Archive for the ‘New Technologies’ Category

Personal AI Helps Convert Social CRM for Recruiting

Thursday, June 26th, 2014

Given the need for more effective content marketing and better quality lead generation, why aren’t the tools better?  Certainly there are lots of applications, SaaS products and services available for all parts of the marketing and sales process.   With BrainBrowser we provide a tool that can understand the content from marketing and match it to bloggers, LinkedIn connections, Twitter followers and find candidates in places you would never look.

Since about one-third of the 7,500+ queries by our testers were using BrainBrowser to search for people, a key objective is to add features to manage the results and integrate them into your workflow.  If you find someone relevant to your work or a potential recruit, you should be able to connect with them right from the list, follow them on Twitter or share lists of candidates with collaborators.

BrainBrowser with Nimble Popup

As a recruiting professional your task is to find the candidates and conversations on the web where conversions will be maximized and get there first.  BrainBrowser does this for you, creating a list of people, companies and sites that match the content of your position and company description.

As a sales professional, you want to use content, either from your marketing department or content you find and create on your own, to engage your network and to identify the people that are talking about and responsible for buying/influencing a purchase.

In our research (using BrainBrowser) we discovered Nimble and a new category of Social CRM vendors with applications driving social selling (check out Gerry Moran’s post for background on content and social selling).  We were immediately hooked and started using Nimble as our company CRM but quickly found it worked well for managing lists of candidates.

Nimble, a new social CRM application, has made integration easy and I’m recommending it to everyone.  All you need to do is sign up for the trial (its only $15 per month if you like it) and install the plug in in your Chrome browser.  You’ll then be able to highlight the name of the person on the list in BrainBrowser, right click, select the Nimble Search and a popup will display the person’s social media pages in LinkedIn, Twitter, Google+ etc.  Click Save and you’ve added them to your Nimble Contacts where you can then view their social media messages, profile and decide whether to connect or follow.   Tag them and you’ve creating a recruiting hot list you can track in Nimble.

Here’s a video clip I tweeted to CEO Jon Ferrara demonstrating how/why we love it.  This was in response to his video clip to Larry Nipon following up on my referral.

Let me know how you like it.  They do a great job but if you have any questions on the difference between CRM and Social CRM, and how we’re using it for recruiting.  Be sure to add @ai_one or @tom_semantic if you tweet about this and sign up to request a login for BrainBrowser.

As of today, there are only 22 slots left for FREE registrations under the Alpha test program.  Participation gets you a year free on the platform.  Email or tweet @tom_semantic to sign up.

Context, Graphs and the Future of Computing

Friday, June 20th, 2014

Robert Scoble and Shel Israel’s latest book, Age of Context, is a survey of the contributions across the globe to the forces influencing technology and our lives today.  The five forces are mobile, social media, data, sensors and location.  Scoble calls these the five forces of context and harnessed, they are the future of computing.

Pete Mortensen also addressed context in his brilliant May 2013 article in Fast Company “The Future of Technology Isn’t Mobile, It’s Contextual.”   So why is context so important (and difficult)?  First, context is fundamental to our ability to understand the text we’re reading and the world we live in.  In semantics, there is the meaning of the words in the sentence, the context of the page, chapter, book and prior works or conversations, but also the context the reader’s education and experience add to the understanding.  As a computing problem, this is the domain of text analytics.

Second, if you broaden the discussion as Mortensen does to personal intelligent agents (Siri, Google Now), the bigger challenge is complexity.  Inability to understand context has always made it difficult for computers and people to work together.  People and the language we use to describe our world is complex, not mathematical, You can’t be reduced to a formula or rule set, no matter how much data is crunched. Mortensen argues (and we agree) that the five forces are finally giving computers the foundational information needed to understand “your context” and that context is expressed in four data graphs.  These data graphs are

  • Social (friends, family and colleagues),
  • Interest (likes & purchases),
  • Behavior (what you do & where) and
  • Personal (beliefs & values).

While Google Glass might be the poster child of a contextual UX, ai-one has the technology to power these experiences by extracting Mortensen’s graphs from the volumes of complex data generated by each of us through our use of digital devices and interaction with increasing numbers of sensors known as the Internet of Things (IoT).  The Nathan API is already being used to process and store unstructured text and deliver a representation of that knowledge in the form of a graph.  This approach is being used today in our BrainDocs product for eDiscovery and compliance.

Age of Context by Scoble and IsraelIn Age of Context, ai-one is pleased to be recognized as a new technology addressing the demands of these new types of data.  The data and the applications that use them are no longer stored in silos where only domain experts can access them.  With Nathan the data space learns from the content, delivering a more relevant contextual response to applications in real time with user interfaces that are multi-sensory, human and intuitive.

We provide developers this new capability in a RESTful API. In addition to extracting graphs from user data, they can build biologically inspired intelligent agents they can train and embed in intelligent architectures.   Our new Nathan is enriched with NLP in a new Python middleware that allows us to reach more OEM developers.  Running in the cloud and integrated with big data sources and ecosystems of existing APIs and applications, developers can quickly create and test new applications or add intelligence to old ones.

For end users, the Analyst Toolbox (BrainBrowser and BrainDocs) demonstrates the value proposition of our new form of artificial intelligence and shows developers how Nathan can be used with other technologies to solve language problems.  While we will continue to roll out new features to this SaaS offering for researchers, marketers, government and compliance professionals, the APIs driving the applications will be available to developers.

Mortensen closes, “Within a decade, contextual computing will be the dominant paradigm in technology.”  But how?  That’s where ai-one delivers.  In coming posts we will discuss some of the intelligent architectures built with the Nathan API.

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

Collaboration, Artificial Intelligence and Creativity

Thursday, April 4th, 2013

We are thrilled to publish this guest blog by Dan Faggella – a writer with a focus on the future of consciousness and technology. ai-one met Dan online through his interest in the beneficial developments of human and volitional (sentient) potential.  Dan is national martial arts champion in Brazilian Jiu Jitsu and Masters graduate from the prestigious Positive Psychology program at the University of Pennsylvania. His eclectic writings and interviews with philosophers and technology experts can be found online at www.SentientPotential.com

Artificial Intelligence as a Source for Collaboration

At a recent copywriting event in Las Vegas, I heard a nationally renown writer of sales letters and magazine ads mention something that resonated with me. He said that copywriters are generally isolated people who like to work at him on a laptop, not in a big room with other people, or in a cubicle in an office – but that some of the absolute best ad agencies were getting their best results by “forcing” (in his words) their best copywriters to work together on important pitches and sales letters – delivering a better product than any of them could have alone.

Some people in the crowd seemed surprised, and the copywriter on stage mentioned that many “a-list” copywriters tend to think that their creativity and effectiveness will be stifled by the pandering to the needs of other writers, or arguing over methods and approaches to writing. In my opinion, however, this notion of the “genius of one” is on the way out, even in fields where creativity rules.

If we take the example of sports, the need for feedback and collaboration is for some reason more obvious. A professional football team does not have one genius coach, they have offensive, defensive, and head coaches with teams of assistant coaches. In addition, top athletes from basketball to wrestling to soccer are usually eager to play with and against a variety of teammates and opponents in order to broaden their skills and test their game in new ways. The textbooks on the development of expertise are full of examples from the world of sport; especially pertaining to feedback, coaching, and breaking from insularity.

The focus of my graduate studies at UPENN was in the domain of skill development, where the terms “feedback” (perspective and advice from experts outside oneself) and “insularity” (a limited scope of perspective based on an inability or unwillingness to seek out or take in the perspective of other experts) are common. In sport, insularity is clearly seen as negative. However, in literature or philosophy, it seems that the “genius of one” still seems to reign.

Why might this be the case, when in so many other fields (chess, sports, business, etc…) we se collaboration proliferated? I believe that the answer to this question lies partially in the individual nature of these fields, but that new approaches in collaboration – and particularly new applications of artificial intelligence – will eventually break down the insularity in these and many other “creative” fields.

What is Creativity & Collaboration All About, Anyway?

Creativity, in short, is the ability to create, or to bend rules and convention in order to achieve an end. Collaboration is working jointly on a project. Both, in my mind, imply the application of more intelligence to a particular problem.

Just as three top copywriters can put together a better sales letter (generally) than one copywriter, three top chess players are more likely to defeat a computer chess program (generally) than one top chess player alone.

Technology allows us to bring more to bare when it comes to applying intelligence. Even in the relatively simple task of putting together this article, I am able to delete, reorganize, link, and research thanks to my laptop and the internet. I bring more than my brain and a pen on paper could do alone. I may not be “collaborating,” but I am applying and the information and research of others to my own work in real time.

Artificial intelligence ads an entirely new level of “applied intelligence” to projects that may extend beyond what internet research and human collaboration could ever achieve. For our purposes today, the progression of “less” to “more” applied intelligence will be: working alone, working with others, working with others and researching online, and applying artificial intelligence. We already have tremendous evidence of this today in a vast number of fields.

Applications Already Underway

I will argue that, in general, collaboration and the application of artificial intelligence will be prevalent in a field based primarily on: the competitiveness of that field (in sports and business, for instance, competition is constant, and so testing and evaluating can be constant), popularity / perceived importance of the field (trivial matters rarely hold the attention of groups of smart people, and are even less likely to garner grants or resources), and the lucrative-ness of that field (such as finance).

In finance, for example, the highly competitive, the highly lucrative and high-speed work of number-crunching and pattern-recognition has been one of the most prominent domains of AI’s applications. Not only are human decisions bolstered by amazingly complex real-time data, but many “decisions” are no longer made by humans at all, but are completely or mostly automated based on streaming data and making sense of patterns. It is estimated that nearly 50% of all trades in American and European markets are made automatically – and are likely to increase.

Anyone who’s visited Amazon.com, Google, or Facebook knows that advertisements or promoted products are calibrated specifically to each user. This is not done by a team of guessing humans, individually testing ads and success rates, but is performed by intelligent, learning algorithms that use massive amounts of data from massive numbers of users (including data from off of their own sites) to present the advertisements or products more likely to generate sales.

The above applications seem like obvious first applications of the expensive technologies of AI because of the amount of money involved, and the necessity for businesses to stay ahead in a competitive marketplace (generating maximum revenue, giving customers offers that they want, etc…). Implications have already been seen in sports, with companies like Automatic Insights providing intelligent sports data and statistics in regular, human language in real time. My guess is that in the big-money world of professional sport, even this kind of advanced reporting will only be the very tip of the iceberg.

However, the implications will soon also reverberate into the worlds of more “complex” systems of meaning, as well as fields where the economic ramifications are less certain. I believe that the humanities (poetry, literature, philosophy) will see a massive surge of applied intelligence that will not only break the mold of the “genius of one,” but will also open doors to all of the future possibilities of AIs contributing to “creative” endeavors.

Future Implications of AI in “Creative” Fields / The Humanities

It seems perfectly reasonable that more applications for AI have been found in the domain of finance than in the domain of philosophy or literature. Finance involves numbers and patterns, while literature involves more complex and arbitrary ideas of “meaning” and a system of much more complicated symbols.

However, I must say that I am altogether surprised with the fact that there seems to be very little application of AI to the domain of the humanities. In part, I believe this to be a problem of applying AI to complex matters of “meaning” and subjective standards of writing quality (there is not clear “bottom line” as there is in finance), but the notion of the “genius of one” invariably plays a part in this trend as well, as even collaboration among humans (never mind collaboration with an AI) is often comparatively limited in these fields.

Not being an novelist, I can hardly say that if writers collaborated with other expert writers more often, they would create “better” overall works. I have an inkling, however, that this might be the case.

In the world of psychology, I believe that outside the desire to “hog the glory,” expert researchers would almost certainly take on the opportunity to collaborate on their most important projects with other expert researchers in the field. In the world of flowing data streams, applying AI and statistical models might also seem more applicable.

In philosophy – where works are generally still seen to be completed by lone, pensive thinkers in dark, pensive rooms – I believe that collaboration and AI will eventually transcend the “genius of one,” and rid us of the notion that the best work is done by solo minds.

If one philosopher spent 12 months aiming to compare and find connections between the ethics of Aristotle and Epictetus, I would argue that 12 very smart philosophers working together for 12 months might achieve much more insight.

Similarly, if intelligent algorithms could be created that could detect commonalities in terms, symbols, and meanings – entirely new connections and insights might be made possible, and much more vast reams of philosophical text could be analyzed in a much more uniform fashion – producing an objective perspective completely unattainable to human beings without an AI aide. I believe that this is already possible, though it’s applications in philosophy and the humanities in general seem almost nonexistent outside of a few events and experiments.

I believe very much in the power of the individual mind, and mean no disrespect to human capacity or to individual thinkers when I say that the era of the “genius of one” is going to progressively evaporate. In 1920, you might be able to win the Nobel Prize in your 40’s with a small team of researchers. In 2020, you’re more likely to win the Nobel Prize in your 60’s with a global research team that’s been hard at work for decades. Even the more “creative” domains of the humanities will experience a similar shift as collaboration becomes more common, research becomes more simple, and intelligence becomes more and more prevalent and nuanced.

Conclusion: Robot Shakespeare?

It is interesting to pose that at some point – potentially within this century, the best prose, the best novels, and the best philosophical insight will come not from individual geniuses, not even from teams of researchers, but almost entirely from AI.

This is not to say that I believe a “robot Shakespeare” will be in our midst anytime soon – but rather that we aught keep our minds open to the idea of AI being something other than calculators and cars that drive themselves. The nuanced connections of meaning can already be used to supplement human efforts with insights in so many domains, an in a period of 20, 40, or 60 years, we may see all elements of human capacity (not just statistical number-crunching) enhanced a billion-fold by AI’s of the future.

The ethical, political, and other implications aside, let us keep our eyes open for the implications of applied intelligence across all fields of human endeavor. We may question technology’s ability to contribute, but remember that it was less than 70 years between the early flights of the Wright brothers and landing on the moon. Might we seem a similar time frame between the advent of Amazon’s intelligent product offers and the replacement of humans at the helm of creative endeavor in writing, philosophy, poetry, and beyond. Only time will tell.

Thinking forward,

-Daniel Faggella

Self-Aware, Self-Defending Adaptive Network Appliance Software (SASDANAS)

Thursday, January 12th, 2012

On November 29, 2011, our consulting partner Ariston Consulting submitted a proposal to the US Air Force to develop a new form of defense for cyber assets using machine learning for cyber awareness and resilience.  This proposal was partially developed by ai-one in an effort to bring the most advanced machine learning technologies to the Air Force at the lowest possible cost. 

Our proposal (below) was in response to BAA Number  AFRL-PK-11-0001 as a Rapid Innovation Funding program. Our proposal met all four operational criteria yet was rejected on January 6, 2012 due to our lack of prior history with the US Air Force. The AF simply preferred to do business with a company that they knew rather than a new vendor.

However, on December 20, 2011 the Air Force released a request to build a system very similar to what we proposed to build below under the contract BAA-RIK-12-03. Both projects were issued by the Department of the Air Force, Air Force Materiel Command, AFRL – Rome Research Site, AFRL/Information Directorate, 26 Electronic Parkway, Rome, NY, 13441-4514.

We are not accusing the Air Force of any wrong doing nor is there any evidence that they copied and pasted our ideas into another BAA. Quite to the contrary, the Air Force is a big place and we are not the only people thinking of ways for networks to defend themselves using autonomic machine learning technologies. However, we feel that our technology can be deployed at very minimal cost compared to the budget provided in the BAA issued a month after we proposed a smaller, more rapid solution.

We think it is valuable to share this information with the public for several reasons:

  1. To publish our findings in a public forum to prevent any other party from obtaining a patent for cyber security applications or network defense applications using the approach described herein.
  2. To encourage major defense contractors to contact Ariston Consulting and to use ai-one’s biologically inspired intelligence in cyber security applications.
  3. To encourage the Air Force to consider reducing the budget allocated for BAA-RIK-12-03 by 90%. There is simply no business reason to spend 10-times what we proposed.

Title:     SASDANAS: A network that protects itself from cyber attacks.

BAA Number:  AFRL-PK-11-0001

Firm:         Ariston Consulting LLC

P.O. Box 1721

Sierra Vista, AZ 85636

http://www.aristonhq.com

Phone: (520) 378-6112

CAGE CODE: 61E85

Duration of Effort:         24 months

Estimated Cost of Effort:          $2,800,000

Self Certification of Applicant:   Service-Disabled Veteran-Owned Small Business (SDVOSB)

Air Force Need Area:  02. Cyberspace Superiority and Mission Assurance

Air Force Primary User:  24th Air Force Wing, San Antonio, TX

Programs/Platforms for Proposed Technology:

DoD-Reimbursed IR&D:  NO

Proposed Approach Relate to Prior DoD-Funded SBIR or STTR:  NO

Foreign Participants for Effort:  NO

Funded by DoD or Another Federal Agency: NO

Percentage of Effort

by Offerer:                    60%

by Others:                    40%

 Preferred Funding Instrument:    Contract

Technical POC:     Jonathan Woodruff, CEO, Ariston Consulting

Phone: 520.378.6112

Email:  jonathan.woodruff@aristonhq.com

 

Business POC:        Steve Mecham, COO, Ariston Consulting

Phone: 520.378.6112

Email: steve.mecham@aristonhq.com

 

Project Description/Objective:  SASDANAS: A network that protects itself from cyber attacks.

Ariston Consulting LLC proposes to develop a Self-Aware, Self-Defending Adaptive Network Appliance Software (SASDANAS) system that acts as an intelligent agent to monitor network activity, content and behavior to augment the capacity of human analysts to identify and counteract all forms of cyber threats.

Ariston Consulting is a Service-Disabled Veteran-Owned Small Business (SDVOSB) based in Sierra Vista, AZ, provides advanced technology testing and engineering solutions. Expertise and experience in providing non-personal scientific and engineering services to test Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) systems in support of the US Air Force (USAF), US Army, and DISA.

SASDANAS is an intelligent agent that learns and understands the threat level posed by every byte-pattern across a network. The software system uses a new form of machine learning to monitor every detail of a network to identify and isolate cyber security threats – including malware, application high-jacking, sabotage and illicit access, hacking and unauthorized use. It enables the Air Force to make all cyber assets self-aware, self-protecting and adaptive to any external or internal threat. The approach eliminates the opportunity for zero-day attacks because it detects all anomalous packet behavior and content. Furthermore, SASDANAS provides the Air Force with a first-mover advantage as the system learns through use and thus becomes more intelligent over time.

SASDANAS is a 64-bit multithread, massively parallel application that is deployable through a REpresentational state transfer (REST) architecture. Each instance of SASDANAS may be deployed in series and/or in parallel. This architecture provides the USAF the greatest degree of flexibility when deploying into field operations. This approach enables the USAF to use SANDANAS in either: a) moving-windows approach to read every packet as it flows across the network; or, b) identifying threats by capturing an image of the topology of network at byte- or packet-level of detail to understand the behavior and content of network. Each instance of SASDANAS will have the capacity to understand up to 18 exabytes of data at a time. Speed of SASDANAS is dependent on available memory and processing capacity. When deployed in parallel, SASDANAS has the theoretical capacity to monitor the activity of the entire Internet.

Unlike current approaches to cyber security, SASDANA uses a new technology called a HoloSemantic DataSpace (HSDS) to detect, classify and store every byte pattern. The HSDS is thus able to recognize every packet’s behavior and content to determine if the byte-pattern conforms to expectations or is anomalous and therefore subject to further scrutiny to determine if it is a threat. The HSDS is an adaptive, associative network that detects the relationship of every byte that is fed into the system. Thus, the HSDS is capable of identifying both known threat patterns while concurrently identifying and isolating anomalous patterns that may signify a zero-day attack or non-compliant use of the network (e.g., sabotage).

The HSDS is a newly discovered form of neuronal network that mimics the neurophysiology of the neocortex. It is commercially trademarked as a “biologically inspired intelligence” and operates similar to a human brain. It learns autonomically by detecting byte-patterns at the moment of stimulation. The HSDS stores each unique byte pattern only once regardless of how many times it encounters that specific pattern. It registers and adjusts the semiotic value for each byte pattern each time it is stimulated – adjusting the size of the net automatically. It determines the semiotic value for each byte pattern with the following dimensions, each of which may have many values: time of stimulation, place of stimulation, syntax of surrounding byte patterns, and packet payload and addressing. Thus, the HSDS creates an n-dimensional representation of the semiotic value of every byte-pattern; thereby capturing every detail within the complexity of data.

The HSDS technology is commercially available from ai-one inc. since June 2011. It is currently in use at Orange (France Telecom) and more than 40 additional installation sites around the world. The commercial version of the HSDS is offered in three versions: Topic-Mapper to analyze human languages, graphalizer to analyze sensor data, and Ultra-Match to analyze visual images. The technology has been used by The Federal Criminal Police Office of Germany (Bundeskriminalamt or BKA) to build a crime scene analysis tool for the Swiss Federal Department of Justice and Police (Eidgenössische Justiz- und Polizeidepartement or EJPD). The commercial versions of HSDS have a technology readiness level (TRL) of 9. The TRL for the proposed customization of current HSDS COTS technology is 7. Ariston Consulting will license ai-one’s technology to create a new software application to meet the unique needs of protecting USAF cyber assets.  The HSDS differs from current forms of neural networks, machine learning and artificial intelligence technologies in the following ways:

Transparency – HSDS generates a lightweight ontology (LWO) that adjusts dynamically with each passing byte (and/or packet). The LWO describes the relationship of every byte within the network. The LWO is machine generated, machine curated and accessible by humans.

Benefit: Humans can see how SASDANAS interprets the value and threat level of every packet.

 

Autonomic:  HSDS learns without any human intervention. It does not require any prior conditions or neighborhood functions. Rather, it automatically generates computational and data cells within the network as needed immediately upon network stimulation – just like the human brain.

Benefit: SASDANAS is objective and subject to cognitive biases that may distort threat detection.

 

Speed, Accuracy, Sensitivity: HSDS captures every detail regardless of the degree of complexity. In incremental learning situations, the proposed 64-bit architecture is expected to be at least 105 faster than latent Dirichlet allocation (LDA) or vectoring approaches such as COStf-idf.

Benefit: SASDANAS is very fast and accurate – even by neural net standards.

 

Trainability: The system can be trained and untrained by humans. It is aware of which patterns are learned through training and which patterns have been taught from humans.

Benefit: SASDANAS eliminates the risk of overtraining. It is flexible.

 

Compatible with Existing Technologies: The system is deployable using industry standard approaches as a cloud-based application.

Benefit: SASDANAS reduces the cost of maintaining and protecting cyber assets while extending their functionality.

Ariston Consulting proposes to build SASDANAS as a software proof-of-concept for further development as a hardware solution called Self-Aware, Self-Defending Adaptive Network Appliance Chipsets (SASDANACS). Based on preliminary tests of the core commercial technology, Ariston estimates that the hardware version will operate at least 10,000 times faster than the software version. This speed, combined with an estimated capacity of 18 exabytes per instance, enables the hardware version to monitor and protect cyber assets at wire-speed and at Internet scale.

SASDANA is deployable at any layer with network (from switch layers 1 through 7) and is compatible with known specifications for Wireless Network After Next (WNAN) as described in unclassified DARPA and AFRL reports. Its architecture provides the AF with a wide range of deployment options.

Approach:

Ariston Consulting LLC will adapt commercial-off-the-shelf (COTS) HSDS software from ai-one inc. to build SASDANA. Ariston Consulting has secured rights to license and modify technologies owned by ai-one inc.for the purpose of creating custom applications for agencies of the United States Government, including the Department of Defense.

Critical Need/JUPM Challenge Area Addressed:

02. Cyberspace Superiority and Mission Assurance

Benefits to the Warfighter:

Cyber security – Networks monitor and defend themselves.

Force leverage – SASDANA drastically increases the analytical capacity of human analysis.

Morale – SASDANA makes network security analysis and counter measures more interesting by eliminating mundane tasks.

Funding/Cost:              $2,800,000.

Program Plan:

a)     Period of Performance:  Not more than 24 months from commencement of contract for Phase 1.

i)      Ariston Consulting shall report progress on technical design, engineering and prototype development every 30 days throughout the project.

b)    Schedule – Total of 24 months:

i)      Detailed technical specification including use and test cases:  3 months

ii)     Technical development of software using Agile methodology: 12 months

iii)    Software testing: 3 months

iv)    Software revisions: 3 months

v)     Preparation and submission of final technical report: 3 months

c)     Deliverables:

i)      Scientific and Technical Reports every three (3) months, Final Report at conclusion

ii)     Funds and Man-hour Expenditure Report every three (3) months, Final Report at conclusion

iii)    Contract Status Report (CFSR)

iv)    Status Report

v)     Presentation Materials

vi)    Software: As proposed, on CD-ROM

d)    Metrics/Measure of Success:

i)      Ability to detect known malware compared to industry standard technology (e.g., McAfee).

ii)     Ability to detect unknown malware threat imposed by AFRL Red Team.

iii)    Ability to detect anomalous behavior of a packet within a network.

e)     Facilities/Equipment:

i)      All development will be completed at an Ariston consulting controlled Top Secret (TS) facility.

f)     Risk:

i)      Technical risk of SASDANAS is minimal as the technology currently is available for commercial use by ai-one inc. Ariston Consulting will mitigate risk by employing ai-one engineers to train Ariston staff, transfer knowledge and provide guidance based on commercial experience.

g)    Proposed Transition Plan:

i)      Technical data: Unlimited rights granted to USAF.

ii)     Non-commercial software (NCS): Unlimited rights granted for each additional instance of SASDANAS software shall be sold to the US Government.

iii)    NCS Documentation: Unlimited rights granted to USAF.

iv)    Commercial computer software rights: Not applicable. SASDANAS will be a modified version of ai-one technology that will not be commercially available.

v)     There are no restrictions on the use of a licensed instance of SASDANAS for use within the United States Air Force. The Air Force may deploy SASDANAS at its own discretion, in any manner it so chooses.

vi)    SASDANA’s application program interface (API) may be accessed by any entity authorized by the USAF.

h)     Other Key Participants:

i)      Commercial supplier of HSDS technology, software development kit and technical training:

ai-one inc. (a Delaware C-corporation)

Atten: Olin Hyde, Vice President

5711 La Jolla Blvd., La Jolla, CA 92037

Phone: 1-858-381-5897/Email: oh@ai-one.com

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.

“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.

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.