1 . Art Builds Understanding

Despite the long history of scholarship on experiences of art, researchers have yet to capture and understand the most meaningful aspects of such experiences, including the thoughts and insights we gain when we visit a museum, the sense of encounter after seeing a meaningful work of art, or the changed thinking after experiences with art. These powerful encounters can be inspiring, uplifting, and contribute to well-being and flourishing.

    1     It contributes to facilitating a better understanding of ourselves, the human condition, and moral and spiritual concepts. The question is how that happens — what are the attributes of meaningful experiences of art?

According to the mirror model of art developed by Pablo P. L. Tinio, aesthetic reception corresponds to artistic creation in a mirror-reversed fashion. Artists aim to express ideas and messages about the human condition or the world at large.     2     This results in the build-up of layers of materials — from initial studies and sketches to the final, refined piece. A viewer’s initial interaction with an artwork starts where the artist has left off. Their interaction first involves the processing surface features, such as color, texture, and the finishing touches applied by the artist during the final stages of the creative process.     3    

In addition, art making and art viewing are connected by creative thinking. Research in a lab at Yale University shows that an educational program that uses art appreciation activities builds creative thinking skills. It showed that the more time visitors spent engaging with art and the more they reflected on it, the greater the correspondence with the artists’ intentions and ideas.     4    

Correspondence in feeling and thinking suggests a transfer — between creator and viewer — of ideas, concepts, and emotions contained in the works of art. Art has the potential to communicate across space and time.     5     What it takes for this to happen is active engagement with art in contexts that facilitate this engagement, especially museums.

2 . Last week, I sent the same request to ChatGPT, the latest artificial-intelligence chatbot from OpenAI. “Upon the Firth of Forth, a bridge doth stand,” it began. In less than a minute, the program had created in full a rhyming Shakespearean sonnet (莎士比亚十四行诗). Tools like ChatGPT seem poised to change the world of poetry — and so much else — but poets also have a lot to teach us about artificial intelligence. If algorithms (算法) are getting good at writing poetry, it’s partially because poetry was always an algorithmic business.

Even the most rebellious (叛逆的) poets follow more rules than they might like to admit. When schoolchildren are taught to imitate the structure of sonnet, they are effectively learning to follow algorithmic constraints. Should it surprise us that computers can do so, too?

But considering how ChatGPT works, its ability to follow the rules for sonnets seems a little more impressive. No one taught it these rules. It is based on a newer kind of AI known as a large language model (LLM). To put it simply, LLMs analyze large amounts of human writing and learn to predict what the next word in a string of text should be, based on context. One frequent criticism of LLMs is that they do not understand what they write; they just do a great job of guessing the next word.

When a private verse by Dickinson makes us feel like the poet speaks directly to us, we are experiencing the effects of a technology called language. Poems are made of paper and ink — or, these days, electricity and light. There is no one “inside” a Dickinson poem any more than one by ChatGPT. Of course, every Dickinson poem reflects her intention to create meaning. When ChatGPT puts words together, it does not intend anything. Some argue that writings by LLMs therefore have no meaning, only the appearance of it. If I see a cloud in the sky that looks like a giraffe, I recognize it as an accidental similarity. In the same way, this argument goes, we should   regard the writings of ChatGPT as merely imitating real language, meaningless and random as cloud shapes.

When I showed my friends the sonnet by ChatGPT, they called it “soulless and barren.” Despite following all the rules for sonnets, the poem is predictable. But is the average sonnet by a human any better? If we now expect computers to write not just poems but good poems, then we have set a much higher bar.

3 . Debate about artificial intelligence (AI) tends to focus on its potential dangers: algorithmic bias (算法偏见) and discrimination, the mass destruction of jobs and even, some say, the extinction of humanity. However, others are focusing on the potential rewards. Luminaries in the field such as Demis Hassabis and Yann LeCun believe that AI can turbocharge scientific progress and lead to a golden age of discovery. Could they be right?

Such claims are worth examining, and may provide a useful counterbalance to fears about large-scale unemployment and killer robots. Many previous technologies have, of course, been falsely hailed as panaceas (万灵药). But the mechanism by which AI will supposedly solve the world’s problems has a stronger historical basis.

In the 17th century microscopes and telescopes opened up new vistas of discovery and encouraged researchers to favor their own observations over the received wisdom of antiquity (古代), while the introduction of scientific journals gave them new ways to share and publicize their findings. Then, starting in the late 19th century, the establishment of research laboratories, which brought together ideas, people and materials on an industrial scale, gave rise to further innovations. From the mid-20th century, computers in turn enabled new forms of science based on simulation and modelling.

All this is to be welcomed. But the journal and the laboratory went further still: they altered scientific practice itself and unlocked more powerful means of making discoveries, by allowing people and ideas to mingle in new ways and on a larger scale. AI, too, has the potential to set off such a transformation.

Two areas in particular look promising. The first is “literature-based discovery” (LBD), which involves analyzing existing scientific literature, using ChatGPT-style language analysis, to look for new hypotheses, connections or ideas that humans may have missed. The second area is “robot scientists”. These are robotic systems that use AI to form new hypotheses, based on analysis of existing data and literature, and then test those hypotheses by performing hundreds or thousands of experiments, in fields including systems biology and materials science. Unlike human scientists, robots are less attached to previous results, less driven by bias—and, crucially, easy to replicate. They could scale up experimental research, develop unexpected theories and explore avenues that human investigators might not have considered.

The idea is therefore feasible. But the main barrier is sociological: it can happen only if human scientists are willing and able to use such tools. Governments could help by pressing for greater use of common standards to allow AI systems to exchange and interpret laboratory results and other data. They could also fun d more research into the integration of AI smarts with laboratory robotics, and into forms of AI beyond those being pursued in the private sector. Less fashionable forms of AI, such as model-based machine learning, may be better suited to scientific tasks such as forming hypotheses.

4 . 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.

6 . Travelling abroad can present many challenges, including long journeys, language problems, and culture shocks, plus the expense of transport and accommodation.     1     It is rewarding to get to experience life in different ways.

One of the wonderful benefits of going abroad is that you can learn history and culture without real effort. There’s a natural tendency to absorb other cultures and pick up historical concepts, while enjoying yourself at the same time.     2    

Another great benefit is that living in a foreign culture is the only real way to fully understand its language.     3     Even if you’re a complete beginner and only manage to pick up a few key phrases of the local language, you still know more than when you arrive.

    4     People in other countries can demonstrate different ways of doing things and alternative ways of being. It can open up your mind and generate new ideas. A direct encounter with a fresh expression of arts, religions, architecture, and food can contribute to an enlightening and valuable experience.

Removing yourself from the familiar and travelling to a new country can be a very powerful tool for gaining self-awareness and deeper understanding.     5     It can help you shift how you see yourself. Perhaps ironically, visiting a foreign country can also teach you things about your homeland. You get to view your land of birth from another perspective and have something to compare it with.

7 . 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.

10 . When you ask people to judge others by their speech, a trend emerges: Listeners dislike disfluency. Slow talkers producing loads of ums and pauses(停顿)are generally perceived as less charming. But science tells us there may be even more to disfluency.

Disfluencies do not occur in arbitrary positions in sentences. Ums typically occur right before more difficult or low-frequency words. Imagine you’re having dinner with a friend at a restaurant,and there’re three items on the table: a knife, a glass, and a wine decanter(醒酒器). Your friend turns to you and says, “Could you hand me the...um...” What would you assume they want? Since it’s unlikely that they will hesitate before such common words as knife, and glass, chances are you’ll pick up the decanter and ask, “You mean this?”

This is exactly what we demonstrated through controlled eye-tracking studies in our lab. Apparently, listeners hear the um and predict that an uncommon word is most likely to follow.Such predictions, though, reflect more than just simple association between disfluencies and difficult words; listeners are actively considering from the speaker’s point of view. For example, when hearing a non-native speaker say the same sentence but with a thick foreign accent, listeners don’t show a preference for looking at low-frequency objects. This is probably because listeners assume non-native speakers may have as much trouble coming up with the English word for a common object, like a knife, as for unusual ones and can’t guess their intention.

In another experiment, listeners were presented with an atypical speaker who produced disfluencies before simple words and never before difficult words. Initially, participants displayed the natural predictive strategy: looking at uncommon objects. However, as more time went by, and they gained experience with this atypical distribution of disfluencies, listeners started to demonstrate the contrary predictive behavior: They tended to look at simple objects when hearing the speaker say um.

These findings represent further evidence that the human brain is a prediction machine: We continuously try to predict what will happen next, even though not all disfluencies are created equal.