Moore’s Law Part 3 Possible extrapolations over the next 15 years and impact

This is the third 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.

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More Moore
We examine data from the ITRS 2012 Overall Roadmap Technology Characteristics (ORTC 2012), and select notable interpolations; The chart below shows chip size trends up to the year 2026 along with the “Average Moore’s Law” line. Additionally, in the ORTC 2011 tables we find data on 3D chip layer increases (up to 128 layers), including costs. Finally, the ORTC 2011 index sheet estimates that the DRAM cost per bit at production will be ~0.002 microcents per bit by ~2025. From these sources we draw three More Moore (MM) extrapolations, that by the year 2025:

• 4Tb Flash multi-level cell (MLC) memory will be in production
• There will be ~100 billion transistors per microprocessing unit (MPU)
• 1TB RAM Memory will cost less than $100

More than Moore
It should be emphasized that “More than Moore” (MtM) technologies do not constitute an alternative or even a competitor to the digital trend as described by Moore’s Law. In fact, it is the heterogeneous integration of digital and non-digital functionalities into compact systems that will be the key driver for a wide variety of application fields. Whereas MM may be viewed as the brain of an intelligent compact system, MtM refers to its capabilities to interact with the outside world and the users.

As such, functional diversification may be regarded as a complement of digital signal and data processing in a product. This includes the interaction with the outside world through sensors and actuators and the subsystem for powering the product, implying analog and mixed signal processing, the incorporation of passive and/or high-voltage components, micro-mechanical devices enabling biological functionalities, and more. While MtM looks very promising for a variety of diversification topics, the ITRS study does not give figures from which “solid” extrapolations can be made. However, we can make safe/not so safe bets going towards 2025, and examine what these extrapolations mean in terms of the user.

Today we have a 1TB hard disk drives (HDD) for $100, but the access speed to data on the disk does not allow to take full advantage of this data in a fully interactive, or even practical, way. More importantly, the size and construction of HDD does not allow for their incorporation into mobile devices, Solid state drives (SSD), in comparison, have similar data transfer rates (~1Gb/s), latencies typically 100 times less than HDD, and have a significantly smaller form factor with no moving parts. The promise of offering several TB of flash memory, cost effectively by 2025, in a device carried along during the day (e.g. smartphone, watch, clothing, etc.) represents a paradigm shift with regard of today’s situation; it will empower the user by moving him/her from an environment where local data needs to be refreshed frequently (as with augmented reality applications) to a new environment where full contextual data will be available locally and refreshed only when critically needed.

If data is pre-loaded in the order of magnitude of TBs, one will be able to get a complete contextual data set loaded before an action or a movement, and the device will dispatch its local intelligence to the user during the progress of the action, regardless of network availability or performance. This opens up the possibility of combining local 3D models and remote inputs, allowing applications like 3D conferencing to become available. The development and use of 3D avatars could even facilitate many social interaction models. To benefit from such applications the use of personal devices such as Google Glass may become pervasive, allowing users to navigate 3D scenes and environments naturally, as well as facilitating 3D conferencing and their “social” interactions.

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.

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Tone An experimental Chrome extension for instant sharing over audio

Posted by Alex Kauffmann, Interaction Researcher, and Boris Smus, Software Engineer

Sometimes in the course of exploring new ideas, well stumble upon a technology application that gets us excited. Tone is a perfect example: its a Chrome extension that broadcasts the URL of the current tab to any machine within earshot that also has the extension installed. Tone is an experiment that we’ve enjoyed and found useful, and we think you may as well.

As digital devices have multiplied, so has the complexity of coordinating them and moving stuff between them. Tone grew out of the idea that while digital communication methods like email and chat have made it infinitely easier, cheaper, and faster to share things with people across the globe, theyve actually made it more complicated to share things with the people standing right next to you. Tone aims to make sharing digital things with nearby people as easy as talking to them.

The first version was built in an afternoon for fun (which resulted in numerous rickrolls), but we increasingly found ourselves using it to share documents with everyone in a meeting quickly, to exchange design files back and forth while collaborating on UI design, and to contribute relevant links without interrupting conversations.

Tone provides an easy-to-understand broadcast mechanism that behaves like the human voice—it doesnt pass through walls like radio or require pairing or addressing. The initial prototype used an efficient audio transmission scheme that sounded terrible, so we played it beyond the range of human hearing. However, because many laptop microphones and nearly all video conferencing systems are optimized for voice, it improved reliability considerably to also include a minimal DTMF-based audible codec. The combination is reliable for short distances in the majority of audio environments even at low volumes, and it even works over Hangouts.

Because its audio based, Tone behaves like speech in interesting ways. The orientation of laptops relative to each other, the acoustic characteristics of the space, the particular speaker volume and mic sensitivity, and even where youre standing will all affect Tones reliability. Not every nearby machine will always receive every broadcast, just like not everyone will always hear every word someone says. But resending is painless and debugging generally just requires raising the volume. Many groups at Google have found that the tradeoffs between ease and reliability worthwhile—it is our hope that small teams, students in classrooms, and families with multiple computers will too.

To get started, first install the Tone extension for Chrome. Then simply open a tab with the URL you want to share, make sure your volume is on, and press the Tone button. Your machine will then emit a short sequence of beeps. Nearby machines receive a clickable notification that will open the same tab. Getting everyone on the same page has never been so easy!

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Download the Hottest mobile ringtones over the web

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Tim Berners Lee didnt expect kittens to take over the web

You may have noticed that its the 25th anniversary of the World Wide Web. Its inventor, Sir Tim Berners-Lee, has naturally been receiving quite a lot of media attention. At an Ask Me Anything event for Reddit last week Tim Berners-Lee was asked the following question:

Q: "What was one of the things you never thought the internet would be used for, but has actually become one of the main reasons people use the internet?"

"Porn," several Redditors prompted.

Tim Berners-Lee replied: "Kittens."

And its true that the Web, and YouTube in particular, have been taken over by cats: kittens, small cats, big cats, LOLcats, Grumpy Cats. So I cant really end this blog post without a cat photo can I.

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

Put the internet to work for you.

via Personal Recipe 895909

Recommended for you

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Syntactic Ngrams over Time

Posted by Yoav Goldberg, Professor at Bar Ilan University & Post-doc at Google 2011-2013

We are proud to announce the release of a very large dataset of counted dependency tree fragments from the English Books Corpus. This resource will help researchers, among other things, to model the meaning of English words over time and create better natural-language analysis tools. The resource is based on information derived from a syntactic analysis of the text of millions of English books.

Sentences in languages such as English have structure. This structure is called syntax, and knowing the syntax of a sentence is a step towards understanding its meaning. The process of taking a sentence and transforming it into a syntactic structure is called parsing. At Google, we parse a lot of text every day, in order to better understand it and be able to provide better results and services in many of our products.

There are many kinds of syntactic representations (you may be familiar with sentence diagramming), and at Google weve been focused on a certain type of syntactic representation called "dependency trees". Dependency-trees representation is centered around words and the relations between them. Each word in a sentence can either modify or be modified by other words. The various modifications can be represented as a tree, in which each node is a word.

For example, the sentence "we really like syntax" is analyzed as:



The verb "like" is the main word of the sentence. It is modified by a subject (denoted nsubj) "we", a direct object (denoted dobj) "syntax", and an adverbial modifier "really".

An interesting property of syntax is that, in many cases, one could recover the structure of a sentence without knowing the meaning of most of the words. For example, consider the sentence "the krumpets gnorked the koof with a shlap". We bet you could infer its structure, and tell that group of something which is called a krumpet did something called "gnorking" to something called a "koof", and that they did so with a "shlap".

This property by which you could infer the structure of the sentence based on various hints, without knowing the actual meaning of the words, is very useful. For one, it suggests that a even computer could do a reasonable job at such an analysis, and indeed it can! While still not perfect, parsing algorithms these days can analyze sentences with impressive speed and accuracy. For instance, our parser correctly analyzes the made-up sentence above.



Lets try a more difficult example. Something rather long and literary, like the opening sentence of One hundred years of solitude by Gabriel García Márquez, as translated by Gregory Rabassa:

Many years later, as he faced the firing squad, Colonel Aureliano Buendía was to remember that distant afternoon when his father took him to discover ice.



Pretty good for an automatic process, eh?

And it doesn’t end here. Once we know the structure of many sentences, we can use these structures to infer the meaning of words, or at least find words which have a similar meaning to each other.

For example, consider the fragments:
"order a XYZ"
"XYZ is tasty"
"XYZ with ketchup"
"juicy XYZ"

By looking at the words modifying XYZ and their relations to it, you could probably infer that XYZ is a kind of food. And even if you are a robot and dont really know what a "food" is, you could probably tell that the XYZ must be similar to other unknown concepts such as "steak" or "tofu".

But maybe you dont want to infer anything. Maybe you already know what you are looking for, say "tasty food". In order to find such tasty food, one could collect the list of words which are objects of the verb "ate", and are commonly modified by the adjective "tasty" and "juicy". This should provide you a large list of yummy foods.

Imagine what you could achieve if you had hundreds of millions of such fragments. The possibilities are endless, and we are curious to know what the research community may come up with. So we parsed a lot of text (over 3.5 million English books, or roughly 350 billion words), extracted such tree fragments, counted how many times each fragment appeared, and put the counts online for everyone to download and play with.

350 billion words is a lot of text, and the resulting dataset of fragments is very, very large. The resulting datasets, each representing a particular type of tree fragments, contain billions of unique items, and each dataset’s compressed files takes tens of gigabytes. Some coding and data analysis skills will be required to process it, but we hope that with this data amazing research will be possible, by experts and non-experts alike.

The dataset is based on the English Books corpus, the same dataset behind the ngram-viewer. This time there is no easy-to-use GUI, but we still retain the time information, so for each syntactic fragment, you know not only how many times it appeared overall, but also how many times it appeared in each year -- so you could, for example, look at the subjects of the word “drank” at each decade from 1900 to 2000 and learn how drinking habits changed over time (much more ‘beer’ and ‘coffee’, somewhat less ‘wine’ and ‘glass’ (probably ‘of wine’). There’s also a drop in ‘whisky’, and an increase in ‘alcohol’. Brandy catches on around 1930s, and start dropping around 1980s. There is an increase in ‘juice’, and, thankfully, some decrease in ‘poison’).

The dataset is described in details in this scientific paper, and is available for download here.

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