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He also tried to build a Marconi transmitter from metal player-piano parts. When he plugged the contraption in, the entire house went dark. He never did get that transmitter to work. When the Arab-Israeli War began in , Ziv was in high school. Drafted into the Israel Defense Forces, he served briefly on the front lines until a group of mothers held organized protests, demanding that the youngest soldiers be sent elsewhere.

Ziv's reassignment took him to the Israeli Air Force, where he trained as a radar technician. When the war ended, he entered Technion—Israel Institute of Technology to study electrical engineering. After completing his master's degree in , Ziv returned to the defense world, this time joining Israel's National Defense Research Laboratory now Rafael Advanced Defense Systems to develop electronic components for use in missiles and other military systems.

The trouble was, Ziv recalls, that none of the engineers in the group, including himself, had more than a basic understanding of electronics.

Their electrical engineering education had focused more on power systems. It wasn't enough. The group's goal was to build a telemetry system using transistors instead of vacuum tubes. They needed not only knowledge, but parts. Ziv contacted Bell Telephone Laboratories and requested a free sample of its transistor; the company sent In , Ziv was selected as one of a handful of researchers from Israel's defense lab to study abroad.

That program, he says, transformed the evolution of science in Israel. 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. Decoding in this way is even simpler, because the decoder doesn't have to identify unique sequences.

Instead it finds the locations of the sequences by following the pointers and then replaces each pointer with a copy of the relevant sequence. The algorithm did everything Ziv and Lempel had set out to do—it proved that universally optimum lossless compression without preprocessing was possible. Eventually, though, researchers recognized the algorithm's practical implications, Weissman says. Ziv and Lempel kept working on the technology, trying to get closer to entropy for small data files.

That work led to LZ Ziv says LZ78 seems similar to LZ77 but is actually very different, because it anticipates the next bit. You can imagine these two sequences as the first branches of a tree. And then you extend the dictionary by adding two more possibilities to the selected branch of the tree. As you do that repeatedly, sequences that appear more frequently will grow longer branches. The methods became known as LZ77 and LZ78 and are still in use today.

They knew their development would be commercially useful, and they wanted to patent it. But they said that it's not possible to get a patent unless it's a piece of hardware, and they were not interested in trying. Supreme Court didn't open the door to direct patent protection for software until the s. However, Lempel's employer, Sperry Rand Corp. It got around the restriction on software patents by building hardware that implemented the algorithm and patenting that device.

It was the LZW variant that spread most widely. It made us famous, and we also enjoyed the research it led us to. One concept that followed came to be called Lempel-Ziv complexity, a measure of the number of unique substrings contained in a sequence of bits. The fewer unique substrings, the more a sequence can be compressed. This measure later came to be used to check the security of encryption codes; if a code is truly random, it cannot be compressed.

Lempel-Ziv complexity has also been used to analyze electroencephalograms—recordings of electrical activity in the brain—to determine the depth of anesthesia , to diagnose depression , and for other purposes. Researchers have even applied it to analyze pop lyrics , to determine trends in repetitiveness.

Over his career, Ziv published some peer-reviewed papers. While the and papers are the most famous, information theorists that came after Ziv have their own favorites. For Shlomo Shamai, a distinguished professor at Technion, it's the paper that introduced the Wyner-Ziv algorithm , a way of characterizing the limits of using supplementary information available to the decoder but not the encoder.

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