Posts Tagged ‘Machine Learning API’

Gartner Names ai-one Cool Vendor 2012 for Content Analytics

Tuesday, May 15th, 2012

Gartner Cool Vendor in Content Analytics, 2012

 

*GARTNER named ai-one in Cool Vendors in Content Analytics, 2012. The report reviews five vendors from around the world that offer potentially disruptive innovations for analyzing data to find actionable insights. Unlike traditional business intelligence solutions, these vendors provide technologies that can understand multiple types of information — including both structured and unstructured data.

The core value of ai-one’s technology is to make it easy for programmers to build intelligence into any application. Our APIs provide a way to mimic the way people detect patterns. “This is why we call it biologically inspired intelligence,” says founder and CEO

Answering the Most Important Questions, Mr. Walter Diggelmann, “because it works just like the human brain.”

 These companies have received tremendous publicity. Both are funded by traditional Silicon Valley venture capital firms. No surprise that they strive to provide comprehensive machine learning solutions rather than a tool for the general programming public.

“We do something completely different! We provide a general purpose tool that you can combine with other technologies to solve a specific problem. We do not try to do everything. Rather we just do one thing: We find the answer to the question you didn’t know to ask.” says Diggelmann

The advantage of ai-one’s approach to developers is that using the API is easy. The tool finds the inherent meaning of any data by detecting patterns. For example, feed it text and it will find every keyword and determine the association words that give each keyword context. Together, keywords and associations provide a complete and accurate summary of a document. The API gives precise results almost instantly and does not require any specialized training to use. Moreover, it is autonomic — as it works without any human intervention.

ai-one follows a technology licensing model — much like Qualcomm. The company makes money when licensees embed the API into commercial applications. ai-one works closely with its OEM partners to ensure that their products are successful.

ai-one’s technology enables programmers to build hybrid analytics solutions that integrate content from almost any digital source, in any language, regardless of its structure (or lack of structure). This capability has the potential to transform the way we think about business intelligence. “90% of the world’s data is unstructured,” says Diggelmann, “but 100% of the major business intelligence systems can’t read or understand it.  We provide a tool to bridge the gap.”

*Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings.  Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact.  Garner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

 

Use Case: Passenger Name List (PNL) for Secure Flight Program

Wednesday, December 14th, 2011

Case Study Summary:

The Passenger Name List application was developed by ai-one for one of the largest airline ground handling services company in the world.

The PNL Matcher is being used by airlines at the JFK, FRA, and ZHR airports to efficiently and accurately match a PNL (Passenger Name List) with the different suspect lists (no-fly list) supplied by official sources such as the U.S. Department of Homeland Security (DHS) Secure Flight Program.

This application uses the core ai-one™ technology in a limited but very effective way.  The challenge in this area is the need to comply quickly with new U.S. DHS requirements to effectively screen passengers before boarding a flight.

The challenge for such an application is, when a ticketing agent creates a ticket for a passenger from a country that does not use the western alphabetic character set and phonetically spells the name.  Since the spelling could be very different from different agents, the software has to be intelligent enough to find and match suspect passengers to the DHS list.  Additionally phonetic use of characters is varies from country to country but must meet U.S. requirements for quality.

PNL Matcher for Swissport's Secure Flight Program

 PNL Benefits:

  • Fast, very accurate response to a ticket agent
  • Phonetic spelled names can be matched with compliance to all regulations
  • Easy to add new lists
  • Works in any language or character set (language agnostic)

Topic:

Visualize all associations within written text

Kind:

Custom implementation of Topic-Mapper API

Status:

Solutions installed runs since its implementation in 2007 productive
Partner:

Swissport International

Rico Barandun, Product Manager

Application areas:

  • Pattern Matching
  • Security

Target Industries

  • Transportation
  • Homeland security
  • Law enforcement

Big Data Just Got Smaller: New Approach to Find Information

Tuesday, November 15th, 2011

Press Release

For Immediate Release

ai-Fingerprint

ai-Fingerprint shows a graphical representation of the knowledge within a news article

San Diego, CA – Artificial intelligence vendor ai-one will unveil a new approach to graphically represent knowledge at the SuperData conference in San Diego on Wednesday November 16, 2011. The discovery, named ai-Fingerprint, is a significant breakthrough because it allows computers to understand the meaning of language much like a person. Unlike other technologies, ai-Fingerprints compresses knowledge in way that can work on any kind of device, in any language and shows how clusters of information relate to each other. This enables almost any developer to use off-the-shelf and open-source tools to build systems like Apple’s SIRI and IBM Watson.

Ondrej Florian, ai-one’s VP of Core Technology invented ai-Fingerprints as a way to find information by comparing the differences, similarities and intersections of information on multiple websites. The approach is dynamic so that the ai-Fingerprint transforms as the source information changes. For example, the shape for a Twitter feed adapts with the conversation. This enables someone to see new information evolve and immediately understand its significance.

“The big idea is that we use artificial intelligence to identify clusters and show how each cluster relates to another,” said Florian. “Our approach enables computers to compare ai-Fingerprints across many documents to find hidden patterns and interesting relationships.”

The ai-Fingerprint is the collection of all the keywords and their associations identified by ai-one’s Topic-Mapper tool. Each keyword and its associations is a coordinate – much like what you would find on a map. The combination of these keywords and associations forms a graph that encapsulates the entire meaning of the document.

The real-world applications are impressive. “It solves a lot of so-called Big Data problems because the system learns by itself,” said Olin Hyde who worked with Florian on the project. “ai-Fingerprints work with existing computer languages and standards. So it only took us about a week to create a generic tool, called BrainBrowser, to find relationships in complex texts – such as summarizing news articles, searching for a job, or identifying new uses for a drug.”

To build BrainBrowser, the team fed ai-Fingerprint results from Topic-Mapper into a natural language processing tool, OpenNLP, so that the computer could understand the rules of grammar then tag parts of speech, chunk phrases and classify words into categories (also called named-entity recognition). The ai-Fingerprint is continuously updated by Topic-Mapper so that the computer can understand how information changes over time – as it does in a human conversation.

Next, the team built a little tool in Java that converted the output into a continuous data feed using an open-standard format called XGMML. This format shares the knowledge of a document as a network of words, sentences and relationships.

Finally, they visualized the result with an open-source bioinformatics tool, called Cytoscape, to show the differences, similarities and identify anomalous information among documents. The result is a graphic representation of knowledge that can show clusters, extract summaries and compare many documents at the same time.

The approach is easy for others to replicate with other technologies. “We used Topic-Mapper with Java, OpenNLP and Cytoscape,” said Florian, “But you could easily do this with Python, MATLAB and NLTK. Heck, you could throw a voice recognition tool on it, like Dragon or Nuance, and you can build an intelligent agent just like SIRI.”

ai-Fingerprint works in any language because Topic-Mapper looks only at byte-patterns. “The approach can give false positives if you don’t teach it the rules of language” warned Florian, “but it is very accurate once it learns the grammar from an outside source of information – such as a natural language processing system or an external database.”

ai-one’s engineering team sees ai-Fingerprints as a way to make it easier, faster and less expensive for their partners to develop intelligent systems. The team is now testing it for applications in advertising, financial analysis, medical research and search engine optimization (SEO).

“Our mission is to make powerful AI available to all developers. This is a big step in that direction,” said ai-one’s chief operating officer Tom Marsh. “We are eager to find academic and consulting partners who can build upon what we started.”

“BrainBrowser is just a minimally viable product (MVP) to prove the concept,” added Hyde. “The sky is the limit for those that want to build commercial applications. Just take the MVP code and customize to your needs.”

A demo of the system can be seen on www.ai-one.com and the semsys YouTube channel.  ai-one intends to provide the source code for ai-Fingerprint as part of its Topic-Mapper software development kit.

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.