Computers start to learn like us

Its always nice to see an article on A.I. in the local newspaper, The New Zealand Herald. So the article titled Autocorrect fails could be distant mammary as computers start to learn like us, by Sarah Knapton made for an interesting read. Heres a quote: "Most mobile phone users have suffered the indignity of an embarrassing autocorrect mistake, but smartphones may soon be smart enough to understand what we are trying to say." Scientists claim that for the first time, they have programmed a machine to learn in the same way as humans. I recommend the full article.

from The Universal Machine http://universal-machine.blogspot.com/

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Free Language Lessons for Computers

Posted by Dave Orr, Google Research Product Manager

Not everything that can be counted counts.

Not everything that counts can be counted.

- William Bruce Cameron

50,000 relations from Wikipedia. 100,000 feature vectors from YouTube videos. 1.8 million historical infoboxes. 40 million entities derived from webpages. 11 billion Freebase entities in 800 million web documents. 350 billion words’ worth from books analyzed for syntax.

These are all datasets that we’ve shared with researchers around the world over the last year from Google Research.

But data by itself doesn’t mean much. Data is only valuable in the right context, and only if it leads to increased knowledge. Labeled data is critical to train and evaluate machine-learned systems in many arenas, improving systems that can increase our ability to understand the world. Advances in natural language understanding, information retrieval, information extraction, computer vision, etc. can help us tell stories, mine for valuable insights, or visualize information in beautiful and compelling ways.

That’s why we are pleased to be able to release sets of labeled data from various domains and with various annotations, some automatic and some manual. Our hope is that the research community will use these datasets in ways both straightforward and surprising, to improve systems for annotation or understanding, and perhaps launch new efforts we haven’t thought of.

Here’s a listing of the major datasets we’ve released in the last year, or you can subscribe to our mailing list. Please tell us what you’ve managed to accomplish, or send us pointers to papers that use this data. We want to see what the research world can do with what we’ve created.

50,000 Lessons on How to Read: a Relation Extraction Corpus

What is it: A human-judged dataset of two relations involving public figures on Wikipedia: about 10,000 examples of “place of birth” and 40,000 examples of “attended or graduated from an institution.”
Where can I find it: https://code.google.com/p/relation-extraction-corpus/
I want to know more: Here’s a handy blog post with a broader explanation, descriptions and examples of the data, and plenty of links to learn more.

11 Billion Clues in 800 Million Documents

What is it: We took the ClueWeb corpora and automatically labeled concepts and entities with Freebase concept IDs, an example of entity resolution. This dataset is huge: nearly 800 million web pages.
Where can I find it: We released two corpora: ClueWeb09 FACC and ClueWeb12 FACC.
I want to know more: We described the process and results in a recent blog post.

Features Extracted From YouTube Videos for Multiview Learning

What is it: Multiple feature families from a set of public YouTube videos of games. The videos are labeled with one of 30 categories, and each has an associated set of visual, auditory, and and textual features.
Where can I find it: The data and more information can be obtained from the UCI machine learning repository (multiview video dataset), or from Google’s repository.
I want to know more: Read more about the data and uses for it here.

40 Million Entities in Context

What is it: A disambiguation set consisting of pointers to 10 million web pages with 40 million entities that have links to Wikipedia. This is another entity resolution corpus, since the links can be used to disambiguate the mentions, but unlike the ClueWeb example above, the links are inserted by the web page authors and can therefore be considered human annotation.
Where can I find it: Here’s the WikiLinks corpus, and tools can be found to help use this data on our partner’s page: Umass Wiki-links.
I want to know more: Other disambiguation sets, data formats, ideas for uses of this data, and more can be found at our blog post announcing the release.

Distributing the Edit History of Wikipedia Infoboxes

What is it: The edit history of 1.8 million infoboxes in Wikipedia pages in one handy resource. Attributes on Wikipedia change over time, and some of them change more than others. Understanding attribute change is important for extracting accurate and useful information from Wikipedia.
Where can I find it: Download from Google or from Wikimedia Deutschland.
I want to know more: We posted a detailed look at the data, the process for gathering it, and where to find it. You can also read a paper we published on the release.

Note the change in the capital of Palau.

Syntactic Ngrams over Time

What is it: We automatically syntactically analyzed 350 billion words from the 3.5 million English language books in Google Books, and collated and released a set of fragments -- billions of unique tree fragments with counts sorted into types. The underlying corpus is the same one that underlies the recently updated Google Ngram Viewer.
Where can I find it: http://commondatastorage.googleapis.com/books/syntactic-ngrams/index.html
I want to know more: We discussed the nature of dependency parses and describe the data and release in a blog post. We also published a paper about the release.

Dictionaries for linking Text, Entities, and Ideas

What is it: We created a large database of pairs of 175 million strings associated with 7.5 million concepts, annotated with counts, which were mined from Wikipedia. The concepts in this case are Wikipedia articles, and the strings are anchor text spans that link to the concepts in question.
Where can I find it: http://nlp.stanford.edu/pubs/crosswikis-data.tar.bz2
I want to know more: A description of the data, several examples, and ideas for uses for it can be found in a blog post or in the associated paper.

Other datasets

Not every release had its own blog post describing it. Here are some other releases:

• Automatic Freebase annotations of Trec’s Million Query and Web track queries.
• A set of Freebase triples that have been deleted from Freebase over time -- 63 million of them.

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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.

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

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Teach Yourself About Desktop Computers Right Now!

By Lee Smith

If you want to find out more about computers, theres a lot of information out there. Thats why this article has a lot of information, so you do not need to search that hard. Put this information to use to find the right computer for you.

You should always have antivirus software running. Make sure that you have this protection program to block viruses. Such dangerous software has the ability to take personal data and also slow down the computer. For continuous protection, you can schedule the anti virus to run and fix problems on a regular basis.

Learn about the add-ons before buying a computer. Many of these models have additional accessories that you can buy. Be sure that you only buy what you need. Also, be aware that some add-ons will be cheaper at other sites. Anything purchased from the computer manufacturer itself is normally priced substantially higher.

Inspect a computers add-ons before you buy it. Youll often be able to purchase extra accessories with the machine. Be sure that you are only buying what you will actually use. Shop around for add-ons. You may find a better price. The manufacturers usually sell their goods at the highest possible prices.

When you are building your own PC, choose the products carefully. Some motherboards will only be compatible with particular processors. Certain RAM units only work well with particular motherboards. When buying the individual computer components it is important they are compatible with each other.

This can save a considerable amount of time and headaches if you build your own computer.

If youre considering a Mac desktop but have PC programs, consider getting Parallels. Parallels for Mac is a type of software that gives you the ability to run the operating system for PC on your Mac computer. That way, you are able to run all your PC programs! You will also need to buy the actual operating system separately.

Go online to read a variety of user reviews before deciding which desktop computer is right for you. There are many options that can be overwhelming, so know what you want to buy ahead of time.

Most computer manufacturers have gone digital with their product information, therefore, be thorough in your research. Make sure the information online is enough information for you. You should always be able to access software and driver downloads.

Diligence is essential considering how many PC makers have started skipping printing out paper documents about their systems in lieu of just posting that information online. Ensure that you can find software, drivers and instructions online, if they are not included in paper form.

Once youve got an idea of the type of desktop thatll work for you, shopping becomes a lot easier. You have the information needed to make a good solid choice. Keep all of these tips in mind when you are shopping.

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Learn more about cloud computing. Stop by Desktop Anywheres site where you can find out all about Desktop Anywhere and what it can do for you.

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