vox--2020-08-13--GPT-3, explained: This new language AI is uncanny, funny — and a big deal

Last month , OpenAI , the Elon Musk-founded artificial intelligence research lab , announced the arrival of the newest version of an AI system it had been working on that can mimic human language — a model called GPT-3 .
In the weeks that followed , people got the chance to play with the program .
If you follow news about AI , you may have seen some headlines calling it a huge step forward — even a scary one .
I ’ ve now spent the past few days looking at GPT-3 in greater depth and playing around with @ @ @ @ @ @ @ tell you — the hype is real .
It has its shortcomings , but make no mistake : GPT-3 represents a tremendous leap for AI .
A year ago I sat down to play with GPT-3 ’ s precursor dubbed ( you guessed it ) GPT-2 .
My verdict at the time was that it was pretty good .
When given a prompt — say , a phrase or sentence — GPT-2 could write a decent news article , making up imaginary sources and organizations and referencing them across a couple of paragraphs @ @ @ @ @ @ @ — it didn ’ t really understand the world — but it was still an uncanny glimpse of what it might be like to interact with a computer that does .
A year later , GPT-3 is here — and it ’ s smarter .
A lot smarter .
OpenAI took the same basic approach it had taken for GPT-2 ( more on this below ) , and spent more time training it with a bigger dataset .
The result is a program that is significantly better at passing various tests of language @ @ @ @ @ @ @ to compare our computer programs .
( You can sign up to play with GPT-3 , but there ’ s a waitlist . )
But that description understates what GPT-3 is , and what it does .
“ It surprises me continuously , ” Arram Sabeti , an inventor with early access to GPT-3 who has published hundreds of examples of results from the program , told me .
“ A witty analogy , a turn of phrase — the repeated experience I have is ‘ there ’ s no way it just @ @ @ @ @ @ @ feel very much like general intelligence.
” Not everyone agrees .
“ Artificial intelligence programs lack consciousness and self-awareness , ” researcher Gwern Branwen wrote in his article about GPT-3 .
“ They will never be able to have a sense of humor .
They will never be able to appreciate art , or beauty , or love .
They will never feel lonely .
They will never have empathy for other people , for animals , for the environment .
They will never enjoy music or fall in love , or cry @ @ @ @ @ @ @ Sorry , I lied — GPT-3 wrote that .
Branwen fed it a prompt — a few words expressing skepticism about AI — and GPT-3 came up with a long and convincing rant about how computers won ’ t ever be really intelligent .
Branwen himself told me he was taken aback by GPT-3 ’ s capabilities .
As GPT-style programs scale , they get steadily better at predicting the next word .
But up to a point , Branwen said , that improved prediction “ just makes it a little more accurate @ @ @ @ @ @ @ English grammar , a little better at trivia questions.
” GPT-3 suggests to Branwen that “ past a certain point , that [ improvement at prediction ] starts coming from logic and reasoning and what looks entirely too much like thinking.
” GPT-3 is , in some ways , a really simple program .
It takes a well-known , not even state-of-the-art approach from machine learning .
Fed most of the internet as data to train itself on — news stories , wiki articles , even forum posts and fanfictions — and given @ @ @ @ @ @ @ on it , GPT-3 emerges as an uncannily clever language generator .
That ’ s cool in its own right , and has big implications for the future of AI .
To understand what a leap GPT-3 represents , it would be helpful to review two basic concepts in machine learning : supervised and unsupervised learning .
Until a few years ago , language AIs were taught predominantly through an approach called “ supervised learning.
” That ’ s where you have large , carefully labeled data sets that contain inputs and desired @ @ @ @ @ @ @ to produce the outputs given the inputs .
That can produce good results — sentences , paragraphs , and stories that do a solid job mimicking human language — but it requires building huge data sets and carefully labeling each bit of data .
Supervised learning isn ’ t how humans acquire skills and knowledge .
We make inferences about the world without the carefully delineated examples from supervised learning .
In other words , we do a lot of unsupervised learning .
Many people believe that advances in general AI capabilities will @ @ @ @ @ @ @ AI gets exposed to lots of unlabeled data and has to figure out everything else itself .
Unsupervised learning is easier to scale since there ’ s lots more unstructured data than there is structured data ( no need to label all that data ) , and unsupervised learning may generalize better across tasks .
GPT-3 ( like its predecessors ) is an unsupervised learner ; it picked up everything it knows about language from unlabelled data .
Specifically , researchers fed it most of the internet , from popular Reddit posts to @ @ @ @ @ @ @ .
GPT-3 uses this vast trove of information to do an extremely simple task : guess what words are most likely to come next , given a certain initial prompt .
For example , if you want GPT-3 to write a news story about Joe Biden ’ s climate policy , you might type in : “ Joe Biden today announced his plan to fight climate change.
” From there , GPT-3 will take care of the rest .
Here ’ s what GPT-3 can do OpenAI controls access to GPT-3 ; you @ @ @ @ @ @ @ business idea , or just to play around , though there ’ s a long waiting list for access .
( It ’ s free for now , but might be available commercially later . )
Once you have access , you can interact with the program by typing in prompts for it to respond to .
GPT-3 has been used for all kinds of projects so far , from making imaginary conversations between historical figures to summarizing movies with emoji to writing code .
Sabeti prompted GPT-3 to write Dr. Seuss poems @ @ @ @ @ @ @ GPT-3 can even correctly answer medical questions and explain its answers ( though you shouldn ’ t trust all its answers ; more about that later ) : You can ask GPT-3 to write simpler versions of complicated instructions , or write excessively complicated instructions for simple tasks .
At least one person has gotten GPT-3 to write a productivity blog whose bot-written posts performed quite well on tech news aggregator Hacker News .
Of course , there are some things GPT-3 shouldn ’ t be used for : having casual conversations and @ @ @ @ @ @ @ two .
Tester after tester has pointed out that GPT-3 makes up a lot of nonsense .
This isn ’ t because it doesn ’ t “ know ” the answer to a question — asking with a different prompt will often get the correct answer — but because the inaccurate answer seemed plausible to the computer .
Relatedly , GPT-3 will by default try to give reasonable responses to nonsense questions like “ how many bonks are in a quoit ” ?
That said , if you add to the prompt that @ @ @ @ @ @ @ questions , then it will do that .
So GPT-3 shows its skills to best effects in areas where we don ’ t mind filtering out some bad answers , or areas where we ’ re not so concerned with the truth .
Branwen has an extensive catalog of examples of fiction writing by GPT-3 .
One of my favorites is a letter denying Indiana Jones tenure , which is lengthy and shockingly coherent , and concludes : Want to try it yourself ?
AI Dungeon is a text-based adventure game powered in @ @ @ @ @ @ @ uncanny abilities as a satirist , poet , composer , and customer service agent aren ’ t actually the biggest part of the story .
On its own , GPT-3 is an impressive proof of concept .
But the concept it ’ s proving has bigger ramifications .
For a long time , we ’ ve assumed that creating computers that have general intelligence — computers that surpass humans at a wide variety of tasks , from programming to researching to having intelligent conversations — will be difficult to make , and will @ @ @ @ @ @ @ , consciousness , and reasoning .
And for the last decade or so , a minority of AI researchers have been arguing that we ’ re wrong — that human-level intelligence will arise naturally once we give computers more computing power .
GPT-3 is a point for the latter group .
By the standards of modern machine-learning research , GPT-3 ’ s technical setup isn ’ t that impressive .
It uses an architecture from 2018 — meaning , in a fast-moving field like this one , it ’ s already out of @ @ @ @ @ @ @ ’ t fix the constraints on GPT-2 , such as its small window of “ memory ” for what it has written so far , which many outside observers criticized .
“ GPT-3 is terrifying because it ’ s a tiny model compared to what ’ s possible , trained in the dumbest way possible , ” Branwen tweeted .
That suggests there ’ s potential for a lot more improvements — improvements that will one day make GPT-3 look as shoddy as GPT-2 now does by comparison .
GPT-3 is a piece @ @ @ @ @ @ @ been hotly debated among AI researchers : Can we get transformative AI systems , ones that surpass human capabilities in many key areas , just using existing deep learning techniques ?
Is human-level intelligence something that will require a fundamentally new approach , or is it something that emerges of its own accord as we pump more and more computing power into simple machine learning models ?
These questions won ’ t be settled for another few years at least .
GPT-3 is not a human-level intelligence even if it can , in @ @ @ @ @ @ @ of one .
Skeptics have argued that those short bursts of uncanny imitation are driving more hype than GPT-3 really deserves .
They point out that if a prompt is not carefully designed , GPT-3 will give poor quality answers — which is absolutely true , though that ought to guide us toward better prompt design , not give up on GPT-3 .
They also point out that a program that is sometimes right and sometimes confidently wrong is , for many tasks , much worse than nothing .
( There are ways @ @ @ @ @ @ @ a guess , but even while using those , you certainly shouldn ’ t take the program ’ s outputs at face value . )
They also note that other language models purpose-built for specific tasks can do better on those tasks than GPT-3 .
All of that is true .
GPT-3 is limited .
But what makes it so important is less its capabilities and more the evidence it offers that just pouring more data and more computing time into the same approach gets you astonishing results .
With the GPT architecture @ @ @ @ @ @ @ more you get .
If there are eventually to be diminishing returns , that point must be somewhere past the $ 10 million that went into GPT-3 .
And we should at least be considering the possibility that spending more money gets you a smarter and smarter system .
Other experts have reassured us that such an outcome is very unlikely .
As a famous artificial intelligence researcher said earlier this year , “ No matter how good our computers get at winning games like Go or Jeopardy , we don ’ t @ @ @ @ @ @ @ .
Our minds are much , much bigger than that.
” Narrow AI has seen extraordinary progress over the past few years .
AI systems have improved dramatically at translation , games like chess and Go , important research biology questions like predicting how proteins fold , and generating images .
AI systems determine what you ’ ll see in a Google search or in your Facebook Newsfeed .
They compose music and write articles that , at a glance , read as if a human wrote them .
They play strategy games @ @ @ @ @ @ @ drone targeting and detect missiles .
But narrow AI is getting less narrow .
Once , we made progress in AI by painstakingly teaching computer systems specific concepts .
To do computer vision — allowing a computer to identify things in pictures and video — researchers wrote algorithms for detecting edges .
To play chess , they programmed in heuristics about chess .
To do natural language processing ( speech recognition , transcription , translation , etc . )
, they drew on the field of linguistics .
But recently , we ’ @ @ @ @ @ @ @ that have generalized learning capabilities .
Instead of mathematically describing detailed features of a problem , we let the computer system learn that by itself .
While once we treated computer vision as a completely different problem from natural language processing or platform game playing , now we can solve all three problems with the same approaches .
GPT-3 is not the best AI system in the world at question answering , summarizing news articles , or answering science questions .
It ’ s distinctly mediocre at translation and arithmetic .
But it @ @ @ @ @ @ @ — it can do all of these things and more with just a few examples .
And AI systems to come will likely be yet more general .
Our AI progress so far has enabled enormous advances , but also raised urgent ethical questions .
When you train a computer system to predict which convicted felons will reoffend , you ’ re using inputs from a criminal justice system biased against Black people and low-income people — so its outputs will likely be biased against Black and low-income people , too .
Making @ @ @ @ @ @ @ your revenue but bad for your users .
Releasing a program that writes convincing fake reviews or fake news might make those widespread , making it harder for the truth to get out .
Rosie Campbell at UC Berkeley ’ s Center for Human-Compatible AI argues that these are examples , writ small , of the big worry experts have about AI in the future .
The difficulties we ’ re wrestling with today with narrow AI don ’ t come from the systems turning on us or wanting revenge or considering us @ @ @ @ @ @ @ the disconnect between what we tell our systems to do and what we actually want them to do .
For example , we tell an AI system to run up a high score in a video game .
We want it to play the game fairly and learn game skills — but if it has the chance to directly hack the scoring system , it will do that to achieve the goal we set for it .
It ’ s doing great by the metric we gave it .
But we aren ’ @ @ @ @ @ @ @ One of the most disconcerting things about GPT-3 is the realization that it ’ s often giving us what we asked for , not what we wanted .
If you prompt GPT-3 to write you a story with a prompt like “ here is a short story , ” it will write a distinctly mediocre story .
If you instead prompt it with “ here is an award-winning short story , ” it will write a better one .
Why ?
Because it trained on the internet , and most stories on the @ @ @ @ @ @ @ text .
It isn ’ t motivated to come up with the best text , or the text we most wanted ; just the text that seems most plausible .
Telling it the story won an award changes what text seems most plausible .
With GPT-3 , this is harmless .
And though people have used GPT-3 to write manifestos about GPT-3 ’ s schemes to fool humans , GPT-3 is not anywhere near powerful enough to pose the risks that AI scientists warn of .
But someday we may have computer systems @ @ @ @ @ @ @ If they ’ re made with deep learning , they will be hard for us to interpret , and their behavior will be confusing and highly variable — sometimes seeming much smarter than human and sometimes much less so .
And many AI researchers believe that that combination — exceptional capabilities , goals that don ’ t represent what we “ really want ” but just what we asked for , and incomprehensible inner workings — will produce AI systems that exercise a lot of power in the world .
Not for the @ @ @ @ @ @ @ against humanity , but toward goals that aren ’ t what we want .
Handing over our future to them would be a mistake — but one it ’ d be easy to make step-by-step , with each step half an accident .
Will you become our 20,000th supporter ?
When the economy took a downturn in the spring and we started asking readers for financial contributions , we weren ’ t sure how it would go .
Today , we ’ re humbled to say that nearly 20,000 people have chipped in @ @ @ @ @ @ @ surprising : Readers told us that they contribute both because they value explanation and because they value that other people can access it , too .
We have always believed that explanatory journalism is vital for a functioning democracy .
That ’ s never been more important than today , during a public health crisis , racial justice protests , a recession , and a presidential election .
But our distinctive explanatory journalism is expensive , and advertising alone won ’ t let us keep creating it at the quality and volume this @ @ @ @ @ @ @ not constitute a donation , but it will help keep Vox free for all .
Contribute today from as little as $ 3 .

Last month, OpenAI, the Elon Musk-founded artificial intelligence research lab, announced the arrival of the newest version of an AI system it had been working on that can mimic human language, a model called GPT-3.
In the weeks that followed, people got the chance to play with the program. If you follow news about AI, you may have seen some headlines calling it a huge step forward, even a scary one.
I’ve now spent the past few days looking at GPT-3 in greater depth and playing around with it. I’m here to tell you: The hype is real. It has its shortcomings, but make no mistake: GPT-3 represents a tremendous leap for AI.
A year ago I sat down to play with GPT-3’s precursor dubbed (you guessed it) GPT-2. My verdict at the time was that it was pretty good. When given a prompt — say, a phrase or sentence — GPT-2 could write a decent news article, making up imaginary sources and organizations and referencing them across a couple of paragraphs. It was by no means intelligent — it didn’t really understand the world — but it was still an uncanny glimpse of what it might be like to interact with a computer that does.
A year later, GPT-3 is here, and it’s smarter. A lot smarter. OpenAI took the same basic approach it had taken for GPT-2 (more on this below), and spent more time training it with a bigger data set. The result is a program that is significantly better at passing various tests of language ability that machine learning researchers have developed to compare our computer programs. (You can sign up to play with GPT-3, but there’s a waitlist.)
But that description understates what GPT-3 is, and what it does.
“It surprises me continuously,” Arram Sabeti, an inventor with early access to GPT-3 who has published hundreds of examples of results from the program, told me. “A witty analogy, a turn of phrase — the repeated experience I have is ‘there’s no way it just wrote that.’ It exhibits things that feel very much like general intelligence.”
Not everyone agrees. “Artificial intelligence programs lack consciousness and self-awareness,” researcher Gwern Branwen wrote in his article about GPT-3. “They will never be able to have a sense of humor. They will never be able to appreciate art, or beauty, or love. They will never feel lonely. They will never have empathy for other people, for animals, for the environment. They will never enjoy music or fall in love, or cry at the drop of a hat.”
Sorry, I lied. GPT-3 wrote that. Branwen fed it a prompt — a few words expressing skepticism about AI — and GPT-3 came up with a long and convincing rant about how computers won’t ever be really intelligent.
Branwen himself told me he was taken aback by GPT-3’s capabilities. As GPT-style programs scale, they get steadily better at predicting the next word. But up to a point, Branwen said, that improved prediction “just makes it a little more accurate a mimic: a little better at English grammar, a little better at trivia questions.” GPT-3 suggests to Branwen that “past a certain point, that [improvement at prediction] starts coming from logic and reasoning and what looks entirely too much like thinking.”
GPT-3 is, in some ways, a really simple program. It takes a well-known, not even state-of-the-art approach from machine learning. Fed most of the internet as data to train itself on — news stories, wiki articles, even forum posts and fanfiction — and given lots of time and resources to chew on it, GPT-3 emerges as an uncannily clever language generator. That’s cool in its own right, and it has big implications for the future of AI.
How GPT-3 works
To understand what a leap GPT-3 represents, it would be helpful to review two basic concepts in machine learning: supervised and unsupervised learning.
Until a few years ago, language AIs were taught predominantly through an approach called “supervised learning.” That’s where you have large, carefully labeled data sets that contain inputs and desired outputs. You teach the AI how to produce the outputs given the inputs.
That can produce good results — sentences, paragraphs, and stories that do a solid job mimicking human language — but it requires building huge data sets and carefully labeling each bit of data.
Supervised learning isn’t how humans acquire skills and knowledge. We make inferences about the world without the carefully delineated examples from supervised learning. In other words, we do a lot of unsupervised learning.
Many people believe that advances in general AI capabilities will require advances in unsupervised learning, where AI gets exposed to lots of unlabeled data and has to figure out everything else itself. Unsupervised learning is easier to scale since there’s lots more unstructured data than there is structured data (no need to label all that data), and unsupervised learning may generalize better across tasks.
GPT-3 (like its predecessors) is an unsupervised learner; it picked up everything it knows about language from unlabeled data. Specifically, researchers fed it most of the internet, from popular Reddit posts to Wikipedia to news articles to fanfiction.
GPT-3 uses this vast trove of information to do an extremely simple task: guess what words are most likely to come next, given a certain initial prompt. For example, if you want GPT-3 to write a news story about Joe Biden’s climate policy, you might type in: “Joe Biden today announced his plan to fight climate change.” From there, GPT-3 will take care of the rest.
Here’s what GPT-3 can do
OpenAI controls access to GPT-3; you can request access for research, a business idea, or just to play around, though there’s a long waiting list for access. (It’s free for now, but might be available commercially later.) Once you have access, you can interact with the program by typing in prompts for it to respond to.
GPT-3 has been used for all kinds of projects so far, from making imaginary conversations between historical figures to summarizing movies with emoji to writing code.
Sabeti prompted GPT-3 to write Dr. Seuss poems about Elon Musk. An excerpt:
But then, in his haste,
he got into a fight.
He had some emails that he sent
that weren’t quite polite. The SEC said, “Musk,
your tweets are a blight.
Not bad for a machine.
GPT-3 can even correctly answer medical questions and explain its answers (though you shouldn’t trust all its answers; more about that later):
So @OpenAI have given me early access to a tool which allows developers to use what is essentially the most powerful text generator ever. I thought I’d test it by asking a medical question. The bold text is the text generated by the AI. Incredible... (1/2) pic.twitter.com/4bGfpI09CL — Qasim Munye (@QasimMunye) July 2, 2020
You can ask GPT-3 to write simpler versions of complicated instructions, or write excessively complicated instructions for simple tasks. At least one person has gotten GPT-3 to write a productivity blog whose bot-written posts performed quite well on the tech news aggregator Hacker News.
Of course, there are some things GPT-3 shouldn’t be used for: having casual conversations and trying to get true answers, for two. Tester after tester has pointed out that GPT-3 makes up a lot of nonsense. This isn’t because it doesn’t “know” the answer to a question — asking with a different prompt will often get the correct answer — but because the inaccurate answer seemed plausible to the computer.
Relatedly, GPT-3 will by default try to give reasonable responses to nonsense questions like “how many bonks are in a quoit”? That said, if you add to the prompt that GPT- 3 should refuse to answer nonsense questions, then it will do that.
So GPT-3 shows its skills to best effects in areas where we don’t mind filtering out some bad answers, or areas where we’re not so concerned with the truth.
Branwen has an extensive catalog of examples of fiction writing by GPT-3. One of my favorites is a letter denying Indiana Jones tenure, which is lengthy and shockingly coherent, and concludes:
It is impossible to review the specifics of your tenure file without becoming enraptured by the vivid accounts of your life. However, it is not a life that will be appropriate for a member of the faculty at Indiana University, and it is with deep regret that I must deny your application for tenure. ... Your lack of diplomacy, your flagrant disregard for the feelings of others, your consistent need to inject yourself into scenarios which are clearly outside the scope of your scholarly expertise, and, frankly, the fact that you often take the side of the oppressor, leads us to the conclusion that you have used your tenure here to gain a personal advantage and have failed to adhere to the ideals of this institution.
Want to try it yourself? AI Dungeon is a text-based adventure game powered in part by GPT-3.
Why GPT-3 is a big deal
GPT-3’s uncanny abilities as a satirist, poet, composer, and customer service agent aren’t actually the biggest part of the story. On its own, GPT-3 is an impressive proof of concept. But the concept it’s proving has bigger ramifications.
For a long time, we’ve assumed that creating computers that have general intelligence — computers that surpass humans at a wide variety of tasks, from programming to researching to having intelligent conversations — will be difficult to make and will require detailed understanding of the human mind, consciousness, and reasoning. And for the last decade or so, a minority of AI researchers have been arguing that we’re wrong, that human-level intelligence will arise naturally once we give computers more computing power.
GPT-3 is a point for the latter group. By the standards of modern machine-learning research, GPT-3’s technical setup isn’t that impressive. It uses an architecture from 2018 — meaning, in a fast-moving field like this one, it’s already out of date. The research team largely didn’t fix the constraints on GPT-2, such as its small window of “memory” for what it has written so far, which many outside observers criticized.
“GPT-3 is terrifying because it’s a tiny model compared to what’s possible, trained in the dumbest way possible,” Branwen tweeted.
That suggests there’s potential for a lot more improvements that will one day make GPT-3 look as shoddy as GPT-2 now does by comparison.
GPT-3 is a piece of evidence on a topic that has been hotly debated among AI researchers: Can we get transformative AI systems, ones that surpass human capabilities in many key areas, just using existing deep learning techniques? Is human-level intelligence something that will require a fundamentally new approach, or is it something that emerges of its own accord as we pump more and more computing power into simple machine learning models?
These questions won’t be settled for another few years at least. GPT-3 is not a human-level intelligence even if it can, in short bursts, do an uncanny imitation of one.
Skeptics have argued that those short bursts of uncanny imitation are driving more hype than GPT-3 really deserves. They point out that if a prompt is not carefully designed, GPT-3 will give poor-quality answers — which is absolutely the case, though that ought to guide us toward better prompt design, not give up on GPT-3.
They also point out that a program that is sometimes right and sometimes confidently wrong is, for many tasks, much worse than nothing. (There are ways to learn how confident GPT-3 is in a guess, but even while using those, you certainly shouldn’t take the program’s outputs at face value.) They also note that other language models purpose-built for specific tasks can do better on those tasks than GPT-3.
All of that is true. GPT-3 is limited. But what makes it so important is less its capabilities and more the evidence it offers that just pouring more data and more computing time into the same approach gets you astonishing results. With the GPT architecture, the more you spend, the more you get. If there are eventually to be diminishing returns, that point must be somewhere past the $10 million that went into GPT-3. And we should at least be considering the possibility that spending more money gets you a smarter and smarter system.
Other experts have reassured us that such an outcome is very unlikely. As a famous artificial intelligence researcher said earlier this year, “No matter how good our computers get at winning games like Go or Jeopardy, we don’t live by the rules of those games. Our minds are much, much bigger than that.”
Actually, GPT-3 wrote that.
AIs getting smarter isn’t necessarily good news
Narrow AI has seen extraordinary progress over the past few years. AI systems have improved dramatically at translation, games like chess and Go, important research biology questions like predicting how proteins fold, and generating images. AI systems determine what you’ll see in a Google search or in your Facebook News Feed. They compose music and write articles that, at a glance, read as though a human wrote them. They play strategy games. They are being developed to improve drone targeting and detect missiles.
But narrow AI is getting less narrow. Once, we made progress in AI by painstakingly teaching computer systems specific concepts. To do computer vision — allowing a computer to identify things in pictures and video — researchers wrote algorithms for detecting edges. To play chess, they programmed in heuristics about chess. To do natural language processing (speech recognition, transcription, translation, etc.), they drew on the field of linguistics.
But recently, we’ve gotten better at creating computer systems that have generalized learning capabilities. Instead of mathematically describing detailed features of a problem, we let the computer system learn that by itself. While once we treated computer vision as a completely different problem from natural language processing or platform game playing, now we can solve all three problems with the same approaches.
GPT-3 is not the best AI system in the world at question answering, summarizing news articles, or answering science questions. It’s distinctly mediocre at translation and arithmetic. But it is much more general than previous systems; it can do all of these things and more with just a few examples. And AI systems to come will likely be yet more general.
That poses some problems.
Our AI progress so far has enabled enormous advances, but it has also raised urgent ethical questions. When you train a computer system to predict which convicted felons will reoffend, you’re using inputs from a criminal justice system biased against Black people and low-income people, so its outputs will likely be biased against Black and low-income people, too. Making websites more addictive can be great for your revenue but bad for your users. Releasing a program that writes convincing fake reviews or fake news might make those widespread, making it harder for the truth to get out.
Rosie Campbell at UC Berkeley’s Center for Human-Compatible AI argues that these are examples, writ small, of the big worry experts have about AI in the future. The difficulties we’re wrestling with today with narrow AI don’t come from the systems turning on us or wanting revenge or considering us inferior. Rather, they come from the disconnect between what we tell our systems to do and what we actually want them to do.
For example, we tell an AI system to run up a high score in a video game. We want it to play the game fairly and learn game skills, but if it has the chance to directly hack the scoring system, it will do that to achieve the goal we set for it. It’s doing great by the metric we gave it. But we aren’t actually getting what we wanted.
One of the most disconcerting things about GPT-3 is the realization that it’s often giving us what we asked for, not what we wanted.
If you prompt GPT-3 to write you a story with a prompt like “here is a short story,” it will write a distinctly mediocre story. If you instead prompt it with “here is an award-winning short story,” it will write a better one.
Why? Because it trained on the internet, and most stories on the internet are bad, and it predicts text. It isn’t motivated to come up with the best text or the text we most wanted, just the text that seems most plausible. Telling it the story won an award changes what text seems most plausible.
With GPT-3, this is harmless. And though people have used GPT-3 to write manifestos about GPT-3’s schemes to fool humans, GPT-3 is not anywhere near powerful enough to pose the risks that AI scientists warn of.
But someday we may have computer systems that are capable of human-like reasoning. If they’re made with deep learning, they will be hard for us to interpret, and their behavior will be confusing and highly variable, sometimes seeming much smarter than humans and sometimes not so much.
And many AI researchers believe that that combination — exceptional capabilities, goals that don’t represent what we “really want” but just what we asked for, and incomprehensible inner workings — will produce AI systems that exercise a lot of power in the world. Not for the good of humanity, not for vengeance against humanity, but toward goals that aren’t what we want.
Handing over our future to them would be a mistake, but one it’d be easy to make step by step, with each step half an accident.
Related The case for taking AI seriously as a threat to humanity
Will you become our 20,000th supporter? When the economy took a downturn in the spring and we started asking readers for financial contributions, we weren’t sure how it would go. Today, we’re humbled to say that nearly 20,000 people have chipped in. The reason is both lovely and surprising: Readers told us that they contribute both because they value explanation and because they value that other people can access it, too. We have always believed that explanatory journalism is vital for a functioning democracy. That’s never been more important than today, during a public health crisis, racial justice protests, a recession, and a presidential election. But our distinctive explanatory journalism is expensive, and advertising alone won’t let us keep creating it at the quality and volume this moment requires. Your financial contribution will not constitute a donation, but it will help keep Vox free for all. Contribute today from as little as $3.


vox--2020-08-13--Wild, chaotic, and deeply silly, the animated Harley Quinn series is a terrific watch

One Good Thing is Vox ’ s recommendations feature .
In each edition , find one more thing from the world of culture that we highly recommend .
Harley Quinn is one of those characters I always feel like I should appreciate more than I do .
The Joker ’ s former psychiatrist-turned-girlfriend is certainly a lot of fun at her best .
She whiplashes her way through moods like she ’ s on a particularly unstable carnival ride , and she has an acidic sense of humor .
But too often , writers have struggled to capture the balance @ @ @ @ @ @ @ work .
( Arguably , nobody has quite done it since Paul Dini and Bruce Timm invented the character for Batman : The Animated Series in 1992 . )
In 2016 ’ s Suicide Squad , Harley ’ s chaotic energy is turned all the way up , but that movie ’ s slipshod filmmaking results in a character who has no core .
She ’ s all wild-child energy and occasional wisecracks , with a healthy dose of the camera ogling Margot Robbie , who plays her .
In 2020 ’ s @ @ @ @ @ @ @ film — Harley moves to the center of the story in a way that ’ s fun but also a bit of a headache .
She keeps interrupting the story ’ s momentum to rewind time and show us what really happened .
And neither of these films capture her skill with a snarky one-liner .
So where do I turn to become a Harley Quinn fan like so many others are ?
The animated Harley Quinn series — which debuted on the DC Universe platform in 2019 and now has both its @ @ @ @ @ @ @ feels like a great start .
It skews far more toward comedy than chaos , but that ’ s just as well .
If there ’ s going to be a character who rips the pages of DC Comics to shreds and reassembles them into a papier-maché sculpture of herself , it ’ s likely Harley .
What ’ s perhaps most surprising about Harley Quinn is that the shows it bears the most resemblance to aren ’ t other superhero shows but , rather , single gals in the big city shows @ @ @ @ @ @ @ .
In the series ’ first episode , Harley and Joker finally break up , and while she keeps trying to win him back , she soon realizes ( with the help of Poison Ivy , her very own Rhoda Morgenstern ) that she ’ s gon na make it after all .
Soon , she ’ s recruiting a crew of low-level supervillains and getting involved in elaborate schemes and heists .
The real fun , however , is in the way Harley and her crew interact with each other .
King @ @ @ @ @ @ @ around on land — might be able to bite off people ’ s heads , but he ’ s also a social media maven .
Clayface is a method actor who keeps developing new characters to turn himself into , a skill that Harley only cares about insofar as it can get her access to places where there ’ s stuff she might steal .
The series also takes place in a goofily skewed version of the DC Expanded Universe , full of superheroes , their arch-nemeses , and lots and lots of @ @ @ @ @ @ @ make great comedic targets .
( Condiment King and Kite Man have far larger presences than you might expect , given how ridiculous their names are . )
Everything about it reflects a deep understanding of both comics lore and sitcom lore , and watching it reminds me of a 10-year-old idly watching TV Land and reading comics at the same time .
( Do kids still do this ? )
The “ deep understanding of both comics lore and sitcom lore ” piece of the show has its roots in the team @ @ @ @ @ @ @ Halpern , Patrick Schumacker , and Dean Lorey .
Halpern and Schumacker , in particular , have been funny writers waiting for the right vehicle for a bit .
Both have bounced around a series of shows that almost worked but either got canceled or never quite pulled it together ( such as the short-lived “ regular office workers in a world of superheroes ” series Powerless ) .
Harley Quinn captures their strength at crafting great jokes and endlessly flinging them at you .
But they have also created a series that @ @ @ @ @ @ @ See , Harley Quinn is sneakily one of TV ’ s queerest shows .
It ’ s heavily implied that Batman and the Joker won ’ t kill each other because they just might be in love with each other , while the relationship between Harley and Ivy at first nods toward the romantic chemistry fans have long read into the two characters , then falls headfirst into it .
I won ’ t tell you how this all resolves , but the will-they/won ’ t-they between Harley and Ivy in the show @ @ @ @ @ @ @ my favorites in a long while .
It ’ s impressive for how their storyline immediately understands that , yeah , Harley and Ivy would kind of be into each other , and that doesn ’ t have to be salacious or out of the ordinary .
They just are .
And , look , there are only 26 episodes of Harley Quinn ( so far , hopefully ) , and they ’ re all a little over 20 minutes long .
Watching all of them won ’ t take you that long @ @ @ @ @ @ @ skip to the show ’ s second season ( when it gets more overtly queer ) , you won ’ t have to do a lot of research to figure out what ’ s happening .
It turns out the only thing I needed to truly love Harley Quinn was to see that she , too , could be another single gal in the city , getting over a bad breakup , trying to have it all .
Who knew ?
Harley Quinn ’ s two seasons are streaming on HBO Max .
@ @ @ @ @ @ @ Universe , but given recent layoffs at WarnerMedia , that service doesn ’ t seem long for this world .
Will you become our 20,000th supporter ?
When the economy took a downturn in the spring and we started asking readers for financial contributions , we weren ’ t sure how it would go .
Today , we ’ re humbled to say that nearly 20,000 people have chipped in .
The reason is both lovely and surprising : Readers told us that they contribute both because they value explanation and because they @ @ @ @ @ @ @ , too .
We have always believed that explanatory journalism is vital for a functioning democracy .
That ’ s never been more important than today , during a public health crisis , racial justice protests , a recession , and a presidential election .
But our distinctive explanatory journalism is expensive , and advertising alone won ’ t let us keep creating it at the quality and volume this moment requires .
Your financial contribution will not constitute a donation , but it will help keep Vox free for all .
Contribute @ @ @ @ @ @ @ .

One Good Thing is Vox’s recommendations feature. In each edition, find one more thing from the world of culture that we highly recommend.
Harley Quinn is one of those characters I always feel like I should appreciate more than I do. The Joker’s former psychiatrist-turned-girlfriend is certainly a lot of fun at her best. She whiplashes her way through moods like she’s on a particularly unstable carnival ride, and she has an acidic sense of humor. But too often, writers have struggled to capture the balance between laughter and chaos that makes Harley work. (Arguably, nobody has quite done it since Paul Dini and Bruce Timm invented the character for Batman: The Animated Series in 1992.)
In 2016’s Suicide Squad, Harley’s chaotic energy is turned all the way up, but that movie’s slipshod filmmaking results in a character who has no core. She’s all wild-child energy and occasional wisecracks, with a healthy dose of the camera ogling Margot Robbie, who plays her. In 2020’s Birds of Prey — a much better film — Harley moves to the center of the story in a way that’s fun but also a bit of a headache. She keeps interrupting the story’s momentum to rewind time and show us what really happened. And neither of these films capture her skill with a snarky one-liner.
So where do I turn to become a Harley Quinn fan like so many others are? The animated Harley Quinn series — which debuted on the DC Universe platform in 2019 and now has both its current seasons available on HBO Max — feels like a great start. It skews far more toward comedy than chaos, but that’s just as well. If there’s going to be a character who rips the pages of DC Comics to shreds and reassembles them into a papier-maché sculpture of herself, it’s likely Harley.
What’s perhaps most surprising about Harley Quinn is that the shows it bears the most resemblance to aren’t other superhero shows but, rather, single gals in the big city shows, particularly The Mary Tyler Moore Show. In the series’ first episode, Harley and Joker finally break up, and while she keeps trying to win him back, she soon realizes (with the help of Poison Ivy, her very own Rhoda Morgenstern) that she’s gonna make it after all. Soon, she’s recruiting a crew of low-level supervillains and getting involved in elaborate schemes and heists.
The real fun, however, is in the way Harley and her crew interact with each other. King Shark — a man-sized shark who walks around on land — might be able to bite off people’s heads, but he’s also a social media maven. Clayface is a method actor who keeps developing new characters to turn himself into, a skill that Harley only cares about insofar as it can get her access to places where there’s stuff she might steal.
The series also takes place in a goofily skewed version of the DC Expanded Universe, full of superheroes, their arch-nemeses, and lots and lots of side characters taken just seriously enough to make great comedic targets. (Condiment King and Kite Man have far larger presences than you might expect, given how ridiculous their names are.) Everything about it reflects a deep understanding of both comics lore and sitcom lore, and watching it reminds me of a 10-year-old idly watching TV Land and reading comics at the same time. (Do kids still do this?)
The “deep understanding of both comics lore and sitcom lore” piece of the show has its roots in the team that developed it: sitcom vets Justin Halpern, Patrick Schumacker, and Dean Lorey. Halpern and Schumacker, in particular, have been funny writers waiting for the right vehicle for a bit. Both have bounced around a series of shows that almost worked but either got canceled or never quite pulled it together (such as the short-lived “regular office workers in a world of superheroes” series Powerless). Harley Quinn captures their strength at crafting great jokes and endlessly flinging them at you. But they have also created a series that has a surprising amount of heart.
See, Harley Quinn is sneakily one of TV’s queerest shows. It’s heavily implied that Batman and the Joker won’t kill each other because they just might be in love with each other, while the relationship between Harley and Ivy at first nods toward the romantic chemistry fans have long read into the two characters, then falls headfirst into it. I won’t tell you how this all resolves, but the will-they/won’t-they between Harley and Ivy in the show’s second season is one of my favorites in a long while. It’s impressive for how their storyline immediately understands that, yeah, Harley and Ivy would kind of be into each other, and that doesn’t have to be salacious or out of the ordinary. They just are.
And, look, there are only 26 episodes of Harley Quinn (so far, hopefully), and they’re all a little over 20 minutes long. Watching all of them won’t take you that long, and if you just want to skip to the show’s second season (when it gets more overtly queer), you won’t have to do a lot of research to figure out what’s happening. It turns out the only thing I needed to truly love Harley Quinn was to see that she, too, could be another single gal in the city, getting over a bad breakup, trying to have it all. Who knew?
Harley Quinn’s two seasons are streaming on HBO Max. You can also watch them on DC Universe, but given recent layoffs at WarnerMedia, that service doesn’t seem long for this world.
Will you become our 20,000th supporter? When the economy took a downturn in the spring and we started asking readers for financial contributions, we weren’t sure how it would go. Today, we’re humbled to say that nearly 20,000 people have chipped in. The reason is both lovely and surprising: Readers told us that they contribute both because they value explanation and because they value that other people can access it, too. We have always believed that explanatory journalism is vital for a functioning democracy. That’s never been more important than today, during a public health crisis, racial justice protests, a recession, and a presidential election. But our distinctive explanatory journalism is expensive, and advertising alone won’t let us keep creating it at the quality and volume this moment requires. Your financial contribution will not constitute a donation, but it will help keep Vox free for all. Contribute today from as little as $3.


vox--2020-08-13--Justice Alito’s jurisprudence of white racial innocence

Earlier this year , the Supreme Court held , 6-3 , in Ramos v. Louisiana that criminal defendants in state court may be convicted only by a unanimous jury .
The practical impact of Ramos is small — until recently , only two states , Louisiana and Oregon , permitted a non-unanimous jury to convict a defendant .
And Louisiana recently amended its constitution to eliminate this practice .
But advocates saw in the ruling a big symbolic change in favor of racial justice .
As the Court ’ s lead opinion pointed out , non-unanimous juries are a practice @ @ @ @ @ @ @ took umbrage with that invocation of racism : Justice Samuel Alito .
His dissent was the latest in a string of opinions bristling at the idea that racism still shapes many policymakers ’ decisions today , and that the legacy of past racism still affects people of color .
In the most noteworthy of those opinions , 2018 ’ s Abbott v. Perez , Alito convinced a majority of his colleagues to write such a strong presumption of white racial innocence into the law governing racial voter discrimination that it is now virtually @ @ @ @ @ @ @ that state lawmakers acted with racist intent .
Alito does not appear driven by broad skepticism of racial issues .
While he has repeatedly lashed out at the mere suggestion that white policymakers may have been motivated by racism , he took a drastically different tone in Ricci v. DeStefano ( 2009 ) .
In that case , Alito wrote a lengthy concurring opinion suggesting that a cohort of mostly white firefighters were denied promotions due to a conspiracy between New Haven Mayor John DeStefano and a local black preacher .
In other @ @ @ @ @ @ @ have been on the receiving end of allegedly racist treatment , Alito preaches that we shouldn ’ t jump to such conclusions ; yet in a case where white people were allegedly harmed , he wasn ’ t so cautious .
With his Ramos opinion , Alito continues to build a distinctive profile as a jurist : He has emerged as the Court ’ s foremost defender of white racial innocence .
Alito scolded his fellow Republican justices for pointing to the legacy of racism Alito sometimes scolds liberals for attributing modern-day policy @ @ @ @ @ @ @ , much of his vitriol was directed at two of his fellow Republican justices .
Indeed , the racist origins of non-unanimous juries were discussed prominently in the Court ’ s lead opinion in Ramos , which was authored by conservative Trump appointee Neil Gorsuch .
In his opinion , Gorsuch offered a brief history of how the practice of allowing non-unanimous juries to decide a defendant ’ s fate is rooted in white supremacy .
The delegates who drafted Louisiana ’ s 1898 constitution , Gorsuch argues , “ sought to undermine @ @ @ @ @ @ @ juries to resolve cases in a 10 to 2 verdict ( the idea was that only a small number of black jurors were likely to serve on the jury in the first place ) .
Gorsuch also argues that Oregon ’ s use of non-unanimous juries “ can be similarly traced to the rise of the Ku Klux Klan and efforts to dilute ‘ the influence of racial , ethnic , and religious minorities on Oregon juries.
’ ” Gorsuch ’ s decision to invoke this dark history produced a livid response from @ @ @ @ @ @ @ injury , the Court tars Louisiana and Oregon with the charge of racism for permitting nonunanimous verdicts , ” Alito writes in the introduction to his dissent .
He adds that “ too much public discourse today is sullied by ad hominem rhetoric , that is , attempts to discredit an argument not by proving that it is unsound but by attacking the character or motives of the argument ’ s proponents , ” and accuses the majority of his colleagues of engaging in such rhetoric .
Alito goes on to make a @ @ @ @ @ @ @ may have originally permitted non-unanimous jury verdicts to advance white supremacy , “ both States readopted their rules under different circumstances in later years.
” Louisiana , for example , originally provided for non-unanimous juries at an 1898 constitutional convention dominated by white supremacists .
But the state “ adopted a new , narrower rule ” at a new constitutional convention in 1974 .
Alito suggests that it is unfair to attribute the results of the 1974 convention to racism , if the delegates to that particular convention were not actually motivated by @ @ @ @ @ @ @ see whether he applies a similarly forgiving rule in a pending case alleging that provision of Montana ’ s 1972 constitution is tainted by 19th-century anti-Catholic bias .
( It ’ s also worth noting that liberal Justice Elena Kagan joined most of Alito ’ s opinion , most likely because Kagan is the Court ’ s most stalwart defender of the idea that the justices should be reluctant to overrule precedents , and Ramos overruled a 1972 decision . )
But Alito ’ s Ramos dissent also fits into a broader pattern @ @ @ @ @ @ @ where there is clear evidence that modern-day lawmakers acted with invidious racial intentions , Alito treats the mere suggestion that anti-black or anti-brown racism may still play a role in policymaking with contempt .
Alito ’ s brand of racial resentment is unique on the Supreme Court It ’ s notable that both Gorsuch and Justice Brett Kavanaugh penned opinions in Ramos that acknowledged what Kavanaugh described as “ the racist origins of the non-unanimous jury.
” And then there ’ s the Court ’ s other white conservative , Chief Justice John @ @ @ @ @ @ @ are animated by his belief that any legal acknowledgment of race is odious , regardless of whether the purpose of a race-conscious law is to foster white supremacy or to tear it down .
“ The way to stop discrimination on the basis of race is to stop discriminating on the basis of race , ” Roberts famously wrote in an opinion arguing that two race-conscious plans to desegregate public schools were unconstitutional .
Roberts ’ s form of color-blindness is often actively hostile to civil rights laws .
Hence his decision in @ @ @ @ @ @ @ later decision in Shelby County v. Holder ( 2013 ) , which struck down much of the Voting Rights Act .
And yet , there is daylight between Roberts and Alito .
Though Roberts joined Alito ’ s opinion in Ramos , he did not join Alito ’ s Ricci concurrence .
And Roberts broke rather sharply with Alito in a recent dispute about whether the Trump administration could add a question to the 2020 census form that would have discouraged many immigrants from participating in the census .
Department of Commerce v. @ @ @ @ @ @ @ Trump administration ’ s attempt to add a question to the 2020 census form asking whether each respondent is a US citizen .
The idea of adding a citizenship question to the main census form is opposed by prominent census experts in both parties .
As top Census officials from the Reagan and Bush I administration warned , adding such a question “ could seriously jeopardize the accuracy of the census , ” because “ people who are undocumented immigrants may either avoid the census altogether or deliberately misreport themselves as legal residents.
@ @ @ @ @ @ @ claim that it added this question to help enforce the Voting Rights Act — a statute this administration has shown little interest in enforcing .
But , while the New York case was pending before the Supreme Court , leaked documents revealed that the administration may have had a very different motive .
A late Republican strategist , Thomas Hofeller , who urged the Trump administration to include a citizenship question on the 2020 Census form , had determined that such a question would ” clearly be a disadvantage to the Democrats ” @ @ @ @ @ @ @ Whites.
” In any event , a 5-4 Supreme Court struck down the citizenship question , with Roberts coming very close to accusing the Trump administration of lying .
The claim that a citizenship question was needed to enforce the Voting Right Act , Roberts concluded , “ rested on a pretextual basis.
” Alito began his dissent with characteristic anger at the idea that anyone would dare accuse the Trump administration of racism .
In a preview of the sort of rhetoric he later deployed in his Ramos dissent , Alito wrote @ @ @ @ @ @ @ our time that the inclusion of a question about citizenship on the census has become a subject of bitter public controversy and has led to today ’ s regrettable decision ” in the opening paragraph of his New York dissent .
For Alito , it was fundamentally wrong to attack “ the decision to place such a question on the 2020 census questionnaire ... as racist.
” Yet , while Alito ’ s jurisprudence of white racial innocence is distinct , Alito ’ s views on race still overlap a great deal with @ @ @ @ @ @ @ triumph of Alito ’ s efforts to write white innocence into the law came in Abbott v. Perez , where Alito wrote the majority opinion .
The facts of Perez are , to say the least , complicated .
In 2011 , Texas ’ s Republican-controlled legislature drew congressional maps that , as a federal court eventually determined , included some districts that were illegally racially gerrymandered .
These maps never took effect , in large part because a different federal court determined that they violated the Voting Rights Act .
That left @ @ @ @ @ @ @ 2012 , the state still had no lawful maps that it could use in its upcoming congressional elections , and the state ’ s primaries for these congressional races were just a few months away .
As a stopgap measure , a federal court in Texas drew interim maps that the state could use in its 2012 elections .
Many of the districts in these hastily drawn interim maps closely resembled the racially gerrymandered districts drawn by the Texas legislature in 2011 .
The court , moreover , emphasized that “ this interim @ @ @ @ @ @ @ the merits of any claims ” that some parts of the map were illegal racial gerrymanders .
The court , in other words , would allow Texas to use imperfect maps for one election only , given the risk that Texas would not be able to hold an election otherwise .
But the court was also equally clear that it might strike down some of the state ’ s racially gerrymandered districts at a later date .
Nevertheless , in 2013 , the Texas legislature passed a new law ratifying these interim maps @ @ @ @ @ @ @ that were still being challenged as racial gerrymanders .
And Alito ’ s Perez opinion held that this new law reenacting the racial gerrymanders should be upheld .
“ The primary question ” in Perez , according to Alito , “ is whether the Texas court erred when it required the State to show that the 2013 Legislature somehow purged the ‘ taint ’ that the court attributed to the defunct and never-used plans enacted by a prior legislature in 2011.
” According to Alito , courts must apply a strong presumption that @ @ @ @ @ @ @ — even under the unusual facts that existed in the Perez case .
“ Whenever a challenger claims that a state law was enacted with discriminatory intent , ” Alito wrote , “ the burden of proof lies with the challenger , not the State.
” Having laid out this standard , Alito then swiftly absolved the Texas legislature of any racial guilt .
“ The only direct evidence brought to our attention suggests that the 2013 Legislature ’ s intent was legitimate , ” Alito wrote in Perez .
“ It wanted @ @ @ @ @ @ @ ’ s districting plans to an end as expeditiously as possible.
” Alito ’ s argument , in other words , is that the 2013 maps weren ’ t enacted to preserve a racial gerrymander ; they were enacted to shut down litigation challenging a racial gerrymander .
And this distinction is sufficient to cleanse the state legislature of any allegation of racism .
It ’ s as if the school districts on the losing end of Brown v Board of Education ( 1954 ) had passed a new law recreating the same @ @ @ @ @ @ @ the Brown litigation , but claimed that these segregated schools should be upheld because the new law had a legitimate purpose — to bring the litigation challenging public school segregation to an end as expeditiously as possible .
Alito sees a racial conspiracy when white people are the alleged victims The common thread animating Alito ’ s opinions in Ramos , New York , and Perez is that he views allegations of racial animus with extreme skepticism .
Someone who reads these cases in isolation , in other words , could easily conclude @ @ @ @ @ @ @ of the sort of color-blindness favored by Chief Justice Roberts .
But then there is Alito ’ s opinion in Ricci v. DeStefano .
Ricci was a difficult case involving the exam that New Haven , Connecticut used to determine which firefighters would be eligible for promotion to lieutenant or captain .
The 2003 exam produced significant racial disparities .
As Justice Ruth Bader Ginsburg laid out in her Ricci dissent , “ the pass rate for African-American candidates was about one-half the rate for Caucasian candidates ” on the lieutenant exam , @ @ @ @ @ @ @ candidates was even lower.
” On the captain exam , “ both African-American and Hispanic candidates passed at about half the rate of their Caucasian counterparts.
” These results led to allegations that the test itself was racially biased , and the city eventually decided to disregard the examinations .
After a cohort of firefighters who performed well on the exam sued , the Supreme Court voted 5-4 to reinstate the tests .
Alito joined the majority , but he also wrote a separate concurring opinion suggesting that the city decided to discard @ @ @ @ @ @ @ good-faith concern that the tests ’ disparate impact on racial minorities arose from a flaw in the test , but because of a conspiracy involving the mayor and a prominent local black activist .
Alito ’ s concurring opinion describes , at length , the relationship between then-Mayor John DeStefano and the Reverend Boise Kimber , whom Alito described as “ a politically powerful New Haven pastor and a self-professed ‘ kingmaker.
’ ” Alito quotes DeStefano ’ s former campaign manager , who described Kimber as “ very good at organizing people @ @ @ @ @ @ @ a result of his ties to labor , his prominence in the religious community and his long-standing commitment to roots , ” and Alito also claims that “ Rev .
Kimber adamantly opposed certification of the test results—a fact that he or someone in the Mayor ’ s office eventually conveyed to the Mayor.
” The implication of Alito ’ s opinion , in other words , is that the tests were scuttled due to a corrupt bargain between the city mayor and a local black activist that DeStefano needed to turn out @ @ @ @ @ @ @ a bit .
His ultimate conclusion is that “ a reasonable jury ” could conclude that the city tossed out the exams due to pressure from Kimber .
But Alito ’ s Ricci opinion shows none of the caution — and certainly none of the anger — that Alito musters when someone suggests that a white policymaker might have been motivated by racism against people of color .
Alito raises his allegations of a racially motivated conspiracy , moreover , despite the fact that there is considerable reason to reject this theory of @ @ @ @ @ @ @ .
Among other things , as Ginsburg points out in her dissent , “ the decision against certification of the exams was made neither by Kimber nor by the mayor and his staff.
” Rather , “ the relevant decision was made by the [ New Haven Civil Service Board ] , an unelected , politically insulated body.
” It is still possible that the Civil Service Board felt pressured to throw out the test by the mayor , and it is equally possible that the mayor acted the way he did due @ @ @ @ @ @ @ s opinion does not reject Alito ’ s theory outright — though it does express a great deal of skepticism .
As mentioned above , Ricci was a tough case .
And it ’ s doubtful we ’ ll ever know with certainty why the city decided to discard the tests .
But if there were plausible reasons to suspect that invidious racial motives played a role in Ricci , there was far more reason to suspect such motives in Abbott v. Perez .
Both cases required judges who were , at the @ @ @ @ @ @ @ that racial animus tainted the government ’ s decisions .
Alito is not that judge .
Every day at Vox , we aim to answer your most important questions and provide you , and our audience around the world , with information that has the power to save lives .
Our mission has never been more vital than it is in this moment : to empower you through understanding .
Vox ’ s work is reaching more people than ever , but our distinctive brand of explanatory journalism takes resources — particularly during @ @ @ @ @ @ @ Your financial contribution will not constitute a donation , but it will enable our staff to continue to offer free articles , videos , and podcasts at the quality and volume that this moment requires .
Please consider making a contribution to Vox today .

Earlier this year, the Supreme Court held, 6-3, in Ramos v. Louisiana that criminal defendants in state court may be convicted only by a unanimous jury.
The practical impact of Ramos is small — until recently, only two states, Louisiana and Oregon, permitted a non-unanimous jury to convict a defendant. And Louisiana recently amended its constitution to eliminate this practice. But advocates saw in the ruling a big symbolic change in favor of racial justice. As the Court’s lead opinion pointed out, non-unanimous juries are a practice rooted in white supremacy.
One justice took umbrage with that invocation of racism: Justice Samuel Alito. His dissent was the latest in a string of opinions bristling at the idea that racism still shapes many policymakers’ decisions today, and that the legacy of past racism still affects people of color. In the most noteworthy of those opinions, 2018’s Abbott v. Perez, Alito convinced a majority of his colleagues to write such a strong presumption of white racial innocence into the law governing racial voter discrimination that it is now virtually impossible for voting rights plaintiffs to prove that state lawmakers acted with racist intent.
Alito does not appear driven by broad skepticism of racial issues. While he has repeatedly lashed out at the mere suggestion that white policymakers may have been motivated by racism, he took a drastically different tone in Ricci v. DeStefano (2009). In that case, Alito wrote a lengthy concurring opinion suggesting that a cohort of mostly white firefighters were denied promotions due to a conspiracy between New Haven Mayor John DeStefano and a local black preacher.
In other words, when black or brown people have been on the receiving end of allegedly racist treatment, Alito preaches that we shouldn’t jump to such conclusions; yet in a case where white people were allegedly harmed, he wasn’t so cautious.
With his Ramos opinion, Alito continues to build a distinctive profile as a jurist: He has emerged as the Court’s foremost defender of white racial innocence.
Alito scolded his fellow Republican justices for pointing to the legacy of racism
Alito sometimes scolds liberals for attributing modern-day policy outcomes to racism. But in Ramos, much of his vitriol was directed at two of his fellow Republican justices. Indeed, the racist origins of non-unanimous juries were discussed prominently in the Court’s lead opinion in Ramos, which was authored by conservative Trump appointee Neil Gorsuch.
In his opinion, Gorsuch offered a brief history of how the practice of allowing non-unanimous juries to decide a defendant’s fate is rooted in white supremacy. The delegates who drafted Louisiana’s 1898 constitution, Gorsuch argues, “sought to undermine African-American participation on juries” by allowing juries to resolve cases in a 10 to 2 verdict (the idea was that only a small number of black jurors were likely to serve on the jury in the first place).
Gorsuch also argues that Oregon’s use of non-unanimous juries “can be similarly traced to the rise of the Ku Klux Klan and efforts to dilute ‘the influence of racial, ethnic, and religious minorities on Oregon juries.’”
Gorsuch’s decision to invoke this dark history produced a livid response from Alito. “To add insult to injury, the Court tars Louisiana and Oregon with the charge of racism for permitting nonunanimous verdicts,” Alito writes in the introduction to his dissent. He adds that “too much public discourse today is sullied by ad hominem rhetoric, that is, attempts to discredit an argument not by proving that it is unsound but by attacking the character or motives of the argument’s proponents,” and accuses the majority of his colleagues of engaging in such rhetoric.
Alito goes on to make a fair point. Though Louisiana and Oregon may have originally permitted non-unanimous jury verdicts to advance white supremacy, “both States readopted their rules under different circumstances in later years.” Louisiana, for example, originally provided for non-unanimous juries at an 1898 constitutional convention dominated by white supremacists. But the state “adopted a new, narrower rule” at a new constitutional convention in 1974.
Alito suggests that it is unfair to attribute the results of the 1974 convention to racism, if the delegates to that particular convention were not actually motivated by racism. It will be interesting to see whether he applies a similarly forgiving rule in a pending case alleging that provision of Montana’s 1972 constitution is tainted by 19th-century anti-Catholic bias. (It’s also worth noting that liberal Justice Elena Kagan joined most of Alito’s opinion, most likely because Kagan is the Court’s most stalwart defender of the idea that the justices should be reluctant to overrule precedents, and Ramos overruled a 1972 decision.)
But Alito’s Ramos dissent also fits into a broader pattern. In multiple cases, including cases where there is clear evidence that modern-day lawmakers acted with invidious racial intentions, Alito treats the mere suggestion that anti-black or anti-brown racism may still play a role in policymaking with contempt.
Alito’s brand of racial resentment is unique on the Supreme Court
It’s notable that both Gorsuch and Justice Brett Kavanaugh penned opinions in Ramos that acknowledged what Kavanaugh described as “the racist origins of the non-unanimous jury.”
And then there’s the Court’s other white conservative, Chief Justice John Roberts. Roberts’s race opinions are animated by his belief that any legal acknowledgment of race is odious, regardless of whether the purpose of a race-conscious law is to foster white supremacy or to tear it down. “The way to stop discrimination on the basis of race is to stop discriminating on the basis of race,” Roberts famously wrote in an opinion arguing that two race-conscious plans to desegregate public schools were unconstitutional.
Roberts’s form of color-blindness is often actively hostile to civil rights laws. Hence his decision in that school segregation case, and his later decision in Shelby County v. Holder (2013), which struck down much of the Voting Rights Act.
And yet, there is daylight between Roberts and Alito. Though Roberts joined Alito’s opinion in Ramos, he did not join Alito’s Ricci concurrence.
And Roberts broke rather sharply with Alito in a recent dispute about whether the Trump administration could add a question to the 2020 census form that would have discouraged many immigrants from participating in the census. Department of Commerce v. New York (2019) involved the Trump administration’s attempt to add a question to the 2020 census form asking whether each respondent is a US citizen.
The idea of adding a citizenship question to the main census form is opposed by prominent census experts in both parties. As top Census officials from the Reagan and Bush I administration warned, adding such a question “could seriously jeopardize the accuracy of the census,” because “people who are undocumented immigrants may either avoid the census altogether or deliberately misreport themselves as legal residents.”
The Trump administration made the implausible claim that it added this question to help enforce the Voting Rights Act — a statute this administration has shown little interest in enforcing. But, while the New York case was pending before the Supreme Court, leaked documents revealed that the administration may have had a very different motive. A late Republican strategist, Thomas Hofeller, who urged the Trump administration to include a citizenship question on the 2020 Census form, had determined that such a question would ”clearly be a disadvantage to the Democrats” and “advantageous to Republicans and Non-Hispanic Whites.”
In any event, a 5-4 Supreme Court struck down the citizenship question, with Roberts coming very close to accusing the Trump administration of lying. The claim that a citizenship question was needed to enforce the Voting Right Act, Roberts concluded, “rested on a pretextual basis.”
Alito began his dissent with characteristic anger at the idea that anyone would dare accuse the Trump administration of racism. In a preview of the sort of rhetoric he later deployed in his Ramos dissent, Alito wrote that “it is a sign of our time that the inclusion of a question about citizenship on the census has become a subject of bitter public controversy and has led to today’s regrettable decision” in the opening paragraph of his New York dissent. For Alito, it was fundamentally wrong to attack “the decision to place such a question on the 2020 census questionnaire ... as racist.”
No other justice joined Alito’s dissent.
Alito’s racial views have fundamentally reshaped American voting rights law
Yet, while Alito’s jurisprudence of white racial innocence is distinct, Alito’s views on race still overlap a great deal with his fellow Republican justices. The greatest triumph of Alito’s efforts to write white innocence into the law came in Abbott v. Perez, where Alito wrote the majority opinion.
The facts of Perez are, to say the least, complicated. In 2011, Texas’s Republican-controlled legislature drew congressional maps that, as a federal court eventually determined, included some districts that were illegally racially gerrymandered. These maps never took effect, in large part because a different federal court determined that they violated the Voting Rights Act.
That left Texas in a bind. In early 2012, the state still had no lawful maps that it could use in its upcoming congressional elections, and the state’s primaries for these congressional races were just a few months away.
As a stopgap measure, a federal court in Texas drew interim maps that the state could use in its 2012 elections. Many of the districts in these hastily drawn interim maps closely resembled the racially gerrymandered districts drawn by the Texas legislature in 2011. The court, moreover, emphasized that “this interim map is not a final ruling on the merits of any claims” that some parts of the map were illegal racial gerrymanders.
The court, in other words, would allow Texas to use imperfect maps for one election only, given the risk that Texas would not be able to hold an election otherwise. But the court was also equally clear that it might strike down some of the state’s racially gerrymandered districts at a later date.
Nevertheless, in 2013, the Texas legislature passed a new law ratifying these interim maps as its own — including the districts that were still being challenged as racial gerrymanders. And Alito’s Perez opinion held that this new law reenacting the racial gerrymanders should be upheld.
“The primary question” in Perez, according to Alito, “is whether the Texas court erred when it required the State to show that the 2013 Legislature somehow purged the ‘taint’ that the court attributed to the defunct and never-used plans enacted by a prior legislature in 2011.”
According to Alito, courts must apply a strong presumption that lawmakers did not act with racist intent — even under the unusual facts that existed in the Perez case. “Whenever a challenger claims that a state law was enacted with discriminatory intent,” Alito wrote, “the burden of proof lies with the challenger, not the State.”
Having laid out this standard, Alito then swiftly absolved the Texas legislature of any racial guilt. “The only direct evidence brought to our attention suggests that the 2013 Legislature’s intent was legitimate,” Alito wrote in Perez. “It wanted to bring the litigation about the State’s districting plans to an end as expeditiously as possible.”
Alito’s argument, in other words, is that the 2013 maps weren’t enacted to preserve a racial gerrymander; they were enacted to shut down litigation challenging a racial gerrymander. And this distinction is sufficient to cleanse the state legislature of any allegation of racism.
It’s as if the school districts on the losing end of Brown v Board of Education (1954) had passed a new law recreating the same racially segregated schools that were challenged in the Brown litigation, but claimed that these segregated schools should be upheld because the new law had a legitimate purpose — to bring the litigation challenging public school segregation to an end as expeditiously as possible.
Alito sees a racial conspiracy when white people are the alleged victims
The common thread animating Alito’s opinions in Ramos, New York, and Perez is that he views allegations of racial animus with extreme skepticism. Someone who reads these cases in isolation, in other words, could easily conclude that Alito is a particularly strident defender of the sort of color-blindness favored by Chief Justice Roberts.
But then there is Alito’s opinion in Ricci v. DeStefano.
Ricci was a difficult case involving the exam that New Haven, Connecticut used to determine which firefighters would be eligible for promotion to lieutenant or captain. The 2003 exam produced significant racial disparities. As Justice Ruth Bader Ginsburg laid out in her Ricci dissent, “the pass rate for African-American candidates was about one-half the rate for Caucasian candidates” on the lieutenant exam, and the “pass rate for Hispanic candidates was even lower.” On the captain exam, “both African-American and Hispanic candidates passed at about half the rate of their Caucasian counterparts.”
These results led to allegations that the test itself was racially biased, and the city eventually decided to disregard the examinations. After a cohort of firefighters who performed well on the exam sued, the Supreme Court voted 5-4 to reinstate the tests.
Alito joined the majority, but he also wrote a separate concurring opinion suggesting that the city decided to discard the exams, not because of a good-faith concern that the tests’ disparate impact on racial minorities arose from a flaw in the test, but because of a conspiracy involving the mayor and a prominent local black activist. Alito’s concurring opinion describes, at length, the relationship between then-Mayor John DeStefano and the Reverend Boise Kimber, whom Alito described as “a politically powerful New Haven pastor and a self-professed ‘kingmaker.’”
Alito quotes DeStefano’s former campaign manager, who described Kimber as “very good at organizing people and putting together field operations, as a result of his ties to labor, his prominence in the religious community and his long-standing commitment to roots,” and Alito also claims that “Rev. Kimber adamantly opposed certification of the test results—a fact that he or someone in the Mayor’s office eventually conveyed to the Mayor.”
The implication of Alito’s opinion, in other words, is that the tests were scuttled due to a corrupt bargain between the city mayor and a local black activist that DeStefano needed to turn out votes.
Alito’s concurrence hedges a bit. His ultimate conclusion is that “a reasonable jury” could conclude that the city tossed out the exams due to pressure from Kimber. But Alito’s Ricci opinion shows none of the caution — and certainly none of the anger — that Alito musters when someone suggests that a white policymaker might have been motivated by racism against people of color.
Alito raises his allegations of a racially motivated conspiracy, moreover, despite the fact that there is considerable reason to reject this theory of why the city tossed out the tests. Among other things, as Ginsburg points out in her dissent, “the decision against certification of the exams was made neither by Kimber nor by the mayor and his staff.” Rather, “the relevant decision was made by the [New Haven Civil Service Board], an unelected, politically insulated body.”
It is still possible that the Civil Service Board felt pressured to throw out the test by the mayor, and it is equally possible that the mayor acted the way he did due to pressure from Kimber. Ginsburg’s opinion does not reject Alito’s theory outright — though it does express a great deal of skepticism. As mentioned above, Ricci was a tough case. And it’s doubtful we’ll ever know with certainty why the city decided to discard the tests.
But if there were plausible reasons to suspect that invidious racial motives played a role in Ricci, there was far more reason to suspect such motives in Abbott v. Perez. Both cases required judges who were, at the very least, open to the possibility that racial animus tainted the government’s decisions.
Alito is not that judge.