Here is look at top 5 breakthrough technologies from 2018 that will create revolution. 3-d metal printing, artificial embryos, dueling neural networks, babel-fish earbuds and much more…

1. 3-D Metal Printing

Metal 3-D Printing holds a unique position in modern-day product development. The technology can create lighter, stronger parts, and complex shapes that aren’t possible with conventional metal fabrication methods. It facilitates tooling for conventional manufacturing technologies, reducing costs and lead times. It can also provide more precise control of the microstructure of metals. In 2017, researchers from the Lawrence Livermore National Laboratory announced they had developed a 3-D-printing method for creating stainless-steel parts twice as strong as traditionally made ones.

Also in 2017, 3-D-printing company 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 to ship its first metal prototyping machines in December 2017. It plans to begin selling larger machines, designed for manufacturing, that are 100 times faster than older metal printing methods.

The printing of metal parts is also getting easier. Desktop Metal now offers software that generates designs ready for 3-D printing. Users tell the program the specs of the object they want to print, and the software produces a computer model suitable for printing.

2. Artificial Embryos

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

Synthetic human embryos would be a boon to scientists, letting them tease apart events early in development. And since such embryos start with easily manipulated stem cells, labs will be able to employ a full range of tools, such as gene editing, to investigate them as they grow.

Artificial embryos, however, pose ethical questions. What if they turn out to be indistinguishable from real embryos? How long can they be grown in the lab before they feel pain? We need to address those questions before the science races ahead much further, bioethicists say.

3. AI for Everybody

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.

What’s the solution? Machine-learning tools based in the cloud are bringing AI to a far broader audience. These products will be essential if the AI revolution is going 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 the advances in artificial intelligence. Sectors such as medicine, manufacturing, and energy could also be transformed if they were able to implement the technology more fully, with a huge boost to economic productivity.

4. Dueling Neural Networks

Artificial intelligence is getting 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 do that, it would be able to create gobs of realistic but synthetic pictures depicting pedestrians in various settings, which a self-driving car could use to train itself without ever going out on the road.

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, or GAN, takes two neural networks—the 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, known as the generator, is tasked with creating variations on images it’s already seen—perhaps a picture of a pedestrian with an extra arm. The second, known as the discriminator, is asked to identify whether the example it sees is like the images it has been trained on or a fake produced by the generator—basically, is that three-armed person likely to be real?

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

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

5. Babel-Fish Earbuds

In the cult sci-fi classic The Hitchhiker’s Guide to the Galaxy, you slide a yellow Babel fish into your ear to get translations in an instant. 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.

Google Translate already has a conversation feature, and its iOS and Android apps let two users speak as it automatically figures out what languages they’re using and then translates them. But background noise can make it hard for the app to understand what people are saying, and also to figure out when one person has stopped speaking and it’s time to start translating.

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