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Podcast: AI finds its voice



Podcast: AI finds its voice

Today’s voice assistants are still a far cry from the hyper-intelligent thinking machines we’ve been musing about for decades. And it’s because that technology is actually the combination of three different skills: speech recognition, natural language processing and voice generation.

Each of these skills already presents huge challenges. In order to master just the natural language processing part? You pretty much have to recreate human-level intelligence. Deep learning, the technology driving the current AI boom, can train machines to become masters at all sorts of tasks. But it can only learn one at a time. And because most AI models train their skillset on thousands or millions of existing examples, they end up replicating patterns within historical data—including the many bad decisions people have made, like marginalizing people of color and women.

Still, systems like the board-game champion AlphaZero and the increasingly convincing fake-text generator GPT-3 have stoked the flames of debate regarding when humans will create an artificial general intelligence—machines that can multitask, think, and reason for themselves. In this episode, we explore how machines learn to communicate—and what it means for the humans on the other end of the conversation. 

 We meet:

  • Susan C. Bennett, voice of Siri
  • Cade Metz, The New York Times
  • Charlotte Jee, MIT Technology Review


This episode was produced by Jennifer Strong, Emma Cillekens, Anthony Green, Karen Hao and Charlotte Jee. We’re edited by Michael Reilly and Niall Firth.


[TR ID]  

Jim: I don’t know if it was AI… If they had taken the recording of something he had done… and were able to manipulate it… but I’m telling you, it was my son. 

Strong: The day started like any other for a man.. we’re going to call Jim. He lives outside Boston. 

And by the way… he has a family member who works for MIT.

We’re not going to use his last name because they have concerns about their safety.

Jim: It was a Tuesday or Wednesday morning, nine o’clock I’m deep in thought working on something, 

Strong: That is … until he received this call. 

Jim: The phone rings… and I pick it up and it’s my son. And he is clearly agitated. This, this kid’s a really chill guy but when he does get upset, he has a number of vocal mannerisms. And this was like, Oh my God, he’s in trouble.

And he basically told me, look, I’m in jail, I’m in Mexico. They took my phone. I only have 30 seconds. Um, they said I was drinking, but I wasn’t and people are hurt. And look, I have to get off the phone, call this lawyer and it gives me a phone number and has to hang up.

Strong: His son is in Mexico… and there’s just no doubt in his mind… it’s him.

Jim: And I gotta tell you, Jennifer, it, it was him. It was his voice. It was everything. Tone. Just these little mannerisms, the, the pauses, the gulping for air, everything that you could imagine.

Strong: His heart is in his throat…

Jim: My hair standing on edge 

Strong: So, he calls that phone number… A man picks up… and he offers more details on what’s going on.

Jim: Your son is being charged with hitting this car. There was a pregnant woman driving whose arm was broken. Her daughter was in the back seat.. is in critical condition and they are, um, they booked him with driving under the influence. We don’t think that he has done that. This is we’ve, we’ve come across this a number of times before, but the most important thing is to get him out of jail, get him safe, as fast as possible.

Strong: Then the conversation turns to money… he’s told bail has been set… and he needs to put down ten percent.

Jim: So as soon as he started talking about money, you know, the, the flag kind of went up and I said, excuse me, is there any chance that this is a scam of some sort? And he got really kind of, um, irritated. He’s like, “Hey, you called me. Look, I find this really offensive that you’re accusing me of something.” And then my heart goes back in my throat. I’m like, this is the one guy who’s between my son and even worse jail. So I backtracked… 


My wife walks in 10 minutes later and says, well, you know, I was texting with him late last night. Like this is around the time probably that he would have been arrested and jailed. So, of course we text him, he’s just getting up. He’s completely fine. 

Strong: He’s still not sure how someone captured the essence of his son’s voice. But he has some theories…

Jim: They had to have gotten a recording of something when he was upset. That’s the only thing that I can say, cause they couldn’t have mocked up some of these things that he does.. They couldn’t guess at that. I don’t think, and so they, I think they had certainly some raw material to work with and then what they did with it from there. I don’t know.

Strong:  And it’s not just Jim who’s unsure… We have no idea whether AI had anything to do with this. 

But, the point is… we now live in a world where we also can’t be sure that it didn’t. 

It’s incredibly easy to fake someone’s voice with even a few minutes of recordings… and teenagers like Jim’s son? They share countless recordings through social media posts and messages…  

Jim: …was quite impressed with how good it was. Um, like I said, I’m not easily fooled and man, they had it nailed. So, um, just caution.

Strong: I’m Jennifer Strong and this episode we look at what it takes to make a voice.


Zeyu Gin: You guys have been making weird stuff online.

Strong: Zeyu Jin is a research scientist at Adobe… This is him speaking  at a company conference about five years ago… showing how software can rearrange the words in this recording.

Key: I jumped on the bed and I kissed my dogs and my wife—in that order.

Zeyu: So how about we mess with who he actually kissed. // Introducing Project VoCo. Project VoCo allows you to edit speech in text. So let’s bring it up. So I just load this audio piece in VoCo. So as you can see we have the audio waveform and we have the text under it. //

So what do we do? Copy paste. Oh! Yeah it’s done. Let’s listen to it. 

Key: And I kissed my wife and my dogs.

Zeyu: Wait there’s more. We can actually type something that’s not here.

Key: And I kissed Jordan and my dogs.

Strong: Adobe never released this prototype… but the underlying technology keeps getting better.

For example, here’s a computer-generated fake of podcaster Joe Rogan from 2019… It was produced by Square’s AI lab called Dessa to raise awareness about the technology.

Rogan: 10-7 “Friends I’ve got something new to tell all of you. I’ve decided to sponsor a hockey team made up entirely of chimps.” 

Strong: While it sounds like fun and games… experts warn these artificial voices could make some types of scams a whole lot more common. Things like what we heard about earlier.

Mona Sedky: Communication focused crime has historically been lower on the totem pole. 

Strong: That’s federal Prosecutor Mona Sedky speaking last year at the Federal Trade Commission about voice cloning technologies.

Mona Sedky: But now with the advent of things like deep fake video…  now deep fake audio you… you can basically have anonymizing tools and be anywhere on the internet you want to be…. anywhere in the world… and communicate anonymously with people. So as a result there has been an enormous uptick in communication focused crime. 

Balasubramaniyan: But imagine if you as a CFO or chief controller gets a phone call that comes from your CEO’s phone number. 

Strong: And this is Pindrop Security CEO Vijay Balasubramaniyan at a security conference last year.

Balasubramaniyan: It’s completely spoofed… so it actually uses your address book, and it shows up as your CEOs name……and then on the other end you hear your CEO’s voice with a tremendous amount of urgency. And we are starting to see crazy attacks like that. There was an example that a lot of press media covered, which is a $220,000 wire that happened because a CEO of a UK firm thought he was talking to his parent company… so he then sent that money out. But we’ve seen as high as $17 million dollars go out the door. 

Strong: And the very idea of fake voices… can be just as damaging as a fake voice itself… Like when former President Donald Trump tried to blame the technology for some offensive things he said that were caught on tape. 

But like any other tech… it’s not inherently good or bad… it’s just a tool… and I used it in the trailer for season one to show what the technology can do.

Strong: If “seeing is believing”… 

How do we navigate a world where we can’t trust our eyes… or ears? 

And so you know… what you’re listening to… It’s not just me speaking.  I had some help from an artificial version of my voice… filling in words here and there.

Meet synthetic Jennifer. 

Synthetic Jennifer: “Hi there, folks!”

Strong: I can even click to adjust my mood…  

Synthetic Jennifer: “Hi there.”

Strong: Yeah, let’s not make it angry..

Strong: In the not so distant future this tech will be used in any number of ways… for simple tweaks to pre-recorded presentations… even… to bring back the voices of animated characters from a series… 

In other words, artificial voices are here to stay. But they haven’t always been so easy to make… and I called up an expert whose voice might sound familiar.. 

Bennet: How does this sound? Um, maybe I could be a little more friendly. How are you? 

Hi, I’m Susan C Bennet, the original voice of Siri. 

Well, the day that Siri appeared, which was October 4th, 2011, a fellow voice actor emailed me and said, ‘Hey, we’re playing around with this new iPhone app, isn’t this you?’ And I said, what? I went on the Apple site and listened… and yep. That was my voice. [chuckles]

Strong: You heard that right. The original female voice that millions associate with Apple devices…? Had no idea. And, she wasn’t alone. The human voices behind other early voice assistants were also taken by surprise. 

Bennet: Yeah, it’s been an interesting thing. It was an adjustment at first as you can imagine, because I wasn’t expecting it. It was a little creepy at first, I’ll have to say, I never really did a lot of talking to myself as Siri, but gradually I got accepting of it and actually it ended up turning into something really positive so…

Strong: To be clear, Apple did not steal Susan Bennett’s voice. For decades, she’s done voice work for companies like McDonald’s and Delta Airlines… and years before Siri came out …she did a strange series of recordings that fueled its development.

Bennet:  In 2005, we couldn’t have imagined something like Siri or Alexa. And so all of us, I’ve talked to other people who’ve had the same experience, who have been a virtual voice, you know we just thought we were doing just generic phone voice messaging. And so when suddenly Siri appeared in 2011, it’s like, I’m who, what, what is this? So, it was a genuine surprise, but I like to think of it as we were just on the cutting edge of this new technology. So, you know, I choose to think of it as a very positive thing, even though, we, none of us, were ever paid for the millions and millions of phones that our voices are heard on. So that’s, that’s a downside.

Strong: Something else that’s awkward… she says Apple never acknowledged her as the American voice of Siri … that’s despite becoming an accidental celebrity… reaching millions.

Bennet: The only actual acknowledgement that I’ve ever had is via Siri. If you ask Siri, who is Susan Bennett, she’ll say, I’m the original voice of Siri. Thanks so much Siri. Appreciate it. 

Strong: But it’s not the first time she’s given her voice to a machine. 

Bennet: In the late seventies when they were introducing ATMs I like to say it was my first experience as a machine, and you know, there were no personal computers or anything at that time and people didn’t trust machines. They wouldn’t use the ATMs because they didn’t trust the machines to give them the right money. They, you know, if they put money in the machine they were afraid they’d never see it again. And so a very enterprising advertising agency in Atlanta at the time called McDonald and Little decided to humanize the machine. So they wrote a jingle and I became the voice of Tilly the all time teller and then they ultimately put a little face on the machine.

Strong:  The human voice helps companies build trust with consumers…  

Bennet: There are so many different emotions and meanings that we get across through the sound of our voices rather than just in print. That’s why I think emojis came up because you can’t get the nuances in there without the voice. And so I think that’s why voice has become such an important part of technology.

Strong:  And in her own experience, interactions with this synthetic version of her voice have led people to trust and confide in her… to call her a friend, even though they’ve never met her.

Bennet: Well, I think the oddest thing about being the voice of Siri, to me is when I first revealed myself it was astounding to me how many people considered Siri their friend or some sort of entity that they could really relate to. I think they actually in many cases think of her as human.

Strong: It’s estimated the global market for voice technologies will reach nearly 185-billion dollars this year…and AI-generated voices? are a game changer. 

Bennet: You know, after years and years of working on these voices, it’s really, really hard to get the actual rhythm of the human voice. And I’m sure they’ll probably do it at some point, but you will notice even to this day, you know, you’ll listen to Siri or Alexa or one of the others and they’ll be talking along and it sounds good until it doesn’t, is like, Oh, I’m going to the store. You know, there’s some weirdness in the rhythmic sense of it. 

Strong: But even once human-like voices become commonplace…she’s not entirely sure that will be a good thing.  

Bennet:  But you know, the advantage for them is they don’t really have to get along with Siri. They can just tell Siri what to do if they don’t like what she says, they can just turn it off. So it is not like real human relations. It’s like maybe what people would like human relations to be. Everybody does what I want. (laughter) Then everybody’s happy. Right?

Strong: Of course, voice assistants like Siri and Alexa aren’t just voices. Their capabilities come from the AI behind the scenes too.

It’s been explored in science fiction films like this one, called Her… about a man who falls in love with his voice assistant.

Theodore: How do you work?

Samantha (AI): Well… Basically I have intuition. I mean.. The DNA of who I am is based on the millions of personalities of all the programmers who wrote me, but what makes me me is my ability to grow through my experiences. So basically in every moment I’m evolving, just like you.

Strong: But today’s voice assistants are a far cry from the hyper-intelligent thinking machines we’ve been musing about for decades. 

And it’s because that technology… is actually many technologies. It’s the combination of three different skills…speech recognition, natural language processing and voice generation.

Speech recognition is what allows Siri to recognize the sounds you make and transcribe them into words. Natural language processing turns those words into meaning…and figures out what to say in response. And voice generation is the final piece…the human element…that gives Siri the ability to speak.

Each of these skills is already a huge challenge… In order to master just the natural language processing part? You pretty much have to recreate human-level intelligence.

And we’re nowhere near that. But we’ve seen remarkable progress with the rise of deep learning… helping Siri and Alexa be a little more useful.

Metz: What people may not know about Siri is that original technology was something different.

Strong: Cade Metz is a tech reporter for The New York Times. His new book is called Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World. 

Metz: The way that Siri was originally built… You had to have a team of engineers, in a room, at their computers and piece by piece, they had to define with computer code how it would recognize your voice. 

Strong: Back then… engineers would spend days writing detailed rules meant to show machines how to recognize words and what they mean.

And this was done at the most basic level… often working with just snippets of voice at a time.

Just imagine all the different ways people can say the word “hello” … or all the ways we piece together sentences … explaining why “time flies” or how some verbs can also be nouns. 

Metz: You can never piece together everything you need, no matter how many engineers you have no matter how rich your company is. Defining every little thing that might happen when someone speaks into their iPhone… You just don’t have enough person-power to build everything you need to build. It’s just too complicated. 

Strong: Neural networks made that process a whole lot easier… They simply learn by recognizing patterns in data fed into the system. 

Metz: You take that human speech… You give it to the neural network… And the neural network learns the patterns that define human speech. That way it can recreate it without engineers having to define every little piece of it. The neural network literally learns the task on its own. And that’s the key change… is that a neural network can learn to recognize what a cat looks like, as opposed to people having to define for the machine what a cat looks like.

Strong: But even before neural networks… Tech companies like Microsoft aimed to build systems that could understand the everyday way people write and talk.

And in 1996, Microsoft hired a linguist … Chris Brocket… to begin work on what they called natural language AI.

Metz: The guy’s not a computer scientist, but what his job was was to define the way that language is pieced together, right. For a computer. And that is just an incredibly difficult task, right? Why do we as English speakers order our words, the way we do, right? And he, he spent years, literally years, five or six years at Microsoft, you know, slowly, you know, trying to tell the computer the way that English is, is put together. So then the computer can do that.

Strong: Then, one afternoon in 2003… a small group at Microsoft… down the hall from Brockett… started work on a new project. They were building a system that translated languages using a technique based on statistics. 

The idea being if a set of words in one language appeared with the same frequency and context in another, that was the likely translation. 

Metz: They put together a prototype in a matter of weeks and showed it off to a group at the Microsoft research center—including Chris Brocket. 

Strong: The system is… pretty cobbled together. It only works when applied to pieces of a sentence… And even then… the translations were jumbled. 

Metz: As he sees them demonstrate this.. he has a panic attack to the point where he literally thinks he’s having a heart attack because he realizes that his career might be over. That everything he has spent the past six years on // is pointless and has been made pointless by the system that these guys built in a matter of weeks. 

Strong: At that time we didn’t have the amount of data needed to train a neural network, nor the processing power… but the idea of one has been around since the 1980s.

And one of those ideas came in the form of NetTalk…which was developed by AI pioneer Terry Sejnowski. 

The system could learn to speak words on its own by studying children’s books. 

Metz: Terry had this incredible demo that he would show to people at conferences. It was sort of time-lapsed because it took a while for the neural network to learn, but he could show that as it started to analyze the patterns in these children’s books, they could start to babble…

[Sounds from NetTalk Demo]

Metz: and then it could babble a little better, and then it could start to piece words together, and then suddenly it could pronounce these words. 

[Sounds from NetTalk Demo]

Metz: He could show his audience // with this demo, how a neural network could learn.  

Strong: It would be another 2 decades before the computing power existed to really make this useful..   

Metz: So natural language was an area where even after the success of neural networks with speech and image, people thought, Oh, well, it’s not going to work with natural language. Well, it has. That doesn’t mean it’s perfect. 

Strong: Deep learning, (the technology driving the current AI boom), can train machines to become masters at all sorts of tasks. But it can only learn things one at a time. And because most AI models train their skillset on thousands or millions of examples, they end up repeating patterns found in old data—including the many bad decisions that people have made, like marginalizing people of color and women.

And any big advances stir up this debate about when humans will create an artificial general intelligence—or machines that can multitask, think, and reason for themselves. Recently, that’s been advances like the board-game champion AlphaZero… and the increasingly convincing fake-text generator GPT-3…

Metz: It can, it can generate blog posts. It can generate tweets, emails. It can generate computer programs. You know, it works maybe half the time, but when it does work, you cannot tell the difference between its English and your English. Okay. That is progress. It’s not the brain, it’s not even close, but it’s progress.

Strong: And these and other tools are also… incredibly divisive. 

Metz: Can we, in the near future, build a system that can do anything the human brain can do. Right. And people will argue about this, like foaming at the mouth on either side. The reality is we have no idea. Like there are people who are completely sure this is going to happen pretty soon, but they don’t know what the path is there. None of us can predict the future. And so it’s an argument about nothing that can be fundamentally decided. So of course the argument never ends. You go back to the fifties and it’s, it’s all the same stuff, right?

Strong: But if we are to someday replicate that intelligence… might we also be able to replicate ourselves? 

…That’s after the break. 


[Music transition]

Strong: Artificial voices have been around for a while…but they didn’t start getting more human-like until really the last five years.

Like when Deepmind’s text-to-speech algorithm called WaveNet came onto the scene… which is the basis of Google’s assistant Duplex…the one that can book your hair appointment or restaurant reservation.

[Sounds of Google Duplex scheduling a hair appointment on user’s behalf] 

*Phone ringing*

Stylist: “Hello. How can I help you?”

AI: “Hi. I’m calling to book a women’s haircut for a client. Umm.. I’m looking for something on May 3rd.”

Stylist: “Sure. Give me one second..” 

Strong: Since then, many other companies have worked to commercialize similar voice technologies. Lyrebird…Descript…Respeecher…just to name a few. 

There’s also a slew of companies geared toward preserving the memory of our loved ones… by creating interactive, digital versions of them. 

One company.. called HereAfter.. turns them into chatbots that can be used with voice assistants. 

And Tech Review reporter Charlotte Jee tried it out with her parents…

Jee: Alexa, open, HereAfter.

Synthetic voice: Welcome to HereAfter. Would you rather speak with Paul or with Jane? 

Jee: I’d rather speak with Jane.

Virtual Jane: Hello, this is Jane Jee, and I’m happy to tell you about my life.


Virtual Jane: How are you today?

Jee:  Uh I’m well, thanks mum. How are you?

Virtual Jane: Good. 

Strong: The system’s listening skills… aren’t the best. You can only ask short, simple questions… and you have to wait until the system has finished speaking before you can ask a new question. 

Virtual Jane: There’s so much to talk about. My childhood career and my interests, which of those sounds best?

Jee:  Uh… your childhood. 

Virtual Jane: Sure. I got into trouble as a child because I was very independent and I liked to exercise my freedom…

Strong:  But what you’re hearing now… isn’t actually a fake or synthetic version of Charlotte’s mother. In fact, this experience relies on surprisingly less AI than you’d think. 

Her parents were interviewed for hours by HereAfter with questions supplied by Charlotte and her sister. That interview was then edited and broken into topic sections.. which can be brought up and played by the system based on the questions they ask. 

But.. as we’ve seen.. voice is powerful. Especially when it’s presented as an interactive experience. 

Jee: Oh my God. (laughter) That was so weird!

That was like hearing my mom.. as a machine. That was really freaky. 

I felt more emotional listening to that than I kind of expected to? When, like, the voice relaxed and it sounded like her.

Strong: This feels a lot like something we’ve seen before. Like in an episode of Black Mirror…  where a woman uses her partner’s smartphone data to create a synthetic version of his voice after he dies. 

[Sounds from Black Mirror – AI sifting through shared media, montage of audio clips from the woman’s deceased partner] 

Strong: It sifts through old videos, texts, voicemails, and social media posts to build a system capable of mimicking his voice.. and personality.  

AI: “Hello?”

Woman: “…Hello! You… sound just like him..” 

AI: “Almost creepy isn’t it? I say creepy…. I mean, it’s totally batshit crazy I can even talk to you. I mean…I don’t even have a mouth.”
Woman: “Thats…That’s just…

AI: “That’s what?”

Woman: “That’s just the sort of thing he would say.”

AI: “Well…that’s why I said it.” 

Strong: Which brings up a thorny issue… is she building trust with her AI partner … or is it just telling her what she wants to hear… ?

And beyond how we might develop voice technologies capable of common sense or self-improvement… lies yet another question we’re just starting to raise… which is..… how do we reckon with this newfound power… to synthesize something as personal as someone’s voice? 


Strong: Next episode… We look at the role of automation on our credit. 

Michele Gilman: The witness for the state who was a nurse, couldn’t explain anything about the algorithm. She just kept repeating over and over that it was internationally and statistically validated, but she couldn’t tell us how it worked, what data was fed into it, what factors it weighed, how the factors were weighed. And so my student attorney looks at me and we’re looking at each other thinking, how do we cross examine an algorithm…

Strong: This episode was made by me, Emma Cillekens, Anthony Green, Karen Hao and Charlotte Jee. We’re edited by Michael Reilly and Niall Firth.

Thanks for listening, I’m Jennifer Strong. 



The Download: Algorithms’ shame trap, and London’s safer road crossings




This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How algorithms trap us in a cycle of shame

Working in finance at the beginning of the 2008 financial crisis, mathematician Cathy O’Neil got a firsthand look at how much people trusted algorithms—and how much destruction they were causing. Disheartened, she moved to the tech industry, but encountered the same blind faith. After leaving, she wrote a book in 2016 that dismantled the idea that algorithms are objective. 

O’Neil showed how every algorithm is trained on historical data to recognize patterns, and how they break down in damaging ways. Algorithms designed to predict the chance of re-arrest, for example, can unfairly burden people, typically people of color, who are poor, live in the wrong neighborhood, or have untreated mental-­health problems or addictions.

Over time, she came to realize another significant factor that was reinforcing these inequities: shame. Society has been shaming people for things they have no choice or voice in, such as weight or addiction problems, and weaponizing that humiliation. The next step, O’Neill recognized, was fighting back. Read the full story.

—Allison Arieff

London is experimenting with traffic lights that put pedestrians first

The news: For pedestrians, walking in a city can be like navigating an obstacle course. Transport for London, the public body behind transport services in the British capital, has been testing a new type of crossing designed to make getting around the busy streets safer and easier.

How does it work? Instead of waiting for the “green man” as a signal to cross the road, pedestrians will encounter green as the default setting when they approach one of 18 crossings around the city. The light changes to red only when the sensor detects an approaching vehicle—a first in the UK.

How’s it been received? After a trial of nine months, the data is encouraging: there is virtually no impact on traffic, it saves pedestrians time, and it makes them 13% more likely to comply with traffic signals. Read the full story.

—Rachael Revesz

Check out these stories from our new Urbanism issue. You can read the full magazine for yourself and subscribe to get future editions delivered to your door for just $120 a year.

– How social media filters are helping people to explore their gender identity.
– The limitations of tree-planting as a way to mitigate climate change.

Podcast: Who watches the AI that watches students?

A boy wrote about his suicide attempt. He didn’t realize his school’s software was watching. While schools commonly use AI to sift through students’ digital lives and flag keywords that may be considered concerning, critics ask: at what cost to privacy? We delve into this story, and the wider world of school surveillance, in the latest episode of our award-winning podcast, In Machines We Trust.

Check it out here.

ICYMI: Our TR35 list of innovators for 2022

In case you missed it yesterday, our annual TR35 list of the most exciting young minds aged 35 and under is now out! Read it online here or subscribe to read about them in the print edition of our new Urbanism issue here.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 There’s now a crazy patchwork of abortion laws in the US
Overturning Roe has triggered a legal quagmire—including some abortion laws that contract others within the same state. (FT $)
+ Protestors are doxxing the Supreme Court on TikTok. (Motherboard)
+ Planned Parenthood’s abortion scheduling tool could share data. (WP $)
+ Here’s the kind of data state authorities could try to use to prosecute. (WSJ $)
+ Tech firms need to be transparent about what they’re asked to share. (WP $)
+ Here’s what people in the trigger states are Googling. (Vox)

2 Chinese students were lured into spying for Beijing
The recent graduates were tasked with translating hacked documents. (FT $)
+ The FBI accused him of spying for China. It ruined his life. (MIT Technology Review)

3 Why it’s time to adjust our expectations of AI
Researchers are getting fed up with the hype. (WSJ $)
+ Meta still wants to build intelligent machines that learn like humans, though. (Spectrum IEEE)
+ Yann LeCun has a bold new vision for the future of AI. (MIT Technology Review)
+ Understanding how the brain’s neurons really work will aid better AI models. (Economist $)

4 Bitcoin is facing its biggest drop in more than 10 years
The age of freewheeling growth really is coming to an end. (Bloomberg $)
+ The crash is a threat to funds worth millions stolen by North Korea. (Reuters)
+ The cryptoapocalypse could worsen before it levels out. (The Guardian)
+ The EU is one step closer towards regulating crypto. (Reuters)

5 Singapore’s new online safety laws are a thinly-veiled power grab
Empowering its authoritarian government to exert even greater control over civilians. (Rest of World)

6 Recommendations algorithms require effort to work properly
Telling them what you like makes it more likely it’ll present you with decent suggestions. (The Verge)

7 China’s on a mission to find an Earth-like planet
But what they’ll find is anyone’s guess. (Motherboard)
+ The ESA’s Gaia probe is shining a light on what’s floating in the Milky Way. (Wired $) 

8 Inside YouTube’s meta world of video critique
Video creators analyzing other video creators makes for compelling watching. (NYT $)
+ Long-form videos are helping creators to stave off creative burnout. (NBC)

9 Time-pressed daters are vetting potential suitors over video chat
To get the lay of the land before committing to an IRL meet-up. (The Atlantic $)

10 How fandoms shaped the internet
For better—and for worse. (New Yorker $)

Quote of the day

“This is no mere monkey business.”

—A lawsuit filed by Yuga Labs, the creators of the Bored Ape NFT collection, against conceptual artists Ryder Ripps, claims Ripps copied their distinctive simian artwork, Gizmodo reports.

The big story

This restaurant duo want a zero-carbon food system. Can it happen?

September 2020

When Karen Leibowitz and Anthony Myint opened The Perennial, the most ambitious and expensive restaurant of their careers, they had a grand vision: they wanted it to be completely carbon-neutral. Their “laboratory of environmentalism in the food world” opened in San Francisco in January 2016, and its pièce de résistance was serving meat with a dramatically lower carbon footprint than normal. 

Myint and Leibowitz realized they were on to something much bigger—and that the easiest, most practical way to tackle global warming might be through food. But they also realized that what has been called the “country’s most sustainable restaurant” couldn’t fix the broken system by itself. So in early 2019, they dared themselves to do something else that nobody expected. They shut The Perennial down. Read the full story.

—Clint Rainey

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ A look inside the UK’s blossoming trainspotting scene (don’t worry, it’s nothing to do with the Irvine Welsh novel of the same name.)
+ This is the very definition of a burn.
+ A solid science joke.
+ This amusing Twitter account compiles some of the strangest public Spotify playlists out there (Shout out to Rappers With Memory Problems)
+ Have you been lucky enough to see any of these weird and wonderful buildings in person?

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The US Supreme Court just gutted the EPA’s power to regulate emissions



The US Supreme Court just gutted the EPA’s power to regulate emissions

What was the ruling?

The decision states that the EPA’s actions in a 2015 rule, which included caps on emissions from power plants, overstepped the agency’s authority.

“Capping carbon dioxide emissions at a level that will force a nationwide transition away from the use of coal to generate electricity may be a sensible ‘solution to the crisis of the day,’” the decision reads. “But it is not plausible that Congress gave EPA the authority to adopt on its own such a regulatory scheme.”

Only Congress has the power to make “a decision of such magnitude and consequence,” it continues. 

This decision is likely to have “broad implications,” says Deborah Sivas, an environmental law professor at Stanford University. The court is not only constraining what the EPA can do on climate policy going forward, she adds; this opinion “seems to be a major blow for agency deference,” meaning that other agencies could face limitations in the future as well.

The ruling, which is the latest in a string of bombshell cases from the court, fell largely along ideological lines. Chief Justice John Roberts authored the majority opinion, and he was joined by his fellow conservatives: Justices Samuel Alito, Amy Coney Barrett, Neil Gorsuch, Brett Kavanaugh, and Clarence Thomas. Justices Stephen Breyer, Elena Kagan, and Sonia Sotomayor dissented.

What is the decision all about?

The main question in the case was how much power the EPA should have to regulate carbon emissions and what it should be allowed to do to accomplish that job. That question was occcasioned by a 2015 EPA rule called the Clean Power Plan.

The Clean Power Plan targeted greenhouse-gas emissions from power plants, requiring each state to make a plan to cut emissions and submit it to the federal government.

Several states and private groups immediately challenged the Clean Power Plan when it was released, calling it an overreach on the part of the agency, and the Supreme Court put it on hold in 2016. After a repeal of the plan during Donald Trump’s presidency and some legal back-and-forth, a Washington, DC, district court ruled in January 2021 that the Clean Power Plan did fall within the EPA’s authority.

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How to track your period safely post-Roe



How to track your period safely post-Roe

3. After you delete your app, ask the app provider to delete your data. Just because you removed the app from your phone does not mean the company has gotten rid of your records. In fact, California is the only state where they are legally required to delete your data. Still, many companies are willing to delete it upon request. Here’s a helpful guide from the Washington Post that walks you through how you can do this.

Here’s how to safely track your period without an app.

1. Use a spreadsheet. It’s relatively easy to re-create the functions of a period tracker in a spreadsheet by listing out the dates of your past periods and figuring out the average length of time from the first day of one to the first day of the next. You can turn to one of the many templates already available online, like the period tracker created by Aufrichtig and the Menstrual Cycle Calendar and Period Tracker created by Laura Cutler. If you enjoy the science-y aspect of period apps, templates offer the ability to send yourself reminders about upcoming periods, record symptoms, and track blood flow.

2. Use a digital calendar. If spreadsheets make you dizzy and your entire life is on a digital calendar already, try making your period a recurring event, suggests Emory University student Alexa Mohsenzadeh, who made a TikTok video demonstrating the process

Mohsenzadeh says that she doesn’t miss apps. “I can tailor this to my needs and add notes about how I’m feeling and see if it’s correlated to my period,” she says. “You just have to input it once.” 

3. Go analog and use a notebook or paper planner. We’re a technology publication, but the fact is that the safest way to keep your menstrual data from being accessible to others is to take it offline. You can invest in a paper planner or just use a notebook to keep track of your period and how you’re feeling. 

If that sounds like too much work, and you’re looking for a simple, no-nonsense template, try the free, printable Menstrual Cycle Diary available from the University of British Columbia’s Centre for Menstrual Cycle and Ovulation Research.

4. If your state is unlikely to ban abortion, you might still be able to safely use a period-tracking app. The crucial thing will be to choose one that has clear privacy settings and has publicly promised not to share user data with authorities. Quintin says Clue is a good option because it’s beholden to EU privacy laws and has gone on the record with its promise not to share information with authorities. 

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