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Emily Blunt and Charlize Theron in one of their many scenes that look like spreads for Vogue Antarctica

I’ve never seen Frozen, but I suspect they ripped it off.

Jake Gyllenhaal continues his quest to have the widest-ranging body of work among his peers in Demolition, a comedy about grief by Jean-Marc Vallée (Dallas Buyers Club, Wild). Jake plays a Wall Street guy who loses his wife but does not know how to grieve or be angry. So everything becomes a metaphor to him. He starts taking machines apart and trying to put them back together to see how they work—this is his way of figuring out what his marriage was. Later he takes apart larger things.

Demolition is very engaging, if a bit too cheerful for a movie about sorrow and loss, but Jake is awesome. In each of his roles, be he a boxer, a gay cowboy, an ambitious lowlife, an academic and his doppelganger, or literally a body in a box, he looks, sounds, and moves differently. In Nightcrawler he looked like a stick insect with giant compound eyes; here, he looks like a very fit, slow-moving investment banker. Demolition also stars Naomi Watts (who would be an even bigger star kung marunong lang siyang magmaganda), Chris Cooper whose sorrow is too real for this movie, and a wonderful new actor named Judah Lewis who plays a teenager who worries he may be gay.

In one scene Jake wears headphones and walks through Wall Street in an interpretative dance of confusion and bottled-up rage. Watch this movie.

We don’t know Drogon’s exact date of birth. He just showed up on our doorstep three years ago, when he was about a year old. Shortly afterwards our eldest cat Koosi died at age 14, and following the Roman Church’s practice of taking a date of significance to the pagans and assigning it to a Church holiday, we declared that Drogon would have the same birthday as Koosi, April 12. Happy Birthday to our white, blue and pink baby dragon! Yes, Drogon looks like those birth announcement cards.

Today, Drogon is The Oracle. You can ask him anything. Post your questions in Comments.

For instance: Drogon, is Jon Snow truly dead?

Drogon: Nope. R+L=J.

In the first few episodes Jon Bernthal makes us think the show is called Marvel’s The Punisher.

DAREDEVIL, the vigilante of Hell’s Kitchen, New York, is not the biggest badass in this season of the Netflix series that bears his name. He’s not even the second. More like the fourth or fifth. However you may feel about comic book adaptations — and there are people who will not watch comic book adaptations because they have fixed notions about grown-up behavior — you will find yourself agreeing that Daredevil has the best and most brutal fight scenes in the business. They’re not just cleverly choreographed (by Philip J. Silvera) or wonderfully photographed (by Martin Ahlgren) or thrillingly edited, they look like they hurt.

Read our TV column, The Binge.

Chow mein photo from norecipes.com

Have They Run Out of Provinces Yet?
by Calvin Trillin

Have they run out of provinces yet?
If they haven’t, we’ve reason to fret.
Long ago, there was just Cantonese.
(Long ago, we were easy to please.)
But then food from Szechuan came our way,
Making Cantonese strictly passé.
Szechuanese was the song that we sung,
Though the ma po could burn through your tongue.
Then when Shanghainese got in the loop
We slurped dumplings whose insides were soup.
Then Hunan, the birth province of Mao,
Came along with its own style of chow.
So we thought we were finished, and then
A new province arrived: Fukien.
Then respect was a fraction of meagre
For those eaters who’d not eaten Uighur.
And then Xi’an from Shaanxi gained fame,
Plus some others—too many to name.

Now, as each brand-new province appears,
It brings tension, increasing our fears:
Could a place we extolled as a find
Be revealed as one province behind?
So we sometimes do miss, I confess,
Simple days of chow mein but no stress,
When we never were faced with the threat
Of more provinces we hadn’t met.
Is there one tucked away near Tibet?
Have they run out of provinces yet?

Calvin Trillin defends his poem.

Since the earliest days of computing, computers have been used to search out ways of optimizing known functions. Deep Blue’s approach was just that: a search aimed at optimizing a function whose form, while complex, mostly expressed existing chess knowledge. It was clever about how it did this search, but it wasn’t that different from many programs written in the 1960s.

AlphaGo also uses the search-and-optimization idea, although it is somewhat cleverer about how it does the search. But what is new and unusual is the prior stage, in which it uses a neural network to learn a function that helps capture some sense of good board position. It was by combining those two stages that AlphaGo became able to play at such a high level.

This ability to replicate intuitive pattern recognition is a big deal. It’s also part of a broader trend. In an earlier paper, the same organization that built AlphaGo — Google DeepMind — built a neural network that learned to play 49 classic Atari 2600 video games, in many cases reaching a level that human experts couldn’t match. The conservative approach to solving this problem with a computer would be in the style of Deep Blue: A human programmer would analyze each game and figure out detailed control strategies for playing it.

By contrast, DeepMind’s neural network simply explored lots of ways of playing. Initially, it was terrible, flailing around wildly, rather like a human newcomer. But occasionally the network would accidentally do clever things. It learned to recognize good patterns of play — in other words, patterns leading to higher scores — in a manner not unlike the way AlphaGo learned good board position. And when that happened, the network would reinforce the behavior, gradually improving its ability to play.

Read the full article in Quanta.