It was the last Sunday in September 2009.
I had just turned in my work, and was about to return to my office when my computer started to show a red crossword with a tiny skull at the bottom.
I immediately flipped it over and started to play.
Then I realized it was the one that was meant to be on the crossword.
I looked down at the red cross.
It was a blank.
I opened the red box and started searching for clues.
When I couldn’t find one, I looked for the clue again.
The answer was nowhere to be found.
I called my mother and told her that I had found the answer.
I also told her about a new technology called a neural network, a kind of supercomputer that I’d seen in the news recently and that I was excited to see for myself.
This was the kind of thing that the news media had been telling me about for years, but the tech hadn’t really caught on in a way that made sense.
But now, thanks to the invention of neural networks, they were beginning to make a difference.
They were making it possible to solve crossword puzzles in real time.
A few weeks later, I was able to win a $50,000 prize from Google.
It made sense to me then that I would be winning the $100,000 reward from Nervous System Solutions, a company based in Dallas, Texas, that had built neural networks into its computer-driven puzzles.
Neural networks were built in a similar way to how we use computers today.
We had a supercomputer built in the 1960s called the DEC Alpha.
It ran on data and had a lot of computing power.
A neural network could do things that were difficult to do on an old computer.
For example, neural networks could be able to handle complex math problems that would take months to solve.
Neural nets could do tasks that humans could never do.
For instance, neural nets could understand what it takes to find a certain number of stars in a galaxy, and could calculate the likelihood of finding a certain star in a particular galaxy.
And the more sophisticated the neural network was, the more likely it was to learn the information it needed to solve the puzzle.
A machine can learn a language and understand what that language means, and that means it can be used to solve a crossword or other crossword-type problem.
Neural machines are very fast, and a lot like humans, they learn and remember.
If you put neural networks to a real-world problem, it can learn.
They are able to process information in a relatively short amount of time.
Neural net experts have said that this can make them a powerful tool for solving puzzles.
The question is: What is it that neural networks are able do?
Neural nets are essentially a super computer.
They have a huge amount of computing horsepower.
They can solve problems that we would never be able for an ordinary computer to do.
The way that neural nets learn and solve problems is a bit different than the way that computers learn and work.
A computer can learn things by using lots of different types of algorithms.
A typical computer would have to take lots of images and then do lots of calculations to figure out what that image shows.
A human would have the ability to solve problems like the crosswords, but it takes a lot more computing power to figure things out, and it requires a lot less training data.
Neural network experts say that the reason neural nets are able, at least initially, to solve these types of puzzles is because they can learn by doing lots of experiments.
A natural question to ask is: How can you use a neural net to solve any kind of crossword?
It’s easy to imagine a network of neurons that can process thousands of images.
But this isn’t the way the brain works.
If a neuron in your brain is trained, the neurons that receive instructions to perform that task can learn something by doing more than just processing a single image.
The brain also learns by working with lots of data.
If it’s a simple task like picking a letter, the brain might learn by sending out a bunch of random letters and seeing how they work.
And it might learn through many different kinds of training.
But for crosswords that have a lot going on, it might be the case that the brain has to learn a lot by doing a lot.
In other words, neural net training involves learning a lot from lots of small examples.
If your brain has learned a lot, then it can solve a lot when it comes to crosswords.
Neural systems can learn as much as a human can learn in one lifetime.
The challenge for neural networks is learning the things that are really important to them.
A lot of neural systems learn by looking at lots of very small things.
For the most part, they just take the data that they’re trained on and learn how to solve them.
But when they have to do