There’s a new achievement in 3D printing that’s beginning to come into focus: food. Recent development has made possible machines that print, cook, and serve foods on a mass scale(大规模地). And the industry isn’t stopping there.

With a 3D printer, a cook can print complicated chocolate sculptures and beautiful pieces for decoration   on a wedding cake. Not everybody can do that—it takes years of experience, but a printer makes it easy. A restaurant in Spain uses a Foodini to “re-create forms and pieces” of food that are “exactly the same”, freeing cooks to complete other tasks. In another restaurant, all of the dishes and desserts it serves are 3D-printed, rather than farm to table.

Future 3D food printers could make processed food healthier. Hod Lipson, a professor at Columbia University, said, “Food printing could allow consumers(消费者) to print food with customized(定制的) nutritional content, like vitamins. So instead of eating a piece of yesterday’s bread from the supermarket, you’d eat something baked just for you on demand.”

Despite recent advancements in 3D food printing, the industry has many challenges to overcome. Currently, most ingredients (烹饪原料) must be changed to a paste（糊状物）before a printer can use them, and the printing process is quite time-consuming, because ingredients interact with each other in very complex ways. On top of that, most of the 3D food printers now are limited to dry ingredients, because meat and milk products may easily go bad. Some experts are doubtful about 3D food printers, believing they are better suited for fast food restaurants than homes and high-end restaurants.

For several decades, there has been an extensive and organized campaign intended to generate distrust in science, funded by regulated industries and libertarian think tanks (自由主义智囊团) whose interests and beliefs are threatened by the findings of modern science. In response, scientists have tended to stress the success of science. After all, scientists have been right about most things, from the structure of the universe to the relativity of time and space.

Quoting successes isn’t wrong, but for many people it’s not persuasive. What is typically declared to be the scientific method — develop a supposition, then design an experiment to test it — isn’t what scientists actually do. Science is active so that new methods get invented and old ones get abandoned. The scientific method doesn’t always work. False theories can produce true results, so even if an experiment works, it doesn’t prove that the theory it was designed to test is true.

If there is no identifiable scientific method, then what is the guarantee for trust in science?

The answer is the methods by which those claims are evaluated. A scientific claim is never accepted as true until it has gone through a long process of examination by fellow scientists. Until this point, scientific feedback is typically fairly friendly. But the next step is different: once the paper is ready, it is presented to a scientific journal, where things get a whole lot tougher. Editors deliberately send scientific papers to people who are not friends or colleagues of the authors, and the job of the reviewer is to find errors or other inadequacies. We call this process “peer review” because the reviewers are scientific peers but they act in the role of a superior who has both the right and the obligation to find fault. It is only after the reviewers and the editor are satisfied that any problems have been fixed that the paper is accepted for publication and enters the body of “science.”

Does this process ever go wrong? Of course. Scientists are human. But if we look carefully at historical cases where science went wrong, typically there was no agreement reached by all. Some people argue that we should not trust science because scientists are “always changing their minds.” While examples of truly settled science being overturned are far fewer than is sometimes claimed, they do exist. But the beauty of this scientific process is that it explains what might otherwise appear paradoxical (矛盾的): that science produces both novelty (新颖性) and stability. New observations, ideas, interpretations introduce novelty: trans-formative questioning leads to collective decisions and the stability of scientific knowledge. Scientists do change their minds in the face of new evidence, but this is a strength of science, not a weakness.

A new service gives new life to the past by using artificial intelligence to change still images into moving videos.

Called Deep Nostalgia, the service creates animations (动画) by using deep learning to analyze a single facial photo. Then, the system animates the facial image through a “driver" — a pre-determined sequence (次序) of movements and gestures. The process is completely automated.

Launched by the Israeli genealogy company My Heritage, some of Deep Nostalgia's early results are impressive. But that's not to say the animations are perfect. There's still an uncanny air to the images, with some of the facial movements appearing slightly unnatural. What's more, Deep Nostalgia is only able to create deepfakes of one person's face from the neck up, so you couldn't use it to animate group photos, or photos of people doing any sort of physical activity.

But for a free deep-fake service, Deep Nostalgia is pretty impressive, especially considering you can use it to create deepfakes of any face, human or not. So, is creating deepfakes of long-dead people a bit frightening? Some people seem to think so. “Some people love the feature with Deep Nostalgia and consider it magical while others think it is scary and dislike it, ”My Heritage wrote on its website. “We invite you to create movies using this feature and share them on social media to see what your friends and relatives think. This feature is intended for nostalgic (怀旧的) use, that is, to give life back to beloved ancestors.”

Deep Nostalgia isn't the first project to create deepfakes from single images. In 2019, researchers working at the Samsung AI Center in Moscow published a paper describing how machine-learning techniques can produce deepfakes after “looking" at only one or a few images.

While the results from the Samsung researchers were impressive, the Deep Nostalgia project shows how deepfake technology is advancing at a rapid pace. As these tools have become increasingly popular, media experts have raised concerns about how bad actors might use deepfakes and “cheap fakes” to trick the public. My Heritage seemed to sense Deep Nostalgia's potential or abuse, writing: “Please use this feature on your own historical photos and not on photos of living people without their agreement.”