basic form during scientific courses, but advanced principles are largely left to individual mentorship. This creates a great deal of variability in how well researchers really understand the principles of scientific rigor. As a result, a lot of research falls short of scientific ideals. This creates a great deal of waste in the system. NIH, as a funding institution, has a great deal of incentive to reduce this waste.

The primary mechanism will be to create teaching modules that then can be made freely available to educational and research institutions. These modules would cover:

biases in research; logical fallacies around causality; how to develop hypotheses; designing literature searches; identifying experimental variables; and reducing confounding variables in research.

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Political neuroscientists are trying to answer a basic question – what is the relationship between political ideology and brain function? Actually this is a horrifically complex question, but we are making some incremental progress, and a recent study adds a new layer of information. But let me first back up and give some thoughts on the entire enterprise.

Obviously political ideology is a brain phenomenon, in the way that all cognitive function is a brain phenomenon. But there are interesting deeper questions. How much of political ideology is learned or absorbed from the environment, and how much is a function of our genetic neurological predisposition (nature vs nurture)? The number one predictor of political ideology is the ideology of one’s parents. But this, of course, cuts both ways – we inherit genes from our parents, but they also dominantly affect the environment of our childhood. Twin studies (looking at the political ideology of twins separated at birth) suggest that political ideology is at least partly genetic. So unsurprisingly the answer is that genetic and environmental factors are likely working together to influence political ideology.

Another layer to consider is how we define political ideology? For studies based in the US, a typical liberal vs conservative scale is used. But we have to ask – is American liberal vs conservative politics fundamental to human psychology, or is it a particular cultural manifestation that may only indirectly relate to basic cognitive function? Perhaps, in other words, we’re looking in the wrong place, where the lighting is good but not necessarily where the phenomenon is really located. All research that looks for neuroanatomic correlates suffers from this fundamental question. If we look for the neuroanatomical correlates of depression, for example, we have to ask what depression is, and if it is a foundational phenomenon or an epiphenomenon. Is it a fundamental property of neurological functioning, or just a manifestation of a deeper neurological functioning?

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