Category Archives: Entertainment

The four act Bond

How to write a thrilling story.

A typical Bond 007 style sequence:

(jeopardy + conflict + risk = penalty/reward) +
(jeopardy + conflict + risk = reward) +
(jeopardy + conflict + risk = penalty) +
big jeopardy + big conflict + big risk = final reward

Some family, data or city is in jeopardy.
Bond arrives and conflict ensues.
At a crux in the conflict Bond makes a rash decision:
He partially succeeds but must pay an unexpected price. (Bond is fallible.)

Some greater entity is at risk.
Bond, known to the villain, cat and mouses in conflict.
Bond, attempting to redeem his initial failure, takes a greater risk:
He succeeds to acclaim. (Bond is arrogant.)

Bond is now in jeopardy along with a state or country or system.
Villain escalates conflict with Bond and the system.
Bond risks yet again, with hubris from prior success:
He fails, loses almost all, humiliated. (Bond is humble.)

World is in peril now.
Bond fights not only villain but the system too.
Bond risks all in last ditch attempt to beat villain and justify prior actions:
He succeeds, world is saved, Bond is hero, villains beware. (Bond is resilient.)


The Problem with Star Wars

The problem with Star Wars and Star Trek and many other “Star blah blah blah” type story lines is this: where are the robots?

No, I’m not talking about the cute comic-relief characters, nor am I talking about the droid-wars robots.

Here’s the thing, Space Is Hard. Even Elon admits this. Biologically based creatures die — really easily — in space. They die if they don’t eat, don’t get liquids, don’t get enough to breathe, get squeezed or stretched or ripped. Biological creatures are fragile. A biological military force, or agents, or workers or what-have-you would be a society’s LAST resort. The first thing a sentient species would do when they start exploring space is to build up the biggest, baddest, smartest, most versatile space-force using ROBOTS they could.

People? We’re not gonna use PEOPLE — hell no!

Look, Humans suck at space. Right now about 1 out of 20 rockets we launch BLOW UP! And that’s good. That’s the best we’ve gotten so far. Imagine if 1 out of every 20 commercial flights that took off from airports just today BLEW UP!  About 100k flights occur everyday. Imagine if 5000 of them exploded in the last 24 hour. Hell No!

So, between our really really bad track record for sending rockets into space and our super-duper track record for flying airplanes, we have a long way to go.

Now, let’s examine our robotic and computer track record. We’ve got some amazing technology there. Robots are going to be replacing humans for most manual labor, and most complex logistics and management in the next 20 years.

Let’s think about this. Humanity will have an amazing robotic work force and superior artificial intelligences in just another generation.

But we won’t have a reliable means of space travel for at least two or three generations.

By the time we can blast around the solar system (or galaxy) in a Millennium Falcon humans will have constructed an incredible robotic space-force. And with that space-force we would be sending ROBOTS out with vast AIs in our space craft to do our exploring, and our patrolling and our space war fighting. We wouldn’t send frail, easy to puncture organic HUMANS! Hell No!

Extraterrestrial Sentient Species in our galaxy would be even smarter than us. They would have even better robots and artilects. They would never use their biological selves to do the work robots would do so much more effectively.

That’s the problem with Star Wars and Star Trek. Their story lines rely on bags of animated organic chemicals and not robots; which is just — implausible.

REF:
http://www.spacelaunchreport.com/logdec.html
https://www.quora.com/How-many-airplanes-fly-each-day-in-the-world


Spike up the Nog – a song

Spike up the Nog

Punch – it – up,
dump in the rum,
kick in the brandy,
gotta have sum.
Oh, gi’me oh gi’me oh gi’me a mug-a-nog.

Headin’ to a party,
where the tame folk hang,
down at the office, a neighbors,
or with the family gang.

But we’ll take a long a secret,
tucked in our tog,
a couple of bottles,
to crank up the nog,

Punch – it – up,
dump in the rum,
kick in the brandy,
gotta have sum.
Oh, gi’me oh gi’me oh gi’me a mug-a-nog.

It’s sweet like Cindy,
It’s spicy like Nell,
It’s smooth like Tanya,
and it kicks like,
I’ll tell ya,
Oh, gi’me oh gi’me oh gi’me a mug-a-nog.

Punch – it – up,
dump in the rum,
kick in the brandy,
gotta have sum.
Oh, gi’me oh gi’me oh gi’me a mug-a-nog.

Tonight at the party,
When the guests all nod,
We’ll pour in the secret,
and spike up the nog,

I said what? (2, 3, 4)…
SPIKE UP THE NOG,
I said what? (2, 3, 4)…
SPIKE UP THE NOG,

Oh, gi’me oh gi’me oh gi’me a mug-a-nog.
Punch – it – up,
dump in the rum,
kick in the brandy,
gotta have sum.
(fade)


Squirrels and Nuts – a song

[chorus]
Nuts, nuts, squirrels and nuts,
toss ’em down,
scatter ’round,
pack ’em up,
dig ’em up,
save ’em in a dixie cup,
Nuts, nuts, squirrels and nuts.
 
[verse 1]
There’s a squirrel in a tree,
and he’s lookin’ down at me.
I give him my evil canine stare.
But the squirrel, he doesn’t care.
 
[chorus]
Nuts, nuts, squirrels and nuts,
toss ’em down,
scatter ’round,
pack ’em up,
dig ’em up,
save ’em in a dixie cup,
Nuts, nuts, squirrels and nuts.
 
[verse 2]
The branch bends wild,
he’s steppin’ careful style.
His tail is nothing but a tease,
the thought of it just makes we want to sneeze.
 
[chorus]
Nuts, nuts, squirrels and nuts,
toss ’em down,
scatter ’round,
pack ’em up,
dig ’em up,
save ’em in a dixie cup,
Nuts, nuts, squirrels and nuts.
 
[break]
His back is turned,
he’s in the grass.
If I’m to catch him,
I must be fast.
I tiptoe in,
he doesn’t see me.
I think I smell him,
it’s kinda dreamy.
closer, closer, must get closer, closer, closer…. [pause]
now!
 
[verse 3]
There’s a squirrel in a tree,
and he’s laughin’ down at me.
I lookup and now I finally know,  [spoken]
that I’m just too dog’on slow. [spoken]
 
[chorus]
Nuts, nuts, squirrels and nuts,
toss ’em down,
scatter ’round,
pack ’em up,
dig ’em up,
save ’em in a dixie cup,
Nuts, nuts, squirrels and nuts.
 
[fade]
Nuts, nuts, squirrels and nuts.
Nuts, nuts, squirrels and nuts.
Nuts, nuts, squirrels and nuts.

Snow Angel – a song

Snow Angel

It was this time,
last November,
headed out to see the folks.

The roads were icy,
I don’t remember,
how I lost you, your dreams,  your hopes.

Like feathers falling,
onto pillows,
where you rest your little head,
my snow angel,

Tiny foot prints,
in the snow drifts,
lead me back,
to where you lay.

Wings a ready,
smile gleaming,
with rosy cheeks,
I hear you say…

Daddy, see the pretty angel,
I made for you here in the snow,
help me up and leave it perfect,
I’m getting cold, it’s time to go…

White feathers falling,
forming pillows,
on which you rest your little head,
my snow angel,

And here I find you,
sleeping soundly,
white surrounds us,
here we hide.

My little darling,
how I miss you,
I hope you find,
the other side,

Soft feather falling,
making pillows,
on which you rest your little head,
my snow angel.


Irish Joe – a song

The autumn leaves have fallen,
In the air, the hint of snow,
The wind blows, and you know
you’re gonna have ta’ have just one more cup-a-joe.
(Irish Joe…)
 
[chorus]
Some like it dressed with cream and sugar,
Some like it black as midnight’s toll.
Some like it laced with mint and molly.
For me, a little ‘Daniels, hard and bold.
 
Down the street, the lights are glarin’
and the kids keep right on starin’
And you know, that the show
only lasts through December, so…
 
we go..
 
.. and head out cuz the cold won’t stop us
struggle with the cash and fuss,
with the ribbons and bows, don’t you know,
that the sales are ending,
and the families, each of them sending,
you photos, you pose, touch your nose,
 
give a wink and you think that the day after Christmas is sad,
that’s too bad,
aren’t you glad,
that it only comes once every year,
like a fad.
I certainly hope that you like the two shirts that we got you,
they’re plaid.
Insert chorus
 
[chorus]
Some like it dressed with cream and sugar,
Some like it black as midnight’s toll.
Some like it laced with mint and molly.
For me, a little ‘Daniels, hard and bold.
 
But you know,
when the old year is ending,
winter, its cold fingers tending,
the garden of icicle rows,
hanging low, seem to flow,
down into the mountains of snow;
that it’s time for an extra delightful,
hot piping, steam curling,
tall cup of joe,
Don’t you know.
(Irish Joe…)
 
[chorus]
Some like it dressed with cream and sugar,
Some like it black as midnight’s toll.
Some like it laced with mint and molly.
For me, a little ‘Daniels, hard and bold.
 
(Irish Joe…,
hot and strong and heady,
smooth Irish Joe…)

So you wrote a novel…

So, you wrote a novel.
Hey, so did I!

And you know what? So did at least fifty thousand other folks — just this year alone.

(That number might be as high as 100k, or as low as 30k, but 50k is a conservative number to work with. And I’m going to over simplify here, as I do throughout this blog, but the general premises are worthy of exploration.)

50,000 authors who are looking to get published.
50,000 authors who want YOU to read their story.
50,000 authors, 90% of whom, will never see the inside of a bookstore.

Most authors will self-publish, because, that’s the only way to get their creation onto those warm white printed pages. (www.blurb.com)

But *all* authors will probably try to get their work submitted to a publisher. Which means that’s 50,000 queries and manuscripts that need to be analyzed — by humans. Let’s stand back and take a look at this as an information processing problem. Literary agents and publishers are sifting through tens of thousands of novels, the haystack, to find the ten or fifty needles that thread the reader onto its string of emotional attachment. Stories that will win awards, rise in the ratings and hopefully pay for themselves and all the wannabe novels that flop.

That’s a shit ton of critical analysis reading to do — accurately and quickly.

Enter, stage left, Deepmind.

Google’s Deepmind neural networked general intelligence platform is designed to take data, any data, and learn it. Want Deepmind to find cats in a picture? Find terrorist threats in email? Learn to mimic the human voice, or parse and replicate Shakespeare? First you need to train it. Engineers take a training set, say the top 500 most popular novels of the 20th century, and the worst 500, and they feed them to Deepmind. DM eats them like lollipops, licking and linking all the nuance of language, cadence, sentence structure, word selection, scene usage from these novels. It doesn’t “understand” them, but it doesn’t need to. It just has to learn: that’s good, and that’s not.

Then you take the next 100 top sellers and a the next worst 100 and test the dynamically constructed mind that was created from the original training. Editors would stand by to give hints and advice to the neural network, edge cases that Deepmind would miss. Eventually, the Automated Literary Analysis Neural Network ALANN, could now be opened to the public. Budding authors like you and me could submit our full manuscripts (no queries or synopsis nonsense) and ALANN would swallow up our words and spit back a thumbs up or down, and maybe a critique of what needs improvement.

ALANN wouldn’t be fool proof. But statistically, it would easily reduce the number of needles that proved worthy of closer inspection. ALANN would house manuscripts for years, waiting for trends to return. ALANN would be the single stop shop for finding material for publication.

The idea is not new. Ten years ago the concept was put to work for screenplays:
http://www.forbes.com/2006/12/03/hollywood-dvd-writers-guild-ent-sales-cx_kw_1201wharton.html

Imagine how well a Deepmind ALANN would perform today. If this doesn’t exist today, it will, soon. Billions of dollars ride on the discovery of the next Twilight, Hunger Games, or Harry Potter literary phenomena.