1 . “Blame My Brain” by Nicola Morgan, reviewed by Rosalie Warren

As someone who constantly blames my brain for all sorts of things (not my fault — my brain did it!), I was _______ by the title of Nicola Morgan’s book and only slightly disappointed to learn that it was _______ teenagers. Since I have many days when I feel that I am barely out of my teens (though the mirror, sadly, does not bear this out), I decided that the book might still be relevant to me, and indeed to my relationships with my no-longer-teenage children.

The subtitle is “The amazing teenage brain revealed” and amazing is, I soon _______, exactly what the teenage brain is. I learned that one of the things that _______ to the brain in our early teenage years is a flurry of growth of the dendrites (connections between neurons), followed a few years later by a major pruning session where many of the relatively unused connections are culled and the remaining ones are strengthened and coated with a fatty myelin layer ready for adult life. Not _______, all this activity is not without its downside for the person “within”, and it goes along way towards _______ why teenagers can be so difficult to live with and to cope with themselves.

There are also brain-based explanations of why teenagers need so much sleep, why they don’t tidy their rooms, why they come _______ when the rest of the world is going to sleep and why some of them, at least, are risk-takers. There’s stuff about alcohol and drugs and why such things are not good for the brain, but all of it is written in a very _______ and understanding way that I think teenagers will warm to.

Nicola Morgan is not a neurologist or a _______ scientist, but she clearly had done a great deal of research and ________ experts including Professor Simon Baron-Cohen and Professor Susan Greenfield, among many other eminent names in the field. Morgan has a gift for simplifying and explaining ________ subject matter with a light but precise touch, and she is careful to ________ between established facts and theories and speculations of her own and other people’s.

There’s plenty of humour and a good few well-deserved digs at the stupidity of parents and other well-meaning but misguided adults, which teenagers will ________. There are diagrams, tests, photos relating to questions like What emotions can you recognize? Do you ________ other emotions with anger? What kind of thinker are you? Which mental tasks do you find comparatively easy or difficult? There’s also sound advice for addiction, self-harm, depression and other ________ illnesses, and some pointers towards recognizing when you may need to seek help.

The illustrations by Andy Baker are great, too. And oh yes — there’s some interesting discussion on the differences between girls’ brains and boys’, if there are any. You’ll have to read it to find out...

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.

Key elements of a great film

Nowadays films play an important role in our life. And every great film must involve many key elements. Opinions about them vary among people. Below are my ideas about key elements of a great film.

Of course, there are many other elements leading to a great film. Anyway an outstanding film must come from its inner shining elements. What about your idea?

6 . 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.