Category Archives: wikipedia

Deep learning

Deep learning (deep machine learning, or deep structured learning, or hierarchical learning, or sometimes DL) is a branch ofmachine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures or otherwise, composed of multiple non-linear transformations.[1][2][3][4][5]

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc.. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition[6]) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.[7]

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.[8]

Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks andrecurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.

Alternatively, deep learning has been characterized as a buzzword, or a rebranding of neural networks.[9][10]

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Flat Earther

Eratosthenes calculated the circumference of the Earth without leaving Egypt. He knew that at local noon on the summer solstice in Syene (modern Aswan, Egypt), the Sun was directly overhead. He knew this because the shadow of someone looking down a deep well at that time in Syene blocked the reflection of the Sun on the water. He measured the Sun’s angle of elevation at noon on the same day in Alexandria. The method of measurement was to make a scale drawing of that triangle which included a right angle between a vertical rod and its shadow. This turned out to be about 7°, or 1/50th of the way around a circle. Taking the Earth as spherical, and knowing both the distance and direction of Syene, he concluded that the Earth’s circumference was fifty times that distance.

His knowledge of the size of Egypt was founded on the work of many generations of surveying trips. Pharaonic bookkeepers gave a distance between Syene and Alexandria of 5,000 stadia (a figure that was checked yearly).  Some say that the distance was corroborated by inquiring about the time that it took to travel from Syene to Alexandria by camel. Carl Sagan says that Eratosthenes paid a man to walk and measure the distance. Some claim Eratosthenes used the Olympic stade of 176.4 m, which would imply a circumference of 44,100 km, an error of 10%,[16] but the 184.8 m Italian stade became (300 years later) the most commonly accepted value for the length of the stade,[16] which implies a circumference of 46,100 km, an error of 15%.[16] It was unlikely, even accounting for his extremely primitive measuring tools, that Eratosthenes could have calculated an accurate measurement for the circumference of the Earth. He made three important assumptions (none of which is perfectly accurate):

  1. That the distance between Alexandria and Syene was 5000 stadia,
  2. That the Earth is a perfect sphere.
  3. That light rays emanating from the Sun are parallel.

Eratosthenes later rounded the result to a final value of 700 stadia per degree, which implies a circumference of 252,000 stadia, likely for reasons of calculation simplicity as the larger number is evenly divisible by 60.[16] In 2012, Anthony Abreu Mora repeated Eratosthenes’ calculation with more accurate data; the result was 40,074 km, which is 66 km different (0.16%) from the currently accepted polar circumference of the Earth.

Seventeen hundred years after Eratosthenes’ death, while Christopher Columbus studied what Eratosthenes had written about the size of the Earth, he chose to believe, based on a map by Toscanelli, that the Earth’s circumference was one-third smaller. Had Columbus set sail knowing that Eratosthenes’ larger circumference value was more accurate, he would have known that the place that he made landfall was not Asia, but rather the New World.

kryptos

Kryptos sculpture at CIA headquarters

In 1988, as a new headquarters for the American Central Intelligence Agency (CIA) was being built in Langley, Virgina, sculptor Jim Sanborn was commissioned to create artwork for the courtyard of the new building.

In 1999, a computer scientist named Jim Gillogly announced that he had solved most of the puzzle. There were four distinct parts to the code, and he had managed to solve the first three.

After his announcement, the CIA revealed that they had actually solved the first three parts internally, the year before. (Later, the U.S. National Security Administration (NSA) also claimed to have solved the first three parts in 1992.)

The fourth section of the code remained unsolved, and to date, no one has brought forth a credible solution for part 4. The code has proven so difficult that its creator, cryptographer and sculptor Jim Sanborn, has provided a tantalizing clue to the New York Times: Characters 64 through 69, the letters N-Y-P-V-T-T, are decoded as B-E-R-L-I-N.

When commenting in 2006 about his error in section 2, Sanborn said that the answers to the first three sections contain clues to the fourth section.[17] In November 2010, Sanborn released a clue, publicly stating that “NYPVTT”, the 64th-69th letters in part four, become “BERLIN” after decryption.

Sanborn gave The New York Times another clue in November 2014: the letters “MZFPK”, the 70th-74th letters in part four, become “CLOCK” after decryption.[20] The 74th letter is K in both the plaintext and ciphertext, meaning that it is possible for a character to encrypt to itself. This means it does not have a weakness, where a character could never be encrypted as itself, that was known to be inherent in the German Enigma machine. It is believed that the “BERLINCLOCK” plaintext may be a direct reference to the Berlin Clock.

Sanborn further stated that in order to solve section 4, “You’d better delve into that particular clock,” but added, “There are several really interesting clocks in Berlin.”[

So what if NSA’s cryptanalysts have also spent their lunch hours—aided by classified code-breaking techniques, massive computing power, and giant stores of data—cracking some of the world’s other great, unsolved cryptographs?

rongorongo

The Bishop questioned the Rapanui wise man, Ouroupano Hinapote, the son of the wise man Tekaki [who said that] he, himself, had begun the requisite studies and knew how to carve the characters with a small shark’s tooth. He said that there was nobody left on the island who knew how to read the characters since the Peruvians had brought about the deaths of all the wise men and, thus, the pieces of wood were no longer of any interest to the natives who burned them as firewood or wound their fishing lines around them!

A. Pinart also saw some in 1877. [He] was not able to acquire these tablets because the natives were using them as reels for their fishing lines!
— Chauvet 1935:381–382

Easter Island was so renamed after Dutch explore Jacob Roggeveen “discovered” the land on Easter Sunday in 1722. Rapa Nui (“Big Island”) is the indigenous name for the island, its inhabitants, and its language.

Rapa Nui is most famous for its iconic Moai. These are large stone statues that were carved between 1100-1680. There are some 900 Moai scattered across the island; some staring out to sea like guardians of the island, but most face inland, appearing to oversee its inhabitants.

At some point in Rapa Nui’s history, its population diminished rapidly. Until recently, the popular theory to explain this was that the primitive, superstitious natives destroyed their natural resources in order to build the Moai. The prevailing theory is not of ecocide, but of genocide. It appears that contact with colonizers caused the near-annihilation of this ethnic group. In the 19th century, thousands of Rapa Nui were kidnapped by Peruvians and forced into slavery in mines and plantations. Some Rapa Nui were eventually returned to their homeland, but disease and hard labor killed many of them. Those who returned brought back an epidemic of smallpox that decimated the already diminished population. Others emigrated to South America or other Polynesian islands. Today, there are only about 3,000 Rapa Nui left, and it’s been a struggle to piece together their history and preserve their culture.

The indigenous people write the Rapa Nui or Spanish languages using the Latin alphabet, but Rapa Nui once had its own writing system: Rongorongo. In the Rapa Nui language, Rongorongo means to “recite” or “chant”. This writing dates back to the 17th century and its origin is unknown. It may have originated in South America or Polynesia. Alternatively, the script may have been invented on the island. If so, Rongorongo would be one of the world’s few writing systems that evolved independently.

The major discovery of the Rongorongo glyphs occurred in 1868 almost accidentally. The Bishop of Tahiti was given a strange gift of one of these texts (Martin). The text consisted of hieroglyphic writing carved on a small wooden board. However, he was unable to find anyone on Easter Island who understood the language and could decipher the text due to the fact so many of the indigenous people had been lost to disease and slavery.

Although the Rongorongo texts have never been interpreted, cryptographers and historians have determined certain characteristics of the hieroglyphics. The texts were primarily written as historical accounts of the Polynesian people and were not intended to be secret texts. Rather, they chronicled all the historical events of their civilization. At first, the texts were written on paper created from banana leaves; however, after the leaves started to rot, the King had the elite class rewrite the historical texts onto toromiro wood tablets (Martin).

The major impediment to translating the Rongorongo texts is the sheer number of glyphs. The texts contain over one hundred twenty different basic glyphs with almost five hundred other variations on these glyphs (Stollznow). The glyphs include human and animal forms along with geometric shapes. The animals include many birds while the shapes often represent common items the Polynesian people used on Rapa Nui. Since it is a distinctive language and not a text representing other letters, there is not a special key for decoding it.

It is thought that Rongorongo glyphs may represent idiosyncratic mnemonic devices meant to remind the reader of something that is representative of something else, such as using a “knot” symbol used to represent marriage (Martin). This differs from almost all written forms of languages today that have characters representing only sounds or only letters.

Rongorongo texts contain a mixture of symbols and a phonetic alphabet written in a unique style known as reverse boustrophedon (Ager). The text begins in the lower left corner and is read left-to-right. Then the text must be turned one hundred and eighty degrees to read the next line left-to-right, and the process is repeated with each line.

There have been numerous attempts to decipher the rongorongo script of Easter Island since its discovery in the late nineteenth century. As with most undeciphered scripts, many of the proposals have been fanciful. Apart from a portion of one tablet which has been shown to deal with a lunar calendar, none of the texts are understood, and even the calendar cannot actually be read. There are three serious obstacles to decipherment: the small number of remaining texts, comprising only 15,000 legible glyphs; the lack of context in which to interpret the texts, such as illustrations or parallels to texts which can be read; and the fact that the modern Rapanui language is heavily mixed with Tahitian and is unlikely to closely reflect the language of the tablets—especially if they record a specialized register such as incantations—while the few remaining examples of the old language are heavily restricted in genre and may not correspond well to the tablets either.[1]

Since a proposal by Butinov and Knorozov in the 1950s, the majority of philologists, linguists and cultural historians have taken the line that rongorongo was not true writing but proto-writing, that is, an ideographic– and rebus-based mnemonic device, such as the Dongba script of the Nakhi people,[note 1] which would in all likelihood make it impossible to decipher.[3] This skepticism is justified not only by the failure of the numerous attempts at decipherment, but by the extreme rarity of independent writing systems around the world. Of those who have attempted to decipher rongorongo as a true writing system, the vast majority have assumed it was logographic, a few that it was syllabic or mixed. Statistically it appears to have been compatible with neither a pure logography nor a pure syllabary.[4] The topic of the texts is unknown; various investigators have speculated they cover genealogy, navigation, astronomy, or agriculture. Oral history suggests that only a small elite were ever literate, and that the tablets were considered sacred.

lunar Rapa Nui calendar, none of the texts are understood. There are three serious obstacles to decipherment, assuming rongorongo is truly writing: the small number of remaining texts, the lack of context such as illustrations in which to interpret them, and the poor attestation of the Old Rapanui language, since modern Rapanui is heavily mixed with Tahitian and is therefore unlikely to closely reflect the language of the tablets.[57]

The prevailing opinion is that rongorongo is not true writing but proto-writing, or even a more limited mnemonic device for genealogy, choreography, navigation, astronomy, or agriculture. For example, the Atlas of Languages states, “It was probably used as a memory aid or for decorative purposes, not for recording the Rapanui language of the islanders.”[58] If this is the case, then there is little hope of ever deciphering it.[note 19] For those who believe it to be writing, there is debate as to whether rongorongo is essentially logographic or syllabic, though it appears to be compatible with neither a pure logography nor a pure syllabary.[59]

Glyph 001 Glyph 002 Glyph 003 Glyph 004 Glyph 005 Glyph 006 Glyph 007 Glyph 008 Glyph 009 Glyph 010 Glyph 014 Glyph 015 Glyph 016
01 02 03 04 05 06 07 08 09 10 14 15 16
Glyph 022 Glyph 025 Glyph 027 Glyph 028 Glyph 034 Glyph 038 Glyph 041 Glyph 044 Glyph 046 Glyph 047 Glyph 050 Glyph 052 Glyph 053
22 25 27AB 28 34 38 41 44 46 47 50 52 53
Glyph 059 Glyph 060 Glyph 061 Glyph 062 Glyph 063 Glyph 066 Glyph 067 Glyph 069 Glyph 070 Glyph 071 Glyph 074 Glyph 076 Glyph 091
59 60 61 62 63 66 67 69 70 71 74 76 91
Glyph 095 Glyph 099 Glyph 200 Glyph 240 Glyph 280 Glyph 380 Glyph 400 Glyph 530 Glyph 660 Glyph 700 Glyph 720 Glyph 730 Glyph 901
95 99 200 240 280 380 400 530 660 700 720 730 901
This basic inventory of rongorongo, proposed by Pozdniakov & Pozdniakov (2007), accounts for 99.7% of the intact texts, except for the idiosyncratic Staff.

Computer encoding

The SMP range 1CA80–1CDBF has been tentatively allocated for encoding the Rongorongo script.[60] An encoding proposal has been written by Michael Everson.[61]

Artificial Intelligence – Depth First Search(DFS)

Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. One starts at the root (selecting some arbitrary node as the root in the case of a graph) and explores as far as possible along each branch before backtracking.

A version of depth-first search was investigated in the 19th century by French mathematician Charles Pierre Trémaux[1] as a strategy for solving mazes.

Algorithmic Thoughts - Artificial Intelligence | Machine Learning | Neuroscience | Computer Vision

Okay! So this is my first blog post!

I will start by talking about the most basic solution to search problems, which are an integral part of artificial intelligence.

What the hell are search problems?

In simple language, search problems consist of a graph, a starting node and a goal(also a node). Our aim while solving a search problem is to get a path from the starting node to the goal.

Consider the diagram below, we want to get to the node G starting from the node S.

Which path will we get on solving the search problem? How do we get the path? This is where algorithms come into picture and answer all our questions! We will look at Depth First Search which can be seen as a brute force method of solving a search problem.

Creating the search tree

So how do we simplify this problem? If we…

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Artificial Intelligence – A* Search Algorithm

In computer science, A* (pronounced as “A star” ( listen)) is a computer algorithm that is widely used in pathfinding and graph traversal, the process of plotting an efficiently directed path between multiple points, called nodes. It enjoys widespread use due to its performance and accuracy. However, in practical travel-routing systems, it is generally outperformed by algorithms which can pre-process the graph to attain better performance,[1] although other work has found A* to be superior to other approaches.[2]

Peter Hart, Nils Nilsson and Bertram Raphael of Stanford Research Institute (now SRI International) first described the algorithm in 1968.[3] It is an extension of Edsger Dijkstra’s 1959 algorithm. A* achieves better performance by using heuristics to guide its search.

Algorithmic Thoughts - Artificial Intelligence | Machine Learning | Neuroscience | Computer Vision

We will try to improve the efficiency of the Uniform Cost Search algorithm by using heuristics which we discussed in the previous post. By improving the efficiency I mean that the algorithm will expand less of the search tree and will give the optimal result faster. We start with the same search problem and search tree that we used for Uniform Cost Search. If you don’t know what search problems are or how search trees are created, visit this post.

searchProblem
searchTree
A* Search Algorithm

We saw that Uniform Cost Search was optimal in terms of cost for a weighted graph. Now our aim will be to improve the efficiency of the algorithm with the help of heuristics. If you don’t know what heuristics are, visit this post. Particularly, we will be using admissible heuristics for A* Search.

A* Search also makes use of a priority queue just like Uniform…

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