In this post, we take a look at MIT Technology Review’s 2018 list. By breakthrough, this list includes some picks that haven’t reached widespread use yet, while others are on the cusp of becoming available commercially. Each of these technologies are seen as having a profound effect on our lives.
The 2018 list includes: 3D metal printing, artificial embryos, sensing city, AI for everyone, dueling neural networks, babel fish earbuds, zero-carbon natural gas, perfect online privacy, genetic fortune telling, materials’ quantum leap.
3D Metal Printing:
Breakthrough: Now printers can make metal objects quickly and cheaply.
Why it Matters: The ability to make large and complex metal objects on demand could transform manufacturing.
Key Players: Markforged, Desktop Metal, GE
3D printing has been around for years, but it has remained mostly with hobbyists and designers that produce one-off prototypes. Plus, printing objects with anything other than plastics has been expensive and very slow.
Now, it’s becoming cheap and is easy enough that it could potentially be a practical way to manufacture parts. If this new technique is widely adopted, it could change how mass-production of products is done.
Short-term: manufacturers don’t need large inventories. They can print an object whenever someone needs it. Long term: large mass-producing factories that make a limited range of parts could be replaced by smaller ones that have a wide variety of parts.
This new technology is able to create lighter, stronger parts that have complex shapes; which isn’t possible with conventional metal production methods. Plus, it can have more precise control of microstructure of metals. Last year, researchers from the Lawrence Livermore National Laboratory broadcasted that they developed a 3D printing method for creating stainless-steel parts that are twice as strong as ones made traditionally.
Also last year, 3D printing company Markforged, released the first 3D metal printer for under $100K.
Desktop Metal began to ship their first metal prototyping machines in December 2017. They plan to sell larger machines, that are designed for the manufacturing industry, that are 100x faster than older printing methods.
Printing metal objects is getting easier. Desktop Metal offers software that generates designs that are ready to be 3D printed. The user tells the program the specs of the object they wish to print, and the software produces a computer model that is suitable for printing.
GE, which is a company that has been a major supporter of 3D printing in aviation products, has a test version of a new metal printer. The printer is fast enough to make large parts. The company is planning to sell these sometime this year.
Breakthrough: Without using eggs or sperm cells, researchers have made an embryo-like structure from stem cells, providing a whole new route to creating life.
Why it Matters: Artificial embryos will make it easier for researchers to study the mysterious beginnings of a human life, but they’re fuelling new bioethical debates.
Key Players: University of Cambridge, University of Michigan, Rockefeller University
UNIVERSITY OF CAMBRIDGE
This is a breakthrough that redefines how life can be created. Scientists at the University of Cambridge have grown realistic-looking mouse embryos using just stem cells ; cells that are from another embryo.
Researchers knew that stem cells are magical in their potential of what they can do, but researchers didn’t realize that stem cells could self-organize to create something like this.
Researcher Magdelena Zernicka-Goetz says the synthetic embryos probably count have grown into mice. But, this is still a hint that soon we could have mammals born without an egg at all.
Zernicka-Goetz’s goal though is to study how the cells of an early embryo begin taking on specific roles. Then, she says the next step would be to make an artificial embryo out of human stem cells. This is already being pursued by the University of Michigan and Rockefeller University.
These synthetic human embryos would be a huge development for scientists as they would be able to learn more about the early stages in development. Plus, since these embryos would be manipulated stem cells, labs would be able to apply a range of tools, for example gene editing, to investigate them as they grow.
But, there is another point of view to this technology. These artificial embryos pose ethical questions. What if they turn out to be unrecognizable from real embryos? How long can they be grown in a lab before pain can be felt? These questions need to be answered before scientists race ahead much further.
Breakthrough: A Toronto neighborhood aims to be the first place 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, Waterfront Toronto
Availability: Project announced October 2017.
Construction could begin in 2019. This new project, Quayside, is hoping to change the pattern of failures that many smart-cities have run into (ex, delays, dialed down ambitious goals, priced out everyone except the wealthy). This is to be achieved by rethinking an urban neighborhood from the ground up and rebuilding it around the latest digital technologies.
Sidewalk Labs is collaborating with the Canadian government on this high-tech project for Toronto’s waterfront.
One goal for the project is to base decisions about design, policy, and technology on information from an extensive network of sensors. These sensors would gather data on everything from the air quality to people’s activities.
The plan also calls for all vehicles to be autonomous and shared. Robots will wander underground doing tedious chores like delivering mail. Sidewalk Labs says open access to the software and systems that are being created so that other organizations can build services on top of them (like building apps for cell phones).
Public infrastructure will be closely monitored by Sidewalk Labs, which raises a few concerns regarding data governance and privacy. But, the company believe that it can work with the Toronto community and government to reduce these worries.
What makes the Quayside project distinctive is that it’s not extraordinarily ambitious but also has humility. The humility could help Quayside avoid pitfalls that smart-cities of the past have fallen into.
Other North American cities are requesting to be next on the list from Sidewalk Labs (ex, San Francisco, Denver, LA, Boston).
AI for Everyone:
Breakthrough: Cloud-based AI is making the technology cheaper and easier to use.
Why it Matters: Right now, the use of AI is dominated by relatively few companies. But, as a cloud-based service, it could be widely available to many more, giving the economy a boost.
Key Players: Amazon, Google, Microsoft
For many companies, AI has been too expensive and difficult to fully implement. So, what’s the solution to bring AI to more companies? Machine-learning tools that are based in the cloud are bringing AI to a broader audience. Right not, Amazon dominates cloud AI with the AWS subsidiary. Google is competing against that with TensorFlow; which is an open-source AI library that can be used to build machine-learning software. Also, Google recently announced Cloud AutoML – a suite that includes pre-trained systems that could make AI easier to use.
Microsoft Azure, an AI-powered cloud platform, is working with Amazon to offer Gluon, an open-source deep-learning library. Gluon is supposed to make building neural nets—a key technology in AI that mimics how the human brain learns—as easy as building a smartphone app.
Although it’s uncertain which of these companies will become the leader in offering AI cloud services, it’s a huge business opportunity. These new products will be crucial if the AI revolution is going to spread through different parts of the economy.
Right now, AI is mostly used in the tech industry, where efficiencies are produced, and new products and services have been created. But many businesses and industries have had a hard time taking advantage of these advances. Industries such as medicine, manufacturing, and energy change if they implemented the technology more, with a huge boost to the economy.
Most companies don’t have enough personnel who know how to use cloud AI. So, Amazon and Google are setting up consultancy services. Once the cloud has the technology within reach for almost everyone, the real AI revolution can start.
Dueling Neural Networks:
Breakthrough: Two AI systems can fight with each other to create ultra-realistic original images or sounds, something machines have never been able to do before.
Why it Matters: Gives machines something similar to a sense of imagination, which may help them to be less reliant on humans—but also turns them into alarmingly powerful tools for digital fakery.
Key Players: Google Brain, DeepMind, Nvidia
ILLUSTRATION BY DEREK BRAHNEY | DIAGRAM COURTESY OF MICHAEL NIELSEN, “NEURAL NETWORKS AND DEEP LEARNING”, DETERMINATION PRESS, 2015
AI is getting very good at recognizing things: show it a million pictures, and it will be able tell you what ones show a pedestrian crossing a street. But AI is horrible at generating images of pedestrians by itself. If AI could do that, it would be able to create piles of realistic but artificial pictures showing pedestrians in various settings, which means that a self-driving car could train itself without ever going on the road.
The problem is, creating something entirely new requires imagination—and until now that has puzzled AIs.
One solution occurred to Ian Goodfellow, a then PhD student at the University of Montreal, during an academic argument in a bar in 2014. The approach, known as a generative adversarial network, or GAN, takes two neural networks—the simplified mathematical models of the human brain that reinforce modern machine learning—and puts them against each other in a digital game of cat-and-mouse.
Both networks are trained on the same data set. One network, the generator, is responsible for creating alternatives on images it has seen already. The second network, the discriminator, is responsible for identifying whether the image it sees is like the ones it has been trained on or if it’s a fake produced by the generator.
Over time, the generator can become so good at making images that the discriminator can’t spot fakes. Essentially, the generator has been taught to recognize, and then create realistic-looking images.
This technology has become one of the most promising advances in AI in the past decade, to help machines produce results that fool even humans.
GANs have been put to use creating realistic-sounding speech and photorealistic fake imagery. For example, researchers from Nvidia prepared a GAN with photographs of celebrities to create hundreds of credible faces of people who don’t exist. Another research group made questionable fake paintings that look like van Gogh artwork. Pushed further, GANs can reimagine images in different ways. For example, making a sunny road appear snowy, or turning horses into zebras.
The results from GANs aren’t always perfect. But since the images and sounds are often realistic, some believe GANs are beginning to understand the basic structure of the world they see and hear. Which means AI may gain an independent ability to make sense of what it sees in the world.
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 is still a barrier to communication.
Key Players: Google, Baidu
Maybe (or maybe not you’ve seen the movie), The Hitchhiker’s Guide to the Galaxy, and remember that you slide a yellow Babel fish into your ear to get translations in an instant. In our real world, Google has created a $159 pair of earbuds called Pixel Buds. These work with the Pixel smartphones and the Google Translate app to produce a real-time translation.
One person wears the earbuds, while another holds a phone. The earbud wearer speaks in their language—English is the default—and the app translates the talking and plays it out loud 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 the iOS and Android apps let two users speak as it figures out what languages they’re using and then translates them. But background noise can make it difficult for the app to understand what people are saying and figure out when one person has stopped speaking and it’s time to start translating.
Pixel Buds get around these issues because the wearer taps and holds a finger on the right earbud when talking. Splitting the interaction between the phone and the earbuds gives each person control of a microphone and helps the speakers maintain eye contact instead of passing a phone back and forth.
The Pixel Buds were widely criticized for design. They look silly, and they may not fit well in your ears. Plus, they can be hard to set up with a phone.
But, clunky hardware can be fixed. Pixel Buds show the promise of mutually intelligible communication between languages in close to real time.
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% of electricity in the USA is produced with natural gas, accounting for around 30% of the power sector’s carbon emissions.
Key Players: 8 Rivers Capital, Exelon Generation, CB&I
Availability: 3-5 years
The world is probably stuck with natural gas as one of the primary sources of electricity for the foreseeable future. Cheap and readily available, it now accounts for more than 30% of the electricity in the USA and 22 percent of world electricity. Although it’s cleaner than coal, it’s still 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. The company behind the project, Net Power, believes it can generate power at least as cheaply as standard natural gas plants and essentially capture all the carbon dioxide released in the process.
If so, 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 activated up and down on demand, avoiding the high capital costs of nuclear power and sidestepping the unsteady supply that renewables generally provide.
Net Power is a collaboration between technology development firm 8 Rivers Capital, Exelon Generation, and energy construction firm CB&I. The company is in the process of commissioning the plant and has begun initial testing. It intends to release results from early evaluations in the future.
The plant puts the carbon dioxide released from burning natural gas under high pressure and heat, using the resulting 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 the costs depends on selling the carbon dioxide. Today the main use is in helping to extract oil from petroleum wells. That’s a limited market, and not a particularly green one. 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 the problems with natural gas, particularly on the extraction side. But as long as we’re using natural gas, we might as well use it as cleanly as possible. Of all the clean-energy technologies in development, Net Power’s is one of the furthest along to promise more than a minimal advance in cutting carbon emissions.
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 to get something done online, it will be easier to do so without risking your privacy or exposing yourself to identity theft.
Key Players: Zcash, JPMorgan Chase, ING
True internet privacy could finally become possible thanks to a new tool that can let you prove you’re over 18 without revealing your date of birth, or prove you have enough money in the bank for a transaction without revealing your balance or other details. Which will be able to limit the risk of a privacy breach or identity theft.
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 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 a 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, in which transactions are visible to everyone. Though these transactions are theoretically anonymous, they 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 their 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 so-called “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.
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
Believe it or not, one day, babies will get DNA report cards at birth. The 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.
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, are a result of not one or a few genes but many acting together. Using the data from large ongoing genetic studies, scientists are creating what they call “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 such illnesses as Alzheimer’s or heart disease. By picking volunteers who are 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 being screened, and then develops cancer anyway?
Polygenic scores are also controversial because they can predict any trait, not only diseases. For example, they can now forecast about 10% of a person’s performance on IQ tests. As the scores improve, it’s likely that DNA IQ predictions will 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 is what makes the new technology “simultaneously exciting and alarming.”
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: 5-10 years
The prospect of powerful new quantum computers comes with a puzzle. They’ll be capable of feats of computation unimaginable with today’s machines, but we haven’t yet figured out what we might do with those powers.
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 a 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 the electrons in even a relatively simple molecule and you run into complexities far beyond the capabilities of today’s computers.
But it’s a natural problem for quantum computers, which instead of digital bits representing 1s and 0s use “qubits” that are themselves quantum systems. Recently, IBM researchers used a quantum computer with seven qubits 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.
We hope that you were able to learn more about these 10 breakthrough technologies. What do you think of them? Are there specific ones that you see yourself using in the future or ones that the general public will want to adopt? Please share your thoughts below!