The first season of Westworld wasted no time in going from “hey cool, robots!” to “well, that was bleak.” Death, destruction, android torture—it’s all been there from the pilot onward. Then again, on a show about a theme park staffed with sentient robots—sorry, “hosts”—those outcomes are exactly what audiences have come to expect. If science fiction has taught folks anything, it’s that the machines will always rise up against humankind. But why does sci-fi always veer dystopian? Westworld’s creators have a theory.

Storytelling, according to show co-creator Jonathan Nolan, serves an evolutionary purpose, allowing us to try out different realities. With sci-fi, because it’s so often forward-looking, “we’re inventing cautionary tales for ourselves,” Nolan said today at WIRED’s 2017 Business Conference in New York. In other words, when your creations would hurt a fly, it’s time to start worrying.

‘It's not going to be a beautiful lady or a beautiful man who is going to come and be your overlord.’

So does that mean Nolan and his co-creator (and wife) Lisa Joy think Westworld is a foreseeable future? Not entirely. For Nolan, the robots on his show represent more of an allegory for human behavior than a cautionary tale. And Joy sees Westworld, and sci-fi in general, as an opportunity to talk about what humanity could or should do if things start to go wrong, especially now that advancements in artificial intelligence technologies are making things like androids seem far more plausible than before. “We’re leaping into the age of the unfathomable, the time when machines [can do things we can’t],” Joy said.

Even though their show loosely taps into the dystopian trope of the AI uprising, Westworld’s co-creators don’t think the actual AI being developed right now will lead to total apocalypse. Instead, Joy said, little bits of AI—like Amazon predicting your purchasing needs or cars helping you drive home—will take over people’s lives piecemeal. Right now, however, “these technologies don’t have a moral compass yet,” Nolan said, adding that thanks to the algorithms telling people what news they might want to read and what opinions they might want to hear, “we’re in a time of artificial ignorance.”

If and when true androids do arrive, though, don’t expect them to look like the passing-for-human hosts of Westworld, though. “We anthropomorphized what the AI will look like,” Joy says. “That’s not going to happen. It’s not going to be a beautiful lady or a beautiful man who is going to come and be your overlord.”

Andy Rubin is an excellent sharer. The longtime inventor, founder of Android, and current CEO of two companies—tech incubator Playground and gadget maker Essential—believes strongly that the best way to invent the future is to do it together. That’s why he encourages the companies he funds and advises at Playground to share technology, he said today at WIRED’s 2017 Business Conference in New York. “A lot of those building blocks are pretty repetitive,” Rubin said. “So a lot of the work this team does is repeatable.” For the same reason, Rubin’s already sharing details of how Essential plans to organize and orchestrate the smart home, and then help others do the same.

Essential only launched publicly at the end of May, introducing two products: a phone and a smart-home hub. The phone, Rubin told the Business Conference crowd, is a necessary first step for the company. “Your phone is your main screen,” he said. “You probably sleep with your phone two feet away from you on your nightstand.” (Guilty.) The screen in your pocket is your primary point of access for all the communication, work, and time-wasting you do in a day. Rubin wanted to make a great phone, earn space in your pocket, and then start to build new tech atop that platform.

The long-term vision for Essential, Rubin said, is more closely aligned with the Essential Home, which Rubin hopes you’ll put in your kitchen or living room and use to control all the connected devices where you live. Just don’t call it a smart speaker, or compare it to an Amazon Echo. “It’s beginning to explore the relationships of the mixed-mode interface,” Rubin said. “You have a touchscreen and an LCD combined with speech, combined with the other sensors you have in your home like cameras and doorbells and locks.”

The Home’s job is to take your August Smart Lock, your Nest thermostat, your Philips Hue lights, and that Samsung fridge you bought with a huge touchscreen, and make them all work together. Right now, they don’t; they’re all built on different ecosystems and platforms that refuse to talk together. “Everybody’s building islands,” Rubin said. “And they’re expecting people to plug into them. When really what has to happen is—and this happened with Android—these islands need bridges.”

Rubin admitted this is hard work, and a long road. But he’s even starting to think about Essential’s next product. There’s Home, Phone—and maybe soon, Car. Rubin specifically mentioned the potential power of a really smart dashcam anyone can put in their car. Before he gets there, Rubin has a phone and a smart-home hub to ship. And a whole lot of bridges to build.

Jennifer Doudna was sitting in her UC Berkeley office when she got the first call from a reporter asking what she thought about scientists using Crispr to modify embryos. At the time, the embryos in question were monkeys. It was late 2014, and Doudna was just beginning to become the face of Crispr/Cas9—the bacterial enzyme behind today’s gene editing revolution. Since then she has fielded an ongoing avalanche of questions about the implications of her discovery. How it’s going to change the future of everything from medicine to agriculture to energy production. But inevitably the questions always get around to super-babies.

Today, at WIRED’s 2017 Business Conference in New York, it took just a few minutes. Doudna said it was exactly this possibility—Crispr custom-designed human offspring—that made her take a step back from her own research and get involved in public discussions around the technology. For the last few years she’s been speaking to scientists, politicians, and federal regulators around the world about the potential risks and rewards of Crispr. “I think it’s really likely that in the not-too-distant future it will cure genetic disease,” she said. “But globally we need to come up with a consensus on moving forward in a responsible way.”

In 2015, Doudna was part of a broad coalition of leading biologists who agreed to a worldwide moratorium on gene editing to the “germ line,” which is to say, edits that get passed along to subsequent generations. But it’s legally non-binding, and scientists in China have already begun experiments that involve editing the genome of human embryos. Using Crispr to cure inheritable genetic diseases is still a long way off, and fraught with ethical potholes. Which is why Doudna said people who are excited about the possibilities of Crispr shouldn’t look to the clinic for its first big successes, but rather to the farm field.

“When I think about where we are likely to see the biggest impacts in the shortest amount of time, I really think it’s going to be in agriculture,” she said. Plant breeders have always been geneticists at heart. And with the precision and ease of Crispr, identifying and separating out desirable traits has the potential to speed up new crop development by several orders of magnitude. Agro-giants DuPont and Monsanto have invested in Crispr licenses to accelerate their R&D efforts toward creating crops that can withstand changing climates and new disease and pest burdens. In test plots around the world gene edited crops are already growing—from longer-lasting potatoes and flood-resistant rice to drought-hardy corn and mildew-proof wheat, to name just a few.

As a tomato farmer, Doudna was most excited about a paper that came out just last month. In it, scientists from Cold Spring Harbor Laboratory in New York tackled some of the crop’s trickiest modern traits. While wild plants benefit from dropping fruit—it helps seed dispersal—farmers want plants where the fruit stays on, so mechanical pickers have an easier time harvesting them. When breeders found a trait called ‘jointless’ that did keep the fruit on the vine, they rushed to incorporate it into their domesticated tomato varieties. But when they crossed ‘jointless’ into existing tomato breeds, the resulting plants put out all these extra branches, actually diminishing the number of fruits they produced.

Using genetics to trace back 10,000 years of tomato domestication, Cold Spring Harbor researchers discovered which genes led to that weird branching. Then they used Crispr to edit their activity. The result—tomato plants with great yields that don’t drop their fruits.

“For me, that really illustrates the potential for this,” Doudna said. “Crispr allows plant breeders to do things that would have been very difficult, sometimes impossible in the past.”

Google is poised to begin a grand experiment in using machine learning to widen access to healthcare. If it is successful, it could see the company help protect millions of people with diabetes from an eye disease that leads to blindness.

Last year researchers at the search and ads company announced that they had trained image recognition algorithms to detect signs of diabetes-related eye disease roughly as well as human experts. The software examines photos of a patient’s retina to spot tiny aneurisms indicating the early stages of a condition called diabetic retinopathy, which causes blindness if untreated.

At the 2017 WIRED Business Conference in New York City today, a leader of Google’s project said that work has begun on integrating the technology into a chain of eye hospitals in India. The country is one of the many places around the world where a lack of ophthalmologists means many diabetics don’t get the recommended annual screening for diabetic retinopathy, said Lily Peng, a product manager with the Google Brain AI research group.

“This kind of blindness is completely preventable, but because people can’t get screened, half suffer vision loss before they’re detected,” she said, describing the current situation in India. “One of the promises of this technology is being able to make healthcare more accessible.” There are more than 400 million people worldwide with diabetes, including 70 million in India.

Peng, who is an MD, was featured on WIRED’s Next List of 20 tech visionaries creating the future earlier this year.

In India, Google is working with the Aravind Eye Care System, a network of eye hospitals established in the late 1970s and credited with helping reduce the incidence of blindness caused by cataracts in the country. Aravind helped Google develop its retinal screening system by contributing some of the images needed to train its image parsing algorithms. The system uses the same deep learning technique that allows Google’s image search and photo storage service do things like differentiate between dogs, cats, and people.

Google’s paper last year just described the accuracy of that technology when applied to retinal images, not its use in the clinic. Peng said today that Google has just finished a clinical study in India—meaning the technology was used in real patient care—with Aravind. Work is now under way on getting the technology into routine use with patients, she said.

Peng dismissed suggestions that while this technology might be good for patients, it could mean fewer jobs for doctors. She said Google’s algorithms would instead do screening work not being done today due to skills shortages while freeing physicians for more important tasks. “There’s not enough expertise to go, we need to have our specialists working on treating people who are sick,” said Peng.

Visa is one of the most recognized brands in the world. Its logo is synonymous with the plastic forms of payment for which the company is still best known.

The ability to pay for things with a debit card or your smartphone instead of having to carry around cash (or something to barter) is sort of miraculous. For nearly the whole of human history, and in many parts of the world still, economies have been built on the premise of physical mediums of exchange. If Visa’s innovation chief Jim McCarthy has his way, Visa itself may soon become invisible.

“The magic of Uber and Amazon, they made payment kind of disappear,” McCarthy said at the 2017 Wired Business Conference in New York today. (Visa is the conference’s main corporate sponsor.)

Naturally, McCarthy wants Visa to stay at the center of the payments ecosystem, even as the consumer-side of paying for stuff becomes less visible. And to do that, he says, Visa has focused on making it easier for tech companies like Apple and Samsung to tap into Visa’s services.

As an example, when you use something like Apple Pay or Samsung Pay, the software actually creates a unique card number for each of your devices. So if you have an iPhone, a Samsung Gear watch, and a debit card, each one of those has a unique card number tied back to your bank account. If one of your accounts is compromised, new numbers can be created for the devices in the background without you ever having to know about it

Eventually, that that could mean the end of having to manually change your credit card number with every different service every time you get a new card.

To make all that work, the payment apps actually have to communicate with Visa’s servers to generate and process card numbers. It’s not hard to imagine more radical scenarios, like the ability to simply walk into a store, take what you want, and leave without having to worry about the entire payment process. When that finally happens, Visa wants to be there, making all the hard parts of sending and receiving money around the world look easy.

When David Limp thinks about the future of Alexa, the AI assistant he oversees at Amazon, he imagines a world not unlike Star Trek—a future in which you could be anywhere, asking anything, and an ambient computer would be there to fulfill your every need.

“Imagine a world in the not-so-distant future where you could have infinite computing power and infinite storage,” Limp said today at WIRED’s 2017 Business Conference in New York. “If you take off the constrains of servers and building up infrastructure, what could you do?”

Limp, who has worked at Amazon since 2010 as the senior vice president for devices, sees Alexa as a critical part of this future. Already, you can shout “Hey, Alexa,” and get the assistant to tell you the weather forecast, turn off the lights, hail an Uber, or thousands of other things that Amazon and developers have trained it to do. But Limp says there’s still plenty more work to be done before we live in the AI-assisted future he thinks about every day, and much of that effort has to do with training machines to better understand humans.

Since Alexa made its debut in 2014, the virtual assistant has taken lease in dedicated devices like the Echo, Tap, Echo Dot, Echo Look, Echo Show, as well as dozens of other supported devices. All that interaction with humans has given Alexa plenty of voice data to parse through—data that’s helped train the assistant to understand preferences, recognize different accents, even figure out the intent of a request without specific keywords. A year ago, if you’d told Alexa to “order a car,” it wouldn’t have understood what you meant. (What, like, order one from the Amazon Marketplace?) Now, through improved machine learning, Alexa knows what you mean and will prompt you to enable an Uber or Lyft skill so that it can summon your ride.

Of course, Alexa is far from perfect. Limp says one near-future goal would be improving Alexa’s understanding of anaphora—so if you ask, “Who’s the president of the United States?” and then follow up by asking, “How old is he?” Alexa knows you’re still talking about Donald Trump. Amazon is also tinkering with Alexa’s short-term and long-term memory, so that the bot can recall context from yesterday’s conversation as well as the thing you asked it five seconds ago.

Those changes involve a shift toward making devices that aren’t personal but can work for everyone. Think more like a wall clock in the kitchen, which everyone in a household can glance at to get the time, rather than a smartphone, which is designed for one person to use.

“As we design the interfaces for Alexa, whether voice or graphical, it’s about making it ambient and so that anybody can use it,” Limp said on stage. “If you ask for a timer and I ask for a timer, they’re both going to work.”

In a world where devices will surround people all the time, those gadgets will have to understand what humans mean, however they choose to say it. For anyone who uses Alexa, that education is already under way: Every time someone talks to their Echo, the world inches a little bit closer to that Starship Enterprise future Limp imagines.

Urs Hölzle has a big job. As senior vice president of technical infrastructure at Google, he’s in charge of the hundreds of thousands of servers in data centers spread across the planet to power the company’s ever widening range of services.

He’s also the person that the company’s engineers turn to when all that computing power turns out not to be enough.

Today at the 2017 Wired Business Conference in New York, Hölze explained that even with its enormous resources, Google has had to find ways to economize its operations in order to meet its ambitious goals. Most recently, he said, the company was forced to start building its own artificial intelligence chips because the company’s existing infrastructure just wouldn’t cut it.

Around five years ago, Jeff Dean, who ran Google’s artificial intelligence group, realized that his team’s technique for speech recognition was getting really good. So good in fact, that he thought it was ready to move from the lab to the real world by powering Android’s voice-control system.

But when Dean and Hölzle ran the numbers, they realized that if every Android user in the world used about three minutes of voice recognition time per day, Google would need twice as much computing power to handle it all. The world’s largest computing infrastructure, in other words, would have to double in size.

“Even for Google that is not something you can afford, because Android is free, Android speech recognition is free, and you want to keep it free, and you can’t double your infrastructure to do that,” Hölzle says.

What Google decided to do instead, Hölzle said, is create a whole new type of chip specialized exclusively for machine learning. He likens traditional CPU chips to everyday cars—they have to do a lot of things relatively well to make sure you get where you’r going. An AI chip, on the other hand, has to do just one thing exceptionally well.

“What we built was the equivalent of a drag race car, it can only do one thing, go straight as fast as it can,” he says. “Everything else it is really, really bad at, but this one thing it is super good at.”

Google’s custom chips could handle AI tasks far more efficiently than traditional chips, which meant the company could support not just voice recognition, but a broad range of other tasks as well without breaking the bank.

Pattern Recognition

This pattern has repeated itself again and again during Hölzle’s time at Google. He says that when he started at the company in 1999 (he was somewhere between the seventh and 11th employee hired by Google, depending on how you count), Google only had around 50 servers and was straining to support the number of search queries it received each day. But even with $25 million in venture funding, the company couldn’t afford to buy enough ready-made servers to meet its growing demand.

“If we had done it with the machines, the servers, that people were using, professional servers, real servers, that would have blown our $25 million in an instant,” he says. “It really was not an option, so we were forced to look for other ways to do the same thing more cheaply.”

So Hölzle and company built their own servers out of cheap parts. Each individual server was less powerful and reliable than a professional-grade machine, but together the clusters of computers they assembled was more powerful and reliable than what they could purchased otherwise. Google didn’t invent the idea of using big clusters of cheap machines in lieu of more expensive hardware—that honor might go to the nearly forgotten search engine Inktomi—but it did popularize the model by proving that it could work on a massive scale.

Hölzle and his team had to do something similar years later when it found that off-the-shelf networking gear no longer met its needs. So few companies needed switches that could support the number machines Google had that no established networking company was interested in producing them. So, once again, Hölzle and his team had to build their own gear—something that other companies, like Facebook, now do as well.

“These decisions become a lot easier if all the other alternatives are non-viable,” Hölzle says. “It’s not necessarily that we’re somehow bolder or more insightful, but it’s actually that for many of these things in our history, it was almost a forced choice, you didn’t really have a viable alternative that you could buy.”

But Hölzle probably isn’t giving himself enough credit. Most people, after exhausting all the viable options, would conclude that their task is impossible. When Hölzle ran out of options, he created new ones.

Yasmin Green leads a team at Google’s parent company with an audacious goal: solving the thorniest geopolitical problems that emerge online. Jigsaw, where she is the head of research and development, is a think tank within Alphabet tasked with fighting the unintended unsavory consequences of technological progress. Green’s radical strategy for tackling the dark side of the web? Talk directly to the humans behind it.

That means listening to fake news creators, jihadis, and cyber bullies so that she and her team can understand their motivations, processes, and goals. “We look at censorship, cybersecurity, cyberattacks, ISIS—everything the creators of the internet did not imagine the internet would be used for,” Green said today at WIRED’s 2017 Business Conference in New York.

Last week, Green traveled to Macedonia to meet with peddlers of fake news, those click-hungry opportunists who had such a sway over the 2016 presidential election in the US. Her goal was to understand the business model of fake news dissemination so that she and her team can create algorithms to identify the process and disrupt it. She learned that these content farms utilize social media and online advertising—the same tools used by legit online publishers. “[The problem of fake news] starts off in a way that algorithms should be able to detect,” she said. Her team is now working on a tool that could be shared across Google as well as competing platforms like Facebook and Twitter to thwart that system.

‘It’s mostly good people making bad decisions who join violent extremist groups.’

Along with fake news, Jigsaw is intensely focused on combatting online pro-terror propaganda. Last year, Green and her team travelled to Iraq to speak directly to ex-ISIS recruits. The conversations led to a tool called the Redirect Method, which uses machine learning detect extremist sympathies based on search patterns. Once detected, the Redirect Method serves these users videos that show the ugly side of ISIS—a counternarrative to the allure of the ideology. At the point that they are buying a ticket to join the caliphate, she said, it was too late.

“It’s mostly good people making bad decisions who join violent extremist groups,” Green says. “So the job was: let’s respect that these people are not evil and they are buying into something and lets use the power of targeted advertising to reach them, the people who are sympathetic but not sold.”

Since its launch last year, 300,000 people have watched videos served up by the Redirect Method—a total of more than half a million minutes, Green said.

Beyond fake news and extremism, Green’s team has also created a tool to target toxic speech in comment sections on news organizations’ sites. They created Perspective, a machine-learning algorithm that uses context and sentiment training to detect potential online harassment and alert moderators to the problem. The beta version is being used by the likes of the New York Times. But as Green explained, it’s a constantly evolving tool. One potential worry is that it could be itself biased against certain words, ideas, even tones of speech. To counteract that risk, Jigsaw decided not to open up the API to allow others to set the parameters themselves, fearing that an authoritarian regime might use the tool for full-on censorship.

“We have to take measures to keep these tools from being misused,” she said. Just like the internet itself, which has been used in destructive ways its creators could never have imagined, Green is aware that the solutions her team creates could also be abused. That risk is always on her mind, she says. But it’s not a reason to stop trying.