MIT published 10 breakthrough technologies for 2018

Welcome to MIT’s annual list of 10 technological advances that will shape the way we work and live now and for years to come.

1. 3-D Metal Printing

While 3-D printing has been around for decades, it has largely remained the domain of hobbyists and designers producing one-off prototypes. And printing objects with anything other than plastics, particularly metal, has been expensive and slow.

Now, however, it is becoming cheap and lightweight enough to be a potentially practical way to manufacture parts. If widely adopted, it could change the way many products are mass-produced. In the short term, manufacturers wouldn’t need to maintain large inventories—they could simply print an object, such as a spare part for an aging car, whenever someone needs it.

The technology can create lighter, stronger parts and complex shapes that are impossible with conventional metal fabrication methods. It can also provide more precise control over the microstructure of metals. In 2017, researchers at Lawrence Livermore National Laboratory announced they had developed a 3D printing method for creating stainless steel parts that are twice as tall as traditionally manufactured ones.

2. Artificial Embryos

In a breakthrough that will shape how life can be created, embryologists working at the University of Cambridge in the UK are growing realistic-looking mouse embryos using only stem cells. No egg. No sperm. Just cells plucked from another embryo.

The researchers carefully placed the cells in a 3D scaffold and watched, fascinated, as they began to communicate and fit into the characteristic bullet shape of a mouse embryo a few days later.

«We know that stem cells are magical in the powerful potential of what they can do. «We didn’t realize they could self-organize so beautifully or beautifully,» Magdelena Zernica-Goetz, who led the team, told an interviewer at the time.

Zernica-Goetz says her «synthetic» embryos likely wouldn’t have been able to develop into mice. However, it hints that we may soon have mammals that don’t have eggs.

3. The Sensitive City

Numerous smart city schemes have encountered delays, missed their ambitious targets, or targeted everyone but the super-rich. A new project in Toronto, called Quayside, hopes to change this pattern of failure by reimagining a city block from the ground up and rebuilding it using the latest digital technologies.

Alphab’s Sidewalk Labs, based in New York City, is collaborating with the Canadian government on a high-tech project for Toronto’s industrial waterfront. One of the project’s goals is to base design, policy, and technology decisions on information from a vast network of sensors that collect data on everything from air quality to noise levels to human activity.

The plan calls for all vehicles to be autonomous and shared. Robots will roam the subway, performing menial tasks like delivering mail. Sidewalk Labs says it will open-source the software and systems it creates, allowing other companies to build services on top of them, much like people build mobile apps.

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

4. AI for Everyone

Artificial intelligence has so far been primarily the domain of large tech companies like Amazon, Baidu, Google, and Microsoft, as well as a few startups. For many other companies and parts of the economy, AI systems are too expensive and too complex to fully implement.

The solution? Cloud-based machine learning tools are bringing AI to a much wider audience. Amazon, with its subsidiary AWS, currently dominates cloud AI. Google challenges what’s happening with Tenso rFlow, an open-source AI library that can be used to build other machine learning software, is also available. Google recently announced Cloud AutoML, a set of pre-built systems that could make AI easier to use.

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

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

5. Neural Network Duel

Artificial intelligence is very good at understanding objects: show it a million photographs, and it can tell you with unusual accuracy what a pedestrian crossing the street looks like. But AI is hopeless at generating images of pedestrians on its own. If it could, it could create bundles of realistic but synthetic images of pedestrians in various conditions, which a self-driving car could use for training without ever driving on the street.

The problem is that creating something completely new requires imagination—and this has puzzled AI so far.

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

Both networks are trained on the same dataset. One, known as the generator, is tasked with creating variations on images it has already seen—perhaps an image of a pedestrian with an extra lever. The second, known as the discriminator, is asked to determine whether the example it sees is similar to the images it has trained on, or whether the example produced by the generator is a fake—basically, a three-armed person, perhaps real?

Over time, the generator can become so good that it produces images that the discriminator cannot recognize as fakes. Essentially, the generator was taught to recognize and then create realistic images of pedestrians.

This technology has become one of the most promising advances in AI in the last decade, capable of helping machines produce results that fool even humans.

6. Babel Fish Earbuds

In the cult sci-fi classic «The Hitchhiker’s Guide to the Galaxy,» you insert a yellow «Babel» fish into your ear to get instant translations. In the real world, Google has come up with a temporary solution: a $159 pair of earbuds called Pixel Buds. They work with their Pixel smartphones and the Google Translate app to get near-real-time translation.

One person wears the earbuds, and the other wears the phone. The earbud owner speaks in their language—English by default—and the app translates the conversation and plays it aloud over the phone. The person answering the phone responds; this response is broadcast and played back through the earbuds.

Google Translate already has a conversation feature, and its iOS and Android apps allow two users to talk because it automatically detects which languages ​​they are using and then translates them. But background noise can make it difficult for the app to understand what people are saying and to figure out when one person stops speaking and it’s time to start translating.

Pixel Pillows circumvent these issues because the owner presses and holds their finger on the right earbud while talking. Splitting the interaction between the phone and earbuds gives each person control of the microphone and helps the speakers maintain eye contact, since they don’t try to pass the phone back and forth.

7. Zero-carbon natural gas

The world is likely stuck with natural gas as one of our primary sources of electricity for the foreseeable future. Cheap and readily available, it currently accounts for more than 30 percent of US electricity and 22 percent of global electricity. And while it’s cleaner than coal, it’s still a huge source of carbon dioxide emissions.

An experimental power plant near Houston, the center of the US oil and refining industry, is testing technology that could make clean energy from natural gas a reality. The company behind the 50-megawatt project, Net Power, believes it can generate electricity at least as cheaply as standard natural gas plants and capture essentially all of the carbon dioxide emissions in the process. process.

If true, it would mean the world has a way to produce carbon-free energy from fossil fuels at a reasonable cost. Such natural gas plants could be spun up and down on demand, avoiding the high capital costs of nuclear power and the unstable supply typically provided by renewables.

8. Online Privacy

True online privacy may finally be possible thanks to a new tool that could, for example, allow you to prove you’re under 18 without providing a date birth, or prove you have enough money in the bank for a financial transaction without revealing your balance or other details. This limits the risk of privacy breaches or identity theft.

The tool is a new cryptographic protocol called zero-knowledge proof. Although researchers have been working on it for decades, interest exploded last year, partly due to the growing obsession with cryptocurrencies, most of which are not private.

Much of the practical benefits of zero-knowledge proof apply to Zcash, a digital currency that launched in late 2016. Zcash’s developers used a method called zk-SNARKs (for «zero-knowledge succinct non-interactive argument of knowledge») to allow users to make anonymous transactions.

This is typically not possible in Bitcoin and most other public blockchains, where transactions are visible to everyone. While these transactions are theoretically anonymous, they can be combined with other data to track and even identify users. Vitalik Buterin, creator of the Ethereum network Ethereum, the world’s second-most popular blockchain network, described zk-SNARKs as a «completely game-changing technology.»

9. Genetic Prediction

One day, children will receive DNA report cards at birth. These reports will predict their chances of having a heart attack or cancer, being hooked on tobacco, and being smarter than average.

The science behind these card cards emerged unexpectedly from massive genetic studies involving over a million people.

It turns out that the most common diseases and many behaviors and traits, including intelligence, are the result of more than one or several genes, and many act in concert. Using data from large genetic studies, scientists are creating what they call «polygenic risk scores.»

While the new DNA tests offer probabilities rather than diagnoses, they could significantly benefit medicine. For example, if women at high risk for breast cancer received more mammograms, while those at low risk received fewer, these exams could catch more actual cancers and reduce false alarms.

Pharmaceutical companies could also use scores in clinical trials of preventative drugs for diseases like Alzheimer’s or heart disease. By selecting volunteers who are more likely to get sick, they can more accurately test how well the drugs work.

The problem is that the predictions are far from perfect. Who wants to know they might develop Alzheimer’s? What if someone with a low cancer risk score delays screening and then develops cancer?

10. Quantum Leap of Materials

The prospect of powerful new quantum computers comes with a They will be capable of computational feats unimaginable with today’s machines, but we haven’t yet figured out what we can do with these powers.

A plausible and enticing possibility: the precise design of molecules.

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

We don’t have such things because molecules are ridiculously difficult to simulate on a classical computer. Try to simulate the behavior of electrons in even a relatively simple molecule, and you’ll encounter complexities far beyond the capabilities of today’s computers.