An analysis in 2021 found that 10% of the world’s electricity production is used by computers, including personal use, data centers, the internet and communication centers. The same analysis projected that this was likely to increase to 20% by 2025. This may have been an underestimate because it did not factor in the recent explosion of AI and large language models. Just training a large language model can cost $4-5 million and expend a lot of energy.

I am not trying to doomsay these statistics. Civilization gets a lot of useful work out of all this computing power, and it likely displaces much less efficient ways of doing things. One Zoom meeting vs an in-person meeting can be a huge energy savings. In fact, as long as we use all that computing power reasonably, it’s all good. We can talk about the utility of specific applications, like mining Bitcoins, but overall the dramatic advance of computing is a good thing. But it does shift our energy use, and it does represent the electrification of some technology. We therefore have to factor it in when extrapolating our future electricity uses (just like we need to consider the effect of shifting our car fleet from burning gasoline to using electricity).

The situation also presents an opportunity. As more and more of our energy use is shifted to computers as our world becomes more digital, that means we can have increasing improvement in our overall energy efficiency just by targeting one technology. For example, if computers used 20% of the world’s electricity, a 50% improvement in computer efficiency would result in a 10% drop in our energy demand (once fully implemented). Obviously such improvements would be implemented over years, but it points out how high the stakes are becoming for computer power efficiency. This means the industry needs to focus not just on doing things bigger, better, faster, but also more efficiently. We also need to think twice before adopting wasteful practices.

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One of the key components of the plan to get our civilization to net zero by 2050 is to transform the motor vehicle fleet into all electric vehicles (EVs). This is a worthy goal, as it would eliminate burning gasoline for transportation. In fact it’s necessary if we want to get near net zero. Governments and the auto industry are responding with incentives for EVs, some regulations forcing the phasing out of internal combustion engine (ICE) vehicles, and investment of billions even trillions of dollars to change over production lines, secure raw material sources, and build charging stations.

But EVs have their critics. And some experts point out (a valid point I completely agree with) that we have to consider the optimal pathway to net zero, not just the destination. By 2050 EVs will be an even more mature technology than they are now, and batteries will have at least 4-5 times the energy density. We may also have battery designs that use more abundant and less problematic raw material.  Also by then there should be a robust infrastructure of charging stations, and a green energy grid to support them. So it’ easy to imagine the world of 2050 with an all EV transportation infrastructure that is as close to net zero as possible.

I also have to say, I own a Tesla and it’s the best car I ever owned. The driving experience is great – once you get used to the regenerative breaking, you have more and easier control. Acceleration is instantaneous. Charging at home every night is easy, and you never have to visit a gas station. There is literally almost no maintenance – no oil changes, no tune-ups, no engine parts that wear out. The break pads last much longer because you very rarely use the breaks. At least along the East coast, long trips are no problem.

But what is the optimal path to get to full EV? And this is not just about getting there quickly. EVs may not be the best option for everyone right now. The optimal path may go through bridging technologies, most notably plug-in hybrids. What are the downsides to EVs?

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Despite robust efforts to fight it, malaria remains one of the most significant infectious diseases affecting humans. According to UNICEF – ” In 2021, there were 247 million malaria cases globally that led to 619,000 deaths in total. Of these deaths, 77 per cent were children under 5 years of age.” Efforts to minimize malaria cost about $7 billion per year, through vaccination, drug therapy, and spraying pesticides to kill the mosquitos that carry the disease. Mosquito populations are developing resistance to the pesticides, however, which could raise the costs of control, while available funds can fluctuate.

One potential solution is using genetic engineering to fight malaria, and there are several approaches being developed that are close to being ready for deployment. They all use an approach known as a gene drive, which causes a desired trait to spread more quickly through a population than regular Mendelian genetics would allow. This idea is actually 60 years old, but newer techniques, such as CRISPR, are making it much easier and more powerful.

With sexual reproduction, each offspring has two sets of chromosomes, one from each parent. So organisms have two copies of each gene (each copy is called an allele). They then pass one of their two copies onto each offspring. Mendelian genetics assumes that there is a 50% chance for each allele to be inherited, and this is mostly true. The gene drive phenomenon refers to situations in which one allele has an advantage over the other, so it is more likely to be inherited. There are naturally occurring gene drives, but we’re going to focus on the latest synthetic gene drive, which involves CRISPR.

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DARPA, the US Defense Advanced Research Projects Agency, is now working on developing a magnet-driven silent water propulsion system – the magnetohydrodynamic (MHD) drive. The primary reason is to develop silent military naval craft. Imagine a nuclear submarine with an MHD drive, without moving parts, that can slice through the water silently. No moving parts also means much less maintenance (a bonus I can attest to, owning a fully electric vehicle).

But don’t be distracted by the obvious military application – if DARPA research leads to a successful MHD drive there are implications beyond the military, and there are a lot of interesting elements to this story. Let’s start, however, with the technology itself. How does the MHD work?

The drive was first imagined in the 1960s. That’s generic technology lesson #1 – technology often has deeper roots than you imagine, because development often takes a lot longer than initial hype would suggest. In 1992 Japan built the Yamato-1, a prototype ship with an MHD drive that worked. It was an important proof of concept, but was not practical. Even over 30 years later, we are not there yet. The drive works through powerful magnetic fields, which are place at right angles to an electrical current, producing a Lorentz force. This is a force produced on a particle moving through both an electrical and magnetic field, at right angles to both. Salt water contains charged particles which would feel this Lorentz force. Therefore, if arranged properly, the magnetic and electrical fields could push water toward the back of the ship, providing propulsion.

Sounds pretty straight forward, so what’s the holdup? Well, there are several. The most important aspect of the Yamato-1 is that is provided great research into all the technical hurdles for this technology. The first is that the MHD drive is horribly energy inefficient, which means it was very expensive to operate. What was mainly needed to improve efficiency was more powerful and more efficient magnets. Here we get to generic technology lesson #2 – basic technology developed for one application may have other or even greater utility for other applications. In this case the MHD is partly benefiting from the fusion energy industry, which requires powerful efficient magnets. We can take those same magnet innovations and apply them to MHD drives, making them energy and cost effective.

But there is still one major and one minor problem remaining. The major problem is the electrodes and electronics necessary to generate the electrical current. Electronics and salt water don’t mix – the salt water is highly corrosive, more so when exposed to magnetic fields and electrical current. We therefore need to develop highly corrosive-resistant electrodes. Fortunately, such development is already underway in the battery industry, that also needs robust electrodes. Apparently we are not there yet when it comes to MHD, and that will be a major focus of DARPA research.

There is also the minor problem of the electrodes electrolyzing the salt water, creating bubbles of hydrogen and oxygen. This reduces the efficiency of the system – not a deal-killer, but it would be nice to reduce this effect. I immediately wondered if the created gases can be captured somehow, both solving the problem and making green hydrogen from the shipping industry. In any case, that’s problem #2 for DARPA to solve.

If all goes well, we are probably 10-20 years (or more) still away from working MHD drives on ships. Probably the military applications will come first. I hope they don’t hog the technology, which they might in order to maintain their military technological dominance, but the civilian applications can be huge. The noise generated by shipping has massive negative consequences on marine life, especially whales and other cetaceans who rely on long distance sound to communicate with each other, to navigate, and to migrate. Propellers churning up water is also an ecological problem. If it ever becomes cost effective enough, a working MHD drive could revolutionize ocean travel and shipping. Electrifying ocean propulsion could also help reduce GHG emissions.

Plus, there might be other downstream benefits from the DARPA research. Those robust corrosion resistant electrodes will likely have many applications. It may feed back into battery technology. It may also lead to better electrodes for a brain-machine interface. This reminds me of the book and TV series Connections, by James Burke. This is a brilliant series I have not seen in a while and should probably watch again. It traces long chains of technological developments, from one application to the next, showing how extensively technologies cross-fertilize. A need in one area leads to an advance that makes a completely different application feasible – and so on and so on. I guess that’s generic technology lesson #3.

DARPA has a solid history of accelerating specific technologies in order to bring new industries to fruition more quickly. Hopefully they will be successful here as well. The downstream benefits of an MHD drive could be significant, with spin-off benefits to many industries.

It was six years ago that I first wrote about NASA’s X-59 QueSST project, contracted to Lockheed Martin. Now the plane has finally been built and is ready for testing. At the time it was reported that NASA “had a design” for a quiet supersonic jet, one that would not create a sonic boom, just more of a “thump”. But having a design is not the same thing as having an actual jet – and it took six years of further research and development for Lockheed Martin to produce a prototype. Now we enter the testing phase, and it will likely be years more before there is any jet produced from this design.

One ultimate goal of NASA’s project is to develop the technology for quiet commercial supersonic passenger jets. The idea is that the knowledge gained from testing the X-59 will lead to designs for such commercial aircraft. Why is this a big deal?

As I wrote in that 2017 article, commercial flight times have been relatively flat for the last 50 years. There are several reasons for this, but the bottom line is that fuel efficiency, and therefore cost-effectiveness, is optimal around Mach 0.85, so that is the speed that commercial jets fly. The only practical way to make jet travel significantly faster is to develop supersonic technology. We had this, with the Concorde, but that jet service was ended in 2003. There is debate over exactly why this happened, which I won’t get into, but one factor was that the potential flight paths for the Concorde were limited, because it is generally illegal to fly greater than Mach 1 over land. The Concorde was a New York to Paris flight, mostly over water.

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One of the futuristic technologies that I find most promising is 3D printing (additive manufacturing). 3D printing has already created a revolution in manufacturing, but I think the general public does not have a high awareness of this technology because it is not yet at the point where it is ready to be a routine in-home appliance. It probably seems like an expensive toy. But in reality it is a key and rapidly growing prototyping and manufacturing technology. Importantly, there is tremendous potential for the technology to advance, and we are far away from a fully mature manifestation of 3D printing.

The basic technology connected the virtual world with the physical analog world. You start with a digital design for an object, which can be created using software or by scanning an existing physical object. The 3D printer then prints the object from the digital design, with a print head laying down the material layer by layer, building up the three dimensional object. Sometimes supports need to be added so that the resulting object can stand upright while printing. One limitation of 3D printers is their dimensions – thy can only build object as large as their print area. However, some newer printers have a conveyor belt for a platform which allows for one dimension to be virtually unlimited.

One of the challenges of 3D printing technology has been printing with metals. Dealing with plastics and resins were the low hanging fruit because they can be extruded by the print head and rapidly cool at room temperature to a solid object, maintaining their structure. Hard plastic parts are great for many applications, but not all. The wider a range of material that 3D printers can use the better, and metal 3D printing has been the holy grail.

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The climate change discussion would benefit most from good-faith evidence and science-based discussion. Unfortunately, humans tend to prefer emotion, ideology, motivated reasoning, and confirmation bias. As an example, I was sent an excerpt from a climate change podcast as a “rebuttal” to my position. The content, however, does not address my actual position, and I find many of the arguments highly problematic. This one is coming from a perspective that climate change is real and a definite problem that needs to be addressed, but seems to be advocating that the best solution is to be all-in on wind and solar without needing other solutions.

The podcast is The Energy Transition Show and here is the episode I was sent: https://xenetwork.org/ets/episodes/episode-200-ets-retrospective/. As a rebuttal to my position and what seems to be the position of many experts, their arguments are strawmen, but a particular kind of strawmen. One way to create a strawman argument it to portray the most extreme position as “the” counter opinion to your own. This sets up a false dichotomy – either you agree with us or you are advocating for this extreme and easily refutable position, ignoring vast territory between two extremes. Here’s the beginning of the excerpt:

[00:34:15] ….this argument against the energy transition, which seems to be falling by the wayside since you started in 2015, is this claim that we could never run a power grid with a large share of renewables due to their quote unquote intermittency? Right. You know, back in 2015, there were a lot of people insisting that the power grid couldn’t support more than maybe a high single digit, low double digit percentage of renewable power due to this intermittency, and that we would need to maintain significant amounts of baseload generators that run close to full time, like coal, nuclear plants, to ensure reliable operation of the power grid. But that has not turned out to be true, at least not yet, at the levels of penetration we’re seeing and we’re seeing very high levels of penetration in California. A couple weeks ago, I think 97% renewables at one point in time. And so I don’t really hear those arguments nearly as often anymore. But I am very interested in where you think those arguments have gone.

[00:35:55] Chris Nelder: Yeah, well, we were just talking about terminology and the preference of some people to start calling natural gas fossil gas or methane. I have a strong aversion to the term intermittency. That’s a term that really I think came from the fossil fuel industry as a way of casting doubt on renewables and making them sound unreliable or hard to forecast or in some way or another, not something that we can count on. And that’s just not the case.

The notion that the grid cannot take more than single digits or low double digits of intermittent sources may be a talking point on the climate change denial end of the spectrum, but that is not the mainstream perspective arguing that we should not rely entirely one wind and solar. Also, I have never read the argument from any expert that the grid cannot function with high penetration of wind and solar, only that it becomes more challenging and high penetration. One side note, many sources use the term “renewable” source or explicitly refer to WWS – wind, water, and solar. But hydropower is not intermittent, it can be dispatchable, and can be use for grid storage through pumped hydro. So including that in the discussion muddies the waters.

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I’ve been following AI (artificial intelligence) news very closely, including all the controversies and concerns. I tend to fall on the side of – AI is a powerful tool, we should continue to develop it and use it responsibly. We don’t need to panic, and highly restrictive laws are likely unnecessary and counterproductive. But there are legitimate concerns about the power of AI, especially in the “wrong” hands. I also think the greatest disruption to our lives might not come from cyberterrorists (although a legit concern) or AI run amok, but from marketing. Giving companies who see us only as customers the power to predict our every move gives me pause.

This AI news item falls into this latter category – the use of machine learning AI to predict which songs people will like. Seems innocuous, but I think it furthers a trend that has some serious downsides. This is what the researchers did:

Traditionally, song elements have been measured from large databases to identify the lyrical aspects of hits. We took a different methodological approach, measuring neurophysiologic responses to a set of songs provided by a streaming music service that identified hits and flops. We compared several statistical approaches to examine the predictive accuracy of each technique. A linear statistical model using two neural measures identified hits with 69% accuracy. Then, we created a synthetic set data and applied ensemble machine learning to capture inherent non-linearities in neural data. This model classified hit songs with 97% accuracy.

This kind of approach is called neuroforecasting – predicting people’s likes and dislikes based upon their brain activity and physiological responses (like a lie detector but for your reaction to music). First let me point out that this study used a synthetic set of data, and is therefore just a proof of concept – this approach can theoretically work. They need to test this in the real world, and see if it can predict hits, not just match the model to existing hits. But let’s assume it works, and the 97% accuracy hold up. What will this mean for the music and streaming industries?

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For the first time in over a decade, Apple has announced a new product designed to change computing. There was the transition to personal computing with the Apple computer, then to portable computing with the iPhone, and now they hope to usher in the transition to virtual computing with their Vision Pro. It may be emblematic of the response to their announcement that Apple stock prices dropped during the announcement.

My personal response is mixed. Since I am now in the “futurism” space (I dislike that phrase, but not sure how else to put it) after the publication of my second book, I do tend to follow technology news quite closely. I’m especially interested in how people interact with new technology, and what that tells us about the future of technology. The Vision Pro is an excellent test case, and I think reflects many of the basic principles of futurism when it comes to thinking about future technology.

The product is being presented as a mixed reality application – usable for both virtual reality (VR) in which one’s visual experience is entirely immersed in the virtual world, and augmented reality (AR), in which you can still see the real world around you but digital content is overlayed on the real world. When evaluating any new tech we need to consider three basic types of analysis – how good is the hardware, how will it be used, and is it practical. By all accounts, Apple has definitely made a huge leap in the VR/AR hardware. Their device looks like ski goggles, and has impressive specs. It can run for two hours on battery, which you would wear on your waist, but can also be plugged in if your are sitting at your desk. It has 23 million pixels in each eye, greater than 4k resolution, eye tracking, and surround sound. There are no firm numbers on field of view, but speculation is that it will be a standard 110 degrees, which for me is very disappointing. It would be nice to bump that up even a little.

So by all accounts this would be an excellent VR headset, if nothing else. But in addition it has multiple cameras with which it can view the world and include that feed into what you see, creating an AR experience. The opacity of the digital world can be dialed anywhere from 0 to 100 percent. Also there are no controllers like with standard VR. You simply move your hands to manipulate objects in the virtual world, and also can interact with objects by just looking at them. So again, at the very least, this appears to be a great VR headset.

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There is good evidence that if you want to lose weight, you need to weigh yourself at least weekly. You need the constant feedback of the scale to adjust your behavior. This is a good general principle – having outcome feedback to measure the effect of what you are doing so you can make adjustments. This is the basic concept of many AI learning algorithms. Plug output into input and let it run.

Along these lines, a group of 50 scientists have made a website and report that tracks several useful measures of how the world is doing tackling climate change. They are doing this in part in preparation for the upcoming (and future) IPCC meetings. But it’s also a useful resource for journalists and the public. So – how are we doing? Brace yourself.

One measure they track is the average warming (above pre-industrialized levels) averaged over the last decade running. This helps track one of the primary goals of trying to blunt global warming, keeping peak warming below 1.5 C (2.7 F). In 2019 this average of the previous 10 years was 1.07 C. In 2022, just three years later, this average is up to 1.14 C. My only “note” is that I would put some kind of thermometer or gauge on the website, visually representing current average warming and how close we are getting to 1.5 C. But the numbers tell the tale.

Another way to mark our march toward problematic climate change is known as the carbon budget – how much cumulative CO2 can we release into the atmosphere without pushing warming past 1.5 C? The remaining carbon budget is 250 gigatonnes of CO2. Our current annual rate of CO2 emissions is 41 gigatonnes – so if we stay at the current rate we will exhaust our carbon budget in 2029 – just 6 years. Remember when it was common to report that “we have just 12 years to stop global warming”? That was based on the carbon budget calculation. That’s not really what it means. It’s more accurate to say, we have 12 years, at current CO2 emissions, before we will exceed 1.5 C warming. That figure has now been cut in half, down to 6 years.

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