2 . Atomic shapes are so simple that they can’t be broken down any further. Mathematicians are trying to turn to artificial intelligence (AI) for help to build a periodic table of these shapes, hoping it will assist in finding yet-unknown atomic shapes.

Tom Coates at Imperial College London and his colleagues are working to classify atomic shapes known as Fano varieties, which are so simple that they can’t be broken down into smaller components. Just as chemists arranged element s in the periodic table by their atomic weight and group to reveal new insights, the researchers hope that organizing these atomic shapes by their various properties will help in understanding them.

The team has given each atomic shape a sequence of numbers based on its features such as the number of holes it has or the extent to which it bends around itself. This acts as a bar code (条形码) to identify it. Coates and his colleagues have now created an AI that can predict certain properties of these shapes from their bar code numbers alone, with an accuracy of 98 percent.

The team member Alexander Kasprzyk at the University of Nottingham, UK, says that the AI has let the team organize atomic shapes in a way that begins to follow the periodic table, so that when you read from left to right, or up and down, there seem to be general patterns in the geometry (几何) of the shapes.

Graham Nib lo at the University of Southampton, UK, stresses that humans will still need to understand the results provided by AI and create proofs of these ideas. “AI has definitely got unbelievable abilities. But in the same way that telescopes (望远镜) don’t put astronomers out of work, AI doesn’t put mathematicians out of work,” he says. “It just gives us new backing that allows us to explore parts of the mathematical landscape that are out of reach.”

The team hopes to improve the model to the point where missing spaces in its periodic table could point to the existence of unknown shapes.

3 . Fresh fish should have a mild smell. Strong fishy smells are the first signs to go bad. How do the fishy smells come from?

It can be several days from when the fish are caught to when they reach the supermarket. In that time, bacteria that grow naturally in fish start to consume a substance called trimethylamine N-oxide(TMAO)in fish. These bacteria change TMAO into trimethylamine (TMA), the substance responsible for the fishy smells. Bacteria in fish can also change lysine(赖氨酸)into cadaverine(尸胺), a substance that’s associated with breaking down the fish once they are caught and giving off fishy smell.

Chemical reactions can also lead to fishy smells. This happens through the oxidation(氧化)of fat. Fish are an important source of omega-3 fatty acids. When these fats are exposed to oxygen, they oxidize and break down into the substance that you can smell.

To slow down the fishy smell, what is beyond question is that the less time between when the fish are caught and when they reach the kitchen, the better. But today, fish are often flown across the globe. To keep smell-producing bacteria at bay, the fish must be frozen or kept at the low temperature possible as soon as they are caught and cleaned.

Controlling fat oxidation can function as well, especially for fattier fish species. While freezing slows bacterial growth, it does not stop fat oxidation. This reaction will occur as long as oxygen is present. Fatty fish are usually not frozen because, despite the cold temperature, they’re going to oxidize pretty fast unless they are stored in a low oxygen container. That’s why those species are often canned.

It’s also important to remember that smell is not always an indicator of safety, especially in processed fish products. “What you might consider the fishy smell may be a delicacy in another culture,” said Carl A. Batt, a professor of food science at Cornell University.

4 . Ann has been severely paralysed (瘫痪)for more than 18 years. She cannot speak and normally communicates. But now, she has been able to speak through an image on the computer using technology that translated her brain signals into speech and facial expressions.

The advance raises hopes that the technology could be on the edge of transforming the lives of people who have lost the ability to speak due to conditions like strokes (中风).

The latest technology uses tiny electrodes (电极) put on the top layer of the brain to detect electrical activity in the part of the brain that controls speech and face movements. These signals are translated directly into a digital avatar’s (化身) speech and facial expressions including smiling, frowning or surprise.

The team put 253 paper-thin electrodes on the top layer of Ann’s brain over an area important to speech. Afterwards Ann worked with the team to train the system to detect her unique brain signals for various speech sounds by repeating different phrases repeatedly. The computer learned 39 distinctive sounds and a Chat GPT-style language system was used to translate the signals into understandable sentences. This was then used to control an avatar with a voice personalised to sound like Ann’s voice before the injury, based on a recording of her speaking at her wedding.

The technology was not perfect, translating words incorrectly 28% of the time in a test run involving more than 500 phrases, and it generated brain-to-text at a rate of 78 words a minute, compared with the 110–150 words typically spoken in natural conversation. However, scientists said the latest advances in accuracy and speed suggest the technology is now at a point of being practically useful for patients. A crucial next step is to create a wireless form that could be put beneath the skull (颅骨).

Human memory is notoriously (众所周知地) unreliable. Even people with the sharpest facial recognition skills can only remember so much.

It’s tough to quantify how good a person is     1     remembering. No one really knows how many different faces someone can recall, for example, but various estimates tend to hover in the thousands – based on the number of acquaintances a person     2     have.

Machines aren’t limited this way. Give the right computer a massive database of faces, and it can process what it sees – then recognize a face it     3     (tell) to find – with remarkable speed and precision. This skill is     4     supports the enormous promise of facial-recognition software in the 21^st century. It is also what makes contemporary surveillance (监控) systems so scary.

The thing is, machines still have limitations when it comes to facial recognition. And scientists are only just beginning to understand what those constraints are.     5     (figure) out how computers are struggling, researchers at the University of Washington created a massive database of faces – they call it MegaFace – and     6     (test) a variety of facial-recognition algorithms(算法) as they scales up in complexity. The idea was to test the machines on a database that included up to 1 million different images of nearly 7,000 different people – and not just a large database     7     (feature) a relatively small number of different faces, more consistent with what’s been used in other research.

As the databases grew, machine accuracy dipped across the board. Algorithms     8     were right 95% of the time when they were dealing with a 13,000-image database, for example, were accurate about 70% of the time when     9     (face) with 1 million images. That’s still pretty good, says one of the researchers, Ira Kemelmacher-Shlizerman. “Much better than we expected,” she said,

Machines also had difficulty adjusting for people who look a lot alike –either doppelgangers (长相极相似的人), whom the machine would have trouble       10     (identify) as two separate people, or the same person who appeared in different photos at different ages or in different lighting, whom the machine would incorrectly view as separate people.

9 . In 1953, when visiting his daughter’s maths class, the Harvard psychologist B.F. Skinner found every pupil learning the same topic in the same way at the same speed. Later, he built his first “teaching machine”, which let children tackle questions at their own pace. Since then, education technology (edtech) has repeated the cycle of hype and flop (炒作和失败), even as computers have reshaped almost every other part of life.

Softwares to “personalize” learning can help hundreds of millions of children stuck in miserable classes—but only if edtech supporters can resist the temptation to revive harmful ideas about how children learn. Alternatives have so far failed to teach so many children as efficiently as the conventional model of schooling, where classrooms, hierarchical year-groups, standardized curriculums and fixed timetables are still the typical pattern for most of the world’s nearly 1.5 billion schoolchildren. Under this pattern, too many do not reach their potential. That condition remained almost unchanged over the past 15 years, though billions have been spent on IT in schools during that period.

What really matters then? The answer is how edtech is used. One way it can help is through tailor-made instruction. Reformers think edtech can put individual attention within reach of all pupils. The other way edtech can aid learning is by making schools more productive. In California schools, instead of textbooks, pupils have “playlists”, which they use to access online lessons and take tests. The software assesses children’s progress, lightening teachers’ marking load and allowing them to focus on other tasks. A study suggested that children in early adopters of this model score better in tests than their peers at other schools.

Such innovation is welcome. But making the best of edtech means getting several things right. First, “personalized learning” must follow the evidence on how children learn. It must not be an excuse to revive pseudoscientific ideas such as “learning styles”: the theory that each child has a particular way of taking in information. This theory gave rise to government-sponsored schemes like Brain Gym, which claimed that some pupils should stretch or bend while doing sums. A less consequential falsehood is that technology means children do not need to learn facts or learn from a teacher—instead they can just use Google. Some educationalists go further, arguing that facts get in the way of skills such as creativity. Actually, the opposite is true. According to studies, most effective ways of boosting learning nearly all relied on the craft of a teacher.

Second, edtech must narrow, rather than widen, inequalities in education. Here there are grounds for optimism. Some of the pioneering schools are private ones in Silicon Valley. But many more are run by charter-school groups teaching mostly poor pupils, where laggards (成绩落后者) make the most progress relative to their peers in normal classes. A similar pattern can be observed outside America.

Third, the potential for edtech will be realized only if teachers embrace it. They are right to ask for evidence that products work. But skepticism should not turn into irrational opposition. Given what edtech promises today, closed-mindedness has no place in the classroom.