Since the emergence of artificial intelligence and the first computers in the late 1940s, computer scientists compared the performance of these “giant brains” with human minds, and gravitated to chess as a way of testing the calculating abilities of computers. The game is a collection of challenging problems for minds and machines, but has simple rules, and so is perfect for such experiments.

See full list on optimalegezondheid.com.

Over the years, many computers took on many chess masters, and the computers lost.

IBM computer scientists had been interested in chess computing since the early 1950s. In 1985, a graduate student at Carnegie Mellon University, Feng-hsiung Hsu, began working on his dissertation project: a chess playing machine he called ChipTest. A classmate of his, Murray Campbell, worked on the project, too, and in 1989, both were hired to work at IBM Research. There, they continued their work with the help of other computer scientists, including Joe Hoane, Jerry Brody and C. J. Tan. The team named the project Deep Blue. The human chess champion won in 1996 against an earlier version of Deep Blue; the 1997 match was billed as a “rematch.”

Dag-opa-avonturen-met-een-kleinzoon 3/5 Downloaded from blog.godhatesnerds.net on January 31, 2021 by guest Flap geroofd zijn. Maar gelukkig valt het mee! A plot is a series of events that take place in a story. Often a plot determines the entire organization of the story. There are usually five parts to any plot: the introduction, the rising action, the climax, the falling action, and the aftermath. Met die schraper kan je makkelijk verf en vernis afschrapen. Je moet voldoende druk uitoefenen (dus na 15 minuten begin je al goed te zweten), als het harde verf of vernis is, brokkelt dit af en kom je direct op bloot hout. Als je dan over het hout schraapt, kan je filterdunne laagjes afschrapen gelijk een schaaf.

The champion and computer met at the Equitable Center in New York, with cameras running, press in attendance and millions watching the outcome. The odds of Deep Blue winning were not certain, but the science was solid. The IBMers knew their machine could explore up to 200 million possible chess positions per second. The chess grandmaster won the first game, Deep Blue took the next one, and the two players drew the three following games. Game 6 ended the match with a crushing defeat of the champion by Deep Blue.

The match’s outcome made headlines worldwide, and helped a broad audience better understand high-powered computing. The 1997 match took place not on a standard stage, but rather in a small television studio. The audience watched the match on television screens in a basement theater in the building, several floors below where the match was actually held. The theater seated about 500 people, and was sold out for each of the six games. The media attention given to Deep Blue resulted in more than three billion impressions around the world.

Deep Blue had an impact on computing in many different industries. It was programmed to solve the complex, strategic game of chess, so it enabled researchers to explore and understand the limits of massively parallel processing. This research gave developers insight into ways they could design a computer to tackle complex problems in other fields, using deep knowledge to analyze a higher number of possible solutions. The architecture used in Deep Blue was applied to financial modeling, including marketplace trends and risk analysis; data mining—uncovering hidden relationships and patterns in large databases; and molecular dynamics, a valuable tool for helping to discover and develop new drugs.

Ultimately, Deep Blue was retired to the Smithsonian Museum in Washington, DC, but IBM went on to build new kinds of massively parallel computers such as IBM Blue Gene^®. [Read more about this Icon of Progress.]

The Deep Blue project inspired a more recent grand challenge at IBM: building a computer that could beat the champions at a more complicated game, Jeopardy!.

Over three nights in February 2011, this machine—named Watson—took on two of the all-time most successful human players of the game and beat them in front of millions of television viewers. The technology in Watson was a substantial step forward from Deep Blue and earlier machines because it had software that could process and reason about natural language, then rely on the massive supply of information poured into it in the months before the competition. Watson demonstrated that a whole new generation of human - machine interactions will be possible.

Plot y versus x as lines and/or markers.

Call signatures:

The coordinates of the points or line nodes are given by x, y.

The optional parameter fmt is a convenient way for defining basicformatting like color, marker and linestyle. It's a shortcut stringnotation described in the Notes section below.

You can use Line2D properties as keyword arguments for morecontrol on the appearance. Line properties and fmt can be mixed.The following two calls yield identical results:

When conflicting with fmt, keyword arguments take precedence.

Plotting labelled data

There's a convenient way for plotting objects with labelled data (i.e.data that can be accessed by index obj['y']). Instead of givingthe data in x and y, you can provide the object in the dataparameter and just give the labels for x and y:

All indexable objects are supported. This could e.g. be a dict, apandas.DataFame or a structured numpy array.

Plotting multiple sets of data

There are various ways to plot multiple sets of data.

• The most straight forward way is just to call plot multiple times.Example:

• Alternatively, if your data is already a 2d array, you can pass itdirectly to x, y. A separate data set will be drawn for everycolumn.

  Example: an array a where the first column represents the xvalues and the other columns are the y columns:

• The third way is to specify multiple sets of [x], y, [fmt]groups:

  In this case, any additional keyword argument applies to alldatasets. Also this syntax cannot be combined with the dataparameter.

By default, each line is assigned a different style specified by a'style cycle'. The fmt and line property parameters are onlynecessary if you want explicit deviations from these defaults.Alternatively, you can also change the style cycle using the'axes.prop_cycle' rcParam.

See also

scatter

XY scatter plot with markers of varying size and/or color ( sometimes also called bubble chart).

Notes

Format Strings

A format string consists of a part for color, marker and line:

Each of them is optional. If not provided, the value from the stylecycle is used. Exception: If line is given, but no marker,the data will be a line without markers.

Other combinations such as [color][marker][line] are alsosupported, but note that their parsing may be ambiguous.

Markers

Plots Zweten

Line Styles

Plots Zweten Man

Example format strings:

Colors

The supported color abbreviations are the single letter codes

Plots Zweten In Excel

and the 'CN' colors that index into the default property cycle.

Plots Zweten En Beven

If the color is the only part of the format string, you canadditionally use any matplotlib.colors spec, e.g. full names('green') or hex strings ('#008000').