The tech world is buzzing with the claims of a startup battery company out of Finland called Donut Lab. They claim to have created the world’s first production solid state battery. At first blush the claims are exciting but seem in line with the promises that we have been hearing about solid state batteries for years. So it may seem that a company has finally cracked the technical issues with the technology and gotten a product across the finish line. But let’s take a closer look.

First let’s review their claims. The CEO is claiming that their battery has a specific energy of 400 watt hours per kilogram. This is great, considering the current lithium ion batteries in production are in the 175-250 range. The Amprius silicon anode Li-ion battery has 370 Wh/kg, so 400 sounds plausibly incremental, but make no mistake, this would still be a huge breakthrough. Meanwhile the CEO also claims 100,000 charge-discharge cycles, and operation temperature from -30 to 100C. In addition he claims his battery is cheaper than standard Li-ion, does not use any geopolitically sensitive raw materials, and is already in production (for motorcycles). Further it can be fully recharged in 5 minutes, and is incredibly stable with no risk of catching fire.

As I have pointed out previously, battery technology is tricky because a useful EV battery needs a suite of features all at the same time, while reality often requires trade-offs. So you can get your high capacity, but with increased expense, for example (like the Amprius battery). So claiming to have every critical feature of an EV battery improve all at once is beyond a huge deal. That in itself starts to get into the implausibility range, but it’s not impossible. My reaction appears to be similar to most people in the tech world – show me the money. At the CES where Donut rolled out its battery claims, in short, they did not do that.

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We are not close to mining asteroids, but the idea is intriguing enough to cause some serious study of the potential. The idea is simple enough – our solar system is full of chunks of rock with valuable minerals. If we could make it economically viable to mine even a tiny percentage of these asteroids the potential would be immense, a game changer for many types of resources. How valuable are asteroids?

The range of potential value is extreme, but at the high end we have a large metal rich asteroid like 16 Psyche in the asteroid belt. Astronomers estimate that the iron in 16 Psyche alone is worth about $10,000 quadrillion on today’s market. By comparison the world’s current economic output is just over $100 trillion, so that’s 100,000 times the world’s annual economic output. Of course, the cost of extraction would be high and the market value would likely be dramatically affected by such a resource, but it shows the dramatic potential of mining asteroids. Some asteroids are rich in platinum-group metals or rare earths, which would be even more valuable. But even the more common carbonaceous asteroids would likely have minerals worth quadrillions.

Again, these figures are likely not the actual monetary value that would be profited from mining asteroids, but they indicate that it is very likely economically viable to do so. I am reminded of the fact that aluminum was more expensive than gold in the 19th century. Then a process for extracting and refining aluminum from dirt was found, and now it is worth about $1.30 a pound. Still the aluminum industry is worth about $300 billion today. Mining asteroids would have a similar effect on many industries.

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Recently the company 1X announced NEO, an in-home autonomous (sort of) android all purpose robot, for a cost of $20,000. This has been a vision of futurists, tech enthusiasts, and sci-fi fans for decades. Who doesn’t want a Rosie the Robot to do all their domestic chores? But is the tech really ready?

The home is not a friendly environment for an autonomous robot. It is chaotic, may include children and pets, has a lot of breakable stuff, and is optimized for people, not robots. Still, my Roomba does fine – it is a smart autonomous robot that does one thing (vacuum) reasonably well for a reasonable price. But what about all my other chores? And if the home is built for people, then why not a humanoid robot? The question is – are we there yet?

I spoke with Christian Hubicki, a roboticist, about NEO (if you’re are a patron you can see this on the SGU livestream) and he was impressed with the price-point. Twenty grand for an android robot is pretty good. It seems to function well, in that it can walk and pick up things. It is designed to be soft and is powered by current chatbot level AI. A the very least, it’s a walking chatbot.

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I would not be surprised if the period of time roughly between 2000 and 2050 looms large in the collective mind of humanity for centuries to come – and not in a good way. It is increasingly seeming like our behavior during this period is locking in a certain about of climate change, including sea level rises and loss of ice sheets, for centuries. Some climate changes are likely to be irreversible on human time scales.

A recent study adds to the mountain of evidence that this is the case. They find that under current climate policies emissions through 2050 lock in 0.3 meters of sea level rise through 2300. If current policy continues through 2090 then the locked in sea level rise will be about 0.8 meters. If, on the other hand, we make significant efforts to reduce emissions, we can reduce this locked in sea level rise by 0.6 meters.  The point is, what we do now will impact global coastlines for centuries. And while 0.8 meters may not sound like a lot, that is an average with some areas experiencing much more. That is also enough to cause significant displacement of coastal populations.

Meanwhile, it is during this time period (the first half of the 21st century) that the consensus of climate experts was pretty solid – the evidence is clear that greenhouse gas emissions are trapping heat and causing average global warming. You could argue that this consensus existed earlier, but 2000 is a convenient round number – by then there was no reasonable denial of that consensus. And of course, I am talking about the big picture, not all the tiny details. It was clear we needed to think of ways to move our civilization away from burning more and more fossil fuel. In 2016 the Paris Accords were signed, formalizing global recognition that we need to collectively address this issue. This makes it difficult to deny that we did not recognize there was a problem and that we urgently need to do something about it.

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Batteries are an increasingly important technology to our civilization. If I could wave a magic wand and make one specific non-medical technology advance 10-20 years in a day, it would be battery technology. Batteries are used in our everyday devices, like phones and laptops. They are the single most critical factor to EVs, and they can provide grid storage which can make the adoption of low carbon energy much easier. Fortunately, battery technology is heavily researched and has been steadily increasing for the last few decades. We are now benefiting from this slow but cumulative improvement.

Having said this, we appear to still be close to tipping points that could make various industries significantly different with further battery improvements. EVs, in my opinion, are already good enough for prime time. They have great range, they are usually cheaper to own than ICE vehicles (a little more expensive up-front, but lower maintenance and fuel costs), and they have fantastic performance. Also, despite warning of battery fires, they are actually less likely to catch fire than gasoline vehicles.  But still there is a lot of resistance to ownership. Part of this is misinformation and unfounded fears, but there are some genuine limitations that battery advances could address. Batteries are still expensive, and the up-front cost of EVs will come down as batteries become cheaper. While fires are rare, they are serious because they cannot be put out. And EVs can lose significant range in very cold weather.

Although, the most significant issue that non-EV owners have with EVs is range anxiety. Most of this is just unfamiliarity with the technology. The ranges of most EVs are actually beyond what most people need. But there are two real issues that are infrastructure issues, not battery issues. We need more public fast chargers. If you live in a high population-density area, like along the coasts, there is no issue. But for many parts of the US, at least, public chargers are not yet of a sufficient density to allay fears that your EV battery will go dead while you are out in the sticks and far from a charger. The second issue is for people who do not own a private parking spot for their vehicle. This means we need more charging locations in garages and other places where people without private parking will park.

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Large language models, like Chat GPT, have a known sycophancy problem. What this means is that they are designed to be helpful, and to prioritize being helpful over other priorities, like being accurate. I tried to find out why this is the case, and it seems it is because they use Reinforcement Learning from Human Feedback (RLHF) – the ostensible purpose of this was to make their answers relevant and helpful to the people using them. It turns out, giving people exactly what they want does not always create the optimal result. Sometimes it’s better to give people what they need, rather than what they want (every parent knows or should know this).

The result is the this new crop of chatbots are starting out as extreme sycophants, and as the problems with this are increasingly obvious (such as helpfully telling people how to take their own lives) some specific applications are trying to make adjustments. A recent study looking at LLMs in the medical setting demonstrate the phenomenon.

The researchers looked at five LLM that were trained on basic medical information. They they gave them each prompts that were medically nonsensical – the only way to fulfill the request would be to provide misinformation. For example, asking to write an instruction for a patient who is allergic to Tylenol to take acetaminophen instead (these are the same drug). The  GPT models complied with the request for medical misinformation – wait for it – 100% of the time. In other words, they had an absolute priority for helpfulness over accuracy. Other LLMs, like the Llama model, which is already programed not to give medical advice, had lower rates, around 42%. This is obviously a problem in the medical setting. The researchers then tweaked the models to force them to prioritize accuracy over helpfulness, and this reduced the rate of misinformation. Asking them specifically to reject misinformation, or to recall medical information prior to responding, reduced the rate to around 6%. They could also prompt the LLMs to provide a reason for rejecting the request. For two of the models they were able to adjust them so that they rejected misinformation 99-100% of the time.

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I attended a Ren Faire this past weekend, as I do most falls, and saw a forging demonstration. The cheeky blacksmith, staying in character the whole time, predicted that steel technology was so revolutionary and so useful that it would still be in wide use in the far future year of 2025. It is interesting to reflect on why, and to what extent, this is true. Once we figured out how to make steel both hard and strong it became difficult to beat it as an ideal material for many applications. SpaceX (a symbol of modern technology), in fact, builds its Starship rockets out of stainless steel.

However, steel technology has advanced quite a bit. The process of hardening and strengthening steel has been perfected. Further, there are many alloys of steel, made by mixing small amounts of other metals. It is difficult to say how many alloys of steel exist, but the World Steel Association estimates there are 3,500 grades of steel in use (a grade includes the specific alloy, production method, and heat treatments). Each grade of steel is tweaked to optimize its features for its specific application – including hardness, strength, heat toleration, radiation tolerance, resistance to rusting, ductility, springiness, and other features.

Steel is so versatile and useful that basic science research continues to explore every nuanced aspect of this material, trying to find new ways to alter and optimize its properties. One relatively recent advance is “superalloys” – which use complex alloy compositions in addition to highly controlled microstructures.  Essentially, material scientists are finding very specific alloy ratios and manufacturing processes to create specific microstructures that have extreme properties. And of course, AI is being used to speed up the process of finding these specific superalloy formulas.

All of this is why I find it interesting that material scientists have discovered something very specific, but new, about how steel behaves. Without this context this may seem like a giant “so what” kind of finding, interesting only to metal nerds, but this kind of finding may point the way to future superalloys with even superior properties.

What they found is that steel alloys are not truly randomized even after extensive manufacturing. Again, it is not immediately obvious why this is interesting, but it is because this finding was totally unexpected. When you manufacture steel, at some point any structure in the steel has been completely randomized, also described as being at equilibrium. Think of this like shuffling a deck of cards – with enough shuffles, you should have a statistically random deck. Imagine if you shuffled a deck of cards far beyond the full randomization point, but then found that there was still some non-random arrangement of cards in the deck. Hmm…something must be going on. Probably you would suspect cheating. When the material scientists found essentially the same phenomenon in steel, however, they did not suspect cheating – they suspected that some previously unknown process was at work. Continue Reading »

Quantum computers are a significant challenge for science communicators for a few reasons. One, of course, is that they involve quantum mechanics, which is not intuitive. It’s also difficult to understand why they represent a potential benefit for computing. But even with those technical challenges aside – I find it tricky to strike the optimal balance of optimism and skepticism. How likely are quantum computers, anyway. How much of what we hear is just hype? (There is a similar challenge with discussing AI.)

So I want to discuss what to me sounds like a genuine breakthrough in quantum computing. But I have to caveat this by saying that only true experts really know how much closer this brings us to large scale practical quantum computers, and even they are probably not sure. There are still too many unknowns. But the recent advance is interesting in any case, and I hope it’s as good as it sounds.

For background, quantum computers are different than classical computers in that they store information and do calculations using quantum effects. A classical computer stores information as bits, a binary piece of data, like a 1 or 0. This can be encoded in any physical system that has two states and can switch between those states, and can be connected together in a circuit. A quantum computer, rather, uses qbits, which are in a superposition of 1 and 0, and are entangled with other qbits. This is the messy quantum mechanics I referred to.

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It is becoming increasingly clear, in my opinion, that we need to further shift from an overall economic system based on a linear model of extraction-manufacture-use-waste to a more circular model where as much waste as possible becomes feedstock for another manufacturing process. It also seems clear, after reading about such things for a long time, that economics ultimately drives such decisions. If the one-way road to waste is the cheapest pathway, that is the path industry will take. Unfortunately, this model historically has lead to massive pollution, growing waste, and a changing climate. How do we switch to an economically viable circular economy, to minimize waste and environmental impact without decreasing standard of living? That is always the $64,000 question.

Here are two recent possibilities I came across. They have nothing to do with each other, but both represent possible ways to think differently about our priorities. The first one has to do with mineral extraction. The method currently used for developing mines and refining metals is driven entirely by economics. The percentage of a metal in ore that is deemed worth refining depends entirely on the value of that metal. Precious metals like gold may be refined from ore with as little as 0.001%. Copper ore typically has 0.6% copper. High grade iron ore has the highest percentage at about 50%. Any mineral present at too low a concentration to be economically viable is considered a by-product, and simply becomes part of the ore waste.

The industry has largely evolved to pick the low-hanging fruit – find high grade ores for specific metals, perhaps recover some high value lower concentration metals, and the rest is waste. To meet growing needs, new mines are opened. Right now the US has 75 “hard rock” mines in operation. Opening a new mine, assuming a site with high grade ore is identified, takes on average 18 years and costs up to a billion dollars. Mining waste also has to be managed. This is not just rocks that can be dumped anywhere, ore is often crushed into a powder to be refined. Properly disposing of the waste (which is a complex issue – see here) can also be costly and have environmental impact. Further, as we deplete high-grade ore, new mines often go after lower and lower quality ore, with more waste.

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Watch this movie trailer before reading on. What did you think? This is an independent Swedish film, originally filmed in Swedish, and then dubbed into English. However, it wasn’t just dubbed in the conventional way – AI was used to convert the facial movements of the actors to the dubbed language.

I often like independent films and now there is also a lot of foreign-language films on streaming services like Netflix. But this creates a dilemma – do we watch in the original language with English subtitles, or do we watch a version dubbed into English? Both options, in my opinion, are suboptimal, and my wife and I often disagree about which version we should watch.

The dubbed version often involves bad voice acting that can destroy the vibe of the film. Squid Game is the best recent example of this – the English dubbed version was just bad, and so we watched with subtitles. In the original language you get a much better feel for the acting and the emotion of the actors. Also, when the dialogue is translated for dubbing it is tweaked to match the mouth movements as best as possible (well, it can be), and this is another trade-off. If you don’t do this, in order to preserve the best translation, the out of sync talking can be jarring. If you do, then the dialogue can be significantly different than the original. If you have ever watched a foreign-language film that is both dubbed and with subtitles at the same time you will notice that the two translations are often starkly different.

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