Archive for August, 2011

Artificial Intelligence for Everyone

Wednesday, August 24th, 2011

Artificial Intelligence is the Only Way to Keep Pace

Do yourself (and humanity) a favor — sign up and take Stanford’s class Introduction to Artificial Intelligence (AI). It is free. Open to everyone. And online. You have no excuse. (If the prerequisites of knowing linear algebra and probability theory scare you — then overcome your fear by taking a few of the 10-minute classes offered by Salman Khan at Khan Academy (also free, open and online). I regularly use Sal’s classes to refresh my decades-old memory of many long forgotten math classes. Amazing stuff).

Stanford Professor Sebastian Thrun and Google’s Peter Norvig deserve tremendous credit for making this course available to anyone with an Internet connection. Why?  Because if you don’t understand artificial intelligence you won’t understand the future. Stanford and AAAI are showing the kind of leadership in education that  that can (and probably will) spawn a new wave of innovation that will transform our lives even more than the Internet.

This class is so important that everyone at ai-one signed up — even though we already know a bit about artificial intelligence ourselves (which is often called machine learning). My fiance, father, cousins…even my workout buddy also signed up. If nothing else, this class is taught by two of the smartest people working on how to solve problems faster and more accurately by using machines that can learn how to reason and learn patterns with ever decreasing human intervention.

Machine Learning is Very Different from Machine Programming

I often speak with prospective customer for our technology who immediately ask me what a learning machine can do differently than a computer that is programmed. The answer is simple but profound: Machines can now learn like humans — by detecting the meaning of data by detecting inherent patterns and associations of each element within a data set. This means the machine can learn the meaning of whatever data you feed to it. No, it can’t reason — that is, machine’s can’t spontaneously create new thoughts (yet). They can spontaneously detect how a word is related to other words, documents, websites, etc. This way of determining meaning through association is often called a semantic network. Although the concepts for creating a world wide web of semantically linked data has been around for a long time (notably described by Tim Berners-Lee in his famous paper The Semantic Web in 2001).

Linking data is the only to make sense out of it. Without links it is simply a sea of noise. Noise that is growing at an astonishing rate.

Evolve or Die: Why Everyone Needs to Know About Artificial Intelligence

Human currently doubles every 5 years — your cognitive capacity does not.

In fact, cognitive capacities are much the same for any individual human as they were before we learned enough to form civilizations. So we are only as smart as our capacity to learn — and that capacity has limits. One such limit is the Dunbar Number which is the theoretical limit of the number of people with whom you can maintain meaningful relationships. This is thought to be between 100 and 200 people. So even though I might have over 800 Facebook friends — most are people whom I do not have sustainable, long-term relationships. Many of these “friends” are people I knew in high school and have long since lost contact (except through Facebook). Interestingly, about 142 people made a personal effort to wish me a happy birthday (130 were on Facebook) — reflecting a value  that falls within widely accepted values for the Dunbar Number (which can be thought of as a Dunbar Limit).

The news for your brain gets worse. Knowledge is continuing to grow faster. Several leading indicators  (such as the adoption newly patented technologies) indicate that this pace will increase exponentially — as predicted by Ray Kurzweil in his now famous essay The Law of Accelerating Returns. Data grows even faster than human knowledge. Data includes both the factual information (that is useful) and all the outputs of sensing devices. Knowledge is the extraction of meaning from data.

Cisco’s Dave Evan’s estimates there are about 35 billion sensors connected to the Internet — enabling an internet of things. That works out to 7 devices for every human on the planet — and growing.

Artificial intelligence is the only way for humans to evolve as fast as our data. If only a few people know about artificial intelligence then only a few people will reap the benefits. Knowing about AI is essential for us to ensure a future filled with greater liberties and opportunities for everyone’s mutual benefit.

Example to Illustrate Difference Between Data and Knowledge

Sensors record data. To make that data useful (actionable or meaningful) we must use systems (such as software) to process the data into information. For example, my heart rate monitor records each heartbeat and my location over time (it is GPS enabled). I know the exact time and place for each contraction of my heart as indicated by an electrical signal. Each of these data points is meaningless unless I can see a pattern of how all those heartbeats fit together. My goal is to see a pattern where I run faster at a lower heart rate. My monitor is old — so it takes me about 20 minutes to download the data, look at it (using the really bad software that came with the system), then determine if how I am progressing (or not). NONE of this data links anywhere. So it is useless to my  insurance company — too bad because I’d like them to know that I am fitter than the average person so they can lower my health insurance rates.

Big Data

The explosion of data caused by all the billions of people and billions of sensors offers a tremendous opportunity to find new value — both in terms of new ways to make money and new ways to make discoveries to improve the human condition.

It is comical when business leaders complain about “big data” problems — rather than seeing big data as a massive, unprecedented opportunity to gain competitive advantage by understanding more than competitors. IDC’s 2011 Digital Universe Study provides great insights on how businesses can “extract value from chaos.”

Big data is a relative term. Thirty years ago, it was unimaginable to have a way to access a terabyte of data. Now I can access 10,000x more than that — from my cell phone. Thirty years from now, my great-nieces and nephews will scoff at our struggles to make sense of exabytes of “chaotic” data (absurd because chaos is only a matter of not seeing inherent patterns within data). The story of science is the never ending discovery of new patterns in things we considered random, chaotic (or divine), such as: weather, astronomical events, plagues, diseases, etc.

Making Sense of It All

We recently released an application program interface (API) that enables programmers to build artificial intelligence into software applications. The value of this API is that is generates a lightweight ontology that reveals all patterns and associations within a data set. Feed it data. It tells you how any one element (byte, word, document, etc.) relates to another. Here is a link to a video that describes ai-one’s machine learning technology.

Yes, you can get a no-obligation copy to try for yourself — just contact us.

“Related” — a Magic Word

Tuesday, August 23rd, 2011

Google announced a tool similar to Hyperwords called Google Related.  The magic is that it reveals more information about any web page — such as maps, photos, reviews and videos — that are related to the page but not necessarily included on it. Google Related shows all the “other” information about a website from other websites.  It is a browser extension, but limited to Chrome only.

With Hyperwords, you get an extra layer on top of the word(s) of your choice on a web page, whereas Google Related suggests relevant (or related) bits and pieces to the what you are looking at. The tool will display the suggestions in a bar at the bottom of your page. But after trying it out for an afternoon, it seems to point mostly to Google products — possibly a result of Google Page Rank is highest for Google products.

One of the coolest parts is that videos play directly on the page in a preview box. You can press the +1 button on the bar to share the result. Butin the long list of “View More Articles” the +1 button vanished — ahh did we discover a bug in the Google multiplex? And no training? Ironic that Google wouldn’t provide a quick 30 second video to show how it work.

In my view, Hyperwords is much less obtrusive. You can choose if you want to follow up on something you saw on a page. And then, you have a far greater choice how you want to explore more information. Google Related helps you find more related stuff on what you are already seeing.

Songbirds use grammar rules

Thursday, August 11th, 2011

Researchers have found that songbirds have something that resembles grammar as we know and are very responsive to rule violation. The birds have a syntax in their tweets, maybe not the same concepts like us (that is nouns, pronouns, verbs, adjectives, adverbs and so on), but they have a syntactic structure. Syntax is the study of principles and rules for constructing sentences and grammar rules are a part of syntax.

Language is made up of signs, meanings and a code connecting signs with their meanings. Semiotics is the study that looks at how signs and meanings are combined, used and interpreted ( you can read it up in our paper Semiotics and Intrinsic Semantics).

The research findings are published in Nature and NewScientist.

Machine Learning Startup Acquired by ai-one

Thursday, August 4th, 2011

Press Release

For Immediate Release:  August 4, 2011

San Diego artificial intelligence startup acquired by leading provider of machine learning SDKs as market for advanced applications gets hot.

San Diego CA – ai-one announced today that it acquired Auto-Semantics, a local start-up providing artificial intelligence services to corporate IT departments. The acquisition is the latest in a series of joint-ventures and acquisitions by ai-one that consolidates its leadership position within the emerging market for machine learning technologies.

In less than one year from its founding, Auto-Semantics built a solid pipeline of commercial accounts to apply computational semantics to solve “big data” and marketing problems – such as modeling consumer and investor behaviors. Computational semantics is a set of technologies that enables machines to understand human language. Olin Hyde started the company to build a “smart, personalized mobile GroupOn” application to deliver coupons but was unable attract capital for that concept. “Not getting funding was a blessing – it forced me to pivot from building speculative products into providing professional services. We solved corporate IT problems using machine learning technologies. That led me to ai-one – they had the only SDK to build semantic applications.” said Hyde.

ai-one provides programming tools that enable software developers to build machine learning into applications, websites and mobile phones. The company was founded in Zurich, Switzerland in 2003 and reincorporated as a US corporation in 2009 in anticipation of going public in 2012. Unlike most technology startups, ai-one spent more than eight years developing the core technology using funding from private investors from Europe.

The technology is distributed through consulting partnerships that use it to build custom solutions for corporations and government agencies. “A lot of people ask us about who uses our technology,” said ai-one’s President Tom Marsh, “and the fact is our customers are working on very proprietary, often secret, solutions.  There are many new applications in the pipeline coming from our partners that will hit the market in late 2011 and early 2012. It’s an exciting time for us.”

Hyde met Marsh at a local MeetUp group and later, ai-one founders Walt Diggelmann and Manfred Hoffleisch at the SDSIC SuperMath conference. Auto-Semantics signed up as an ai-one Consulting Partner on the first day the program became available. “It was clear from the beginning that Olin had the pulse on what corporate CIOs were thinking,” said Marsh, “He gets how to communicate the value of our big idea: Machines can learn just like we do, and you don’t have to be IBM or Google to play in this space.  Olin fits our entrepreneurial culture with international business experience.” The stock transaction ties Hyde to ai-one where he will serve as Vice President of Business Development.

ai-one also acquired Berlin-based PPM Data Management GmbH last year and formed two joint-ventures earlier this year that embed ai-one’s advanced pattern recognition technology in commercial services: ai-ibiomics gmbh provides personalized medicine using genetic sequencing and Forensity AG sells shoeprint recognition software to law enforcement agencies and crime laboratories.

About ai-one inc., ai-one provides technologies that enable programmers to build artificial intelligence into software programs. Based in San Diego with offices in Zurich and Berlin, ai-one’s “biologically inspired intelligence” is a virtual brain that learns without human intervention. Technically described as an adaptive holosemantic data space with semiotic capabilities, ai-one’s approach provides more accurate answers than competing technologies.  The Topic-Mapper™ SDK for text enables developers to create intelligent applications that deliver better sense-making capabilities for semantic discovery, lightweight ontologies, knowledge collaboration, sentiment analysis, artificial intelligence and data mining.

Contact: Tom Marsh, Phone: 1-858-531-0674, email: tm@ai-one.com, web: www.ai-one.com

 

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Search needs a shake-up

Thursday, August 4th, 2011

Oren Etzioni, computer scientist at the University of Washington has penned Search needs a shake-up, the August commentary in Nature (the full commentary is available to Natures subscribers or you can get the UW’s news). The piece, in short, is a call to academics and industry researchers to revolutionise how we find information on the web.

Now, searching on the web is typing a keyword (which is just a string of characters) into the search box and the search engine goes off and searches for exactly that string of characters and presents the website with that word. Instead, Etzioni proposes that the web search engine would identify basic entities – persons, places, objects – and point out the relationships between them. Which is exactly our approach. Only, we think, this can be applied in all kinds of documents and not only on the web. Our technology detects intrinsic semantic structure of the text data and works with lightweight ontologies that show the associations and significance of every element. Watch our webcast for more info.

Overview Video of ai-one Topic-Mapper SDK for Machine Learning Applications

Monday, August 1st, 2011

Click this link for an video overview of the ai-one Topic-Mapper SDK for machine learning applications (8 minutes).