10 Revolutionary Technologies of 2018

Since 2001, we have annually chosen what we call the 10 Breakthrough Technologies. People often ask what exactly we mean by “breakthrough”—a fair question, as some picks are not yet widely adopted while others are on the verge of commercial availability. We are looking for a technology, or a set of technologies, that will have a profound effect on our lives.

This year, a new AI technique called GANs gives machines imagination; artificial embryos, despite thorny ethical constraints, are redefining how life can be created and opening a research window into the early moments of human life; and a pilot plant in the heart of Texas’s petrochemical industry is attempting to produce completely clean power from natural gas—likely a major energy source for the foreseeable future. These and the rest of our list are worth watching. —The Editors

This story is part of our March/April 2018 Issue

3-D Metal Printing

Derek Brahney

3-D printing has existed for decades but remained largely in the realm of hobbyists and designers creating one-off prototypes. Printing objects with materials other than plastics—especially metal—was expensive and painfully slow.

Now, however, it is becoming cheap and easy enough to be a potentially practical way of manufacturing parts. If widely adopted, it could transform how we mass-produce many products.

3-D Metal Printing

• Breakthrough: Printers can now make metal objects quickly and cheaply.
• Why It Matters: The ability to produce large and complex metal objects on demand could revolutionize manufacturing.
• Key Players: Markforged, Desktop Metal, GE
• Availability: Now

In the short term, manufacturers wouldn’t need to keep large inventories—they could simply print an object, such as a replacement part for an aging car, whenever needed.

In the longer term, large factories mass-producing a limited range of parts might be replaced by smaller ones making a wider variety, adapting to changing customer needs.

The technology can create lighter, stronger parts and complex shapes impossible with conventional metal fabrication. It also allows more precise control of metal microstructure. In 2017, researchers from Lawrence Livermore National Laboratory announced a 3-D-printing method for stainless-steel parts twice as strong as traditionally made ones.

Also in 2017, Markforged, a small startup based outside Boston, released the first 3-D metal printer for under $100,000.

Another Boston-area startup, Desktop Metal, began shipping its first metal prototyping machines in December 2017. It plans to sell larger manufacturing machines that are 100 times faster than older metal printing methods.

Printing metal parts is also becoming easier. Desktop Metal now offers software that generates designs ready for 3-D printing. Users specify the object’s specs, and the software produces a suitable computer model.

GE, a longtime proponent of 3-D printing in aviation (see “10 Breakthrough Technologies of 2013: Additive Manufacturing”), has a test version of its new metal printer fast enough to make large parts. The company plans to start selling the printer in 2018. —Erin Winick

Artificial Embryos

University of Cambridge

In a breakthrough that redefines how life can be created, embryologists at the University of Cambridge in the UK have grown realistic-looking mouse embryos using only stem cells. No egg. No sperm. Just cells taken from another embryo.

Artificial Embryos

• Breakthrough: Researchers have made embryo-like structures from stem cells alone, without using eggs or sperm, offering a new route to creating life.
• Why It Matters: Artificial embryos will make it easier to study the mysterious beginnings of human life, but they also spark new bioethical debates.
• Key Players: University of Cambridge; University of Michigan; Rockefeller University
• Availability: Now

The researchers placed the cells carefully in a three-dimensional scaffold and watched, fascinated, as they started communicating and lining up into the distinctive bullet shape of a several-day-old mouse embryo.

“We know that stem cells are magical in their powerful potential. We did not realize they could self-organize so beautifully or perfectly,” Magdelena Zernicka-Goetz, who led the team, told an interviewer.

Zernicka-Goetz says her “synthetic” embryos probably couldn’t have grown into mice. Nonetheless, they hint that soon we could have mammals born without an egg at all.

That isn’t Zernicka-Goetz’s goal. She wants to study how early embryo cells begin taking on specialized roles. The next step, she says, is to make an artificial embryo from human stem cells—work being pursued at the University of Michigan and Rockefeller University.

Synthetic human embryos would be a boon to scientists, allowing them to tease apart early developmental events. Since such embryos start with easily manipulated stem cells, labs can use a full range of tools, such as gene editing, to investigate them as they grow.

Artificial embryos, however, pose ethical questions. What if they become indistinguishable from real embryos? How long can they be grown in the lab before they feel pain? Bioethicists say we need to address those questions before the science races ahead. —Antonio Regalado

Sensing City

sidewalk toronto

Numerous smart-city schemes have faced delays, scaled back ambitious goals, or priced out everyone except the super-wealthy. A new project in Toronto called Quayside hopes to change that pattern by rethinking an urban neighborhood from the ground up and rebuilding it around the latest digital technologies.

Sensing City

• Breakthrough: A Toronto neighborhood aims to be the first to successfully integrate cutting-edge urban design with state-of-the-art digital technology.
• Why It Matters: Smart cities could make urban areas more affordable, livable, and environmentally friendly.
• Key Players: Sidewalk Labs and Waterfront Toronto
• Availability: Project announced in October 2017; construction could begin in 2019

Alphabet’s Sidewalk Labs, based in New York City, is collaborating with the Canadian government on the high-tech project, slated for Toronto’s industrial waterfront.

One goal is to base decisions about design, policy, and technology on information from an extensive sensor network gathering data on everything from air quality to noise levels to people’s activities.

The plan calls for all vehicles to be autonomous and shared. Robots will roam underground doing menial chores like delivering mail. Sidewalk Labs says it will open access to the software and systems it creates so other companies can build services on top of them, much like apps for mobile phones.

The company intends to closely monitor public infrastructure, raising concerns about data governance and privacy. But Sidewalk Labs believes it can work with the community and local government to alleviate those worries.

“What’s distinctive about what we’re trying to do in Quayside is that the project is not only extraordinarily ambitious but also has a certain amount of humility,” says Rit Aggarwala, the executive in charge of Sidewalk Labs’ urban-systems planning. That humility may help Quayside avoid pitfalls that plagued previous smart-city initiatives.

Other North American cities are already clamoring to be next on Sidewalk Labs’ list, according to Waterfront Toronto, the public agency overseeing Quayside’s development. “San Francisco, Denver, Los Angeles, and Boston have all called asking for introductions,” says CEO Will Fleissig. —Elizabeth Woyke

AI for Everybody

Miguel Porlan

Artificial intelligence has so far been mainly the plaything of big tech companies like Amazon, Baidu, Google, and Microsoft, as well as some startups. For many other companies and parts of the economy, AI systems are too expensive and too difficult to implement fully.

AI for Everybody

• Breakthrough: Cloud-based AI is making the technology cheaper and easier to use.
• Why It Matters: Currently AI use is dominated by a few companies, but as a cloud service it could become widely available, boosting the economy.
• Key Players: Amazon; Google; Microsoft
• Availability: Now

What’s the solution? Machine-learning tools based in the cloud are bringing AI to a far broader audience. So far, Amazon dominates cloud AI with its AWS subsidiary. Google is challenging that with TensorFlow, an open-source AI library for building machine-learning software. Recently Google announced Cloud AutoML, a suite of pre-trained systems that could make AI simpler to use.

Microsoft, which has its own AI-powered cloud platform Azure, is teaming up with Amazon to offer Gluon, an open-source deep-learning library. Gluon is supposed to make building neural nets—a key AI technology that crudely mimics how the human brain learns—as easy as building a smartphone app.

It is uncertain which company will become the leader in offering AI cloud services, but it is a huge business opportunity for the winners.

These products will be essential if the AI revolution is to spread more broadly through different parts of the economy.

Currently AI is used mostly in the tech industry, where it has created efficiencies and produced new products and services. But many other businesses and industries have struggled to take advantage of AI advances. Sectors such as medicine, manufacturing, and energy could also be transformed if they could implement the technology more fully, with a huge boost to economic productivity.

Most companies, though, still lack enough people who know how to use cloud AI. So Amazon and Google are also setting up consultancy services. Once the cloud puts the technology within reach of almost everyone, the real AI revolution can begin. —Jackie Snow

Dueling Neural Networks

ILLUSTRATION BY DEREK BRAHNEY | DIAGRAM COURTESY OF MICHAEL NIELSEN, “NEURAL NETWORKS AND DEEP LEARNING”, DETERMINATION PRESS, 2015

Artificial intelligence is very good at identifying things: show it a million pictures, and it can tell you with uncanny accuracy which ones depict a pedestrian crossing a street. But AI is hopeless at generating images of pedestrians by itself. If it could, it would create gobs of realistic but synthetic pictures of pedestrians in various settings, which a self-driving car could use to train itself without ever going on the road.

Dueling Neural Networks

• Breakthrough: Two AI systems can spar with each other to create ultra-realistic original images or sounds, something machines have never done before.
• Why It Matters: This gives machines something akin to imagination, which may help them become less reliant on humans—but also turns them into alarmingly powerful tools for digital fakery.
• Key Players: Google Brain, DeepMind, Nvidia
• Availability: Now

The problem is, creating something entirely new requires imagination—and until now that has perplexed AIs.

The solution first occurred to Ian Goodfellow, then a PhD student at the University of Montreal, during an academic argument in a bar in 2014. The approach, known as a generative adversarial network (GAN), takes two neural networks—simplified mathematical models of the human brain that underpin most modern machine learning—and pits them against each other in a digital cat-and-mouse game.

Both networks are trained on the same data set. One, the generator, is tasked with creating variations on images it has already seen—perhaps a picture of a pedestrian with an extra arm. The second, the discriminator, is asked to identify whether the example it sees is like the training images or a fake produced by the generator—basically, is that three-armed person likely to be real?

Over time, the generator becomes so good at producing images that the discriminator can’t spot fakes. Essentially, the generator has learned to recognize and create realistic-looking images of pedestrians.

The technology has become one of the most promising AI advances in the past decade, able to help machines produce results that fool even humans.

GANs have been used to create realistic-sounding speech and photorealistic fake imagery. In one compelling example, researchers from chipmaker Nvidia primed a GAN with celebrity photographs to create hundreds of credible faces of people who don’t exist. Another research group made not-unconvincing fake paintings resembling van Gogh’s works. Pushed further, GANs can reimagine images in different ways—making a sunny road appear snowy, or turning horses into zebras.

The results aren’t always perfect: GANs can conjure up bicycles with two sets of handlebars, or faces with eyebrows in the wrong place. But because the images and sounds are often startlingly realistic, some experts believe GANs are beginning to understand the underlying structure of the world they see and hear. That means AI may gain, along with imagination, a more independent ability to make sense of what it sees. —Jamie Condliffe

Babel-Fish Earbuds

Google

In the cult sci-fi classic The Hitchhiker’s Guide to the Galaxy, you slide a yellow Babel fish into your ear for instant translations. In the real world, Google has come up with an interim solution: a $159 pair of earbuds called Pixel Buds. These work with its Pixel smartphones and Google Translate app to produce practically real-time translation.

Babel-Fish Earbuds

• Breakthrough: Near-real-time translation now works for a large number of languages and is easy to use.
• Why It Matters: In an increasingly global world, language remains a barrier to communication.
• Key Players: Google and Baidu
• Availability: Now

One person wears the earbuds, while the other holds a phone. The earbud wearer speaks in his or her language (English is the default), and the app translates and plays it aloud on the phone. The person holding the phone responds; this response is translated and played through the earbuds.

Google Translate already has a conversation feature, and its iOS and Android apps let two users speak as it automatically identifies languages and translates them. But background noise can make it hard for the app to understand speech and to determine when one person has stopped speaking and it’s time to translate.

Pixel Buds get around these problems because the wearer taps and holds a finger on the right earbud while talking. Splitting the interaction between phone and earbuds gives each person control of a microphone and helps speakers maintain eye contact, since they’re not passing a phone back and forth.

The Pixel Buds were widely panned for subpar design. They do look silly, and they may not fit well in your ears. They can also be hard to set up with a phone.

Clunky hardware can be fixed, though. Pixel Buds show the promise of mutually intelligible communication between languages in close to real time. And no fish required. —Rachel Metz

Zero-Carbon Natural Gas

Miguel Porlan

The world is probably stuck with natural gas as a primary electricity source for the foreseeable future. Cheap and readily available, it now accounts for over 30 percent of US electricity and 22 percent of world electricity. Although cleaner than coal, it remains a massive source of carbon emissions.

A pilot power plant just outside Houston, in the heart of the US petroleum and refining industry, is testing a technology that could make clean energy from natural gas a reality. The company behind the 50-megawatt project, Net Power, believes it can generate power at least as cheaply as standard natural-gas plants and capture essentially all carbon dioxide released in the process.

Zero-Carbon Natural Gas

• Breakthrough: A power plant efficiently and cheaply captures carbon released by burning natural gas, avoiding greenhouse-gas emissions.
• Why It Matters: Around 32 percent of US electricity comes from natural gas, accounting for about 30 percent of the power sector’s carbon emissions.
• Key Players: 8 Rivers Capital; Exelon Generation; CB&I
• Availability: 3 to 5 years

If successful, it would mean the world has a way to produce carbon-free energy from a fossil fuel at a reasonable cost. Such natural-gas plants could be ramped up and down on demand, avoiding the high capital costs of nuclear power and sidestepping the unsteady supply from renewables.

Net Power is a collaboration between technology development firm 8 Rivers Capital, Exelon Generation, and energy construction firm CB&I. The company is commissioning the plant and has begun initial testing. It intends to release results from early evaluations in the months ahead.

The plant puts carbon dioxide from burning natural gas under high pressure and heat, using the resulting supercritical CO2 as the “working fluid” that drives a specially built turbine. Much of the carbon dioxide can be continuously recycled; the rest can be captured cheaply.

A key part of pushing down costs depends on selling that carbon dioxide. Today the main use is in extracting oil from petroleum wells—a limited market and not particularly green. Eventually, however, Net Power hopes to see growing demand for carbon dioxide in cement manufacturing and in making plastics and other carbon-based materials.

Net Power’s technology won’t solve all problems with natural gas, especially on the extraction side. But as long as we use natural gas, we might as well use it as cleanly as possible. Of all clean-energy technologies in development, Net Power’s is one of the furthest along to promise more than a marginal advance in cutting carbon emissions. —James Temple

Perfect Online Privacy

Miguel Porlan

True internet privacy could finally become possible thanks to a new tool that—for instance—lets you prove you’re over 18 without revealing your date of birth, or prove you have enough money in the bank for a financial transaction without revealing your balance or other details. That limits the risk of a privacy breach or identity theft.

Perfect Online Privacy

• Breakthrough: Computer scientists are perfecting a cryptographic tool for proving something without revealing the information underlying the proof.
• Why It Matters: If you need to disclose personal information online, it will be easier to do so without risking your privacy or exposing yourself to identity theft.
• Key Players: Zcash; JPMorgan Chase; ING
• Availability: Now

The tool is an emerging cryptographic protocol called a zero-knowledge proof. Though researchers have worked on it for decades, interest has exploded in the past year, thanks in part to the growing obsession with cryptocurrencies, most of which aren’t private.

Much of the credit for a practical zero-knowledge proof goes to Zcash, a digital currency that launched in late 2016. Zcash’s developers used a method called zk-SNARK (for “zero-knowledge succinct non-interactive argument of knowledge”) to give users the power to transact anonymously.

That’s not normally possible in Bitcoin and most other public blockchain systems, where transactions are visible to everyone. Though theoretically anonymous, these transactions can be combined with other data to track and even identify users. Vitalik Buterin, creator of Ethereum, the world’s second-most-popular blockchain network, has described zk-SNARKs as an “absolutely game-changing technology.”

For banks, this could be a way to use blockchains in payment systems without sacrificing clients’ privacy. Last year, JPMorgan Chase added zk-SNARKs to its own blockchain-based payment system.

For all their promise, though, zk-SNARKs are computation-heavy and slow. They also require a “trusted setup,” creating a cryptographic key that could compromise the whole system if it fell into the wrong hands. But researchers are looking at alternatives that deploy zero-knowledge proofs more efficiently and don’t require such a key. —Mike Orcutt

Genetic Fortune-Telling

Derek Brahney

One day, babies will get DNA report cards at birth. These reports will offer predictions about their chances of suffering a heart attack or cancer, of getting hooked on tobacco, and of being smarter than average.

Genetic Fortune Telling

• Breakthrough: Scientists can now use your genome to predict your chances of getting heart disease or breast cancer, and even your IQ.
• Why It Matters: DNA-based predictions could be the next great public health advance, but they will increase the risks of genetic discrimination.
• Key Players: Helix; 23andMe; Myriad Genetics; UK Biobank; Broad Institute
• Availability: Now

The science making these report cards possible has suddenly arrived, thanks to huge genetic studies—some involving more than a million people.

It turns out that most common diseases and many behaviors and traits, including intelligence, result from many genes acting in concert, not just one or a few. Using data from large ongoing genetic studies, scientists are creating “polygenic risk scores.”

Though the new DNA tests offer probabilities, not diagnoses, they could greatly benefit medicine. For example, if women at high risk for breast cancer got more mammograms and those at low risk got fewer, those exams might catch more real cancers and set off fewer false alarms.

Pharmaceutical companies can also use the scores in clinical trials of preventive drugs for illnesses like Alzheimer’s or heart disease. By picking volunteers more likely to get sick, they can more accurately test how well the drugs work.

The trouble is, the predictions are far from perfect. Who wants to know they might develop Alzheimer’s? What if someone with a low risk score for cancer puts off screening and then develops cancer anyway?

Polygenic scores are also controversial because they can predict any trait, not just diseases. For instance, they can now forecast about 10 percent of a person’s performance on IQ tests. As the scores improve, DNA IQ predictions will likely become routinely available. But how will parents and educators use that information?

To behavioral geneticist Eric Turkheimer, the chance that genetic data will be used for both good and bad makes the new technology “simultaneously exciting and alarming.” —Antonio Regalado

Materials’ Quantum Leap

jeremy liebman

The prospect of powerful new quantum computers comes with a puzzle. They’ll be capable of feats of computation inconceivable with today’s machines, but we haven’t yet figured out what we might do with those powers.

Materials’ Quantum Leap

• Breakthrough: IBM has simulated the electronic structure of a small molecule using a seven-qubit quantum computer.
• Why It Matters: Understanding molecules in exact detail will allow chemists to design more effective drugs and better materials for generating and distributing energy.
• Key Players: IBM; Google; Harvard’s Alán Aspuru-Guzik
• Availability: 5 to 10 years

One likely and enticing possibility: precisely designing molecules.

Chemists are already dreaming of new proteins for far more effective drugs, novel electrolytes for better batteries, compounds that could turn sunlight directly into liquid fuel, and much more efficient solar cells.

We don’t have these things because molecules are ridiculously hard to model on a classical computer. Try simulating the behavior of electrons in even a relatively simple molecule, and you run into complexities far beyond today’s computers.

But it’s a natural problem for quantum computers, which use “qubits” that are themselves quantum systems instead of digital bits representing 1s and 0s. Recently, IBM researchers used a seven-qubit quantum computer to model a small molecule made of three atoms.

It should become possible to accurately simulate far larger and more interesting molecules as scientists build machines with more qubits and, just as important, better quantum algorithms. —David Rotman

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