GARTNER  named ai-one as one of the most promising new technology vendors in its latest report on:

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.

Press Release

New tools will enable machine learning of twitter feeds

La Jolla CA | Zurich | Berlin  February 16 2012 – ai-one inc. and Gnostech Inc. announced a partnership today to build new machine learning applications for the US government and military. The deal brings together two small firms that are well known for developing cutting-edge technologies. Gnostech specializes in simulation and modeling, Command Control Communications Computers and Intelligence Surveillance and Reconnaissance (C4ISR) systems and security engineering and Information Assurance (IA) applications. The partnership with ai-one provides Gnostech with access to technology that enables computers to learn the meaning and context of data in a way that is similar to humans. Called “biologically inspired intelligence” the technology is a new form of machine learning that is particularly useful for understanding complex, unstructured information – such as conversations in social media.

In the past month, the US government has issued six requests for companies to create solutions to help better understand Twitter, Facebook and other social media sources. These broad area announcements (BAAs) are formal requests from the Government to invite companies to provide turn-key solutions. With more than 800 million people actively using Facebook and more than 100 million Twitter users, governments and intelligence agencies know that they need better ways to mine this data to get real-time information to protect national security.“

We now have more than 40 partners worldwide that are experimenting with our technology – but only 3 that specialize in US government applications,” said Tom Marsh, President of ai-one. “Gnostech is local, technically driven and well positioned to develop rapid prototypes using our technology.”

About Gnostech, Since 1981, Gnostech has provided technical and engineering services to the Department of Defense (DOD) and Department of Homeland Security (DHS). Gnostech has a proven reputation for engineering efficiency, systems innovation, and dedicated customer service.

Gnostech Inc. began as an engineering and consulting company in Warminster, PA with expertise in GPS simulations and software, initially supporting the US Navy at the Naval Air Development Center (NADC) in Warminster, PA. Today, Gnostech has grown from a few people to about 50 employees with a satellite office in San Diego, CA and engineering support staff in Norfolk, VA, Morristown, NJ and Philadelphia, PA. Gnostech’s technical expertise expands upon our GPS experience and extends into Mission Planning, Network Engineering, Information Assurance and Security Engineering.  www.gnostech.com

About ai-one inc., ai-one provides an “API for building learning machines”.  Based in San Diego, Zurich and Berlin, ai-one’s software technology is an adaptive holosemantic data space with semiotic capabilities (“biologically inspired intelligence”).  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.  www.ai-one.com

How to find information that is difficult to describe?

For example, you want to find a match between two people to fill an empty job position. What attributes do you use to represent a complex subject (like a person) to find the best fit?

What if the single best answer is hidden within a vast amount of unstructured text?

Let’s say you want to repurpose a drug – such as using the side-effect of a chemical to treat a disease using a newly discovered metabolic pathway. How would you search through the 21+ million research articles in PubMed to find the best match from more than 2,000+ known drug compounds?

What if the textual information is constantly changing?

What if you want to provide personalized marketing to a person based on what they are saying on Facebook, Twitter or LinkedIn?  To do this, you must understand the meaning of what they are saying. The most accurate approach is to have people read and interpret the conversations because we are fantastic at understanding the complexity of language. But to do this with a computer requires a different approach: Machines must learn like humans. They must understand how meaning evolves in a conversation, how to disambiguate, how to detect the single most important concepts, etc.

Big Data Means Big Opportunity

These are classic “Big Data” problems – and they are rampant. Finding a solution would change everything; from how we discover new drugs to what social media would tell us about ourselves.

There have been many attempts to find ways for machines to learn like a human. Artificial intelligence has made bold promises that have been consistently broken for more than 50 years. Yet, we still don’t have a universal approach for machines to learn and understand language like a human.

Now, more than ever, we need to find a new approach to mine unstructured text. As of February 2012, it is estimated that the Internet has more than 614 million websites. More than 1.8 zettabytes of information was created in 2011 – more than much of it unstructured text from our comments on websites, news articles, social media feeds… just about anything where people are communicating with language rather than numbers.

Unstructured text can’t be processed like structured data. Rather it requires an approach that enables knowledge representation in a form that can be processed by machines.

Knowledge representation is a rich field and there has been tremendous effort and innovation – too many to describe here. However, we still live in a world where the overwhelming majority of people (including almost every CIO, developer and consumer) CANNOT find the information they seek with a simple query. Rather, the domain of data mining text analytics is dominated by specialists who use tools that are very difficult to learn and very expensive to deploy (because they require highly skilled programmers).

We set out to create a new toolset that would be easy to use for almost any programmer to build data mining tools for unstructured text.

ai-browser: A prototype for human-machine collaboration

For the past several months, we have been working on a new approach for text analytics and data mining. The idea is to create a tool that enables human-machine collaboration to quickly mine unstructured data to find the single best answer.

We now have a working prototype, called ai-browser, that solves knowledge management and data mining problems involving unstructured text. It combines natural language processing (NLP) and pattern recognition technologies to generate a precise knowledge representation graph.  Our team selected OpenNLP because it is open-source, easy to use and customize. We used the Topic-Mapper API to detect patterns within the text after it was pre-processed to isolate parts of speech. The system also allows users to use ontologies and/or reference documents to sharpen the results. The output is a graph that can be used in a number of ways with 3rd party products, such as:

• Submission to search appliances like Google, Bing, Lucene, etc.
• Analysis with modelling tools like Cytoscape, MATlab, SAS, etc.
• Enterprise systems for reporting, knowledge management and/or decision support

This graph makes it easy to ask questions like, “Find me something like _______!” and get a very tightly clustered group of results – rather than millions of hits.

Even more impressive, ai-browser’s graph is a powerful tool that can be applied to a wide range of applications, such as:

• Healthcare – clinical decision support systems to enable physicians to make better decisions by understanding all the relevant information held in electronic medical records (EMRs) – including emerging trends and relationships within the patient population.
• Social media – detecting and tracking sentiments in conversations over time (such as Twitter) to understand how brands are perceived by customers.
• Innovation management – discovering the relationships of information across disciplines to foster more productive collaboration and interdisciplinary discoveries.
• Information comparison and confirmation – determine the similarities and differences between two different sources of content.
• Human resources – sourcing and placement of the best candidate for a job based on previous work experience.

The intent of the ai-browser design is to provide a starting point for developers to build solutions to meet the specific needs of enterprise customers. For example, modifying the system enables solutions to the following use cases:

• Help a physician determine if additional tests are necessary to confirm a diagnosis.
• Determine how perceptions about a brand are change through conversations on Twitter.
• Find new uses for a drug by reviewing clinical studies published on PubMed and determining if there are relevant patent filings.
• Identify stock market trading opportunities by comparing news feeds and SEC filings on a particular company or industry.
• Finding the best person for a job by searching the internet for someone that is “just like person who has this job last year.”

Enterprise Data Mining: A far easier, lower cost approach.

Unlike other data mining approaches, ai-browser learns the meaning of documents by generating a lightweight ontology – a dynamic file that describes every relationship between every data element. It detects keywords and their association words which provide context. The combination of a keyword and all the association words can be thought of as a coordinate (x,y_0->T) where x is the keyword and y_0->T is the series of association words for that specific keyword. The collection of these coordinates creates a topology for the document: G(V,E) where G is graph and V is the set of vertices (or nodes) represented by each keyword and E is the edge represented by the associations to the keyword.

We call this graph the “ai-fingerprint.” It is a lossless knowledge representation model. It captures the meaning of the document by showing the context of words and the clustering of concepts. It is lossless because it captures every relationship in a directed graph – thereby revealing the significance of a word that may only appear once yet is central to the meaning of a large, complex textual data set.

ai-browser expresses ai-fingerprints uses the XGMML format in REST. This enables it to accommodate dynamic data, so it can change as the underlying text changes (such as in text from social media feeds).

Contact Olin Hyde to schedule a demo of ai-Browser. The source code is available to programmers to license and modify to solve specific problems.

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.

Use Case Summary:

The BON Matcher is an ai-one implementation enabling a leading swiss retail store to analyze all scanned credit card receipts.

After the scan process, all credit card receipts are analyzed and matched against patterns using a-one’s API.

Our solution corrects the errors of the optical character recognition (OCR) system when it fails to recognize 100% of the elements.

This was an early validation of our technology. It  affirmed ai-one’s superiority over alternative artificial intelligence-based solutions as a much faster, better quality, and less expensive solution. The retail chain saved substantial operating costs by automating this process and was able to reduce its workforce by 15 people.

The project was finished after 3 months of development time and is still being used for more than 80 stores.

The feature of the technology used in this application is commonly used in document archiving systems where users need to search for documents that have been scanned with many character errors.

Benefits:

• Improved OCR performance from 80% to 98% in less than a week after implementation.
• Enhancing OCR recognition in a separate, low-cost post processing process
• Faster data availability
• Additional fraud detection possibilities

Deployment:

Customize software development

Status:

Solution in place. Successful since 2006 launch.

Partner:

Swiss Data Safe AG

Application areas:

• OCR recognition
• Numerical series matching
• Data management / Archiving

Target Industries:

• Information management
• Retail

For Immediate Release

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.

San Diego CA | October 20, 2011 – Did you miss the wave? Artificial intelligence is transforming entire industries by finding value in big, complex data.

The San Diego Online Society (SANDIOS) will host a public seminar on Thursday November 17 on how artificial intelligence (AI) is being used by leading edge companies around the world.

Recent advances in AI technology make it easy to build machines that can learn like humans. Now almost any programmer can build systems like Apple’s SIRI and IBM Watson by combining off-the-shelf technologies. A leading vendor of machine learning technology, ai-one, will present case studies from a wide range of customers. The seminar will focus on showing practical ways businesses can use AI.

Questions that will be addressed: 

• What is AI & why everything you think you know about AI has changed
• Business uses for ai-one technology
• Demo of a cutting edge AI application
• AI incubation models
• How to succeed with building an AI business
• AI Product strategy

The event will be hosted by Jones Day which specializes in intellectual property and business law.

Tickets available online at:   http://sandios-11-2011.eventbrite.com/

About ai-one inc., ai-one provides an “API for building learning machines”.  Based inSan Diego,Zurich andBerlin, ai-one’s software technology is an adaptive holosemantic data space with semiotic capabilities (“biologically inspired intelligence”).  The Topic-Mapper™ SDK for text enables developers to create artificial intelligence applications for semantic discovery, knowledge collaboration, sentiment analysis, and data mining.

Contact: Olin Hyde, Ph: 1-858-381-5897, email: oh@ai-one.com, web: www.ai-one.com

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For Immediate Release:  September 12, 2011

DoD vendor Ariston Consulting partners with artificial intelligence startup to provide test & engineering services powered by machines that can learn like humans.

Sierra Vista AZ – Ariston Consulting and ai-one announced a strategic partnership today that will provide the US Army Electronic Proving Grounds with machine learning tools to test and evaluate military Command, Control, Communications, Computers, Cyber, and Intelligence Test Bed (C5ISR) systems.

(Pictured L to R) Steve Mecham COO and Woody Woodruff, CEO of Ariston Consulting; Tom Marsh and Olin Hyde, Pres & VP of ai-one.

“This technology enables us to do far better testing in less time on far less money.” said Woody Woodruff, Founder and CEO of Ariston. “We can now automate the most burdensome tasks for human analysts.”The partnership provides Ariston with a first mover advantage in building custom solutions for the DoD using a new generation of artificial intelligence tools that were released to the market in June 2011. These tools enable software developers to build machine learning into applications so computers can recognize patterns and associations much like a human.

The impact is potentially huge. More than ten percent of the DoD budget is spent on research development test and evaluation according to a report to a May 17, 2011 report to the House Committee on Armed Services.

ai-one’s technology differs from other forms of artificial intelligence because it learns without any human intervention. It detects the inherent structure of data in very complex environments so computers can recognize patterns and associations – even for the faintest signals. It automatically builds a database (called a lightweight ontology that shows humans how any piece of data relates to another. The technology works with any digital file – including text, images and radio signals.

Ariston plans to start using ai-one’s Topic-Mapper product for text analytics to evaluate messaging systems for the US Army Electronic Proving Grounds. Olin Hyde, VP of Business Development for ai-one explained that “It is language agnostic. So it is ideal for finding patterns where people don’t use conventional grammar or words. For example, it can find the sentiments in Twitter feeds with only a few commands.”

Future plans include using it to analyze threat patterns in cyber security, cataloging radio signals, network monitoring and management and other trial analysis programs. “The core technology plays broadly in the C5ISR space,” said Woodruff, “the key will be finding areas where we can prove immediate returns on investment. The cost is low – so we expect it will be a question of just picking the right test environments to focus our efforts.”

About Ariston Consulting LLC., is a Service-Disabled Veteran-Owned Small Business (SDVOSB) based in Sierra Vista, AZ, providing the best testing and engineering solutions designed for today’s defense industry challenges. Ariston’s diversified experts provide solutions for test methodology and design, operational, developmental and interoperability test services, analytical and data services. Current clients include: Joint Interoperability Test Command (JITC) and the U.S. Army Test and Evaluation Command Electronic Proving Grounds (EPG) at Fort Huachuca, AZ and the U.S. Air Force Joint Test and Evaluation Joint UAS Digital Information Exchange (JUDIE) effort at Nellis AFB, NV.  In addition, Ariston is a specialist in providing Department of Homeland Security Independent Test and Evaluation services with staff experience as the Independent Tester of Secure Border Initiative Network and Project 28.

Contact:  Steve Mecham, Phone: 1-520-378-6112, email: steve.mecham@aristonhq.com

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: Olin Hyde, Phone: 1-858-381-5897, email: oh@ai-one.com, web: www.ai-one.com

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