Word assembler game

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Reload to refresh your session. Its organizers didn't steer the selected young engineers and scientists into particular fields. Instead, they let them pursue any type of graduate studies in any Western nation.

Ziv planned to continue working in communications, but he was no longer interested in just the hardware. He had recently read Information Theory Prentice-Hall, , one of the earliest books on the subject , by Stanford Goldman, and he decided to make information theory his focus.

And where else would one study information theory but MIT, where Claude Shannon, the field's pioneer, had started out? Ziv arrived in Cambridge, Mass. His Ph. So if you invest the computational effort, you can know you are approaching the best outcome possible.

Ziv contrasts that certainty with the uncertainty of a deep-learning algorithm. It may be clear that the algorithm is working, but nobody really knows whether it is the best result possible. He found this work less beautiful. That is why I didn't go into real computer science. Then in , with several other coworkers, he joined the faculty of Technion. Jacob Ziv with glasses , who became chair of Technion's electrical engineering department in the s, worked earlier on information theory with Moshe Zakai.

The two collaborated on a paper describing what became known as the Ziv-Zakai bound. The state of the art in lossless data compression at the time was Huffman coding. This approach starts by finding sequences of bits in a data file and then sorting them by the frequency with which they appear.

Then the encoder builds a dictionary in which the most common sequences are represented by the smallest number of bits. This is the same idea behind Morse code: The most frequent letter in the English language, e, is represented by a single dot, while rarer letters have more complex combinations of dots and dashes. It requires two passes through a data file: one to calculate the statistical features of the file, and the second to encode the data.

And storing the dictionary along with the encoded data adds to the size of the compressed file. Ziv and Lempel wondered if they could develop a lossless data-compression algorithm that would work on any kind of data, did not require preprocessing, and would achieve the best compression for that data, a target defined by something known as the Shannon entropy. It was unclear if their goal was even possible. They decided to find out.

The two came up with the idea of having the algorithm look for unique sequences of bits at the same time that it's compressing the data, using pointers to refer to previously seen sequences. This approach requires only one pass through the file, so it's faster than Huffman coding.

Let's say that first incoming bit is a 1. Now, since you have only one bit, you have never seen it in the past, so you have no choice but to transmit it as is. So you enter into your dictionary Say the next bit is a 0. So in your dictionary you now have and also Here's where the pointer comes in. The next time that the stream of bits includes a or a , the software doesn't transmit those bits.

Instead it sends a pointer to the location where that sequence first appeared, along with the length of the matched sequence. The number of bits that you need for that pointer is very small. If the program appeared more than once, they didn't republish the synopsis. They just said, go back to page x.