Gaslight

As an urban kid, my first encounter with the will-o’-the-wisp was through literature. In the dreary journey to Mordor in The Lord of the Rings, Frodo and Sam make their way through the Dead Marshes; Gollum warns them not to follow the lights that can lead them astray. For Harry Potter readers, the equivalent creature is the hinkypunk. Wikipedia defines it as an “atmospheric ghost light… dancing or flowing in a static form, until noticed or followed, in which case they visually fade or disappear”. Today, their lore has been transformed into the kid-friendly version of the jack-o’-lantern at Halloween.

 

We now know something about the chemistry that causes this luminescence – oxidation reactions involving methane and phosphines, gases released in marshland from decaying organic matter. Air currents play a role in the wispy behavior. While I’ve personally never seen this before – I don’t visit marshlands in poorly lighted areas – it is apparently spooky-looking. In his latest book, It’s a Gas, author Mark Miodownik shares historical writings about such spooky observations. I learned that a Major Blesson, from Napoleon’s army, did some early experiments to figure out what was going on and concluded that the wisps were “caused by flammable gas bubbling up from the bottom of the marsh”.

 

Living in an age of electric lights in urban areas, I have scant notion of what it would really be like to have experienced what most of humanity knew when the sun went down. Darkness. Danger. And the fear of not being able to see what might be lurking nearby. (Yes, I could go camping in some remote area to see the stars, but I like my creature comforts.) Miodownik discusses the “anatomy of a flame” from a wood fire, and how careful observations led to mass production of charcoal and tar. Folks also discovered that the invisible released gas was also explosive: Methane. In the marshes we can thank anaerobic microorganisms that eat carbon dioxide and poop methane.

 

Enter the scientists and engineers: Could methane gas be used to light the streets and households of urban areas? Can we shoo away the dark and eliminate the spooky? It was also a safety issue. You might fall into a cesspool or get mugged. In 1801, the inventor Philippe Lebon rigged a system for a hotel in Paris. According to Miodownik, “so marvelous was the spectacle of will-o’-the-wisps flickering away around every corner that the public happily paid three francs to enter and see the wonderland he had created.” But that first system didn’t catch on. It was the stink. Not from odorless methane, but from small amounts of hydrogen sulfide that were naturally part of the gas mix. British engineers eventually figured out how to remove the stink: one step in their refining process involved bubbling the gas mixture through lime water (calcium hydroxide solution) which reacts with acidic hydrogen sulfide.

 

Storing gas was a tricky business. You had to compress it. Then you had to release it at the right pressure to get optimal lighting while avoid too many fumes from incomplete burning. Then there was the problem of gas leaks. Today, a tiny amount of methanethiol, a compound very similar to hydrogen sulfide, is added so our noses can detect the smell of a gas leak. In a mere 25 years after Lebon’s demonstration, any large town in Britain had gaslight. Eventually gaslight was replaced by electric light as science marched onward.

 

The word gaslight has returned to our vocabulary in the twenty-first century. As women entered the workforce in ever-increasing numbers and began to vie for positions in leadership, boorish men took to “gaslighting” them. Miodownik relates that the phrase coms from a 1938 play titled Gaslight whereby a conniving husband tries to manipulate his wife into thinking she is insane “by dimming the gaslights in their home, and when she notices, he claims the lights are not dimmer – it is all in her mind.” In a former age, gaslight illuminated. Now it obscures. What will it do in tomorrow’s age?

Educating AI

One reason my blog writing has fallen off the past year – I’m ambivalent about bots scraping my data to train AI models. But honestly, I’m not that great a writer, and it’s not like the bots are mining gold. I just need to get over myself and keep sharpening my writing practice, be it on this blog or elsewhere.

 

I just finished reading The Alignment Problem by Brian Christian. While the issue of AI ethics and the dangers posed by advanced AI are the main theme, what I spent time mulling over was comparing the educating of AI with the educating of human students. There are differences between human brains and machine learning neural networks, but the bigger difference is the wetware of the entire human body-organism, which cannot be separated into dry hardware and software.

 

Christian launches the historical story with Skinner’s behaviorism, Turing’s computing machines, and the neuron assembly of McCulloch and Pitts. (I didn’t know Pitts was such an enigmatic character until reading this book!) This is the framework of reinforcement learning. The reward hypothesis states that “all of what we mean by goals and purposes [is essentially] the maximization of the cumulative sum of a received scalar reward”. Shoot for the high score! Not surprisingly, Atari and other early video games were utilized in the training process. (I also learned that Montezuma’s Revenge, a game I played in the 1980s, is particularly tricky for an AI to get good at and represented some sort of gold standard.) What made the world pay attention was when AI beat grandmasters at Chess and Go.

 

I appreciate Christian going through the challenges of any training method. (He also carefully distinguishes reinforcement learning from supervised and unsupervised learning.) These include the problem of the terseness of a scalar reward or punishment, compounded by a delay in knowing that a much earlier blundering move may have cost the game. Turns out “reinforcement learning is less like learning with a teacher than learning with a critic. The critic may be every bit as wise, but is far less helpful.” There’s an interesting story on the “dopamine puzzle” that leads to a learning model (known as temporal difference) that what’s really being valued is the “error in its expectation of future rewards”.

 

The most interesting part for me was Chapter 5 (“Shaping”) on the Problem of Sparsity. Essentially, “if the reward is defined explicitly in terms of the end goal, or something fairly close to it, then one must essentially wait until random button-pressing, or random flailing around, produces the desired effect. The mathematic show that most reinforcement-learning algorithms will, eventually, get there…” but it’s inefficient and takes too darn long. The solution is to put together a Curriculum. That’s what we do as human educators. I break down the learning of chemistry into steps; I set tasks for the students; I try to motivate them; and there’s a rewards system in terms of points and a final grade. But creating the right incentives in AI training turns out to be quite tricky. Specifying certain steps along the pathway often does not have the desired outcome. Evolution has had hundreds of millions of years to shape humans, dolphins, elephants, and octopi, all naturally intelligent creatures among many others.

 

Can you get beyond external reinforcement strategies? Can you build in intrinsic curiosity into a computer? Can you value novelty? There are some clever tricks to do this. OpenAI (now famous for ChatGPT) is profiled for their early efforts working on Atari-arcade-like games. Can we learn from how humans and apes learn? Can computers learn through imitation? Do they learn the same way? I learned that human children in some situations over-imitate compared to chimpanzees; “children are from a very young age, acutely sensitive to whether the grown-up demonstrating something is deliberately teaching them, or just experimenting.” Why does this work? It “allows the student (be it human or machine) to learn things that are hard to describe.” The OpenAI folks managed to get an AI to beat Montezuma’s Revenge by watching YouTube videos of many human players.

 

This may be why taking students through worked examples, then letting them try simpler problems, before adding complexity to a more sophisticated problem is a pedagogical approach that works well, at least for the subject of chemistry. Many of these principles came from folks doing research into teaching and learning math. There’s also a tricky balance between intrinsic and extrinsic motivational approaches. It’s not that one always works better than the other. I’m not sure that final grades, which I assign based on numerical scores, are the best value function that most of my students strive towards. I understand that grades loom large for increasingly stressed students in what they perceive to be a global cutthroat career market. My generation did not experience the pressures they are facing now. With AI chomping at their heels as a competitor, the business of educating AI may be existential for them, even if they don’t realize it yet.

Exploring ADOM

I’m thirty years late to Ancient Domains of Mystery, more commonly known by its acronym ADOM. Created by Thomas Biskup in 1994, it is a computer role-playing game (CRPG) that is often described as Rogue-like. There’s an irreverent but informative video that discusses this issue amidst a high-speed playthrough of the ASCII version of ADOM. While there are spoilers, they go by much too fast for you to remember any of them, so I wouldn’t worry about it. I’ve never played the original Rogue so I have no basis for comparison. The three features that stood out to me are that it is turn-based, the dungeons are procedurally generated, and there is permadeath – when your character dies, the game immediately records it and all you can do is restart from scratch with a new character.

 

Most of my experience with computer games was for a half-decade in the mid-to-late 1980s, sometimes referred to as the golden age of CRPGs with plenty of different designs with new spaces to explore. I was first hooked by Ali Baba and the Forty Thieves and got through Ultima V before life took over. In the last year, however, I have been rediscovering the cousins and close descendants of those early games. I’ve been surprised by delightful obscure gems such as Antepenult alongside well-known old classics such as the first Might & Magic.

 

The old CRPGs required patience. You had to grind your way through lots of fights to earn experience, gold, and better weapons and armor. You had to level up your spellcasters to access more powerful destructive and protective magic. The baddies and bosses got harder. You needed special items to access special areas. I don’t have the same patience now as I did forty years ago, but I am enjoying the discovery aspects of ADOM. There’s a huge world to explore (mostly underground since it is a dungeon-crawler) with tons of different items. You don’t know what’s around the next corner so you’d better be prepared to fight or run. There’s a sweet satisfaction with surviving a nail-biting encounter, discovering a strange new space, or coming across an item you’ve never seen before.

 

I think I’m on my tenth or twelfth character in ADOM, and only the second to make it to the mid-game stage. (You die early and often, which is part of the exploration.) My previous troll fighter reached level twelve and made it to Dwarftown but got killed shortly after, somewhere in the Caverns of Chaos. I currently have a hurthling archer that successfully completed many early quests that I’m quite attached to now, so I am save-scumming (allowing me to restart if something bad happens or my character is close to death). What’s a hurthling? It’s like a halfling, hobbit or bobbit. If you recognize any of those names, you’ll know that CRPGs borrow heavily from Tolkien and its D&D derivatives. In ADOM, mithril gear is better than regular gear made of wood, leather, or iron. It’s also lighter – and carrying weight matters! But there’s also adamantium and eternium. ADOM has no problem mixing genres.

 

While I had no idea what I was doing in the first several games, experimenting with different objects and strategies, now that I have mid-level characters, I would prefer to not go back to the beginning and grind my way anew. (I’ve always had Fate decide all aspects of my starting character rather than picking my own stats.) So alongside my natural experimenting within the game world, I’ve also started referring to Internet resources (such as the ADOM Guidebook). In addition, I’m watching my way through a very entertaining play-through on YouTube, where I’m pacing myself episode-wise so my character is roughly at the same level. I’m learning that ADOM is even bigger than I thought, and there are things you can do that I hadn’t even considered. I suppose I’m learning from the large community of those who had gone before me. Long-term players have played thousands of games over the years and built up a lore of knowledge.

 

It’s a bit (or perhaps a lot) like science – discovering the natural “laws” of the world around you, what you can do and what you can’t do. There’s the trial and error approach which I used early on, and there’s learning from the community of those who have trialed-and-errored a lot more and are sharing the results of their labor. Finally, there might be folks who have looked at the code and can say something about the “hidden” underlying rules. This is how we humans learn. Many before us have experimented directly, and the fruits of discovery have been passed down to us so we don’t have to reinvent the wheel. As a teacher, it’s an integral part of my job to pass down this knowledge in my field of expertise, which is chemistry. One thing I convey to students is that we can come up with abstractions to understand the underlying rules of chemistry. This includes mathematical models that powerfully allow us to make predictions of what will happen in a different situation; it’s like discovering the source code of nature!

 

Yes, I could try to “enjoy” grinding my way through ADOM with no outside references. But I think the exploration is enhanced by tapping into the wisdom of the community while being careful to avoid spoilers. Without it, I think I would just give up – ADOM is a hard and unforgiving game. But games also allow you to explore the paths not taken, and ADOM’s many different starting characters and strategies, and its multiple endings (from what I’ve gleaned without looking at any of them in detail), provide a certain satisfaction. The procedurally-generated dungeons add to the game’s high replay value. For someone with my old-school 1980s CRPG background, ADOM provides an exploration experience at its finest. Warts and all. My character recently grew horns due to increased background corruption. That was yet another recent surprise with reaching the mid-game!

Shapeshifting Vine

I’ve been thinking about plants and photon-absorbing pigments having recently read a speculative and interesting origin-of-life article that suggests animals might be gardeners co-opted by plants. Last year, I read about flavor molecules and poisons, which are part of the suite of secondary metabolites released by plants. Right now I’m reading The Light Eaters by Zoe Schlanger, a fascinating look into cutting-edge and controversial research in botany. Do plants “scream in pain” when we pluck a leaf or break a twig? Do they then warn their neighbors with signaling molecules that danger is nearby? Can they listen to sounds? Are they “conscious” in their own way, different from humans or octopi? These are interesting questions, and Schlanger delves deep into the research. Her writing is also thoroughly engaging, aimed at the non-expert, reminding me of Ed Yong’s superb book. Who would have thought botany would be so exciting!

 

Chapter 8 discusses the “chameleon vine”: Boquila trifoliolata. I’d never heard of it before. It is native to Chile and has some interesting cousins in Asia. This vine of a plant is an actual shapeshifter. Not like the lizard chameleon that can only change its colors. Not like the leafy water dragon that has evolved to look like the water plants where it spends its time. Not like many examples of adaptation in nature that take generations. Boquila does its shapeshifting in real time. But this is plant time, measured in hours or days; too slow for us impatient humans who prefer to marvel at it with time-lapsed photography. It has reshaped its leaves to mimic dozens of other plants, some that look very different from each other. Sometimes the details are astonishingly close; sometimes the match is poorer. How does this happen?

 

There are two prevailing theories; there is some experimental evidence for each but scientists are still in the throes of figuring out what’s going on. That’s what the cutting-edge of science looks like. The theories may sound wild. One is the “plants have vision” hypothesis. Plant leaves have plenty of light-sensitive molecules, not just ones used in photosynthesis. Maybe the vine sees its neighbor and mimics it. The other gives primacy to microorganisms; the shared space may allow microorganisms to exchange information (horizontal gene transfer), and to move from one plant to another, that may then translate into building leaves that look identical. Both ideas sound crazy when you first hear them, but there is some evidence for each. Not enough to gain widespread acceptance. Science is conservative, for good reason. You don’t discard an established theory that was built up by initial evidence unless the new evidence that disproves it is sufficient and overwhelmingly so.

 

I don’t know which of the two theories I lean towards. However, both ideas have pushed me to think more about my own research. Since getting into origin-of-life research, I’ve started to pay close attention to microorganisms, bacteria and archaea. Since starting to teach biochemistry, I’ve been marveling at the world of metabolites – plants and fungi are amazing in this regard where secondary metabolism is concerned. I’m starting to see conjugated pi-systems show up in origin-of-life related molecules, and I’ve started to read up on how to analyze photochemical reactions using computational chemistry. There’s something intriguing about the interplay of photons and chemistry that could be the key to why we have dynamic systems, building up molecules and breaking them down, going along the flow of the second law of thermodynamics yet diverting it to one’s own ends. That last phrase might sound speculative and crazy too.

 

This brings me back to pondering the nature of the boggart, one of my early posts when I started this Potions for Muggles blog. We now have a plant boggart in being able to shapeshift, but seemingly limited to being a mimic. While the Harry Potter books do mention interesting plants and their properties, this seems subdued compared to Fantastic Beasts and how to find them. We miss the wondrous nature of plants because they seem so unlike us. Boquila would have been of great interest to Professor Sprout, and I could see her working closely with a Potions Master to delve into the subtle connections between plants and their secondary metabolites that would go into potions!

Spontaneity, Reversibility, Equilibrium

Last week, right after my P-Chem II class, a bright student came up to ask me to help clear up and issue comparing reversible processes that actually move a process forward (you might expect a change in free energy or overall entropy) and equilibrium (you expect delta-G to be zero). Since we only have ten minutes in between when one class gets out and the next one comes in to the room, I did what many professors do – I gave a handwaving explanation. I said something about an overall system being at equilibrium macroscopically (equal rates of forward and reverse reactions), and separated that from a “reversible” process where you’re moving something along via infinitesimal steps. One’s the overall “system”, the other is looking at a series of steps for a specific process, I said. Clearly, or maybe obtusely, I was hedging.

 

Part of the issue here is that in real life you’d never run a process in infinitesimal steps because it would take an infinite amount of time. Essentially nothing is changing if each step takes eternity. I did tell students that, in practice, no process is truly reversible in this infinitesimal-step sense if in fact it actually takes place. Students understand this issue of practically. But since P-Chem II is calculus-infused, we can do the math by taking limits. In particular we go to the limit of the infinitesimal step and use d’s instead of deltas. This leads to beautifully simple calculations to calculate the mechanical work or heat transfer in idealized “reversible” cases, while comparing them to “irreversible” cases that are “less efficient”. In the reversible case, work in equals work out if you did this ideally. In the irreversible case, you get less work out compared to what you put in. Most students are satisfied by all of this, but for this student, there was a bee in her bonnet, and rightly so.

 

Actually, I don’t know if she found my hedging answer satisfactory. I should ask her. I was unimpressed by my own answer even though it was practical in the interest of time (she had to run to another class). In fact, I told her that I needed to be more careful in how I defined the terms “equilibrium” and “reversible process” and that sometimes I’m not careful enough. I provide the students with the formal definitions in our lecture notes, but I slide my way from one term to another in the “heat of the moment” when I’m trying to be dynamic and lively in class. (“Heat” is another of those tricky terms.) In class I only look sporadically at my own notes. Sometimes I remember to emphasize something to watch out for, and other times I simply forget.

 

In my quest to figure out how to not confuse future students, I started scouring the primary literature for inspiration. The article I’ve found most useful thus far is by John Norton titled “The impossible process: Thermodynamic reversibility” (Studies in History and Philosophy of Modern Physics 2016, 55, 43-61). It provides a historical slant and includes many examples where much more famous physicists have also elided their way through. Now I don’t feel so bad about my quick handwave. I think I can tighten up my definitions a little better. And I need to spend a little time talking about the issue of using calculus to take the limit for infinitesimal steps. Many chemistry students are a little calculus-phobic so I try not to emphasize the mechanics of calculus and instead concentrate on the chemistry. But I’m reminded that I need to be more careful in this regard. Norton also points out that on the molecular scale, thermal fluctuations make this ideal-calculus-limit taking a problem. In a big-picture Mack view, all this might be okay, but tiny Mike would protest that there’s a problem! (Mack and Mike represent macroscopic and microscopic views.)

 

I also need to be very clear when I use each of these terms: reversible, equilibrium, and spontaneous. I make a very big deal (multiple times) in both my G-Chem and P-Chem classes that thermodynamic spontaneity has nothing to do with how fast a reaction might take place. All it tells you is which way the reaction is likely to proceed absent any external intrusions on the system. Thankfully, we’re on the verge of changing our G-Chem textbook to, in my opinion, a superior one that excises the confusing term “spontaneity” and instead uses “thermodynamic favorability”. I’m all in favor of that change. I’ll just have to remind students to be careful when they encounter “spontaneity” on the internet because, sadly, that’s where many of them go to look up things rather than their textbook.

 

I think I should stop using the term “reversible” in G-Chem as a thermodynamic definition. I should limit myself to discussing forward and reverse reactions (in the kinetic sense), that both occur, and that if one waits long enough eventually the rates of the forward and reverse reactions are equal. That’s when dynamic equilibrium is reached. If the change in system free energy or the change in the entropy of the thermodynamic universe is zero, then the system is overall at equilibrium. That’s it. No need to belabor the point.

 

In P-Chem I’m considering using the term quasi-reversible to emphasize that taking the infinitesimal limit is actually an impossible situation at the molecular level. Perhaps I should always say quasi-reversible process. This may help emphasize the distinction between the macroscopic system as a whole (which may or may not be at equilibrium) and considering a specific process in getting from one state to another state. I’m not sure I want to go into the language of “a series of connected equilibrium states” since this muddies the waters. Since I don’t use a textbook in P-Chem, I can just change all the notes that I provide students to tighten up these definitions. I will restrict using the word equilibrium to the usage I mentioned above in G-Chem. When I get to the stat mech version of discussing equilibrium, I will focus it on the equilibrium constant as a ratio of the number of product molecules versus reactant molecules, while reminding the students that the state of being at equilibrium is a macroscopic description. There will be a tricky part when I get to transition-state theory in thinking about the transition state as a quasi-equilibrium state; not sure how to handle that terminology-wise. We’ll see how this all works out the next time I teach P-Chem II.

The Optimality of Forgetting

In the education business, we’re often emphasize the business of remembering. Remembering what you learned is good. Forgetting what you learned is bad. Students may wish they had better memories to remember all the stuff I’m telling them. Heck, I often wish for better memory as I age and forgetfulness increases in frequency. So why do we forget when improved remembering seems like what we want? If remembering was so adaptively so much better than forgetting, evolution should have selected for the best memorizers!

 

What has our memory evolved for? And why might forgetting be just important as remembering? One possibility is that in a noisy and ever-changing environment, having specific detailed memories that persist make it difficult to learn new things and adapt appropriately to analogous yet different situations. I didn’t come up with this myself. I just spent the last hour reading a perspective article: “The Persistence and Transience of Memory” by Richards and Frankland (Neuron 2017, 94, 1071-1084). Parts of the article were slow-going because I lack the background related to the experimental work being reviewed, but I think I got the gist of it. And that’s the point! Getting the gist may be what matters adaptively.

 

The authors argue that the interplay between persistence (remembering) and transience (forgetting or erasing memories) is key. In particular, transience “enhances flexibility, by reducing the influence of outdated information on memory-guided decision making, and prevents overfitting to specific past events, thereby promoting generalization.” There are supporting experiments in rats and fruit flies for this hypothesis. Neural network models also suggest a congruence with the experiments: Injecting “noise” into the network, reducing weighting factors, encoding sparsely rather than densely, seem to improve the network’s ability to handle generalized situations.

 

When teaching physical chemistry (and to a lesser extent in general chemistry), I try to emphasize the models underlying the equations we used. The simpler the model, the simpler the equation and the more generalizable it is: the ideal gas law equation (PV = nRT) is an example of a very powerful equation that works for any gas, as long as it behaves close to ideally. The model of an ideal gas imagines a large number of particles moving randomly in a box with plenty of empty space with all collisions being elastic. That’s a good approximation for N₂, O₂, CO₂ and Ar which constitute over 99% in dry air. We can elaborate the model further for “real” gases through the two-parameter van der Waals equation or a multi-parameter virial equation. A mathematical model is powerful because its quantitative aspect allows it to make predictions of future situations to be encountered.

 

But putting in too many parameters can result in over-fitting, which can then result in incorrect predictions. So if we go through life encoding every moment in dense detail, it might actually hamper our ability to see the forest from the trees and adapt to new situations. Everything is a detail and the big picture is lost. The article’s introduction mentions the oft-quoted story of a patient with seemingly photographic memory of his entire life, but had plenty of problems navigating life because of this. I’m also reminded of how we learn when encountering something new. If you’re a novice, you try to absorb as much as you can but you have no idea which “details” are important and which are not. But if you already have some background, you’re able to ignore the artifacts and focus on abstracting the most crucial features. How exactly that happens, I don’t know. But I see it every day in my teaching. I constantly have to remind myself that I have the curse of knowledge in that I can’t quite remember or fathom how hard it was for me to build my chemistry scaffold oh so many years ago.

 

We humans haven’t had enough time to evolve towards learning academic subjects. Or even the seemingly simple acts of reading, writing and arithmetic. I don’t remember how I learned to read. I improved my writing through sheer practice and repetition. I have a vague “memory” that algebra was completely obtuse when I first encountered it; but I had an aha(!) moment at some point in life and somehow grasped it in a gestalt experience. Now algebra is obvious to me, at sometimes I’m at a loss helping students work a chemistry problem and realize they don’t get algebra. (This is a very small number, but I’ve noticed a few more post-pandemic.) Learning is still mysterious to me.

 

What can I do to help students learn chemistry? In class and through homework and practice, I try to emphasize the things students need to remember. I repeat the salient points a lot such that I sound like a broken record, but I think it’s crucial to keep the students attending to the main thing. The first time I say something the strongest students may grasp the salience but the majority of the class hasn’t yet. So I need to keep repeating and emphasizing the most general principles. But I have to do this in the context of multiple examples that look different from each other. Same principle, different example. This is the key to “transfer”, the ability to effectively apply something you’ve learned in a different situation; and this includes knowing the limits of applicability!

 

I also add a lot of tidbits (history, broader applications, interdisciplinary connections) to my lectures. I hope that the students find them interesting, possibly strengthening a neural connection; but even if students forget these, that’s okay. For the things I need them to remember and use, there’s no substitute for repetition to strengthen the memory (both conceptual and procedural). If the students don’t practice retrieving these memories and using them, they will forget. It’s not a bad thing. Transience and persistence go together and I wouldn’t want my students to be maladaptive to new situations. So I’m not looking for them to have better memories (even though they might wish for it), but I’m trying to strengthen the neural connections they do have and maybe even replace some incorrect misconceptions they might have. Forgetting has its place in learning!

Rediscovering Earth

Captain’s log, 25.04-05 in the year 4620. We’ve found the planet they call Earth in the Sol system of the Pythagoras cluster. There are no signs of life but we will try to find the underground station.

 

I’m playing Starflight, released in 1986 for the IBM-PC. The four-color CGA made it hard to distinguish terrain types, and I found it challenging to use the controls via the emulator on my laptop. After a bit more research, I found a “cracked” Amiga version that doesn’t require using the copy-protection codewheel every time I leave starbase. Also, the expanded color palette is very welcome, and the controls were more streamlined. Except for space battle where I still don’t know what I’m doing and randomly shooting at enemy craft.

 

Starflight is impressive. I’m amazed how much game they were able to pack in given memory and disk constraints. You start out as a starship captain from the Arth system, hire a crew, and explore the galaxy. The galaxy is huge! All those little circles represent solar systems, each of which may have multiple planets. There are more hidden in the green blobbed nebulae.

 

I don’t know what the object of the game is yet. Starting out, I needed to make some money to improve my ship and train my crew. To do that, mining is the name of the game. I was told that the innermost rocky planet was a good place to start prospecting so that’s what I did. Then I decided to explore the third planet and found strange artifacts such as a blue bauble, a silver gadget, a bladed toy, and strange cloth. There were ruins and a message telling me about a black egg. Also, there were strange creatures. And despite the simple graphics, the game gives you the feeling that you are indeed an explorer. It feels like you’re in Star Trek exploring strange new worlds. Even the silhouette of my starship reminds me of the Enterprise. Except mine’s much smaller and only has six crew members.

 

Not only is the galaxy large, planets are sizeable areas to explore in one’s all-terrain-vehicle. You pick a landing site and then drive around the local area grabbing minerals and specimens of the local fauna and flora. Sometimes there are ancient ruins where you load up on endurium, fuel for your starship. As a chemist, I’m delighted to read the chemical composition of the atmosphere, hydrosphere, and lithosphere of each planet I visit. We note the climate and the gravitational field, and determine if it’s suitable for colonization. If so, we log the planet and gain a reward upon returning to starbase, which is also where we sell our minerals and specimens, upgrade our ship, visit the bank, and if needed, hire crew replacements.

 

Then I ventured outside the Arth solar system. The nearby system is a K-class star, slightly unstable, apparently. On one of the planets I find a message asking me to report to another planet in a different system but the message is cut off. Clues lead to more clues, and in the meantime, the message board at starbase suggests that things are amiss. The sun of Arth is dying. Other ships were destroyed by androids. And as I venture further into outer space, I encounter aliens! The dialogue system is in real-time and you need to keep your wits about you. Do I try to be friendly? Do I raise my shields and prepare to fight? These encounters are tension-filled and even nerve-wracking, and you can be destroyed quickly by superior foes.

 

I’ve now learned a little more history from some of these encounters. A long time ago there was an old empire, but as starfaring expanded, different alien races encountered each other, and war often broke out. But now something is beginning to threaten all of life. There is no longer any life on Old Earth. It might be a race against time but I don’t know what to do yet.

 

Interestingly, Pluto is not a planet in Sol. At the time Starflight was released, Pluto had not yet been downgraded into a dwarf planet. Mars is mineral-rich and there was a polar station but it has been long deserted. In the meantime, I’ve made some friends and made some enemies. My ship is pretty formidable and I’ve got a good crew. I’ve picked up some useful and interesting alien devices and I have a reasonable guess of what I might need to do to prevent galactic destruction, but I need more specific information. Meanwhile the price of endurium is going up as people look to flee for safer havens. But for now, I’m elated that I rediscovered Earth!