Everything is getting better and worse at the same time

“The truth will set you free, but first it will make you miserable.”

It’s late at night. You shuffle to the kitchen for a snack. Your hand fumbles briefly for the light switch, and… roaches! They quickly scatter, but now you’ve seen them. You know they’re there. From now on, you can’t not think about the roaches in the kitchen. It’s a shame, too, because you always thought of yourself as a neat person with a clean kitchen. But now that image has been ruined. The good news is, now you have better information about the world. You DO have roaches in your kitchen, and you can start to do something about it.

The Roach Reveal story is how I think about one of our modern patterns of experience: everything is getting better and worse at the same time. These days we’re getting ever more powerful sensors that show us the virtual vermin that lurk beneath the veneers of civilization. We’re seeing roaches we never knew were there. It feels like we’re being hammered endlessly by terrible news. But look closer: the roaches were always there. And now we can start to do something about it. And often it’s only when we realize just how bad it is that we finally decide to take action.

Related to this topic, I want to convince you to read The Alignment Problem by Brian Christian. This book addresses the dangers associated with machine learning, smart robots, and clever algorithms of all kinds. The title refers to a subtle and disturbing fact: it’s strangely difficult to tell a computer what you want it to do. You can tell it to do something that is what you THINK you want it to do. And then it will, with dizzying speed and precision, set about making you miserable, all while doing exactly what you asked it to do. This is often described in terms of deal-with-the-devil jokes: tell the computer to stop people from getting malaria, and it will murder everyone before they can catch malaria. “I solved your problem!” says the robot. “You killed my family!” says the programmer. That’s the alignment problem. It’s not a joke.

One of the topics in the book is algorithmic bias. Suppose you want to teach a robot to hire good employees. Or decide who should get a loan. Or maybe decide who should be paroled from jail. After you implement some pretty straightforward machine learning, you are almost certain to be disturbed by the results. The computer has learned from a horribly biased society. What else could it learn from? Naturally it mirrors back to us racism, sexism, and xenophobia.

Machine learning is the kitchen light. It’s illuminating the roaches that have been crawling through our brains and institutions for hundreds of years. Switching on these algorithms feels like a massive step backwards. We are in danger of encoding extraordinarily efficient prejudice. But the book comes with good news too. No matter how unhappy the kitchen light first makes you, it will also help you solve the problem. Seeing how biased our algorithms are, we can set about attacking the root cause. The root cause is not the kitchen light.

Our computers can teach us to be better humans.

How Do You Spell Vaccine?

This post is a follow-up to one that I did back in February: A Modern Magical Spell. There I was ruminating about the fact that, whereas old-school vaccines included little chunks of the virus itself, the Pfizer and Moderna mRNA vaccines are instructions, blueprints, that tell your body how to make those little viral chunks. They are, in effect, cellular DIY projects.

It sounds like a minor point, but the difference is huge. Sending messages is easier and more flexible than sending the thing that the message encodes. Why send cookies if you can just send the recipe? Why send a string quartet if you can just send the sheet music? Blueprints are cheaper than bricks.

Anyway, I’m writing about the same thing again because we now know exactly what text is in the vaccine. Text messages are convenient for a number of reasons, and one is that they’re easy for us to read. Think about this: the syringe that pokes you in the arm is a bottle with a message. Uncork the bottle, unroll the message, and you can see, you can just read off, exactly what protein sequence the vaccine codes for. Some folks at Stanford have done exactly that: Stanford Scientists Post mRNA Sequence for Moderna Vaccine on Github. They didn’t want to get in trouble for stealing anyone’s shot, so they did the equivalent of sifting through the trash for valuable documents: “RNAs were obtained as discards from the small portions of vaccine doses that remained in vials after immunization.”

Pfizer didn’t want to publish this information… only they did publish it. They published millions of copies into little vials and distributed them across the country. For my previous piece, I made a guess as to the sequence that was used for encoding the spike protein. No surprise, my guess was wrong. But the good news is that the actual answer is posted on GitHub. Check out the fancy title: Assemblies of putative SARS CoV2 spike encoding mRNA sequences for vaccines BNT-162b2 and mRNA-1273.

Do you want to know the actual recipe for that BNT-162b2 vaccine? The actual text that would be injected into your bloodstream? Here it is.

GAGAATAAACTAGTATTCTTCTGGTCCCCACAGACTCAGAGAGAACCCGCCACCATGTTCGTGTTCCTGGTGCTGCTGCC
TCTGGTGTCCAGCCAGTGTGTGAACCTGACCACCAGAACACAGCTGCCTCCAGCCTACACCAACAGCTTTACCAGAGGCG
TGTACTACCCCGACAAGGTGTTCAGATCCAGCGTGCTGCACTCTACCCAGGACCTGTTCCTGCCTTTCTTCAGCAACGTG
ACCTGGTTCCACGCCATCCACGTGTCCGGCACCAATGGCACCAAGAGATTCGACAACCCCGTGCTGCCCTTCAACGACGG
GGTGTACTTTGCCAGCACCGAGAAGTCCAACATCATCAGAGGCTGGATCTTCGGCACCACACTGGACAGCAAGACCCAGA
GCCTGCTGATCGTGAACAACGCCACCAACGTGGTCATCAAAGTGTGCGAGTTCCAGTTCTGCAACGACCCCTTCCTGGGC
GTCTACTACCACAAGAACAACAAGAGCTGGATGGAAAGCGAGTTCCGGGTGTACAGCAGCGCCAACAACTGCACCTTCGA
GTACGTGTCCCAGCCTTTCCTGATGGACCTGGAAGGCAAGCAGGGCAACTTCAAGAACCTGCGCGAGTTCGTGTTTAAGA
ACATCGACGGCTACTTCAAGATCTACAGCAAGCACACCCCTATCAACCTCGTGCGGGATCTGCCTCAGGGCTTCTCTGCT
CTGGAACCCCTGGTGGATCTGCCCATCGGCATCAACATCACCCGGTTTCAGACACTGCTGGCCCTGCACAGAAGCTACCT
GACACCTGGCGATAGCAGCAGCGGATGGACAGCTGGTGCCGCCGCTTACTATGTGGGCTACCTGCAGCCTAGAACCTTCC
TGCTGAAGTACAACGAGAACGGCACCATCACCGACGCCGTGGATTGTGCTCTGGATCCTCTGAGCGAGACAAAGTGCACC
CTGAAGTCCTTCACCGTGGAAAAGGGCATCTACCAGACCAGCAACTTCCGGGTGCAGCCCACCGAATCCATCGTGCGGTT
CCCCAATATCACCAATCTGTGCCCCTTCGGCGAGGTGTTCAATGCCACCAGATTCGCCTCTGTGTACGCCTGGAACCGGA
AGCGGATCAGCAATTGCGTGGCCGACTACTCCGTGCTGTACAACTCCGCCAGCTTCAGCACCTTCAAGTGCTACGGCGTG
TCCCCTACCAAGCTGAACGACCTGTGCTTCACAAACGTGTACGCCGACAGCTTCGTGATCCGGGGAGATGAAGTGCGGCA
GATTGCCCCTGGACAGACAGGCAAGATCGCCGACTACAACTACAAGCTGCCCGACGACTTCACCGGCTGTGTGATTGCCT
GGAACAGCAACAACCTGGACTCCAAAGTCGGCGGCAACTACAATTACCTGTACCGGCTGTTCCGGAAGTCCAATCTGAAG
CCCTTCGAGCGGGACATCTCCACCGAGATCTATCAGGCCGGCAGCACCCCTTGTAACGGCGTGGAAGGCTTCAACTGCTA
CTTCCCACTGCAGTCCTACGGCTTTCAGCCCACAAATGGCGTGGGCTATCAGCCCTACAGAGTGGTGGTGCTGAGCTTCG
AACTGCTGCATGCCCCTGCCACAGTGTGCGGCCCTAAGAAAAGCACCAATCTCGTGAAGAACAAATGCGTGAACTTCAAC
TTCAACGGCCTGACCGGCACCGGCGTGCTGACAGAGAGCAACAAGAAGTTCCTGCCATTCCAGCAGTTTGGCCGGGATAT
CGCCGATACCACAGACGCCGTTAGAGATCCCCAGACACTGGAAATCCTGGACATCACCCCTTGCAGCTTCGGCGGAGTGT
CTGTGATCACCCCTGGCACCAACACCAGCAATCAGGTGGCAGTGCTGTACCAGGACGTGAACTGTACCGAAGTGCCCGTG
GCCATTCACGCCGATCAGCTGACACCTACATGGCGGGTGTACTCCACCGGCAGCAATGTGTTTCAGACCAGAGCCGGCTG
TCTGATCGGAGCCGAGCACGTGAACAATAGCTACGAGTGCGACATCCCCATCGGCGCTGGAATCTGCGCCAGCTACCAGA
CACAGACAAACAGCCCTCGGAGAGCCAGAAGCGTGGCCAGCCAGAGCATCATTGCCTACACAATGTCTCTGGGCGCCGAG
AACAGCGTGGCCTACTCCAACAACTCTATCGCTATCCCCACCAACTTCACCATCAGCGTGACCACAGAGATCCTGCCTGT
GTCCATGACCAAGACCAGCGTGGACTGCACCATGTACATCTGCGGCGATTCCACCGAGTGCTCCAACCTGCTGCTGCAGT
ACGGCAGCTTCTGCACCCAGCTGAATAGAGCCCTGACAGGGATCGCCGTGGAACAGGACAAGAACACCCAAGAGGTGTTC
GCCCAAGTGAAGCAGATCTACAAGACCCCTCCTATCAAGGACTTCGGCGGCTTCAATTTCAGCCAGATTCTGCCCGATCC
TAGCAAGCCCAGCAAGCGGAGCTTCATCGAGGACCTGCTGTTCAACAAAGTGACACTGGCCGACGCCGGCTTCATCAAGC
AGTATGGCGATTGTCTGGGCGACATTGCCGCCAGGGATCTGATTTGCGCCCAGAAGTTTAACGGACTGACAGTGCTGCCT
CCTCTGCTGACCGATGAGATGATCGCCCAGTACACATCTGCCCTGCTGGCCGGCACAATCACAAGCGGCTGGACATTTGG
AGCAGGCGCCGCTCTGCAGATCCCCTTTGCTATGCAGATGGCCTACCGGTTCAACGGCATCGGAGTGACCCAGAATGTGC
TGTACGAGAACCAGAAGCTGATCGCCAACCAGTTCAACAGCGCCATCGGCAAGATCCAGGACAGCCTGAGCAGCACAGCA
AGCGCCCTGGGAAAGCTGCAGGACGTGGTCAACCAGAATGCCCAGGCACTGAACACCCTGGTCAAGCAGCTGTCCTCCAA
CTTCGGCGCCATCAGCTCTGTGCTGAACGATATCCTGAGCAGACTGGACCCTCCTGAGGCCGAGGTGCAGATCGACAGAC
TGATCACAGGCAGACTGCAGAGCCTCCAGACATACGTGACCCAGCAGCTGATCAGAGCCGCCGAGATTAGAGCCTCTGCC
AATCTGGCCGCCACCAAGATGTCTGAGTGTGTGCTGGGCCAGAGCAAGAGAGTGGACTTTTGCGGCAAGGGCTACCACCT
GATGAGCTTCCCTCAGTCTGCCCCTCACGGCGTGGTGTTTCTGCACGTGACATATGTGCCCGCTCAAGAGAAGAATTTCA
CCACCGCTCCAGCCATCTGCCACGACGGCAAAGCCCACTTTCCTAGAGAAGGCGTGTTCGTGTCCAACGGCACCCATTGG
TTCGTGACACAGCGGAACTTCTACGAGCCCCAGATCATCACCACCGACAACACCTTCGTGTCTGGCAACTGCGACGTCGT
GATCGGCATTGTGAACAATACCGTGTACGACCCTCTGCAGCCCGAGCTGGACAGCTTCAAAGAGGAACTGGACAAGTACT
TTAAGAACCACACAAGCCCCGACGTGGACCTGGGCGATATCAGCGGAATCAATGCCAGCGTCGTGAACATCCAGAAAGAG
ATCGACCGGCTGAACGAGGTGGCCAAGAATCTGAACGAGAGCCTGATCGACCTGCAAGAACTGGGGAAGTACGAGCAGTA
CATCAAGTGGCCCTGGTACATCTGGCTGGGCTTTATCGCCGGACTGATTGCCATCGTGATGGTCACAATCATGCTGTGTT
GCATGACCAGCTGCTGTAGCTGCCTGAAGGGCTGTTGTAGCTGTGGCAGCTGCTGCAAGTTCGACGAGGACGATTCTGAG
CCCGTGCTGAAGGGCGTGAAACTGCACTACACATGATGACTCGAGCTGGTACTGCATGCACGCAATGCTAGCTGCCCCTT
TCCCGTCCTGGGTACCCCGAGTCTCCCCCGACCTCGGGTCCCAGGTATGCTCCCACCTCCACCTGCCCCACTCACCACCT
CTGCTAGTTCCAGACACCTCCCAAGCACGCAGCAATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAG
CAGTGATTAACCTTTAGCAATAAACGAAAGTTTAACTAAGCTATACTAACCCCAGGGTTGGTCAATTTCGTGCCAGCCAC
ACCCTGGAGCTAGCA

There it is! That’s the payload. That’s what the fuss is all about. It gives me a thrill to look at it. It may look like a mess, but the ribosomes in your cells can read it like a recipe. So we might say that you can’t read it, but “you” can. Because you can. And you will. And it might save your life.

As biology becomes more and more of an information science, many strange and wonderful things will become possible. We’re still at the “Mr. Watson, come here, I want to see you” stage of communication.