In her sadness she trod weary along the road .
Not knowing  where to go
Or  what she should do.
Time wasn't on her side.

The darkness besotted her
She hid from her searing pain
She felt alone
That no-one could possibly empathize.
She was the worst enemy of her soul.

Ron Gavalik Jul 19

Along the shore of the Allegheny River in Pittsburgh
a little girl of about seven, dressed in a track suit
threw chunks of bread to nearby ducks and geese.
The geese, twice the size of their mallard brethren,
aggressively pushed between the ducks
to gobble up each morsel.
The girl placed her hands on her hips
and scolded the winged despots for their greed.
A few of the ducks joined in the protest,
and quacked in solidarity, for justice.
The geese remained undeterred in their conquest.
Clearly frustrated, the little girl gave up.
She handed the bag of bread to her mother
and then ran off to join a group of older children
playing frisbee in a nearby grass field.
The ruling geese and the victimized ducks
continued to swim near the shore,
hungry and confused,
and without that reliable food source.

Observation
Jobira Jul 6

I kept falling down
deep into your dreams
my imagination and body
Completely overtaken by your sins
Thirsty and hungry
By your seductive temptations, which pulled me
Way deeper into the dark shallow water
Like a featherless bird, shutdown  
from the sky by gravity.

At first, my lungs gasped for air
Like balloons for helium
When the foreign allergic
Awakening pleasures of your touches
Made my body react to your sins
Like bitten by a bloodthirsty vampires;

As I kept falling deeper and deeper,
I became more hungrier and thirstier
For your pleasures,
So I lost the good fight and
Gave in to my hunger and thirst
Until my parched body satisfied
With your pleasures.

As I was getting closer to
the bottom of the ocean, before I
hit the ground,
I kept my wings closed tighter and
Took my head out of the game
For the chances of my survival
With the distance and speed
I was traveling was predictable;

By estimation,
the sampled outcome seemed
To have a zero chance
I could avoid your company
And pleasure, and make it alive
Out of the deep dark shadow water
As I kept falling down
deep into your dreams.

So I took my head, out of the game
And keep falling down, deeper and deeper
Into your dreams.

When you get down
On your knees in front of me
I panic:
                 “Please don't.

I'm so used to
  Being the one to kneel
  Being your submissive
  Being the one
To caress your sensitive sugar cookie
With the tip of my tongue
Just not the other way around:
                 “Stop.

I'm scared
Because what if
You don't like the way I taste
What if
I'm not sweet enough for you
The thought
Burns up my insides:
                 “please don't…

But when you plant
A candy coated kiss
On my quivering
Inner thigh
I can feel myself
Dripping
My frosting
Creating a damp oval
On the bed spread
A gentle moan escapes my lips:
                 “stop…

Yet
Your body is hungry
And my words
Only make your stomach growl

So you lean forward anyway
And kiss away at the sugar
My tension growing:
                 “Please don't. Stop.
Dripping and melting
Into a pleading whimper
                 “Please don't stop.

Evidently
I seem to be sweet enough for you.

Just thought I'd have a little fun and write a sequel to Dirty Sweets (for those of you who are a fan of that poem) :D

.
                              Some say the scientific method
                              Is the ultimate algorithm and others
                              Prefer prayer.

For symbolists, all intelligence can be reduced to manipulating symbols, in the same way that a mathematician solves equations by replacing expressions by other expressions. Symbolists understand that you can't learn from scratch: you need some initial knowledge to go with the data. They've figured out how to incorporate pre-existing knowledge into learning, and how to combine different pieces of knowledge on the fly in order to solve new problems. Their master algorithm is inverse deduction, which figures out what knowledge is missing in order to make a deduction go through, and then makes it as general as possible.

                              Tea
                    ­          In its simplicity
                              Can sustain concentration

For connectionists, learning is what the brain does, and so what we need to do is reverse engineer it. The brain learns by adjusting the strengths of connections between neurons, and the crucial problem is figuring out which connections are to blame for which errors and changing them accordingly. The connectionists' master algorithm is back propagation, which compares a system's outputs with the desired one and then successively changes the connections in layer after layer of neurons so as to bring the output closer to what it should be.

                              Hungry and cold
                              A holy condition
                              A warrior's position in the world
                              
Evolutionaries believe that the mother of all learning is natural selection. If it made us, it can make anything, and all we need to do is simulate it on the computer. The key problem that evolutionaries solve is learning structure: not just adjusting parameters, like back propagation does, but creating the brain that these adjustments can then fine-tune. The evolutionaries' master algorithm is genetic programming, which mates and evolves computer programs in the same way that nature mates and evolves organisms.

                              Arithmetic
            ­                  A good shit's the metric
                              Of a dying man

Bayesians are concerned above all with uncertainty. All learned knowledge is uncertain, and learning itself is a form of uncertain inference. The problem then becomes how to deal with noisy, incomplete, and even contradictory information without falling apart. The solution is probabilistic inference, and the master algorithm is Bayes' theorem and its derivatives. Bayes' theorem tell us how to incorporate new evidence into our beliefs, and probabilistic inference algorithms do that as efficiently as possible.

                              I can't believe
                              I won't live forever, therefore,
                              I made up an afterlife to go with reincarnation

For analogizers, the key to learning is recognizing similarities between situations and thereby inferring other similarities. If two patients have similar symptoms, perhaps they have the same disease. The key problem is judging how similar two things are. The analogizers' master algorithm is the support vector machine, which figures out which experiences to remember and how to combine them to make new predictions.

                              Prepare for a powerful anesthesia
                              Chemical processes irresistible
                              A good and perfect rest

www.ronnowpoetry.com

--Domingos, Pedro, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, Basic Books, 2015.
BE McComb Jun 4

spinach has blown
down my neck
and drifted gently
under my ribs

(i'm the salad fork carefully
rolling coffee beans
in drippy melted
warm dark chocolate)


i'm hungry but
not in the way where
my stomach growls
in the way where
i want to cry
but i've got to keep my
$20 teeth fresh and
minty at all times

the mirror
is broken

cracked in so many places
i'm more jagged lines than person
a mosaic of pieces that don't match
and parts i don't like

the truth is i
am flawed
and i will always
be flawed

and i may never
stop looking in
a broken mirror
wishing to smash
my body on its
sharpest edges

but i'm slipping
into a comatose
state of control
and loathing

(the more dead i get
the more alive i look)


when will i snap
out of this
when will i snap
out of this

(I DON'T WANT TO
SNAP OUT OF THIS
I DON'T WANT TO
SNAP OUT OF THIS)


stir the greens
rip the chicken
orange stings
the minty sores

chew chew chew
chew chew chew
chew chew chew
chew chew chew
chew chew chew
chew chew chew
chew chew chew
chew chew chew
chew chew chew
chew chew chew

swallow

take a bite
leave a bite
too much
too little
still hungry

always hungry

but it will all feel better
another ten pounds down

Copyright 6/3/17 by B. E. McComb
Trish Jun 2

Groggy but awake
It's a little late
I want cake

Kendra Mack Apr 23

I like ‘em scrambled
I like ‘em fried
I like ‘em sunny
Don’t matter which side

I like ‘em easy
I like ‘em boiled
I like ‘em cheesy
Just please never spoiled

'Cause that's gross.

I'm hungry
saranade Apr 20

My hand held out...
...to guard your back
When your friendships lacked
...to give money or supplies
When you couldn't survive
...to hold your hand
When you needed support
...to give you a hug
When you needed love
...to high five yours
At all of your endeavors
...to pat on your back
When you succeeded this or that
...to throw a thumbs-up
Because you never gave up

My hand held out...
...to cover my eyes
Through all of the lies
...to hide evidence
When you lacked common sense
...to understand the unreal
Amounts of items you'd steal
...to my chin to stipulate
The way you'd manipulate
...to cover my heart and divert
From your stories that hurt.

I could do this when I had two hands.
I could juggle these separate demands.
My dominant hand is limp now.
The tasks I take on are now simple.
I can only do one thing at a time.
Like, write out this single line rhyme.

When you see my hand out...
...from utter desperation
Please don't tabulate your accommodation
...remember I never asked before my disability
That you had previously admired my stability
...homeless, dirty and hungry
Offer to help me, without charging money
...keep in mind, it's the only one I have
My abilities and tasks all need to be halves
...perhaps don't act put-out or surprised
Because the person who's asking is paralyzed.

I feel like my sister is so concerned with money, she didn't offer help to her newly disabled sister (me) until I could pay her. When things got worse, she didn't even check on me because she knew I had no money.
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