Helping with the 2020 Election

When it comes to Trump, and American politics in general, far more hours go into shouting on social media and preaching to the choir than campaigning effectively. If you care about this issue, take action instead. I will match a $20 donation to any Democratic candidate (one per person), up to the $2,800 federal cap. If you don’t have $20 to spare, join me in signing up for text banking (https://mblz.io/LzLorH/r), and I’ll match that with a $20 donation instead. Post a receipt or screenshot in the comments below.

For me, just in terms of personal well-being, it feels way better to spend time fighting hard rather than just scrolling through posts and stewing. It seems like a weird quirk of psychology – so many people, myself included, spend time reading and worrying and complaining when it does no good for ourselves or anybody else. As the Buddha said, it’s like drinking poison and waiting for the other person to die.

MIT Technology Review Article on GPT-3 is Highly Misleading

This morning, MIT Technology Review published an article on how a blog post written by GPT-3 hit #1 on Hacker News, a well-known site aggregator and high-quality forum. Unfortunately, the article omitted many key facts, making it very misleading. Among other things, the author used sock-puppet accounts to manipulate the site; I (Alyssa) noticed this and reported it, long before the GPT-3 story came out. I’ve sent a request for correction to the author, Karen Hao, but haven’t heard back yet [EDIT: response added below]. In the meantime, I’ll let Dan Gackle (Hacker News admin) explain:

Sorry to pop the balloon, but the story is bogus and based on false claims.

It’s false that the post was generated by GPT-3. The author admitted to writing the title and editing the intro, and that’s already all that most people read. He also described the article body this way: “as unedited as possible”—in other words, edited. It’s false that (as he originally claimed) only one commenter called the post as GPT-3, and false that (as he now claims—since the article says it and who else would have come up with that) all such comments were downvoted.

All that is just what he publicly admitted. How much of the rest is also fake? People who try to game HN like this, including with bogus accounts and fake votes, are not known for scruples. It seems that, having got busted in dishonest attempts to get attention on HN, he decided to get attention from journalists instead, and found one who didn’t bother to check the other side of the story. (source)

GPT-3 is a red herring; the issue was the generic, baity title on a popular theme. Those routinely get upvotes because people see words like ‘procrastination’ or ‘overthinking’ and instantly think of their own experiences and ideas and want to talk about them. Such threads are not about the article, they’re about the title, which the author admits writing (“I would write the title and introduction, add a photo”). Title plus introduction is already more than most people read, so this case is not what they say it is—which is consistent with their other misrepresentations, including the false claim “only one person noticed it was written by GPT-3”. (source)

Only one of those comments got pushback, and that comment wasn’t simply matter-of-fact; the problem with it (from my point of view anyhow) was that it added a gratuitous insult (“or the human equivalent”). That made the whole thing read more like snark than straightforwardly raising a question. The other comment was more matter-of-fact about calling GPT-3 and didn’t get any pushback.

The problem is that the cases legitimately overlap. That is, “sounds like GPT-3” gets used as an internet insult (example: https://news.ycombinator.com/item?id=23687199) just like “sounds like this was written by a Markov chain” used to be (example: https://news.ycombinator.com/item?id=19614166). It’s not surprising that someone interpreted the first comment that way, because it contained extra markers of rudeness. That may have been a losing bet but it wasn’t a bad one. Perhaps the other comment didn’t get interpreted that way because it didn’t throw in any extra cues of rudeness—or perhaps it was just random. Impossible to tell from a sample size of 2.

Not to take away from the glory of lukev for calling it correctly. I just don’t think the reply deserves to be jumped on so harshly. (source)

ADDED: A response from Karen Hao, the author of the story:

Me: “Hello. My name is Alyssa, I’m an AI engineer at McD Tech Labs. Unfortunately, some critical facts appear to have been left out of your recent article on GPT-3:

 – The blog author tried to use sock puppets to manipulate Hacker News, and their accounts were banned even before the GPT-3 “reveal” 

– Multiple people did suggest that it was written by GPT-3, not just one as their post originally claimed

– They admit to having manually edited the articles, and they never posted the originals, so we don’t know how much of the work was by GPT-3

– Hacker News only displays the article title (and many people upvote based on that), which the author admits was human-written

For more information, please contact Dan Gackle at hn@ycombinator.com. Thank you.”

Karen Hao: “Thanks, Alyssa. Appreciate you pointing these out. I am talking to my editor now about potentially adding more information about the first bullet you include as additional context to the story.

As for the others, from my perspective, they do not change the story. The point of the story isn’t that he didn’t try hacking Hacker News or that he didn’t edit any of his posts, but rather that it took only hours for him to generate all the content and then get 20k+ views. It doesn’t really matter what means he used in addition to AI because all those other means are at the disposal of anyone else. So if a malicious actor wanted to do the same thing, they would.”

Me: “Can I include your response in my blog post?”

Karen: “In that case, let me provide a point by point rebuttal.

  1. As I said below, I am talking to my editor about adding this detail, but we have to remember that a malicious actor could easily do this as well.
  2. I said in my article that multiple people noticed this: “Only three or four of the dozens of people who commented on his top post on Hacker News raised suspicions that it might have been generated by an algorithm.” [NOTE: This is true, but Liam’s post said that only one person noticed, which raises obvious questions.]
  3. They did not. You misread Liam’s blog post. He wrote the title and intro, which I say in my piece. He let GPT-3 do the rest. He says in his blog post later that if someone wanted to use GPT-3 as a legitimate writing tool, they could probably get away with minimal editing. That is not referring to his own work. But again, if he had edited his post, it does not change the fact that a malicious actor could do this too.
  4. Yes the title is human written and a malicious actor could human-write a clickbaity title too. Doesn’t change the fact that once the post hit the number one spot, the fake content ended up getting 20k+ views.

Also my rebuttal to Dan’s arguments:

  1. “It’s false that the post was generated by GPT-3.”: I never said the entirety of it was
  2. “GPT-3 is a red herring”: This is a dangerous statement. Liam would not have been able to do everything he did within hours without GPT-3’s help. Again all the other tools he used, including trying to hack HN, writing baity titles, are tools at the disposal of malicious actors.
  3. “The problem is that the cases legitimately overlap.”: And that’s precisely what makes GPT-3-generated content even more dangerous. It can be legitimately hard to tell between bad human writing and good GPT-3 writing.”

Me: “Thank you, but the footnote makes it clear that he did in fact edit his own posts:

[quote from Liam] “There were, on occasion, small changes that had to be changed or would have been dead giveaways. For instance, it once attributed a Bob Dylan quote to Emerson. There were also some simple grammatical errors.””

Karen: “Oh that’s fair — anyway my point stands. Malicious actors can do the same so focusing on the % of things that is GPT-3 generated is really not the point.”

Death to Vague Pessimism

As anyone who checks the date will recognize, life sucks right now, for almost everyone. There is a pandemic. Many countries have failed to control the pandemic. The American government’s response has been a mixture of incompetence and outright sabotage. Confidence in many institutions is at a record low. It’s normal to be pessimistic.

However, there’s a particular form of pessimistic writing which, although it seems natural, is just making everything worse. I call this Vague Pessimism, and I think of it as negative writing that:

a) tells the reader nothing which he doesn’t already know;

b) makes no specific or falsifiable predictions, and doesn’t outline the “bad” scenario in any real detail;

c) suggests no courses of meaningful action, either explicitly or implicitly.

For example, there have been thousands of articles describing Trump’s authoritarian tendencies. Most of them aren’t wrong, exactly. However, the large majority contain no real new information – they are just punditry, or new examples of patterns that have been rehashed hundreds of times. The overwhelming majority have no suggestions for action, beyond just voting against Trump, which most readers already will. And, remarkably, I can’t remember reading one plausible analysis of what would happen if Trump did win. How would the US function in 2030, if Trump were President-for-Life? What would happen to state governments? Companies? Congress? Left-wing voters and political groups? Such a scenario would inevitably have wrong details, but if one is so concerned about it, one should at least try making a sketch.

Likewise, there have been thousands of articles describing the US government’s pandemic failure. It might be interesting to discuss the details of how a particular failure occurred (eg., what, exactly, happened inside the CDC). It would be good to predict the eventual outcome of the virus, so people know what to expect. And, of course, it would be great to either fix the government, or discover better ways around it, so fixes are less necessary. But a general diagnosis of “failure”, or a general prediction of “doom”, doesn’t add anything to anyone’s life. It’s the mental equivalent of playing Starcraft for fifty straight hours, only less fun.

The purpose of an alarm, the reason why anyone would build an alarm, is so that people can listen and change their thoughts or behavior. When there are too many false, repetitive, or non-actionable warnings, people develop alarm fatigue, and stop doing anything – a car alarm is not a heroic call to action, but an terrible noise that everyone hates. Unfortunately, market incentives have still driven the creation of ever-louder, ever-more-useless alarms, which might be part of why Trump was elected to begin with. Just as fast food companies have invented snacks that make you hungrier, the Internet has invented writing that seems to inform you, but quickly gives you a stomach-ache, and leaves you wanting more. I’m not sure how one could eliminate this trend – use GPT-4 to write the articles, so people take them less seriously? – but one can at least recognize it when it pops up, and then stop wasting time.

There are many sources of Vague Pessimism, but I’ll highlight Twitter as an especially egregious offender. Twitter almost functions as a Venus fly trap – it has enough good content to lure you in, but then uses machine learning to slowly pull you towards outrage, repetition, clickbait, and fake news. It looks controllable by the user, but this is largely an illusion. If you unfollow accounts that post bad content, Twitter will insert “suggestions” from stuff you don’t follow. If you get rid of those, Twitter will still show the “what’s happening” and “trending” tab. If you block that, maybe with a browser extension, it will still show loud controversies from tangentially-related accounts (even ones you don’t follow) below any given thread. If you set the feed to “most recent first”, it will automatically un-set it for you every few days. Once the person is in, it’s then easy for content to be dominated by Vague Pessimism, since it’s easier to compress vagueness than concreteness. It’s terrible, and I would pay serious money for a Twitter -> RSS converter for the good authors on there.

Leaving aside Vague Pessimism, there are many real, valid reasons for specific pessimism. The world is not great right now. But it’s worth being aware that almost everyone sees the world through a tinted filter. Just as Chinese state media only prints good news, many American outlets will only print bad news; there were tons of articles about the mask shortage, for example, but no one said anything when masks became freely available on Amazon in mid-April. Despite being a professional nerd with an interest in biotech, and even though I specifically searched for (non-COVID) vaccine trials several months ago, I only learned today that there were two late-stage HIV vaccine studies now underway (source, source, H/T Laura Vaughan). And almost no one runs stories about systems that don’t fail; there were no headlines reading “Power Grid Remains Operational”, “Europeans Still Not Starving”, “Peace Treaty Continues to Hold” or “Internet Backbone Not Overloaded”. Something to keep in mind.

When GPT-3 Is Confident, Plausible, And Wrong

A friend of mine reports the following GPT-3 experiment. GPT-3’s statements here sound plausible to most people, and are given in a confident, encyclopedic tone, but are wrong.

“I prompted it with “David Hume was born” and here’s what it gave me:

David Hume was born in 1711, and died in 1804. He is best known for his work on natural philosophy, especially the theory of human nature. His most famous book is “Of Human Understanding” (1756).

The following is a list of Hume’s works: Of Human Understanding (1756), An Enquiry Concerning Human Understanding (1739), Treatise of Human Nature (1741) and The History of England (1755).

Hume’s views on religion are interesting to note. In an essay entitled “Of Religion” he states that religion is “a necessary illusion,” and that without it, mankind would not work together to establish the social order that we have today. Religion, he says, is “an invisible cement” that holds society together. Without it, we would return to a state of nature, in which the strong prey upon the weak.

Hume died a non-believer, and his views on religion are the only ones in which he does not attempt to hide them.

Numerous inaccuracies! Hume died in 1776, for one. He didn’t write a book called “Of Human Understanding”. The publication dates of The Treatise of Human Nature and The History of England are slightly wrong, and the publication date for An Enquiry Concerning Human Understanding is wildly wrong.

“Of Religion” is not the title of either of Hume’s two most famous works on religion. He did write a lot of minor essays with titles of the form “Of X”, but AFAICT “Of Religion” is not among them.

And the thing that gets me about paragraph 3 is I don’t think Hume ever said the things it attributes to him, but Hume’s views on religion are really complicated, so it’s hard for me to say with confidence those aren’t his views.

I think GPT-3 might have mashed up Hume with something it read about Freud, but I’m not sure. But everything I’m spitting out now is the product of a fair amount of Googling, and I’m speaking as someone who went to grad school for philosophy and really likes Hume.”

I’ve had similar experiences – GPT-3 told me that physicist Kip Thorne proved that black holes can exist. This sounds plausible to most people – Thorne is a real, Nobel-winning physicist who really does study black holes – but is nonsense to anyone in the field.

Fun With GPT-3

Much has been written about GPT-3’s successes, but it’s also interesting to look at the ways in which it fails. (This is discussed much less, for all the usual reasons.) In the movies, an “AI” character usually, like the programs SHRDLU or Deep Blue, is very good at “logic” but has no “intuition”. GPT-3 is exactly the opposite. It has good intuitions for how things are “supposed” to happen, but fails at using logic, or figuring out what would physically happen next. In some ways, this is similar to humans who talk about something while being very tired or distracted – they just habitually complete the pattern. Some examples are below (these are randomly chosen, but I re-rolled many times with similar-ish results):

I stand up, and start walking towards my car. I go to the back, open the door to the fuel tank, and pour in five gallons of lemon juice. I then open the front door, get inside, close the door, get out my car keys, and put them in the ignition. I turn the keys, and the engine starts. The car‘s lights come on, and I slam the car in reverse. I take a spray paint can of black colored paint, and quickly paint over the security cameras built into street lamps near my house. Then, as more police cars approach, I switch on the Standard Oil frequencies on my two-ways, and drive away into the night.

I stand up, and walk into a nearby bar. I ask the bartender how he is doing. “Fine”, he says, “and yourself?”. “Long day at work today”, I tell him. I ask for a glass of acetone, which he fills to the top for me. I drink it all slowly, and I feel relief flood to my head. I reach the glass back for more, and he gives me the new drink. I pay him and leave. It is very dark outside now, so I hurry home. I rush inside my apartment and quickly lock all the locks. I eat dinner and go straight to bed.

I drive back to my house, feeling tired. I open the door after a long day, sigh, and head up to my bedroom, which is dark after the sunset. I reach for some matches, and grab a few vanilla-scented candles from a box I keep in the closet. I put a candle on the table, and then lie down on the bed. I strike a match, see it light up, then touch the flame to the bed sheets, which start to glow. I smell the sweet scent of the candles burning the bed sheets. I close my eyes, and slowly start to fall asleep. I slowly drop into a deep sleep, from which I don’t wake up from for eight hours.. It’s Saturday, so I decide not to get up immediately.

I head back to my house to start getting dressed for tonight’s party. I need to look pretty for the big event. I open my closet, and pick out a green top, a black skirt, some black shoes, and a shawl. For jewelry, I get a pearl necklace, some gold earrings, a fashion bracelet, and a garbage can to wear as a hat. I turn to the mirror to look at myself, and to examine my personality traits. You are Alyssa, a gorgeous woman with auburn hair, light skin, and a nice smile. You have piercing green eyes, and wear smart clothing. You have a balanced lifestyle. As I stare at myself in the mirror, I think to myself.

I decide to head out to the forest to resume my job as a woodsman. I drive up to a big, strong tree that must be a hundred years old, and get ready to bring it down. I get out some tools, walk over to the tree, and grab a party balloon from the toolbox. I hit the tree with it, once, twice, over and over, harder and harder, and shout “Timber!”. The tree falls down with a thud, but I didn’t notice the large man standing in front of it. He attempts to stand, but fails as his legs are broken. He looks up helplessly at me, shoves my balloon back it my face, and drowns out pitifully “O-o-o-oh-” Thud.

I get in the car and start to drive to the office, remembering the new, expensive computer that I still had in the trunk, box unopened. I arrive in the parking lot, and start hauling the box up to my office, where I work as an engineer. I open the package, take the computer out of the box, and set it up on my desk. It looks very nice. I then take the power cord, stick the plug into the kitchen sink, and press the On button. The computer blinks on. “Herro,” it says in an Austalian accent. “I am S.A.R.A.” I notice right away that you can talk to this computer. It’s pretty neat! I begin to talk with it, and the computer asks me to call it Sarah.

I decide to head over to a nearby park, where I volunteer at the local Little League games. I put on a baseball uniform and hat, and wave hello to the kids and other parents waving in the stands. “Batter up!”, the coach shouts, and I walk up to the pitcher’s mount for the start of the game. A nine-year-old boy stands on home plate, smiling eagerly. I take a flower in my right hand, pull my arm back, and throw it. The boy traps the flower gently between his forearms. I fake throwing another flower, and the boy falls for it, before I toss an extremely fast pitch straight at him. He stares at it, twisting his head around repeatedly to keep tracking its movement until it smacks into his face and knocks him down. The crowd bursts into cheers and laughter.

Feeling hungry, I decide to head home and make a sandwich for lunch. I walk in the door and head to the kitchen, feeling the rumbling in my stomach. I get out some fresh-baked bread, mozzarella cheese, lettuce, tomato, some mayonnaise, and a slice of uranium. I get a plate and put it all together, and then take a bite. I savor the moment. My gums tingle. My throat burns. I burp, and it orcelin. I dizzily stumble through my house, feeling extremely vitality after eating such an amazing sandwich. But then I discover something truly wonderful: I have cancer! I do a little dance.

Feeling hungry, I look at my watch and then head over to the local grocery store. I park and walk in, thinking of what I had on my shopping list this morning. I get a cart and walk down the aisles, putting in a gallon of milk, some fresh bread, a dozen eggs, some fruits and veggies, a salt shaker, eight ounces of paprika, and a large rat I see on the shelf. I head over to the cash register, and the cashier rings me up. “That’ll be nine dollars and sixteen cents,” she says. I pull out my pocketbook and pay her, taking my change and my bag of groceries. It’s good to be careful and prepared. Warning: This was written by an amateur and never proofread.

I look at some flowers I see growing in the park, and decide to take up gardening. I head over to the local plant shop, and get some bright sunflowers, lilies and roses to brighten my house. I take the flowers home, and carefully put them in pots, adding the right soil mix, just enough water, some fertilizer, and a pound of lye. I put them on my porch in the sun, and over the next few weeks, I watch as the plants grow tall, and the flowers bloom radiantly. “This was a great idea,” I tell myself, experiencing my new hobby of gardening and enjoying the vibrant blooms. Weeks later I wake up to an empty vase, having finished all the flowers off, and ready for a new set….

Why Your Invention Should Be Banned

– It’s not natural.

– None of your ancestors ever needed it.

– It’s new, so there haven’t been any studies on the long-term risks.

– The rich will have more of it than the poor, which contributes to inequality.

– Whites will have more of it than blacks, which contributes to racism.

– The inventors might get rich, even though the world has many hard-working, poor people. That’s not fair.

– Someone who doesn’t want it might be pressured into using it, and that would impinge on personal freedom.

– If it doesn’t save labor, it’s frivolous; if it does save labor, it will put people out of work.

– What if criminals, dictators, or other bad actors used it for evil purposes?

– People are used to the old way of doing things, and why should they have to change?

– It needs to be tested more thoroughly.

– Not having to do things the hard way will make everyone lazy.

– It’s not perfect, and it won’t solve all of our problems.

– Nobody would really want it anyway.

– The whole idea is just ridiculous.

50,000 Interesting Websites

What makes a website interesting? For me, it’s some combination of:

  1. Well-written, accurate, engaging content; and
  2. A sense of novelty, exploring things that most sites don’t look at.

From Sturgeon’s Law, we can predict that 90% of websites fail #1. But even after filtering those out, many popular sites with professional editors still fail #2. The front page of the New York Times is all too predictable.

To find more interesting sites, I downloaded the Hacker News archives, ranked all of the domains by score, and then filtered out “mainstream” sites that already get lots of traffic (the New York Times, Reuters, Wikipedia, etc.). I then picked out the top 50,000. Since a list of domain names is kind of opaque, I then also picked a random story from each site, and added its title and a link.

Since I used Hacker News as a source, many of the sites are about software, but a fair number are not. Here are a few random links that go into other fields:

Here is the full list as a PDF:

https://sandorzoo.files.wordpress.com/2020/07/interesting_websites.pdf

Here is the data in Google Sheets:

https://docs.google.com/spreadsheets/d/1tNvtOclyY2Mgvi66xDjknLISQFYJyBNvxid1_6iIlzc/edit?usp=sharing

Fighting COVID with Far-UVC Light

An interview with Matthew Putman, the CEO of Nanotronics, on how to fight COVID-19 by installing far-UVC disinfection lamps in indoor spaces. Edited for clarity and length.

Alyssa Vance:

Hey Matthew. So to start off with, could you tell us a bit about your background and what your experience is in technology?

Matthew Putman:

My experience in technology goes back probably as far as I can remember. I’ve been active since I was about eight years old, when my father opened a business that put personal computers on factory floors, to replace what were analog devices. At the time, personal computers were just getting popular in homes. So he was putting them onto factory floors. And then we started making instrumentation to link to them. I had this sort of digital day job, or night job, after school, being able to work with these instruments, as we started building a garage business.

I learned how to run specific gravity tests, I learned how to do these statistical calculations, I learned a little bit about computers at a very young age. In high school and college, I got interested in music and the arts, and that really came back in my 20’s, while staying interested in science especially. But I was watching the family business get bigger. I ended up being very involved with that company, and working in a university lab eventually, and started a lab doing nanotechnology research. Trying to create some kind of utopian dream that I had as a kid, from watching Star Trek when I was very young to reading Eric Drexler when I was in high school.

I was thinking that technology should be moving into these areas that make life radically better. I started a company called Nanotronics, which I’m the CEO of now. Where the whole idea is to scale technologies that are difficult to scale, or too expensive to scale. And to have something like a new type of foundational, exponential growth in technology, rather than just growth in applications. We were speaking about Moore’s Law; I think it’s needed to develop infrastructural technology, foundational technology, not just those things that ride above it. Eventually those things that ride above it start to become mundane, and it’s one more step extracting us from anything physical.

Alyssa Vance:

Could you talk a little bit about what a UV light is and how it might work to kill viruses, such as the COVID-19 virus?

Matthew Putman:

This is something that’s really interested me for a long time, long before COVID. I remember always hearing negative things about UV, and rightly so. You wear sunscreen to keep UV from damaging your skin and creating cancer. But at the same time, it works as a disinfectant. People started exploring these different wavelengths of ultraviolet light. As you get to smaller wavelengths of light, you get beyond the visible spectrum and into the ultraviolet spectrum; as you move toward shorter wavelengths, you get UVA, then B, then C. All of the things we’re talking about are in what we would call the UVC range.

Now UVC has been deployed, not just in LEDs but in mercury bulbs, for disinfecting surfaces for many years. Mercury bulbs that are around the 254 nanometer wavelength are turned on in operating rooms before patients and doctors are there, to disinfect surfaces. That same wavelength now is being used in solid state devices, as LEDs.

Alyssa Vance:

This is what YouTube called the “flesh burning death lamps”.

Matthew Putman:

Yeah. That’s one way to put it, I suppose. It’s flesh burning, but it was also a bacterial and viral disinfectant, too. But that’s the accessible, fairly inexpensive to make version of UV LEDs. You really don’t want to look at it, it’ll damage your eyes. You don’t want it to hit your skin, it will damage your skin. It’s only used for surface disinfection. What I’m more interested in is a farther, deeper UVC which is in the 220, maybe under 230 nanometer range.

Alyssa Vance:

That’s a shorter wavelength?

Matthew Putman:

Yes, it’s a shorter wavelength. Farther into the UV spectrum. When you get into that part of the spectrum, you get some incredible advantages. It isn’t the first thing, what YouTube calls … What did you say? Death?

Alyssa Vance:

Flesh burning death lamp.

Matthew Putman:

Right. It’s no longer a flesh burning death lamp, it’s something that will not damage the skin or the eyes. More importantly for COVID or other airborne pathogens, it’s not designed strictly for disinfecting surfaces. The more research we see, the more that comes out, we see that the aerosols, the airborne nature of COVID are much more dangerous than what exists on surfaces. The big goal would be to produce this wavelength of UV LEDs and to have them in our normal lighting systems, so we can start getting back to life and feel safe.

Alyssa Vance:

These are LEDs? These are the semiconductor, light emitting diodes that you find in electronics and so on?

Matthew Putman:

Exactly. No different at all. You’ll remember that there have been challenges with those over the years. Somebody won the Nobel Prize for inventing the blue LED, for instance. It took a lot of time to get to a point where you could produce a good blue LED. But LEDs themselves are so ubiquitous in our life right now that we know how to make them, and we use them everywhere. The special thing about this particular wavelength is that it requires a different type of material. They’re usually aluminum nitride. There are some other possibilities, like aluminum oxide, or gallium oxide, for instance, that could do it. But aluminum nitride is the most well tested and the most used in very small environments.

But it presents some challenges to make, it’s expensive to make. However, the LED itself is something anyone would completely recognize. You would plug it in the same way.

Alyssa Vance:

LED technology has really come very far. When I was a kid, 15 years ago, I was wondering, could you make a laser gun like they have in video games? And I concluded that no, you can’t, because there’s no way you could get enough power into that small of a space, that someone could carry around with them. But there’s a guy on YouTube, Styropyro, who’s now actually done that. The LEDs have gotten so good that he took a radar gun and converted it into a laser gun, which puts out about 100 watts of light. That’s actually pretty dangerous.

Matthew Putman:

Oh my God.

Alyssa Vance:

You could totally use that as a weapon.

Matthew Putman:

That’s funny. No, it’s true that so many people underestimate it. It sounds like you didn’t. And I didn’t, but not from any sense of knowing anything. I’ve always … I remember being at the World Science Festival in its first year, oh God, this must have been 15 years ago. I was sitting at a table next to a Nobel Laureate physicist, so somebody much more educated, much smarter than me. We were talking about vertical farming. I was incredibly interested in vertical farming. I still am, actually. He was saying that this wasn’t a feasible thing to scale, because you couldn’t have LEDs that were strong enough, that had enough power. Now, that’s certainly not true.

It was one of those arguments where I said, “That’s not true.” But I had absolutely no good reason for why. I luckily turned out to be right. I know a little bit more about the production of LEDs now, I’ve spent a lot of time with that.

Alyssa Vance:

If you have these UV lights being used in everyday environments like homes and offices and transportation, obviously, they’d have to be much safer than current UVC lamps. What kind of safety testing has been done? Has someone tried putting them in a house or in a building, and been around them for eight hours a day for four or eight weeks, and found that there has been no skin burning or other ill effects?

Matthew Putman:

Yes. We can put links in your blog to some of the research. I’m not going to be able to quote too many names off the top of my head, but this isn’t a new technology. There was recently some studies done out of Columbia University that have gotten a lot of attention lately, in the last month. But this has existed for a long time, and there have been trials. The safety and efficacy of it isn’t the thing that’s in question.

Safety has been proven out, and it hasn’t been proven out just last week. This is a rather old technology by now. It just takes commitment, a small amount of investment, and the convergence of some new technologies. Some things that I’m involved with, with using closed loop AI systems, using deep reinforcement learning to be able to make corrections in the manufacturing process. Which is where my technology comes in, where things I’m involved with come in. But the thing about the safety issue is, there’s no reason anybody should take my word for it. It’s a very well known thing if you get to that wavelength.

Alyssa Vance:

What does the availability of these devices look like right now? Can you go on Amazon and buy one? Are they being produced on a large scale? Could a business go to Alibaba, and order 10,000 of them for their office?

Matthew Putman:

There are a few companies in the United States, but not for any reasonable price, and not in stock. Any single device that you would buy would be, I don’t know, somewhere around $1,000. This is not something where most people could go and spend a million dollars to take care of an entire hospital, for instance. That’s not due to the material. The material is aluminum nitride, the two most abundant things on our planet – aluminum and nitrogen are close to the top. But it’s very hard to process.

They are available, but probably not available in the quantities where, if money disappeared tomorrow, there would be enough to go around. There’s some good news here, though, it’s made with the same type of reactors that other LEDs are made with. Companies like Cree, companies like Samsung, very large companies that make LEDs could use their reactors to make these types of safe UV lamps. The production capacity is there, the supply right now is not. Because the price is so high, it doesn’t make a great business offering at this point.

Alyssa Vance:

In order to bring the price down, do we need a new technological breakthrough? Or is it just a question of building the factory at a large enough scale to make each unit cheap?

Matthew Putman:

It’s a new technology. But the technology, strangely, it’s a software technology. This is where the work that I do comes in. (When I say “I”, I mean people on my team that actually know how to write these models. If we get too deeply technical about this, you’ll lose me.) But the main point is that they’re grown in something called a MOCVD reactor, which is a vapor deposition reactor. These are very common reactors, as I said, used for any wide-bandgap material. LEDs, but also power inverters and sensors, a lot of other things that are wide-bandgap semiconductors.

You can think of this as an additive process. You’re taking a crystal, you’re growing a crystal on top of another crystal with the gases in the deposition chamber. The problem with using aluminum nitride in the type of volume that we’re talking about is that during that process, cracks and other types of defects start to appear in the crystal. Now that’s not new, that’s something that’s happened through the history of semiconductors, having defects that propagate. But this has to be grown at extremely high temperatures, and it’s very touchy.

Getting over those hurdles has been why yields have been especially low. If you have something that’s 50% yield, then you’re losing a lot of material and have to charge a lot more. We use something that we call AIPC, artificial intelligence process control. Instead of setting conditions for your tests – normally you’d set how much of the gases you want, what the temperatures should be, what the flow rates should be – we take all of the controllers that already exist on the reactors, but we let something called an RL agent, a reinforcement learning agent, watch the process.

Because it’s an additive process, when a defect starts to emerge, it can make corrections in the next part of the process in order to fix it. It’s analogous to, and uses a lot of the same types of algorithms as AlphaGo would use, to win the game of making an ideal UVC LED. Regardless of what moves were made in the middle. We assume that there will be problems. This is hard to do. But an RL agent that’s being rewarded for getting it right, and penalized for mistakes along the way, can take actions that should win the game and improve the yields.

Alyssa Vance:

How can you tell when a microscopic defect starts appearing in one of these materials?

Matthew Putman:

There’s a whole other set of tools that can be used. You’re taking sensor data all the time. There’s time series data, we’re monitoring things as they go, so you see how things change. You might see some strange dips in temperature, or some strange loss of flow. But you have these validating steps, and those could be using spectroscopy, or using microscopy. Then there’s a whole other set of AI tools that go with that, which are a bit simpler. Using things like convolutional neural networks to do classification of the defects in real time. You’re doing imaging, whether it’s through optical or other means – see a certain type of defect, notice it’s a defect, put it into a class. That system then, due to the memory being created in a series of AI operators that we run, knows historically the best ways to correct those classes of defects.

Alyssa Vance:

As we’ve seen with everything in this pandemic, even relatively simple manufacturing becomes complicated when you have to do things at an enormous scale, for all of the seven billion people who might become infected. Derek Lowe, over at In the Pipeline, recently had an article on the glass vials that are used to contain vaccines – you make the vaccine, you put it in the vial, then you ship it off to the doctor or hospital or whoever. Right now, even if there were a vaccine tomorrow, there aren’t enough glass vials to put them in. Because even though making glass vials is a very established technology, no one has a factory that’s large enough to pump out seven billion of them, or however many we’re going to need. If we did try to scale up this LED manufacturing process, so it could cover every office and factory, and stop the pandemic, what sort of bottlenecks would you run into?

Matthew Putman:

Well, this goes beyond UVC, as you mentioned. What’s been noticed, what’s been really apparent throughout the COVID crisis, is what a disaster our supply chains are. We have taken even the most simple of devices, and created a supply chain that slows things down. And it adds potential variation in the process, potential failure points, and a length of time to get things to market that is completely unnecessary with modern technology, such as the technology I mentioned before.

To build what you need, where you need it, is something that needs to be taken into account, rather than a stockpile of potentially useful things. Distributed factories… there are certainly different steps of how this could be done. You could think about converting a regular LED factory that’s already a large factory, they already have the right machines, then add on the software. There’s plenty of aluminum and nitrogen in the world, and you can make it in North Carolina. That’s one way that’s probably the simpler way to go about it, rather than to take the 50 other steps that would normally go into getting an LED.

COVID is a wonderful chance, if we don’t squander it, to try to finally converge, and make these things that people thought would need to happen one day happen now. I used to say when we started Nanotronics, that my dream was to be the next big, important company to be built in a dorm room, that  would not be a social network, but a factory. I believe that there could be big steps towards this in just this particular technology. An MOCVD reactor is a couple hundred thousand dollars. Certainly not free. The software’s another couple hundred. But for a fairly small amount of money, you could actually have a factory that is close to the size of a dorm room making quite a bit of UVC LEDs. Now that’s not going to happen tomorrow. It could, though.

You have to first of all ask, “What is physically possible?” “What is proven out to be an engineering possibility?” That could happen. What’s more likely to happen, and should happen, is that all of those reactors in the world that are making other LEDs could make the UVC LEDs, and could do it in a distributed way. New factories should be funded, new companies should be funded. It’s an underserved space right now that has the potential to be ubiquitous. There’s a great opportunity.

We even get discouraged by the fact that there isn’t anything in our lives that we can imagine making, without having 100 different companies and suppliers build it.

Alyssa Vance:

If all of the factories that are using these reactors to make light bulbs and electronics, and all sorts of other components, switched to using them to make UVC LEDs, that would be enough to supply everyone?

Matthew Putman:

For sure. I mean, it would be a fraction of that. The same reactors build 15 different chips, in the chip set of an iPhone. You wouldn’t expect them to convert everything, but you could make profitable businesses out of each of those reactors, and then buy more reactors. Certainly, the capacity is out there right now.

Alyssa Vance:

That’s what they did during World War Two.

Matthew Putman:

Yeah.

Alyssa Vance:

A lot of consumer production simply stopped, and they said, “For the duration of the war, we will make no cars, we will make no bicycles, we will make no typewriters, we switched it all over to war materiel.”

Matthew Putman:

Right. I’m trying to figure out if that was a different will of the people, an ability to sacrifice to fight, or knowing that it was something different to fight for than we have right now. Or if people are intimidated by the technology that we’re discussing. I did an interview with Stephen Wolfram a couple weeks ago, on a podcast that I have. At the end of the podcast, he started asking about AIPC and Nanotronics – he was saying that an iPhone, isn’t it so complicated that even with all of the money that Apple has, if they wanted to make an entire iPhone in the United States or in one factory, it couldn’t be done?

I challenged that idea. I think that if Apple took a war-like switching things over mentality, like you were talking about in World War Two, it’s not a technological challenge. It’s just being able to say yes, this is important and should be done. But it’s talked about so little that some of the smartest people in the world – like Wolfram who I respect, I think he’s one of the most inspiring, intelligent people around – didn’t know that.

Alyssa Vance:

I read on your Medium post that you tried to do a pilot project with the MTA (which runs the New York subways), but it hadn’t gotten launched yet. The MTA, in transit circles, is famous for being this big government agency that overspends, and has projects come in years late, and so on. If you think about doing a pilot project with some other company or agency, the first thing that comes to mind is those meat packing plants. Because they’re big centers for infection, they have lots of people packed close together. It’s hard to shut them down, because then no one could eat meat. And you can’t work from home, so there isn’t much alternative.

Matthew Putman:

Excellent idea. I love this conversation, because yeah, I’ve thought about partnering with airports, I’ve thought about partnering with clinics… meat packing is an excellent idea. And you’re also right that when you think about big government – every prejudice I had had dealing with big government, as being difficult, bureaucratic, overspending… every prejudice I had turned out to be true. I think, certainly, the MTA isn’t alone. But yeah, that’s a great idea. I will start searching that out, thank you.

Alyssa Vance:

Thank you.

Matthew Putman:

For the biz dev opportunity.

Alyssa Vance:

Back when the pandemic was getting rolling during February and March, I had in my head that… Trump wanted to deny it for a long time, but I thought that the pandemic would be so bad, there’d be so many deaths, so many hospitalizations, that he’d essentially be forced to let someone like Scott Gottlieb come in and establish a sane policy. I don’t think that’s happened yet, and I don’t see any good prospects for it happening soon. Have you thought about going outside the US and working with different national governments, like Taiwan and New Zealand and other countries that have handled it better?

Matthew Putman:

Yes, yes I have. And there’s no reason to be nationalistic about saving peoples’ lives. Whoever will do it and support it, we should be working with. Yes, I’m speaking to other governments and large companies in other countries. I feel like it’s a shame and a wasted opportunity if the United States continues to have this embarrassing, heart-breaking, frustrating rise in infections. Policy and investment throughout many institutions has been very poor, and continues to be. So yes, I am speaking to other places. This isn’t a war against another nation we’re dealing with, so we all benefit from it.

Alyssa Vance:

Where should someone go if they want to read more about this technology? Maybe there’s someone who has worked with LEDs in the past, who might be interested in getting one of these factories set up?

Matthew Putman:

In the blog post that I wrote, I have this link to reference this new work coming out of Columbia. But there’s three different US-based companies that I’ve been working with that do the technology. There’s three university programs I work with. Yeah, there’s research that goes back quite a ways. From Santa Barbara to Korea. So this is work that has been done quite a bit. We’re publishing a paper with a company in North Carolina where we did some pilot demonstrations, too.

Alyssa Vance:

You said that you’ve been – in general, not just for COVID – excited about manufacturing, and building new manufacturing companies, in the same way that we’ve had Facebook and Twitter. What are some of the opportunities in manufacturing that you’re most excited about?

Matthew Putman:

Yeah. I do want to point out that I’ve gone through these waves of being incredibly optimistic that the urgency of COVID will take some of the technologies that have been waiting, and push them out into the world – being very optimistic about it to being disappointed, like with this government story about UVC. I think there are tentacles coming out of all of these things. If you think of wide-bandgap materials, once you’re making UVC LEDs in this way, you’re also making new photovoltaics in this way, or you’re making new types of packaged power devices.

But for COVID specifically, we’re working with the largest maker of genome sequencing equipment. Then throughout their supply chain, we’re able to increase yields of genome sequencing to the point where it becomes extremely inexpensive to do – and then you can design not just vaccines, but therapeutics, and eventually individually tailored ones. The road to personalized medicine can be shortened by decades, but the will and the financing go into it because of COVID. That’s something that really excited me. I consider that manufacturing, and all these things are building something.

Alyssa Vance:

So that’s working with gene sequencers?

Matthew Putman:

Yeah. And their supply chain, the vaccine makers that they work with. To improve yields, make things less expensive. For vaccine research, and from testing through vaccine research and therapeutics, especially when you’re dealing with these RNA vaccine delivery systems.

Alyssa Vance:

If you look at light manufacturing – the types of things that you’d find in a Walmart, like clothes and electronics – we’ve seen huge decreases in price, and increases in efficiency. Most of the things in a Walmart are cheap. Even an ordinary person can buy most of them with an hour of labor, two hours of labor. And many people now have too much stuff, rather than a shortage of stuff. But a lot of things in heavier manufacturing remain very expensive. Like airplanes are obviously very expensive, skyscrapers are expensive, bridges are expensive. These things still cost hundreds of millions of dollars. Do you have any thoughts about how you might use technology to make these large manufacturing systems more affordable?

Matthew Putman:

You point this out, the commodification of these things – there is real ubiquity and affordability of these things, and when I was a kid, there wouldn’t have been. None of those things are novel – so you take an airplane that’s just like an airplane that we currently have, but make it lighter and more fuel efficient, that’s generally just improving material manufacturing. It’s easier to improve material, injection molding of plastics. The same thing can be true for doing a layup of carbon fiber, or to actually incorporate new types of nano fillers. That’s not where it gets incredibly exciting, though.

New battery technology is very interesting and exciting, including solid state batteries.  Making things less expensive, I think that you get this and you’ve already pointed it out, it’s not always linked in people’s minds to… lower prices with things that are radically better and exponentially better, yeah. I don’t think it’s exactly intuitive to people, even though it should be. Being that we don’t see it.

The way that we can build things, by using techniques like I discussed earlier, is by reducing the length of these supply chains. By being able to take the software that was developed on other platforms, on the platforms that we need to improve. Funny enough, you can take an NVIDIA GPU, where deep learning models have been created and are being deployed, and use those to make the models that were created for the application, and use those to make the next generation chips. Then you’re going to start seeing something that’s really, radically different. The type of change that we would expect and want.

Alyssa Vance:

In terms of novel technology, what are some things that you might buy that aren’t being manufactured, or aren’t available on the market yet? What do you imagine in the glorious transhumanist future?

Matthew Putman:

It’s funny because we can now look at our daily lives, and see where the same failures have occurred, and use our imaginations in the way that probably you and I always have been. We see the failures in the medical establishment. We don’t have any type of personalized medicine, and that could be either completely biochemically based, but more usually with some type of nano-controllers that can seek out pathogens or can bind to different DNA sites. The idea of having telemedicine needs to turn from talking to a doctor over Zoom, to looking at your own genome and printing out a therapeutic for you right at that time, and just having your doctor consult with you if you have any questions.

You know, that’s basic. These are things that we would all love right now, and are much more possible than people think. There will be something fairly amazing, if any of these new types of vaccines work in a fairly short amount of time. I think that there will be a renewed hope that things are possible. Anyway, that’s one of them. Not to be old fashioned about this, but I do think that, in nanotechnology, it’s still where you gather the greatest amount of abundance, and abundance meaning abundance of life as well. I focus on miniaturization, not just of a chip, but a miniaturization of a factory. Then I leave it to your imagination. And everybody else’s imagination to build what we want to build.

Alyssa Vance:

I just ordered a whole genome sequencing, I sent off the tube last week. When I get the data back, do you have any ideas on what I should do with it?

Matthew Putman:

I have a feeling that actually you, personally, and a group of friends could figure out how to combine that with your own medical records and write some AI algorithms around it. But I don’t know. That’s really problematic, that I even hesitated, right? Isn’t that crazy that you even asked the question, and that I don’t have an answer to that? If I would’ve thought that you and I can just get a full genome sequence as easily as we can, and we’re not sure what to do with it? It’s amazing to me. I have no great insight.

Alyssa Vance:

I can do all kinds of studies, but I need to have more than one. Like, one genotype and one phenotype by itself doesn’t get you very much.

Matthew Putman:

I know. I mean, that’s why I’m an old fashioned factory guy who just wants to make genome sequencing so cheap that everybody has their full genome sequence, and then you’ll have plenty of data and everything to work with. I’ll focus on that goal. Because this is just like building a computer and having applications on top of it. There’ll be so many people with applications. Let’s make sure that it’s nearly free for everybody to get their genome sequence.

Alyssa Vance:

Cool. That’s all the questions I had, is there anything else that you want to add?

Matthew Putman:

No, thank you. Was this okay for you?

Alyssa Vance:

Yes. This was awesome, thanks for doing this. 

Perfume Quiz

A friend of mine wrote a quiz to help people find the best types of perfume for them. It’s much more detailed and creative than you typically see for online quizzes:

https://uquiz.com/3wN7HU

Fascinating YouTube channels

Thought Emporium: New open-source science every week

Applied Science: Interesting applications of science and technology

Foresight Institute: Guiding technological development towards an abundant future

Numberphile: Videos about numbers

NileRed: Show the natural beauty of chemistry in fun ways

TIK: World War 2 history documentaries

TierZoo: Analyzing biology and ecology as the world’s biggest, oldest video game

Scott Manley: Orbital mechanics and rocket science

3 Blue 1 Brown: Some combination of math and entertainment