Archive for the ‘text analytics’ Category

ai-one named Finalist in SDBJ Innovation Awards for 2013

Thursday, June 27th, 2013

At the San Diego Business Journal Annual Innovation Award event, ai-one was named a finalist in the technology category. The award was presented at the prestigious event on June 18th at Scripps, attended by several hundred leaders in San Diego’s tech, medical, software and telecom industries. ai-one received the award for its leading edge technology in machine learning and content analytics, as evidenced by the release this year of the new Nathan API for deep learning applications.

The award was accepted by ai-one COO Tom Marsh and partner for defense and intelligence, Steve Dufour, CEO of ISC Consulting of Arizona.

Tom Marsh & Steve Dufour at SDBJ Innovation Awards

Tom Marsh & Steve Dufour at SDBJ Innovation Awards

Ai-one’s Artificial Brain’ Has a Real Eye for Data SDBJ

TECH: Software Can Dig Through and Decipher Information

Software writer ai-one Inc. doesn’t just promise code. The company promises to pull new perspectives and second opinions from seemingly inscrutable data.

SDDT recognizes ai-one’s presentation at CommNexus to SK Telecom of North Korea

Thursday, June 27th, 2013

ai-one was recognized for its participation in the CommNexus MarketLink event June 4th in San Diego California. The event featured companies from all across the US selected by SK Telecom for their potential to add value to SK Telecom’s network. The meeting was also attended by SK’s venture group based in Silicon Valley.
 
Tierney Plumb of the San Diego Daily Transcript reported, “San Diego-based ai-one inc. pitched its offerings Tuesday to the mobile operator. The company, which has discovered a form of biologically inspired neural computing that processes language and learns the way the brain does, was looking for two investments — each about $3 million — from SK. One is a next-generation Deep Personalization Project whose goal is to create an intimate personal agent while providing the user with total privacy control. ”
 
For the full text of this article click  San Diego Source _ Technology _ Startups line up to meet with SK Telecom

Building Intelligent Agents: Google Now versus Apple SIRI?

Friday, December 14th, 2012

It has been a long time since our last blog post. Why? We’ve been busy learning how to build better intelligent agents.

Today, Kurt and I were discussing ways to improve feature detection algorithms for use in a prototype application called ai-BrainDocs. This is a system that detects concepts within legal documents. This is a hard problem because legal concepts (or ideas) use the same words. That is, there are no distinguishing features in the text.

ai-one’s technology is able to solve this problem by understanding how the same word (keyword) can mean different things by its context (as defined by association words). Together, keywords and associations create an array that we call an ai-Fingerprint. This can be thought of as a graph that can be represented as G[V,E]. ai-Fingerprints are easy to build using our Topic-Mapper API.

We pondered how the intelligent agents for Android developed by Google (called Google Now) and Apple iOS (called SIRI) might perform on a simple test. We picked a use case where the words were sparse but unique — looking for the status for a departing flight on American Airlines. Both Google Now and Apple SIRI have a tremendous advantages over ai-one because they: 1) have a lot more money to spend on R&D, 2) use expensive voice recognition technologies, and 3) they store all queries made by every user so they can apply statistical  machine learning to refine results from natural language processing (NLP).

Unlike Apple and Google, ai-one’s approach is not statistical. We use a new form of artificial neural network (ANN) that detects features and relationships without any training or human intervention.  This enables us to do something that Google and Apple can’t: Autonomic learning. This is a huge advantage for situations where you need to develop machine learning applications to find information where you can’t define what you are seeking. This is common in so-called “Big Data” problems. It is also much cheaper, faster and accurate than using the statistical machine learning tools that Apple and Google are pushing.

 

Posted by: Olin Hyde

Lead Analyst Firm Names ai-one “Who’s Who in Text Analytics”

Wednesday, September 19th, 2012

ai-one evaluated as machine learning for text vendor

We are proud to report that the *Gartner cites ai-one in their September 14 report Who’s Who in Text Analytics. Analysts Daniel Yuen and Hans Koehler-Kruener based this report on a survey of 55 vendors conducted in April 2012.  Vendors were included based on offering distinct text analytics offerings, not those whose text analytics technology is part of another product.  ai-one offers a general purpose, autonomic machine learning tool that can be embedded within other applications. Earlier this year, Gartner named ai-one as one of the “Cool Vendors 2012”* for content analytics. We believe the coverage of ai-one as a text analytics provider indicates the importance that Gartner places on the ability to evaluate information that cannot be processed using traditional tools that depend on looking at tables, rows and models.

“Language is not math.”

ai-one uses a completely new form of machine learning to detect the meaning of text. The technology evaluates any length of text to isolate keywords and associations. The keywords are the most important words – the words that are central to the meaning of the document. The association words are the words that give the keywords context.

“Making sense of short text.”

Text analytics is particularly difficult for short texts – such as social media feeds from Facebook and Twitter. Humans are great at seeing the meaning in a few words. Computers are not.

ai-one’s context detection technology provides a easy  solution to this problem. For example, our technology can learn the meaning of a very short text, such as a tweet: “Will Google eat Apple with the new J2ObjC?” It immediately detects the keywords ‘Google,’ ‘Apple’ and ‘J2ObjC’ and the associations ‘eat’ and ‘new.’ The system will learn the meaning of these words by adding additional association words to the keywords as it is fed additional tweets. The more tweets, the more it learns.  No human intervention or training sets are required – although the system learns faster if it is taught. In many ways, ai-one’s technology learns just like a human. It detects context by evaluating the associations of words. Most impressive, it forms concepts by connecting together groups of associations.

 “ai-one thinks different.”

This approach is radically different than the rules-based approach used by IBM and the Bayesian statistical approaches of SAS and Autonomy. ai-one is purely a pattern recognition tool for multiple higher order concepts. It finds the inherent meaning in any text by simply seeing how words connect with each other. Unlike AlchemyAPI, Textifier and other competitors that use ontologies connected to natural language processing (NLP), our technology works equally well in any language.

Prelude to the debut of NathanApp

ai-one’s Topic-Mapper SDK and API will soon be replaced with a cloud-deployable API called NathanApp. NathanApp & NathanNode are REST services where we offer a complete analytics solution as a service.  NathanCore is the native technology where customers build their own interfaces using REST or any other standard. ai-one also plans to offers an open source infrastructure to NathanCore and NathanApp/Node where REST, JSON, and other functions and services are offered as open source code.  Details of NathanApp will be released in a future press release… But it is safe to say that ai-one’s research and development team have spent almost two years developing new technology that will enable ai-one technology to be used by anyone, anywhere on any device.

We are very proud that Gartner has acknowledged ai-one as a Who’s Who and Cool Vendor. Moreover, we look forward to showing you very soon how NathanApp will change everything: Nathan will be the first intelligent agent that any developer can embed in any application. This is what ai-one considers a “smarter planet.”

*Gartner, Inc., Cool Vendors in Content Analytics, Rita L. Sallam, et al, April 26, 2012.  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.

 

Gartner benennt ai-one im “Who’s Who in Text Analytics”

ai-one ist als führende Firma für “machine learning” im Textbereich aufgeführt

Stolz dürfen wir verkünden, das die Gartner ai-one als eine führende Technologie Firma für Textanalyse in neusten Forschungsbericht, dem Who’s Who in Text Analytics vom 14. September aufgeführt hat. Die Analysten Daniel Yuen  und Hanns Koehler-Kruener haben insgesamt 28 Hersteller, inklusive den Industrie- “Schwergewichten” IBM, SAS, SAP und Autonomy untersucht und verglichen.  ai-one wird dabei als einziger Hersteller mit einer unabhängigen Universalanwendung für Aufgabenübergreifende Lösungen aufgeführt.  Bereits im Frühjahr hatte die Gartner  ai-one als “Cool Vendors 2012” für Kontext Analyse gewählt. Gartner bezeichnet Kontext Analyse als eine der wichtigsten zukünftigen Aufgaben im Rahmen der Business Intelligenz Anwendungen, weil es sowohl strukturierte wie auch unstrukturierte Inhalte analysieren und deren Sinn erkennen kann. Die Art wie ai-one als Leader in der Text Analyse dargestellt wird zeigt deutlich, wie wichtig Gartner dieses Thema bewertet. Gartner unterstützt zudem deutlich die neuen Ansätze in der Textanalyse weil Gartner die Wichtigkeit von intelligenten Werkzeugen deutlich machen möchte, welche über das Benutzen von Tabellen und Modellen herausgeht.

“Sprache ist keine Mathematik.”

Im Unterschied zu den anderen im Report gelisteten Firmen hat ai-one einen neuen Ansatz wie maschinelles Lernen intelligenter und präziser gestaltet werden kann. Der ai-one Ansatz kann Texte in jeder Länge analysieren und erkennt spontan Sinn und Schlagworte. Diese Schlagworte „KeyWords” sind die wichtigsten Worte welche in der Kombination den Sinn in einem Text bestimmen. Weiter erkennt ai-one die Assoziations-Worte welche den Schlagworten den Kontextzusammenhang geben.

“ai-one erkennt die Bedeutungen  selbst in kurzen Texten“.

Textanalyse und Sinnerkennung ist vor allem in kurzen Texten sehr schwierig. In Feeds, Tweet‘s und Facebook stehen oft nur kurze Sätze, welche aber in der Fülle durchaus Sinn machen. Ausser ai-one basieren alle anderen Hersteller in Gartners Report auf Sprachabhängigen Regelsystemen und Umweltmodellen.

ai-one kann selbst einen sehr kurzen Text „Tweet“ analysieren wie: “Will Google eat Apple with the new J2ObjC?” Sofort wird automatisch das Wort ‘Google,’ ‘Apple’ und ‘J2ObjC’als Schlagwort erkannt, sowie die Assoziation ‘eat’ and ‘new‘. die ai-one Technologie lernt spontan die Bedeutung der Worte aus dem Zusammenhang mit anderen Tweet‘s. Je mehr Tweet‘s vorhanden sind zu einem Thema, desto exakter versteht ai-one spontan den Sinn und die Zusammenhänge. Je mehr Tweet‘s umso schlauer wird ai-one. Es ist also keine manuelle Intervention nötig – ai-one lernt schneller und bessert je mehr Inhalt vorhanden ist. Man kann sagen, ai-one’s Technologie lernt wie ein Mensch. Sie erkennt den Sinn und die Bedeutungen aus dem Zusammenhang in denen die einzelnen Worte verwendet werden. Darüber hinaus ist ai-one in der Lage verschachtelte Konzepte aus zusammenhängenden Assoziationen zu erkennen.

Im Unterschied zu den andren im Report gelisteten Firmen hat ai-one einen neuen Ansatz wie maschinelles Lernen intelligenter und präziser gestaltet werden kann. Der ai-one Ansatz kann Texte in jeder Länge analysieren und erkennt spontan Sinn und Schlagworte. Diese Schlagworte „KeyWords” sind die wichtigsten Worte welche in der Kombination den Sinn in einem Text bestimmen. Weiter erkennt ai-one die Assoziations-Worte welche den Schlagworten den Kontextzusammenhang geben. ai-one kann selbst einen sehr kurzen Text „Tweet“ analysieren wie: “Will Google eat Apple with the new J2ObjC?” Sofort wird automatisch das Wort ‘Google,’ ‘Apple’ und ‘J2ObjC’als Schlagwort erkannt, sowie die Assoziation ‘eat’ and ‘new‘. Die ai-one Technologie lernt spontan die Bedeutung der Worte aus dem Zusammenhang mit anderen Tweet‘s. Je mehr Tweet‘s vorhanden sind zu einem Thema, desto exakter versteht NathanCore spontan den Sinn und die Zusammenhänge. Je mehr Tweet‘s umso schlauer wird NathanCore. Es ist also keine manuelle Intervention nötig – Nathan lernt schneller und bessert je mehr Inhalt vorhanden ist. Man kann sagen, ai-one’s NathanCore lernt wie ein Mensch. Er erkennt den Sinn und die Bedeutungen aus dem Zusammenhang in denen die einzelnen Worte verwendet werden. Darüber hinaus ist NathanCore in der Lage verschachtelte Konzepte aus zusammenhängenden Assoziationen zu erkennen.

ai-one denkt anders

ai-one verfolgt einen radikal anderen Ansatz als die model- und regelbasierten Systeme welche IBM, SAS oder SAP anwenden. Bayesian und die Statistischen Ansätze können zwar Muster erkennen, benötigen aber immer Modelle und statische Regelwerke.  ai-one’s Nathan findet die inhärente (innewohnenden) Beziehungen und Bedeutungen aus dem Text, weil es die semantischen Verbindungen und assoziativen Bedeutungen erkennt.  Ontologien oder Thesauri, sowie NLP dienen ai-one als Ergänzung und Verfeinerung der Deutungen. Vor allem dann wenn der Text selber in ungenügender Qualität vorliegt. Der ai-one Core ist absolut Sprachunabhängig.

Vorschau auf das Debüt von NathanApp

Der Gartner Report wurde im Juni 2012 evaluiert und ist somit schon fast wieder überholt. ai-one’s damals untersuchtes Topic-Mapper SDK/API ist in der Zwischenzeit mit NathanCore ersetzt worden. ai-one veröffentlicht in Kürze die neue Generation NathanApp, NathanNode & NathanCore. NathanApp & NathanNode sind REST Services als Komplettlösung. NathanCore ist die Basistechnologie in welcher Kunden ihre eigenen Lösungen und Infrastrukturen bauen können. ai-one offeriert zusätzlich open source Infrastruktur mit REST, JSON. Die neuen Versionen werden bald über Pressemitteilung bekannt gemacht. Wir dürfen allerdings schon jetzt verkünden, dass das ai-one Team mehr als 2 Jahre investiert hat, um die Technologie grundlegend zu erweitern damit sie in den neuen Systemarchitekturen (z.B. Cloud) optimal eingesetzt werden kann. Wir sind stolz über die Gartner Bewertungen. Nathan App ist der erste intelligente Agent von ai-one welcher durch jeden Entwickler einfach und mit wenigen Klicks in eine Lösung integriert werden kann. Das ist ai-one’s Beitrag zu einem “smarter planet.”

Posted by: Olin Hyde

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.

 

Building Machine Learning Tools to Mine Unstructured Text

Friday, February 17th, 2012

This presentation describes how to build tools to find the meaning of unstructured text using machine generated knowledge representation graphs using NLP and ai-one’s Topic-Mapper API.
The prototype solution, called ai-Browser, is a generalized approach that can solve the following types of use cases:
  • Sentiment analysis of social media feeds
  • Evaluating electronic medical records for clinical decision support systems
  • Comparing news feeds
  • Electronic discovery for legal purposes
  • Automatically tagging documents
  • Building intelligent search agents
The source code for ai-Browser is available to developers to customize to meet specific requirements. For example:
  • Healthcare providers can use ai-Browser to analyze medical records by using ontologies and medical lexicons.
  • Social media marketing agencies can use ai-Browser to create personal profiles of customers by reading social media feeds.
  • Researchers can use ai-Browser to mine PubMed and other repositories.
Our goal is to get the source code and the API into the hands of commercial companies who want to tailor the application to solve specific problems.
Click here to download the presentation from SlideShare:
View more presentations from ai-one

Partnership to Create New Social Media Intelligence Tools

Thursday, February 16th, 2012

New Partnership Targets Creation of Social Media Intelligence Tools

Press Release

Tweet log

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 TwitterFacebook 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

Mining Unstructured Text: A new machine learning approach

Monday, February 13th, 2012

We believe we have found a new approach to apply a new general purpose machine learning technology to solve domain-specific problems by mining unstructured text. The solution addresses fundamental problems in knowledge management:

ai-browser is a tool for mining unstructured textHow 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.

Growth of Websites

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,y0->T) where x is the keyword and y0->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.

ai-fingerprint of Fox News Article

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.