In my second book (shameless plug alert) – The Skeptics’ Guide to the Future – my coauthors and I try to imagine both the utopian and dystopian versions of the future, brought about by technology, either individually or collectively. This topic has come up multiple times recently on this blog when discussing technology and trust in science and scientists, so I thought it deserved its own discussion.

The overarching point is that science and technology should not be thought of as pure objective good, but rather they are tools, and tools can be used for good or evil. I admit I am a science enthusiast and a technophile, also a bit of an optimist, so when I hear about a new discovery or technology my first thoughts go to all the ways that it might make life better, or at least cooler. I have to remember to consider all the ways in which the technology can also be abused or exploited, which is why we explicitly did this in our futurism book.

So far, on the balance I think science and technology has been an incredible plus to humanity. For most of human existence life was “short, nasty, and brutish.” Science has given us a greater perspective on ourselves and the universe, freeing us from ignorance and superstition. And technology has given us the power to extend our lives, improve our health, and control our environment. It enables us to peer deep into the universe, and see for the first time a microscopic world that was always there but we had no idea existed. It enables us to travel beyond the confines of our planet, and eventually (if we survive) will enable us to be a multi-world, and even multi-system, species.

I do think we have lost touch with how bad life was prior to modern technology. Our period movies, for example, are highly romanticized. A brutally accurate portrayal of life prior to the industrial revolution would show people with horrible dentition ravaged by diseases and living mostly in drudgery. Most people never saw the world beyond their small village.  We get a hint of this sometimes, but never the reality.

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There are several technologies which seem likely to be transformative in the coming decades. Genetic bioengineering gives us the ability to control the basic machinery of life, including ourselves. Artificial intelligence is a suite of active, learning, information tools. Robotics continues its steady advance, and is increasingly reaching into the micro-scale. The world is becoming more and more digital, based upon information, and our ability to translate that information into physical reality is also increasing.

Finally, we are increasingly able to interface ourselves with this digital technology, through brain machine interfaces, and hybrid biological technology. This is the piece I want to discuss today, because of a recent paper detailing a hybrid biopolymer transistor. This is one of the goals of computer technology going forward – to make biological, or at least biocompatible, computers. The more biocompatible our digital technology, the better we will be able to interface that technology with biology, especially the human brain.

This begins with the transistor, the centerpiece of modern computing technology. A transistor is basically a switch that has two states, which can be used to store binary information (1s and 0s). If the switch in on, current flows through the semiconductor, and that indicates a 1, if it is off, current does not flow, indicating a zero. The switch is also controlled by a gate separated by an insulator. These switches can turn on and off 100 billion times a second. Circuits of these switches are designed to process information – to do the operations that form the basis of computing. (This is an oversimplification, but this is the basic idea.)\

This new hybrid transistor uses silk proteins as the insulator around the gates of the transistor. The innovation is the ability to control these proteins at the nano-scale necessary to make a modern transistor. Using silk proteins rather than an inorganic substance allows the transistor to react to its environment in a way that purely inorganic transistors cannot. For example, the ambient moisture will affect the insulating properties of these proteins, changing the operation of the gates.

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The history of aluminum, and what is now happening in the artificial diamond market, may tell us something about a post-scarcity world. Aluminum is the most abundant metal in the Earth’s crust. However, it like to form with other elements and therefore it was very difficulty to purify the metal. In the 1800s methods were discovered for purifying aluminum, but they were slow and expensive, and hence aluminum was scarce and expensive – $16 per pound ($419 in today’s dollars), more expensive than gold at the time. Famously, Napoleon set before his most prestigious guests aluminum cutlery, instead of the less exciting gold cutlery. But then, in 1887, two men (both in their 20s), American Charles Martin Hall and Frenchman Paul Héroult independently discovered the same method for purifying aluminum from ore (usually bauxite). Within two years aluminum fell to $2 per pound, then pennies per pound. Now we make cans and foil out of it.

Will a similar thing happen to the diamond market? Diamonds, despite common belief, are not made from coal. They are created deep underground, 177-241 km, where pressures and temperatures are high. They are brought to the surface through volcanic eruptions, embedded in a mineral known as kimberlite. While natural diamonds are highly valued for their beauty and strength, there is a lot of controversy surrounding the mining of diamonds. The mining process is disruptive to the environment, and tends to involve a great deal of conflict and exploitation.

Further, the pricing of diamonds has been a bit controversial. Natural diamonds are generally considered to be overpriced by marketing and market manipulation. They are marketed as rare and valuable gems, but in reality they are quite plentiful. Marketing of diamonds as a “girls best friend” and for engagement rings also fed this image. Like everything, prices are generally about supply and demand, but demand is kept high through marketing while supply is generally kept secret in order to keep the price elevated. Gem-quality diamonds are also a luxury item, so their value is inherently subjective.

The first artificial diamond was created in 1954, using a method that involves high pressure and temperature. There is now also a newer method called chemical vapor deposition (CVD), which is cheaper and becoming more popular. India in particular has a booming industry in synthetic diamonds, and first to decrease the need for import, but now increasingly for export. The CVD method uses a gas chamber containing carbon, and may also contain other elements that affect the color of the diamond. The temperature and pressure are manipulated to cause the carbon to precipitate out of the gas, forming onto a seed diamond which can grow to large size. Many diamonds can be made at the same time using this method, which takes 1-2 weeks. Periodically graphite needs to be cleaned from the growing diamond.

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This is a bit of a false choice – we can do both, or neither – but it is an important question and a somewhat of a dilemma. Is the optimal path to reductions and eventual elimination of fossil fuel burning through reduced demand or supply? There are some interesting tradeoffs either way, and no perfect answer.

To focus the question, it’s clear that we need to reduce demand as quickly as possible. This is not a question, and there is no dilemma here. Reducing demand is a win-win. We can do this in a number of ways. Switching from internal combustion to battery-electric vehicles is one way. Changing coal and gas power plants to wind, solar, nuclear, hydro, and geothermal is another. Increasing energy efficiency is also important – in homes, cars, and industry. Longer term we can also shift some of our societal patterns of behavior, creating more walkable cities, expanding public transport, and reducing waste.

Basically we need to electrify our technology, switch to green energy, and maximize efficiency. All of these are good things that will reduce pollution, create jobs, foster energy independence, and improve prosperity. Even if you are a climate change denier, you should still favor the green energy revolution (don’t let political propaganda dissuade you). If we were having this conversation in the 1990s, this would be all we need to talk about. How do we accelerate the switch over from fossil fuels by investing in R&D and providing strategic tax incentives? But it’s 2023, and time is basically up.

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The European Space Agency (ESA) has announced they are developing their own commercial space capsule. This will be used initially for cargo, but then eventually for crew as well. They anticipate a maiden voyage in 2028.

I think this is a positive development. It seems we are slowing moving in the direction of increasing collaboration among international government and private space companies. This has existed from the beginning of the space program to some extent, but private companies are taking an increasing role, and more nations are getting involved as well. But what I want to talk about is what this space travel infrastructure will look like, and where that should be heading.

The history of the Apollo mission is a good place to start to frame the challenges of space travel. For Apollos NASA ultimately landed on a complex but workable solution. First you need a really big rocket, one able to life a large payload and give it enough acceleration to get into trans-lunar orbit – to get into orbit around the moon. This required a multi-stage rocket, the Saturn V. On top of that sat the lunar lander then the service module and then the command module (the capsule where the astronauts lived). After acceleration to the Moon was complete, the lunar module would detach and then flip around and dock with the top of the command module, so that astronauts could move between the two vehicles.

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Artificial Intelligence (AI) is coming for your job. This, at least, is increasingly conventional wisdom, but I’m not so sure. In a recent interview, Elon Musk predicted that AI would “make paid work redundant.” I encountered the same opinion watching the latest season of Connections (yes, season 4 of Connections with James Burke), and I had the opportunity to interview James Burke about this (which will be on the next episode of the SGU). Many futurists are predicting a “post scarcity” or “post labor” world.

The idea, explored in many works of science fiction, is that a combination of AI and robotics will be able to do any labor required to run civilization. There would simply be no need for humans to engage in any sort of labor, freeing us for endless leisure. There is a lot of speculation and discussion about what such a world would look like. Will it be a paradise, where all our needs are met by attentive robots, and there is no poverty or want? Or will it be a dystopian nightmare, full of boredom, mindless distraction, a loss of any purpose, and a general atrophying of all human capability? Perhaps it will be both simultaneously. One vision has AI and robots taking care of all our physical needs, while we live our lives in virtual reality, gods of our own digital worlds.

It’s difficult to predict what a post-work world would look like. Partly this difficulty comes from not knowing the state of all other technology. Also, the people is that hypothetical future will not be us. They will exist generations from now, forged by social and technological changes we have not envisioned yet. Our current hand-wringing over what such a world will be like may seem quaint to them, like Medieval people trying to imagine our lives today.

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Should an artificial intelligence (AI) be treated like a legal “subject” or agent? That is the question discussed in a new paper by legal scholars. They recognize that this question is a bit ahead of the technology, but argue that we should work out the legal ramifications before it’s absolutely necessary. They also argue – it might become necessary sooner than we think.

One of their primary arguments is that it is technically possible for this to happen today. In the US a corporation can be considered a legal agent, or “artificial persons”, within the legal system. Corporations can have rights, because corporations are composed of people exerting their collective will. But, in some states it is not explicitly required that a corporation be headed by a human. You could, theoretically, run a corporation entirely by an AI. That AI would then have the legal rights of an artificial person, just like any other corporation. At least that’s the idea – one that can use discussion and perhaps require new legislation to deal with.

This legal conundrum, they argue, will only get greater as AI advances. We don’t even need to fully resolve the issue of narrow AI vs general AI for this to be a problem. An AI does not have to be truly sentient to behave in such a way that it creates both legal and ethical implications. They argue:

Rather than attempt to ban development of powerful AI, wrapping of AI in legal form could reduce undesired AI behavior by defining targets for legal action and by providing a research agenda to improve AI governance, by embedding law into AI agents, and by training AI compliance agents.

Basically we need a well thought-out legal framework to deal with increasingly sophisticated and powerful AIs, to make sure they can be properly controlled and regulated. It’s hard to argue with that.

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It’s pretty clear that we are at an inflection point with adoption of solar power. For the last 18 years in a row, solar PV electricity capacity has increased more (as a percentage increase) than any power source. Solar now accounts for 4.5% of global power generation. Wind generation is at 7.5%, which means wind and solar combined are at 12%. By comparison nuclear is at about 10% generation globally.

Solar PV is currently the cheapest power capacity to add to the grid. Extrapolating gets more complicated as solar penetration increases because we increasingly need to consider the costs of upgrading electrical grids and adding grid storage. But wind and solar still have a long way to go. Adopting these renewable energy source as quickly as possible is helped by technological improvements that make their installation and maintenance less expensive (and material and power hungry) and increase their efficiency. Part of the reason for the steep curves of wind and solar adoption is the fact that these technologies have been steadily improving.

There are numerous research programs looking at various methods for improving on the current silicon PV cells which dominate the market. The current range of energy conversion efficiencies for the top silicon solar cells on the market range from 18.7%–22.8%. Those are great numbers – when I started following the PV solar cell industry closely in the aughts efficiencies were around 15%. The theoretical upper limit for silicon is about 29%, so the technology has some head room. Increased efficiency, of course, means more energy per dollar invested, and fewer panels needed on any specific install.

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I am of the generation that essentially lived through the introduction and evolution of the personal computer. I have decades of experience as an active user and enthusiast, so I have been able to notice some patterns. One pattern is the relationship between the power of computing hardware and the demands of computing software. For the first couple of decades of personal computers, the capacity and speed of the hardware was definitely the limiting factor in terms of the end-user experience. Software was essentially fit into the available hardware, and as hardware continually improved, software power expanded to fill the available space. The same was true for hard drive capacity – each new drive seemed like a bottomless pit at first, but quickly file size increased to take advantage of the new capacity.

During these days my friends and I would intimately know every stat of our current hardware – RAM, hard drive capacity, processor speed, then the number of cores – and we engaged in a friendly arms race as we perpetually leap-frogged each other. The pattern shifted, however, sometime after 2000. For personal computers, hardware power seemed to finally get to the point where it was way more than enough for anything we might want to do. We stopped obsessing with things like processor speed – except, that is, for our gaming computer. Video games were the only everyday application that really stressed the power of our hardware. Suddenly, the stats of your graphics card became the most important stat.

That is beginning to wane also. I know there are gaming jockeys who still build sick rigs, pushing the limits of consumer computing, but for me, as long as my computer is basically up to date, I don’t have to worry about running the latest game. I still pay attention to my gaming card stats, however, especially when VR became a thing. There is always some new application that makes you want a hardware upgrade.

Today that new thing is artificial intelligence (AI), although this is not so much for the consumer as the big data centers. The latest crop of AI, like Chat GPT, which uses pretrained transformer technology, is hardware hungry. Interestingly, they mostly rely on graphics cards, which are the fastest mass-produced processors out there. The same is true for crypto mining, which led to a shortage of graphics cards and a spike in the price (damn crypto miners). Video games really are an important driver of computing hardware.

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The story has become a classic of failed futurism – driverless or self-driving cars were supposed start taking over the roads as early as 2020. But that didn’t happen – it turned that the last 5% of capability was about as difficult to develop as the first 95%. Around 2015 I visited Google and they were excited about the progress they were making with their self-driving car. They told us, clearly proud of their progress, that they used to measure their technology’s performance in terms of interventions per mile – how many times does the human driver have to grab the wheel to keep on the road. But now their metric was miles per intervention. It seemed plausible at the time that we would get to full self-driving capability by 2020. This is common when trying to predict future technology. We tend to overestimate short term progress, mostly because we extrapolate linearly into the future. But problems are often not linear to solve. Initial rapid progress in self-driving technology turned out to be misleading, and it is taking longer to make it over the final hurdle than originally thought (or at least hyped).

Despite not meeting early hype, self-driving technology has continued to progress – so where are we now? The Society for Automotive Engineers (SAE) has developed a system for noting the level of autonomous driving, from L0 to L5. Levels 0-2 require a human driver to be behind the wheel, ready to take control when necessary. These levels are more accurately considered “driver assist” technology, than autonomous or self-driving. Level three can control the steering wheel and brake, can provide lane centering and adaptive cruise control. This level is currently available in Tesla’s and other vehicles.

Level 3 is the first level where the human driver can fully surrender control to the autonomous vehicle, and is not required to pay attention. Level 3 and 4 can fully drive the car but only in limited conditions, whereas level 5 can fully drive the car in all conditions. Right now we are at level 2 and cautiously transitioning to level 3. However, the difference between level 2 and 3 is often a legal rather than a technical one. Even when vehicles might theoretically be capable of level 3, the car manufacturers may not get approval and market them as level 3, because when the vehicle is in full control the manufacturer is legally responsible at that point for whatever happens, not the driver.  Many self-driving cars, therefore, will remain paused at level 2, even as the technology improves, until the manufacturer is confident enough to get level 3 approval. Some market themselves as “level 2+” to reflect this.

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