The answer is Smart technology :)
Hey there,
I believe that your correct answer would be that "<span> some people will tell you what they think you want to hear </span>". When you ask someone about the perceptions of your person trait, they will most likely <span>tell you what they think you want to hear because its what they want to make you happy.
For example: Jimmy ask billy "Am I fat Billy"
Billy tells Jimmy "No, Your not fat, you look just great".
But really, Jimmy is very fat.
The point is that people are going to say things that make you feel happy and something that you want to hear.
~Jurgen</span>
Answer:
Learning opportunities for AI can be extended to all through the inclussion of courses and different teaching methods related to Artificial Intelligence, new technologies, computer science, programming and, in short, everything related to the digital world, in the educational plans at the national level. This is so because artificial intelligence and computer systems are the basis for the development of the new tools jobs of tomorrow. Thus, education in these subjects is essential so that citizens can enter the labor market once they acquire the necessary age, having the knowledge to do so in a proper way.
Answer:
It we were asked to develop a new data compression tool, it is recommended to use Huffman coding since it is easy to implement and it is widely used.
Explanation:
The pros and the cons of Huffman coding
Huffman coding is one of the most simple compressing encoding schemes and can be implemented easily and efficiently. It also has the advantage of not being patented like other methods (e.g. arithmetic codingfor example) which however are superior to Huffman coding in terms of resulting code length.
One thing not mentioned so far shall not be kept secret however: to decode our 96 bit of “brief wit” the potential receiver of the bit sequence does need the codes for all letters! In fact he doesn’t even know which letters are encoded at all! Adding this information, which is also called the “Huffman table” might use up more space than the original uncompressed sentence!
However: for longer texts the savings outweigh the added Huffman table length. One can also agree on a Huffman table to use that isn’t optimized for the exact text to be transmitted but is good in general. In the English language for example the letters “e” and “t” occur most often while “q” and “z” make up the least part of an average text and one can agree on one Huffman table to use that on average produces a good (=short) result. Once agreed upon it doesn’t have to be transmitted with every encoded text again.
One last thing to remember is that Huffman coding is not restricted to letters and text: it can be used for just any symbols, numbers or “abstract things” that can be assigned a bit sequence to. As such Huffman coding plays an important role in other compression algorithms like JPG compression for photos and MP3 for audio files.
The pros and the cons of Lempel-Ziv-Welch
The size of files usually increases to a great extent when it includes lots of repetitive data or monochrome images. LZW compression is the best technique for reducing the size of files containing more repetitive data. LZW compression is fast and simple to apply. Since this is a lossless compression technique, none of the contents in the file are lost during or after compression. The decompression algorithm always follows the compression algorithm. LZW algorithm is efficient because it does not need to pass the string table to the decompression code. The table can be recreated as it was during compression, using the input stream as data. This avoids insertion of large string translation table with the compression data.
