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Showing posts with label history. Show all posts
Showing posts with label history. Show all posts
Sunday, August 28, 2016
This day in history the first tweet
from The Universal Machine http://universal-machine.blogspot.com/
Tuesday, August 9, 2016
Moore’s Law Part 1 Brief history of Moores Law and current state
This is the first entry of a series focused on Moore’s Law and its implications moving forward, edited from a White paper on Moore’s Law, written by Google University Relations Manager Michel Benard. This series quotes major sources about Moore’s Law and explores how they believe Moore’s Law will likely continue over the course of the next several years. We will also explore if there are fields other than digital electronics that either have an emerging Moores Law situation, or promises for such a Law that would drive their future performance.
Moores Law is the observation that over the history of computing hardware, the number of transistors on integrated circuits doubles approximately every two years. The period often quoted as "18 months" is due to Intel executive David House, who predicted that period for a doubling in chip performance (being a combination of the effect of more transistors and their being faster). -Wikipedia
Moore’s Law is named after Intel co-founder Gordon E. Moore, who described the trend in his 1965 paper. In it, Moore noted that the number of components in integrated circuits had doubled every year from the invention of the integrated circuit in 1958 until 1965 and predicted that the trend would continue "for at least ten years". Moore’s prediction has proven to be uncannily accurate, in part because the law is now used in the semiconductor industry to guide long-term planning and to set targets for research and development.
The capabilities of many digital electronic devices are strongly linked to Moores law: processing speed, memory capacity, sensors and even the number and size of pixels in digital cameras. All of these are improving at (roughly) exponential rates as well (see Other formulations and similar laws). This exponential improvement has dramatically enhanced the impact of digital electronics in nearly every segment of the world economy, and is a driving force of technological and social change in the late 20th and early 21st centuries.
Most improvement trends have resulted principally from the industry’s ability to exponentially decrease the minimum feature sizes used to fabricate integrated circuits. Of course, the most frequently cited trend is in integration level, which is usually expressed as Moore’s Law (that is, the number of components per chip doubles roughly every 24 months). The most significant trend is the decreasing cost-per-function, which has led to significant improvements in economic productivity and overall quality of life through proliferation of computers, communication, and other industrial and consumer electronics.
All of these improvement trends, sometimes called “scaling” trends, have been enabled by large R&D investments. In the last three decades, the growing size of the required investments has motivated industry collaboration and spawned many R&D partnerships, consortia, and other cooperative ventures. To help guide these R&D programs, the Semiconductor Industry Association (SIA) initiated the National Technology Roadmap for Semiconductors (NTRS) in 1992. Since its inception, a basic premise of the NTRS has been that continued scaling of electronics would further reduce the cost per function and promote market growth for integrated circuits. Thus, the Roadmap has been put together in the spirit of a challenge—essentially, “What technical capabilities need to be developed for the industry to stay on Moore’s Law and the other trends?”
In 1998, the SIA was joined by corresponding industry associations in Europe, Japan, Korea, and Taiwan to participate in a 1998 update of the Roadmap and to begin work toward the first International Technology Roadmap for Semiconductors (ITRS), published in 1999. The overall objective of the ITRS is to present industry-wide consensus on the “best current estimate” of the industry’s research and development needs out to a 15-year horizon. As such, it provides a guide to the efforts of companies, universities, governments, and other research providers or funders. The ITRS has improved the quality of R&D investment decisions made at all levels and has helped channel research efforts to areas that most need research breakthroughs.
For more than half a century these scaling trends continued, and sources in 2005 expected it to continue until at least 2015 or 2020. However, the 2010 update to the ITRS has growth slowing at the end of 2013, after which time transistor counts and densities are to double only every three years. Accordingly, since 2007 the ITRS has addressed the concept of functional diversification under the title “More than Moore” (MtM). This concept addresses an emerging category of devices that incorporate functionalities that do not necessarily scale according to “Moores Law,” but provide additional value to the end customer in different ways.
The MtM approach typically allows for the non-digital functionalities (e.g., RF communication, power control, passive components, sensors, actuators) to migrate from the system board-level into a particular package-level (SiP) or chip-level (SoC) system solution. It is also hoped that by the end of this decade, it will be possible to augment the technology of constructing integrated circuits (CMOS) by introducing new devices that will realize some “beyond CMOS” capabilities. However, since these new devices may not totally replace CMOS functionality, it is anticipated that either chip-level or package level integration with CMOS may be implemented.
The ITRS provides a very comprehensive analysis of the perspective for Moore’s Law when looking towards 2020 and beyond. The analysis can be roughly segmented into two trends: More Moore (MM) and More than Moore (MtM). In the next blog in this series, we will look in the the recent conclusions mentioned in the ITRS 2012 report on both trends.
The opportunities for more discourse on the impact and future of Moore’s Law on CS and other disciplines are abundant, and can be continued with your comments on the Research at Google Google+ page. Please join, and share your thoughts.
Read More..
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Moores Law is the observation that over the history of computing hardware, the number of transistors on integrated circuits doubles approximately every two years. The period often quoted as "18 months" is due to Intel executive David House, who predicted that period for a doubling in chip performance (being a combination of the effect of more transistors and their being faster). -Wikipedia
Moore’s Law is named after Intel co-founder Gordon E. Moore, who described the trend in his 1965 paper. In it, Moore noted that the number of components in integrated circuits had doubled every year from the invention of the integrated circuit in 1958 until 1965 and predicted that the trend would continue "for at least ten years". Moore’s prediction has proven to be uncannily accurate, in part because the law is now used in the semiconductor industry to guide long-term planning and to set targets for research and development.
The capabilities of many digital electronic devices are strongly linked to Moores law: processing speed, memory capacity, sensors and even the number and size of pixels in digital cameras. All of these are improving at (roughly) exponential rates as well (see Other formulations and similar laws). This exponential improvement has dramatically enhanced the impact of digital electronics in nearly every segment of the world economy, and is a driving force of technological and social change in the late 20th and early 21st centuries.
Most improvement trends have resulted principally from the industry’s ability to exponentially decrease the minimum feature sizes used to fabricate integrated circuits. Of course, the most frequently cited trend is in integration level, which is usually expressed as Moore’s Law (that is, the number of components per chip doubles roughly every 24 months). The most significant trend is the decreasing cost-per-function, which has led to significant improvements in economic productivity and overall quality of life through proliferation of computers, communication, and other industrial and consumer electronics.
 |
| Transistor counts for integrated circuits plotted against their dates of introduction. The curve shows Moores law - the doubling of transistor counts every two years. The y-axis is logarithmic, so the line corresponds to exponential growth |
All of these improvement trends, sometimes called “scaling” trends, have been enabled by large R&D investments. In the last three decades, the growing size of the required investments has motivated industry collaboration and spawned many R&D partnerships, consortia, and other cooperative ventures. To help guide these R&D programs, the Semiconductor Industry Association (SIA) initiated the National Technology Roadmap for Semiconductors (NTRS) in 1992. Since its inception, a basic premise of the NTRS has been that continued scaling of electronics would further reduce the cost per function and promote market growth for integrated circuits. Thus, the Roadmap has been put together in the spirit of a challenge—essentially, “What technical capabilities need to be developed for the industry to stay on Moore’s Law and the other trends?”
In 1998, the SIA was joined by corresponding industry associations in Europe, Japan, Korea, and Taiwan to participate in a 1998 update of the Roadmap and to begin work toward the first International Technology Roadmap for Semiconductors (ITRS), published in 1999. The overall objective of the ITRS is to present industry-wide consensus on the “best current estimate” of the industry’s research and development needs out to a 15-year horizon. As such, it provides a guide to the efforts of companies, universities, governments, and other research providers or funders. The ITRS has improved the quality of R&D investment decisions made at all levels and has helped channel research efforts to areas that most need research breakthroughs.
For more than half a century these scaling trends continued, and sources in 2005 expected it to continue until at least 2015 or 2020. However, the 2010 update to the ITRS has growth slowing at the end of 2013, after which time transistor counts and densities are to double only every three years. Accordingly, since 2007 the ITRS has addressed the concept of functional diversification under the title “More than Moore” (MtM). This concept addresses an emerging category of devices that incorporate functionalities that do not necessarily scale according to “Moores Law,” but provide additional value to the end customer in different ways.
The MtM approach typically allows for the non-digital functionalities (e.g., RF communication, power control, passive components, sensors, actuators) to migrate from the system board-level into a particular package-level (SiP) or chip-level (SoC) system solution. It is also hoped that by the end of this decade, it will be possible to augment the technology of constructing integrated circuits (CMOS) by introducing new devices that will realize some “beyond CMOS” capabilities. However, since these new devices may not totally replace CMOS functionality, it is anticipated that either chip-level or package level integration with CMOS may be implemented.
The ITRS provides a very comprehensive analysis of the perspective for Moore’s Law when looking towards 2020 and beyond. The analysis can be roughly segmented into two trends: More Moore (MM) and More than Moore (MtM). In the next blog in this series, we will look in the the recent conclusions mentioned in the ITRS 2012 report on both trends.
The opportunities for more discourse on the impact and future of Moore’s Law on CS and other disciplines are abundant, and can be continued with your comments on the Research at Google Google+ page. Please join, and share your thoughts.
Wednesday, July 27, 2016
The IT History Society
- A global network of IT historians and archivists
- Our exclusive International Database of Historical and Archival Sites
- IT Honor Roll of people who have made a noteworthy contribution to the industry
- IT Hardware and Companies databases
- Research links and tools to aid in the preservation of IT history
- Technology Quotes
- Calendar of upcoming events
- An active blog
- And more
from The Universal Machine http://universal-machine.blogspot.com/
Put the internet to work for you.
Saturday, July 23, 2016
Explore the history of Pop and Punk Jazz and Folk with the Music Timeline
With newspapers, blogs, and websites wrapping up their Top Ten album lists for 2013, we thought wed take a look at some favorite music a bit further in the past as well.
Today, we’re releasing a visualization to show which music has stood the test of time, and how genres and artists have risen and fallen in popularity. The Music Timeline uses aggregated data from Google Play Music to show the changes in music genres over the decades.
This visualization shows which albums from past eras are still in our music libraries today.
Dig deeper into the chart to highlight key artists in each genre and read their stories, and navigate to Google Play to hear their music. For example, by clicking on the Metal stripe, we can see the handoff from Classic Metal to Hair Metal to Alt Metal within the growth of the overall genre, as well as some of the most popular artists that composed each subgenre.
The overall shape of each major genre shows when it hit the scene and when it retreated -- for example, R&B has a long history of resurgences, but Electronica is a strictly recent phenomenon.
Delve into changes in the vocabulary used to name artists and their work -- funk may be over as a genre, but as a band or album name it seems to be timeless.
Or search for a particular artist to see the trajectory of their career -- contrast U2’s long-running reinvention and re-emergence from the ‘80s up to today, versus a one hit wonder like Los del Río’s 1995 Macarena.
For more explorations of how information visualization can make complex data accessible, useful, and even fun, check out the Big Picture project page at research.google.com/bigpicture. We expect this is the first of many collaborations with the Music Recommendations and Discovery team.
Wednesday, March 30, 2016
Croudfunding for a computer history display IBM 5080
from The Universal Machine http://universal-machine.blogspot.com/
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Sunday, March 20, 2016
Operating system History of computers
Operating System
OS(Operating system), OSs are the backbone software to any computer it is the program that allows evering thing else to be added, it is the first piece of software you install on a newly build computer. OSs as we think of them today did not exist until the early 1960s. There are different types of user the two most common are command line and GUI(Graphical User Interface) command line was big in the 60s and 70s.
The two biggest OSs today are Microsofts Windows OS, and Apples Macintosh OS. One of the first GUIs to hit the market was the Apple lisa. It did not sell very well due to the $10,000 price tag. Then apple came out with the macintosh and it sold well due to the drastic price drop. There are many smaller OSs that are gaining popularity one to note is Linux. Linux is built off of UNIX which is ca command line driven OS. Still used by many large corporations,but not the general public due to its large price tag. This is where linux comes in, linux and its variations which there are too many to list are free and some offer a GUI. Linux is a very popular OS which is one of the few that can act as a server with no 3rd party software.
History of computers
Charles Babbage is the man credited with creating the concept of input,storage, processing,and output, when he outlined his idea of the ultimate calculator. The problem with his design was that it was mechanical and vulnerable to mechanical problems like wear. What made computers a viable thing instead of "Thing of fantasy" was the invention of electronic devices. With that we created the vacuum tube, then the transitor that replaced that, and is now the basis for all of our electronics today.
Sources: History of computers
Operating systems
Read More..
OS(Operating system), OSs are the backbone software to any computer it is the program that allows evering thing else to be added, it is the first piece of software you install on a newly build computer. OSs as we think of them today did not exist until the early 1960s. There are different types of user the two most common are command line and GUI(Graphical User Interface) command line was big in the 60s and 70s.
The two biggest OSs today are Microsofts Windows OS, and Apples Macintosh OS. One of the first GUIs to hit the market was the Apple lisa. It did not sell very well due to the $10,000 price tag. Then apple came out with the macintosh and it sold well due to the drastic price drop. There are many smaller OSs that are gaining popularity one to note is Linux. Linux is built off of UNIX which is ca command line driven OS. Still used by many large corporations,but not the general public due to its large price tag. This is where linux comes in, linux and its variations which there are too many to list are free and some offer a GUI. Linux is a very popular OS which is one of the few that can act as a server with no 3rd party software.
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| Best creative commons example i could find. |
History of computers
Charles Babbage is the man credited with creating the concept of input,storage, processing,and output, when he outlined his idea of the ultimate calculator. The problem with his design was that it was mechanical and vulnerable to mechanical problems like wear. What made computers a viable thing instead of "Thing of fantasy" was the invention of electronic devices. With that we created the vacuum tube, then the transitor that replaced that, and is now the basis for all of our electronics today.
Sources: History of computers
Operating systems
Tuesday, March 8, 2016
Distributing the Edit History of Wikipedia Infoboxes
Aside from its value as a general-purpose encyclopedia, Wikipedia is also one of the most widely used resources to acquire, either automatically or semi-automatically, knowledge bases of structured data. Much research has been devoted to automatically building disambiguation resources, parallel corpora and structured knowledge from Wikipedia. Still, most of those projects have been based on single snapshots of Wikipedia, extracting the attribute values that were valid at a particular point in time. So about a year ago we compiled and released a data set that allows researchers to see how data attributes can change over time.
Figure 1. Infobox for the Republic of Palau in 2006 and 2013 showing the capital change.
Many attributes vary over time. These include the presidents of countries, the spouses of people, the populations of cities and the number of employees of companies. Every Wikipedia page has an associated history from which the users can view and compare past versions. Having the historical values of Infobox entries available would provide a historical overview of change affecting each entry, to understand which attributes are more likely to change over time or have a regularity in their changes, and which ones attract more user interest and are actually updated in a timely fashion. We believe that such a resource will also be useful in training systems to learn to extract data from documents, as it will allow us to collect more training examples by matching old values of an attribute inside old pages.
For this reason, we released, in collaboration with Wikimedia Deutschland e.V., a resource containing all the edit history of infoboxes in Wikipedia pages. While this was already available indirectly in Wikimedia’s full history dumps, the smaller size of the released dataset will make it easier to download and process this data. The released dataset contains 38,979,871 infobox attribute updates for 1,845,172 different entities, and it is available for download both from Google and from Wikimedia Deutschland’s Toolserver page. A description of the dataset can be found in our paper WHAD: Wikipedia Historical Attributes Data, accepted for publication at the Language Resources and Evaluation journal.
What kind of information can be learned from this data? Some examples from preliminary analyses include the following:
- Every country in the world has a population in its Wikipedia attribute, which is updated at least yearly for more than 90% of them. The average error rate with respect to the yearly World Bank estimates is between two and three percent, mostly due to rounding.
- 50% of deaths are updated into Wikipedia infoboxes within a couple of days... but for scientists it takes 31 days to reach 50% coverage!
- For the last episode of TV shows, the airing date is updated for 50% of them within 9 days; for for the first episode of TV shows, it takes 106 days.
While infobox attribute updates will be much easier to process as they transition into the Wikidata project, we are not there yet and we believe that the availability of this dataset will facilitate the study of changing attribute values. We are looking forward to the results of those studies.
Thanks to Googler Jean-Yves Delort and Guillermo Garrido and Anselmo Peñas from UNED for putting this dataset together, and to Angelika Mühlbauer and Kai Nissen from Wikipedia Deutschland for their support. Thanks also to Thomas Hofmann and Fernando Pereira for making this data release possible.
Friday, March 4, 2016
BBC Micro Bit and history repeats
from The Universal Machine http://universal-machine.blogspot.com/
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