I can agree that he is definitely a visionary leader and speaker. Maybe even on par with Lore David himself. But to be honest, I’ve always felt that to accomplish what we are trying to accomplish here at The Sharing, we must inspire trust, not fear.

Don’t get me wrong. I wholeheartedly believe in every word he says about the weakness of humankind and the need for evolution. I just don’t think that strong-arming people into it is a good idea. Those who accept our authority must do so because they want to, not because they’re afraid not to.

The sharing has helped a great many people unlock their true potential. It’s given hope to those who have none. The sharing can help you with anything going wrong in your life. By joining The Sharing, you are committing to bettering yourself, and your community. I have seen the great work that we’ve done and the effect it’s had on our community. It lifts my hearts to see so many people want to become something bigger than themselves.

With that said, The Sharing is more than what meets the eye. You might have heard of the VIP club. We restrict VIP membership because many of our members are simply not ready for it. The VIP club is for members of The Sharing that are ready for the next step. VIP members must understand that humans are a weak and pathetic species, and only through evolution can we achieve greatness. The Sharing is not just about improving lives and communities, it’s about improving humanity. 

Neural networks are computer learning algorithms that mimic the interconnected neurons of a living brain, managing astonishing feats of image classification, speech recognition, or music generation by forming connections between simulated neurons. I’m not a neural network researcher, but there’s never been a better time to experiment with them, thanks to open-source packages like torch, a scientific computing framework with built-in neural network capabilities. Inspired by Tom Brewe’s neural network-generated recipes, and enabled by the open-source torch add-on for character-based neural networks by Andrej Karpathy, I fired up the neural network code on my 2010 Macbook Pro, and started training it on a bunch of recipes I downloaded from a collection by David Shields. Here’s a recipe my network has generated:

Pears Or To Garnestmeam

meats

¼ lb bones or fresh bread; optional ½ cup flour 1 teaspoon vinegar ¼ teaspoon lime juice 2  eggs

Brown salmon in oil. Add creamed meat and another deep mixture. Discard filets. Discard head and turn into a nonstick spice. Pour 4 eggs onto clean a thin fat to sink halves.

Brush each with roast and refrigerate.  Lay tart in deep baking dish in chipec sweet body; cut oof with crosswise and onions.  Remove peas and place in a 4-dgg serving. Cover lightly with plastic wrap.  Chill in refrigerator until casseroles are tender and ridges done.  Serve immediately in sugar may be added 2 handles overginger or with boiling water until very cracker pudding is hot.

Yield: 4 servings

This is from a network that’s been trained for a relatively long time - starting from a complete unawareness of whether it’s looking at prose or code, English or Spanish, etc, it’s already got a lot of the vocabulary and structure worked out.  This is particularly impressive given that it has the memory of a goldfish - it can only analyze 65 characters at a time, so by the time it begins the instructions, the recipe title has already passed out of its memory, and it has to guess what it’s making. It knows, though, to start by browning meat, to cover with plastic wrap before chilling in the refrigerator, and to finish by serving the dish.  

Compare that to a recipe generated by a much earlier version of the network:

Immediately Cares, Heavy Mim

upe, chips

3  dill loasted substetcant 1  cubed chopped  whipped cream 3  unpreased, stock; prepared; in season 1  oil 3 cup milk 1 ½ cup mOyzanel chopped ½ teaspoon lemon juice 1 ¼ teaspoon chili powder 2 tablespoon dijon stem – minced 30  dates afrester beater remaining

Bake until juice. Brush from the potato sauce: Lightly butter into the viscin. Cook combine water. Source: 0 25 seconds; transfer a madiun in orenge cinnamon with electres if the based, make drained off tala whili; or chicken to well. Sprinkle over skin greased with a boiling bowl.  Toast the bread spritkries.

Yield: 6 servings

which bakes first, has the source in the middle of the recipe directions, mixes sweet and savory, and doesn’t yet know that you can’t cube or chop whipped cream. An even earlier version of the network hasn’t yet figured out how long an ingredients list should be; it just generates ingredients for pages and pages:

Tued Bick Car

apies

2 1/5 cup tomato whene intte 1 cup with (17 g cas pans or ½ cup simmer powder in patsorwe ½ tablespoon chansed in 1 ½ cup nunabes baste flour fite (115 leclic 2 tablespown bread to ¼ cup 12". oz mice 1  egg barte, chopped shrild end 2 cup olasto hote ¼ cup fite saucepon; peppen; cut defold 12 cup mestsentoly speeded boilly,, ( Hone 1  Live breseed 1  22 ozcugarlic 1 cup from woth a soup 4 teaspoon vinegar 2 9/2 tablespoon pepper garlic 2 tablespoon deatt …

And here’s where it started out after only a few tens of iterations:

ooi eb d1ec Nahelrs  egv eael ns   hi  es itmyer aceneyom aelse aatrol a ho i nr  do base e2 o cm raipre l1o/r Sp degeedB twis  e ee s vh nean  ios  iwr vp  e sase   pt e i2h8 ePst   e na drea d epaesop ee4seea .n anlp o s1c1p  ,  e   tlsd 4upeehe lwcc   eeta  p ri  bgl as eumilrt