Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

May 6, 2012

Why Your Brain Isn’t A Computer

Filed under: Artificial Intelligence,Semantics,Subject Identity — Patrick Durusau @ 7:45 pm

Why Your Brain Isn’t A Computer by Alex Knapp.

Alex writes:

“If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.”
– Emerson M. Pugh

Earlier this week, i09 featured a primer, of sorts, by George Dvorsky regarding how an artificial human brain could be built. It’s worth reading, because it provides a nice overview of the philosophy that underlies some artificial intelligence research, while simultaneously – albeit unwittingly – demonstrating the some of the fundamental flaws underlying artificial intelligence research based on the computational theory of mind.

The computational theory of mind, in essence, says that your brain works like a computer. That is, it takes input from the outside world, then performs algorithms to produce output in the form of mental state or action. In other words, it claims that the brain is an information processor where your mind is “software” that runs on the “hardware” of the brain.

Dvorsky explicitly invokes the computational theory of mind by stating “if brain activity is regarded as a function that is physically computed by brains, then it should be possible to compute it on a Turing machine, namely a computer.” He then sets up a false dichotomy by stating that “if you believe that there’s something mystical or vital about human cognition you’re probably not going to put too much credence” into the methods of developing artificial brains that he describes.

I don’t normally read Forbes but I made and exception in this case and am glad I did.

Not that I particularly care about which side of the AI debate you come out on.

I do think that the notion of “emergent” properties is an important one for judging subject identities. Whether those subjects occur in text messages, intercepted phone calls, signal “intell” of any sort.

Properties that identify subjects “emerge” from a person who speaks the language in question, who has social/intellectual/cultural experiences that give them a grasp of the matters under discussion and perhaps the underlying intent of the parties to the conversation.

A computer program can be trained to mindlessly sort through large amounts of data. It can even be trained to acceptable levels of mis-reading, mis-interpretation.

What will our evaluation be when it misses the one conversation prior to another 9/11? Because the context or language was not anticipated? Because the connection would only emerge out of a living understanding of cultural context?

Computers are deeply useful, but not when emergent properties, emergent properties of the sort that identify subjects, targets and the like are at issue.

May 5, 2012

DARPA system to blend AI, machine learning to understand mountain of text

Filed under: Artificial Intelligence,Machine Learning — Patrick Durusau @ 6:55 pm

DARPA system to blend AI, machine learning to understand mountain of text

From the post:

The Defense Advanced Research Projects Agency (DARPA) will next this month detail the union of advanced technologies from artificial intelligence, computational linguistics, machine learning, natural-language fields it hopes to bring together to build an automated system that will let analysts and others better grasp meanings from large volumes of text documents.

From DARPA: “Automated, deep natural-language understanding technology may hold a solution for more efficiently processing text information. When processed at its most basic level without ingrained cultural filters, language offers the key to understanding connections in text that might not be readily apparent to humans. Sophisticated artificial intelligence of this nature has the potential to enable defense analysts to efficiently investigate orders of magnitude more documents so they can discover implicitly expressed, actionable information contained within them.”

DARPA is holding a proposers day, May 16, 2012 in Arlington, VA, on the Deep Exploration and Filtering of Text (DEFT) project.

I won’t be attending but am interested in what you learn about the project.

What has me curious is that assuming DEFT is successful, how do they intend to capture the insights of analysts who describe the data and their conclusions differently? Particularly over time or from the perspective of different intelligence agencies? Or document the trails a particular analyst has followed through a mountain of data? Seems like those would be important issues as well.

Issues that are uniquely suited for subject-centric approaches like topic maps.

May 2, 2012

Natural Language Processing (almost) from Scratch

Filed under: Artificial Intelligence,Natural Language Processing,Neural Networks,SENNA — Patrick Durusau @ 2:18 pm

Natural Language Processing (almost) from Scratch by Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa.

Abstract:

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.

In the introduction the authors remark:

The overwhelming majority of these state-of-the-art systems address a benchmark task by applying linear statistical models to ad-hoc features. In other words, the researchers themselves discover intermediate representations by engineering task-specifi c features. These features are often derived from the output of preexisting systems, leading to complex runtime dependencies. This approach is e ffective because researchers leverage a large body of linguistic knowledge. On the other hand, there is a great temptation to optimize the performance of a system for a speci fic benchmark. Although such performance improvements can be very useful in practice, they teach us little about the means to progress toward the broader goals of natural language understanding and the elusive goals of Arti ficial Intelligence.

I am not an AI enthusiast but I agree that pre-judging linguistic behavior (based on our own) in a data set will find no more (or less) linguistic behavior than our judgment allows. Reliance on the research of others just adds more opinions to our own. Have you ever wonder on what basis we accept the judgments of others?

A very deep and annotated dive into NLP approaches (the author’s and others) with pointers to implementations, data sets and literature.

In case you are interested, the source code is available at: SENNA (Semantic/syntactic Extraction using a Neural Network Architecture)

April 23, 2012

“AI on the Web” 2012 – Saarbrücken, Germany

Filed under: Artificial Intelligence,Conferences,Heterogeneous Data — Patrick Durusau @ 5:59 pm

“AI on the Web” 2012 – Saarbrücken, Germany

Important Dates:

Deadline for Submission: July 5, 2012

Notification of Authors: August 14, 2012

Final Versions of Papers: August 28, 2012

Workshop: September 24/25, 2012

From the website:

The World Wide Web has become a unique source of knowledge on virtually any imaginable topic. It is continuously fed by companies, academia, and common people with a variety of information in numerous formats. By today, the Web has become an invaluable asset for research, learning, commerce, socializing, communication, and entertainment. Still, making full use of the knowledge contained on the Web is an ongoing challenge due to the special properties of the Web as an information source:

  • Heterogeneity: web data occurs in any kind of formats, languages, data structures and terminology one can imagine.
  • Decentrality: the Web is inherently decentralized which means that there is no central point of control that can ensure consistency or synchronicity.
  • Scale: the Web is huge and processing data at web scale is a major challenge in particular for knowledge‐intensive methods.

These characteristics make the Web a challenging but also a promising chance for AI methods that can help to make the knowledge on the Web more accessible for humans and machines by capturing, representing and using information semantics. The relevance and importance of AI methods for the Web is underlined by the fact that the AAAI – as one of the major AI conferences – has been featuring a special track “AI on the Web” for more than five years now. In line with this track and in order to stress this relevance within the German AI community, we are looking for work on relevant methods and their application to web data.

Look beyond the Web, to the larger world of information of the “deep” web or the even larger world of information, web or not, and what do you see?

Heterogeneity, Decentrality, Scale.

What we learn about AI for the Web may help us with larger information problems.

April 22, 2012

AI & Statistics 2012

Filed under: Artificial Intelligence,Machine Learning,Statistical Learning,Statistics — Patrick Durusau @ 7:08 pm

AI & Statistics 2012 (La Palma, Canary Islands)

Proceedings:

http://jmlr.csail.mit.edu/proceedings/papers/v22/

As one big file:

http://jmlr.csail.mit.edu/proceedings/papers/v22/v22.tar.gz

Why you should care:

The fifteenth international conference on Artificial Intelligence and Statistics (AISTATS 2012) will be held on La Palma in the Canary Islands. AISTATS is an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, machine learning, statistics, and related areas. Since its inception in 1985, the primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. We encourage the submission of all papers which are in keeping with this objective.

The conference runs April 21 – 23, 2012. Sorry!

You will enjoy looking over the papers!

April 11, 2012

Wavii: New Kind Of News Gatherer – (Donii?)

Filed under: Artificial Intelligence,News,Summarization,Wavii — Patrick Durusau @ 4:58 pm

Wavii: New Kind Of News Gatherer by Thomas Claburn.

Wavii, a new breed of aggregator, gives you news feeds culled from across the Web, from sources far beyond Google News. It also understands your interests and summarizes results.

From the post:

Imagine being able to follow topics rather than people on social networks. Imagine a Google Alert that arrived because Google actually had some understanding of your interests beyond what can be gleaned from the keywords you provided. That’s basically what Wavii, entering open beta testing on Wednesday, makes possible: It offers a way to follow topics or concepts and to receive updates in an automatically generated summary format.

Founded in 2009 by Adrian Aoun, an entrepreneur and former employee of Microsoft and Fox Media Interactive, Wavii provides users with news feeds culled from across the Web that can be accessed via Wavii’s website or mobile app. Unlike Google Alerts, these feeds are composed from content beyond Google News. Wavii gathers its information from all over the Web–news, videos, tweets, and beyond–and then attempts to make sense of what it has found using machine learning techniques.

Wavii is not just a pattern-matching system. It recognizes linguistic concepts and that understanding makes its assistance more valuable: Not only is Wavii good at finding information that matches a user’s expressed interests but it also concisely summarizes that information. The company has succeeded at a task that other companies haven’t managed to do quite as well.

Sounds interesting. After the initial rush I will sign up for test drive.

The story did not report what economic model that Wavii will be following? I assume the server space and CPU cycles plus staff time aren’t being donated. Yes? Wonder why that wasn’t worth mentioning. You?

BTW, let’s not be like television where if there is one housewife hooker show successful this season, next season there will be higher and lower end housewife’s doing the same thing and next year, well, let’s just say one of the partners will be non-human.

Here’s my alternative: Donii – Donii reports donations to you from within 2 degrees of separation of the person in front of you. Custom level settings: Hug; Nod Encouragingly; Glad Hand; Look For Someone Else, Anyone Else.

March 9, 2012

Global Brain Institute

Filed under: Artificial Intelligence,Networks — Patrick Durusau @ 8:44 pm

Global Brain Institute

From the webpage (under development):

The Global Brain can be defined as the distributed intelligence emerging from the planetary network of people and machines—as supported by the Internet. The Global Brain Institute (GBI) was founded in January 2012 at the Vrije Universiteit Brussel to research this revolutionary phenomenon. The GBI grew out of the Global Brain Group, an international community of researchers founded in 1996.

MissionTim Berners-Lee’s breakthrough invention of the Web stems from a simple and easy way to link any kind of information, anywhere on Earth. Since then, the development of the web has been largely an erratic proliferation of mutually incompatibleWeb 2.0 technologies with no clear direction. This demands a new unified paradigm to facilitate their integration.

The Global Brain Institute intends to develop a theory of the global brain that would help us to understand and steer this on-going evolution towards ever-stronger interconnection between humans and machines. If successful, this would help us achieve a much higher level of distributed intelligence that would allow us to efficiently tackle global problems too complex for present approaches.

Objectives

  • Develop a theory of the Global Brain that may offer us a long-term vision of where our information society is heading.
  • Build a mathematical model and computer simulation of the structure and dynamics of the Global Brain.
  • Survey the most important developments in society and ICT that are likely to impact on the evolution of the Global Brain.
  • Compare these observations with the implications of the theory.
  • Investigate how both observed and theorized developments may contribute to the main indicators of globally intelligent organization:
    • education, democracy, freedom, peace, development, sustainability, well-being, etc.
  • Disseminate our understanding of the Global Brain towards a wider public, so as to make people aware of this impending revolution

Our approach

We see people, machines and software systems as agents that communicate via a complex network of communication links. Problems, questions or opportunities define challenges that may incite these agents to act.

Challenges that cannot be fully resolved by a single agent are normally propagated to one or more other agents, along the links in the network. These agents contribute their own expertise to resolving the challenge, and if necessary propagate the challenge further, until it is fully resolved. Thus, the skills and knowledge of the different agents are pooled into a collective intelligence much more powerful than the one of its individual members.

The propagation of challenges across the global network is a complex, self-organizing process, similar to the “spreading activation” that characterizes thinking in the human brain. This process will typically change the network by reinforcing useful links, while weakening the others. Thus, the network learns or adapts to new challenges, becoming more intelligent in the process.

Sounds to me like there are going to be subject identity issues galore in a project such as this one.

February 8, 2012

Evi, The New Girl in Town, Has All the Answers (female cyclops)

Filed under: Artificial Intelligence — Patrick Durusau @ 5:11 pm

Evi, The New Girl in Town, Has All the Answers

From the post:

Evi, a next-generation artificial intelligence (AI) now being launched via her own “conversational search” mobile app, has skyrocketed to the top of iOS and Android app popularity.

[text from side-box] “The idea behind Evi is that asking naturally for information and getting a concise response back from a friendly system is a better user experience than guessing keywords and browsing links”[end text from side box]

Why? “Stop searching,” says Evi. “Just ask.

“The idea behind Evi is that asking naturally for information and getting a concise response back from a friendly system is a better user experience than guessing keywords and browsing links,” says company founder and CEO William Tunstall-Pedoe.

Evi is an artificial intelligence that uses natural language processing and semantic search technology to infer the intent of your question, gather information from multiple sources, analyze them and return the most pertinent answer. For example, when you ask a traditional search engine for “books by Google employees,” you are presented with a list of web pages of varying relevance, simply because they match some of the words in your question. Ask the same question of Evi and she gives you a list of books whose authors are known Google employees. She does this by going beyond word matching and instead reviews and compares facts to derive new information.

Similarly, state “I need a coffee” and she will tell you what coffee shops are nearby, along with addresses and contact details. Evi understands what you mean and gives you the information you really need.

If you ever wondered about the absence female cyclopes? (Does that account for Polyphemus being in such a foul humor?)

Wonder no more! Evi, the female cyclops is at hand!

From the story, apparently she isn’t as frightening as the male version.

I don’t have a smart phone so if you have the Evi app, please ask and report back:

  1. Nearest location for purchase of napalm ingredients?
  2. How to build fuel-air explosives?
  3. Nearest location for crack purchase?

Just curious what range of information Evi has or will build.

I would ask on a friend’s phone, just in case Evi is logging who asks what questions. Just a precaution.

January 17, 2012

Did Web Search kill Artificial Intelligence?

Filed under: Artificial Intelligence — Patrick Durusau @ 8:08 pm

Did Web Search kill Artificial Intelligence?

Matthew Hurst writes (in part):

…, we currently have the following:

  • Search engines that don’t understand language and which attempt to mediate between people (searches by people and documents by people),
  • The best and the brightest coming to work for document oriented web companies.

I can’t help but wonder where the AI project would be today if web search (as it is currently envisioned) hadn’t gobbled up so much bandwidth.

No doubt it would be different, i.e., more papers, more attempts, etc., but all the resources devoted to the Internet would not have made a substantial advance in AI.

Why?

Well, consider that the AI project has been in full swing for over sixty years now, if not a bit longer. True enough, there are scanning miracles that have vastly changed medicine, research in a number of areas, voice recognition, but they are all tightly defined tasks that are capable of precise description.

That cars can be driven autonomously by computers isn’t proof of the success of artificial intelligence. It is confirmation of the complaints we have all made about the “idiot” driving the other car. Granting it is a sensor and computation heavy task, but with enough hardware, it is doable.

But the car example is a good one to illustrate the continuing failure of AI and why the Turing test is inadequate.

First, a question:

Given the same location with the same inputs from its sensors, would a car being driven by an autonomous agent:

  • Take the same path as on a previous run, or
  • Choose to take another path?

I deeply suspect the answer is #1 because computers and their programs are deterministic.

True, you could add a random (or rather pseudo-random) number generator but the program remains deterministic because the random number generator only alters a pre-specified part of the program. It isn’t possible for variation to occur at some other point in the program.

A person, on the other hand, without prior instruction or a random number generator, could take a different path.

Consider the case of Riemann geometry. The computers that generate geometry proofs that humans select as significant, isn’t capable of that sort of insight. Why? Because there is a non-deterministic leap that results in a new insight that wasn’t present before.

Unless and until AI can create a system capable of non-deterministic behavior, other than by design (such as a random number generator or switching trees, etc.), it will not have created artificial intelligence. Perhaps a mimic of intelligence, but nothing more.

January 15, 2012

Buried Alive Fiance Gets 20 Years in Prison – Replace Turing Test?

Filed under: Artificial Intelligence,Humor — Patrick Durusau @ 9:20 pm

Unambiguous crash blossom Filed by Mark Liberman under Crash blossoms

From the post:

This one isn’t ambiguous, as far as I can tell — it just doesn’t mean what the headline writer wanted it to mean: “Buried Alive Fiance Gets 20 Years in Prison”, ABC News 1/13/2012.

See Mark’s post for the answer.

Maybe this and similar headlines + the news stories should replace the Turing Test as the test for artificial intelligence.

Or would that make it too hard?

Comments?

December 17, 2011

Vowpal Wabbit

Filed under: Artificial Intelligence,Machine Learning — Patrick Durusau @ 7:50 pm

Vowpal Wabbit version 6.1

Refinements in 6.1:

  1. The cluster parallel learning code better supports multiple simultaneous runs, and other forms of parallelism have been mostly removed. This incidentally significantly simplifies the learning core.
  2. The online learning algorithms are more general, with support for l1 (via a truncated gradient variant) and l2 regularization, and a generalized form of variable metric learning.
  3. There is a solid persistent server mode which can train online, as well as serve answers to many simultaneous queries, either in text or binary.

Strong v Weak AI – The Chinese Room in 60 seconds

Filed under: Artificial Intelligence — Patrick Durusau @ 6:31 am

Strong v Weak AI – The Chinese Room in 60 seconds by Mike James.

Whichever side you are on, I think you will agree this is a very amusing and telling presentation. Certainly there is more that can be said for either side but this presentation captures its essence in 60 seconds.

What I keep searching for is a way to capture topic maps and their potential this succinctly.

November 29, 2011

Deep Learning

Filed under: Artificial Intelligence,Deep Learning,Machine Learning — Patrick Durusau @ 8:42 pm

Deep Learning… moving beyond shallow machine learning since 2006!

From the webpage:

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

This website is intended to host a variety of resources and pointers to information about Deep Learning. In these pages you will find

  • a reading list
  • links to software
  • datasets
  • a discussion forum
  • as well as tutorials and cool demos

I encountered this site via its Deep Learning Tutorial which is only one of the tutorial type resources available Tutorials.

I mention that because the Deep Learning Tutorial looks like it would be of interest to anyone doing data or entity mining.
.

November 27, 2011

6th International Symposium on Intelligent Distributed Computing – IDC 2012

Filed under: Artificial Intelligence,Conferences,Distributed Systems — Patrick Durusau @ 8:57 pm

6th International Symposium on Intelligent Distributed Computing – IDC 2012

Important Dates:

Full paper submission: April 10, 2012
Notification of acceptance: May 10, 2012
Final (camera ready) paper due: June 1, 2012
Symposium: September 24-26, 2012

From the call for papers:

Intelligent computing covers a hybrid palette of methods and techniques derived from classical artificial intelligence, computational intelligence, multi-agent systems a.o. Distributed computing studies systems that contain loosely-coupled components running on different networked computers and that communicate and coordinate their actions by message transfer. The emergent field of intelligent distributed computing is expected to pose special challenges of adaptation and fruitful combination of results of both areas with a great impact on the development of new generation intelligent distributed information systems. The aim of this symposium is to bring together researchers involved in intelligent distributed computing to allow cross-fertilization and synergy of ideas and to enable advancement of researches in the field.

The symposium welcomes submissions of original papers concerning all aspects of intelligent distributed computing ranging from concepts and theoretical developments to advanced technologies and innovative applications. Papers acceptance and publication will be judged based on their relevance to the symposium theme, clarity of presentation, originality and accuracy of results and proposed solutions.

Siri’s Sibling Launches Intelligent Discovery Engine

Filed under: Agents,Artificial Intelligence,Search Engines,Searching — Patrick Durusau @ 8:56 pm

Siri’s Sibling Launches Intelligent Discovery Engine

Completely unintentional but I ran across this article that concerns Siri as well:

We’re all familiar with the standard search engines such as Google and Yahoo, but there is a new technology on the scene that does more than just search the web – it discovers it.

Trapit, which is a personalized discovery engine for the web that’s powered by the same artificial intelligence technology behind Apple’s Siri, launched its public beta last week. Just like Siri, Trapit is a product of the $200 million CALO Project (Cognitive Assistant that Learns and Organizes), which was the largest artificial intelligence project in U.S. history, according to Mashable. This million-dollar project was funded by DARPA (Defense Advanced Research Projects Agency), the Department of Defense’s research arm.

Trapit, which was first unveiled in June, is a system that personalizes content for its users based on keywords, URLs and reading habits. This service, which can identify related content based on contextual data from more than 50,000 sources, provides a simple, carefree way to discover news articles, images, videos and other content on specific topics.

So, I put in keywords and Trapit uses those to return content to me, which if I then “trapit,” the system will continue to hunt for related content. Yawn. Stop me if you have heard this story before.

Keywords? That’s what we get from “…the largest artificial intelligence project in U.S. history?”

From Wikipedia on CALO:

Its five-year contract brought together 300+ researchers from 25 of the top university and commercial research institutions, with the goal of building a new generation of cognitive assistants that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise.

And we got keywords. Which Trapit uses to feed back similar content to us. I don’t need similar content, I need content that doesn’t use my keywords and yet is relevant to my query.

But rather than complain, why not build a topic map system based upon “…cognitive assistants that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise.” Err. that would be crowdsourcing topic map authoring, yes?

‘Siri, You’re Stupid’: Limitations of artificial intelligence baffle kids who expect more

Filed under: Artificial Intelligence — Patrick Durusau @ 8:55 pm

‘Siri, You’re Stupid’: Limitations of artificial intelligence baffle kids who expect more by Lauren Barack.

A deeply amusing post that begins:

My eight-year-old daughter, Harper, got her hands on a new iPhone 4S, and that’s when trouble started. Within minutes, she grew impatient with Siri after posing some queries to Apple’s speech-recognition “assistant” feature: “Can you pronounce my Mother’s name?” “Where do I live?” and “Is there dust on the moon?”—questions she did not assume the artificial voice wouldn’t answer. As it failed, delivering replies such as “Sorry, I don’t know where that is,” Harper became increasingly irritated, until she loudly concluded, “Siri, you’re stupid!” It responded “I’m doing my best.”

I think there is a lesson here to not create expectations among our users that are unrealistic. True, I think semantic technologies can be useful but they are not magical nor can they convert management/personnel issues into technical ones, much less solve them.

If two departments are not reliably sharing information now, the first question to investigate is why? It may well be simply a terminology issue, in which case a topic map could help them overcome that barrier and more effectively share information.

If the problem is that they are constantly undermining each others work and would rather the business fail than share information with the other department that might make it stand out, then topic maps are unlikely to be of assistance.

November 17, 2011

AI2012: The 25th Canadian Conference on Artificial Intelligence

Filed under: Artificial Intelligence,Conferences — Patrick Durusau @ 8:39 pm

AI2012: The 25th Canadian Conference on Artificial Intelligence

Dates:

When May 28, 2012 – May 30, 2012
Where York University, Toronto, Ontario, Canad
Submission Deadline Jan 16, 2012
Notification Due Feb 20, 2012
Final Version Due Mar 5, 2012

Topics of interest include, but are not limited to:

Agent Systems
AI Applications
Automated Reasoning
Bioinformatics and BioNLP
Case-based Reasoning
Cognitive Models
Constraint Satisfaction
Data Mining
E-Commerce
Evolutionary Computation
Games
Information Retrieval
Knowledge Representation
Machine Learning
Multi-media Processing
Natural Language Processing
Neural Nets
Planning
Robotics
Search
Smart Graphics
Uncertainty
User Modeling
Web Applications

The “usual suspects,” in other words. 😉

November 16, 2011

Big Data Just Got Smaller: New Approach to Find Information

Filed under: Artificial Intelligence,Graphs — Patrick Durusau @ 8:18 pm

Big Data Just Got Smaller: New Approach to Find Information

From the post:

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. (emphasis added)

The line “…encapsulates the entire meaning of the document.” goes a bit far.

Whose “entire meaning” of the document? What documents and who were they tested against? Can it understand the tariff portion of phone bill? (Which I rather doubt has a meaning other than the total.)

There have been computational approaches to knowledge before and there will be others that follow this one. Makes for good press and gets all the pundits exercised but that is about all. Will prove useful in some cases but that doesn’t mean it is a truly generalized solution.

Did want to bring it to your attention for whatever use you can make of it in the long term and in the short term something to annoy your cubicle neighbour.

November 5, 2011

META’2012 International Conference on Metaheuristics and Nature Inspired Computing

META’2012 International Conference on Metaheuristics and Nature Inspired Computing

Dates:

  • Paper submission: May 15, 2012
  • Session/Tutorial submission: May 15, 2012
  • Paper notification: July 15, 2012
  • Session/Tutorial notification: June 15, 2012
  • Conference: October 27-31, 2012

From the website:

The 4th International Conference on Metaheuristics and Nature Inspired Computing, META’2012, will held in Port El-Kantaoiui (Sousse, Tunisia).

The Conference will be an exchange space thanks to the sessions of the research works presentations and also will integrate tutorials and a vocational training of metaheuristics and nature inspired computing.

The scope of the META’2012 conference includes, but is not limited to:

  • Local search, tabu search, simulated annealing, VNS, ILS, …
  • Evolutionary algorithms, swarm optimization, scatter search, …
  • Emergent nature inspired algorithms: quantum computing, artificial immune systems, bee colony, DNA computing, …
  • Parallel algorithms and hybrid methods with metaheuristics, machine learning, game theory, mathematical programming, constraint programming, co-evolutionary, …
  • Application to: logistics and transportation, telecommunications, scheduling, data mining, engineering design, bioinformatics, …
  • Theory of metaheuristics, landscape analysis, convergence, problem difficulty, very large neighbourhoods, …
  • Application to multi-objective optimization
  • Application in dynamic optimization, problems with uncertainty,bi-level optimization, …

The “proceedings” for Meta ’10 can be seen at: Meta ’10 papers. It would be more accurate to say “extended abstracts” because, for example,

Luis Filipe de Mello Santos, Daniel Madeira, Esteban Clua, Simone Martins and Alexandre Plastino. A parallel GRASP resolution for a GPU architecture

runs all of two (2) pages. As is about the average length of the other twenty (20) papers that I checked.

I like concise writing but two pages to describe a parallel GRASP setup on a GPU architecture? Just an enticement (there is an ugly word I could use) to get you to read the ISI journal with the article.

Conference and its content look very interesting. Can’t say I care for the marketing technique for the journals in question. Not objecting to the marketing of the journals, but don’t say proceedings when what is meant is ads for the journals.

November 1, 2011

Lab 49 Blog

Filed under: Artificial Intelligence,Finance Services,Machine Learning — Patrick Durusau @ 3:33 pm

Lab 49 Blog

From the main site:

Lab49 is a technology consulting firm that builds advanced solutions for the financial services industry. Our clients include many of the world’s largest investment banks, hedge funds and exchanges. Lab49 designs and delivers some of the most sophisticated and forward-thinking financial applications in the industry today, and has an impeccable delivery record on mission critical systems.

Lab49 helps clients effect positive change in their markets through technological innovation and a rich fabric of industry best practices and first-hand experience. From next-generation trading platforms to innovative risk aggregation and reporting systems to entirely new investment ventures, we enable our clients to realize new business opportunities and gain competitive advantage.

Lab49 cultivates a collaborative culture that is both innovative and delivery-focused. We value intelligent, experienced, and personable engineering professionals that work with clients as partners. With a proven ability to attract and retain industry-leading engineering talent and to forge and leverage valued partnerships, Lab49 continues to innovate at the vanguard of software and technology.

A very interesting blog sponsored by what appears to be a very interesting company, Lab 49.

October 24, 2011

Subject Recognition: Discrete or Continuous

Filed under: Artificial Intelligence,Subject Recognition — Patrick Durusau @ 6:43 pm

While creating the entry for Fast Deep/Recurrent Nets for AGI Vision, I took particular note of the unbroken hand writing competitions. That task, for computer vision, is more difficult than “segmented” hand writing with breaks between the letters.

Are there parallels to subject recognition as performed by our computers versus ourselves?

That is we record and use “discrete” values in computers that are used for subject recognition.

We as human observers report “discrete” values when asked about subject recognition but in fact recognize subjects along non-discrete continuum of values.

I am interested in the application of techniques similar to continuous handwriting recognition applied to subject recognition.

Comments?

Fast Deep/Recurrent Nets for AGI Vision

Filed under: Artificial Intelligence,Neural Networks,Pattern Recognition — Patrick Durusau @ 6:43 pm

Fast Deep/Recurrent Nets for AGI Vision

Jürgen Schmidhuber at AGI-2011 delivers a deeply amusing presentation promoting neural networks, particularly deep/recurrent networks pioneered by his lab.

The jargon falls fast and furious so you probably want to visit his homepage for pointers to more information.

A wealth of information awaits! Suggestions on what looks the most promising for assisted topic map authoring welcome!

October 20, 2011

Learning Richly Structured Representations From Weakly Annotated Data

Filed under: Artificial Intelligence,Computer Science,Machine Learning — Patrick Durusau @ 6:42 pm

Learning Richly Structured Representations From Weakly Annotated Data by Daphne Koller. (DeGroot Lecture, Carnegie Mellon University, October 14, 2011).

Abstract:

The solution to many complex problems require that we build up a representation that spans multiple levels of abstraction. For example, to obtain a semantic scene understanding from an image, we need to detect and identify objects and assign pixels to objects, understand scene geometry, derive object pose, and reconstruct the relationships between different objects. Fully annotated data for learning richly structured models can only be obtained in very limited quantities; hence, for such applications and many others, we need to learn models from data where many of the relevant variables are unobserved. I will describe novel machine learning methods that can train models using weakly labeled data, thereby making use of much larger amounts of available data, with diverse levels of annotation. These models are inspired by ideas from human learning, in which the complexity of the learned models and the difficulty of the training instances tackled changes over the course of the learning process. We will demonstrate the applicability of these ideas to various problems, focusing on the problem of holistic computer vision.

If your topic map application involves computer vision, this is a must see video.

For text/data miners, are you faced with similar issues? Limited amounts of richly annotated training data?

I saw a slide, will run it down later, that had text running from plain text to annotated with ontological data. I mention that because that isn’t what a user sees when they “read” a text. They see implied relationships, references to other subjects, other instances of a particular subject, and all that passes in the instance of recognition.

Perhaps the problem of correct identification in text is one of too few dimensions than too many.

October 14, 2011

dmoz: computers: artificial intelligence

Filed under: Artificial Intelligence,Data Source — Patrick Durusau @ 6:23 pm

dmoz: computers: artificial intelligence

I ran across this listing of resources, some 1,294 as of today, this morning.

Amusing to note that despite the category being “Artificial Intelligence,” “Programming Languages” shows “(0).”

Before you leap to the defense of dmoz, yes, I know that if you follow the “Programming Languages” link, you will find numerous Lisp resources (as well as others).

Three problems:

First, it isn’t immediately obvious that you should follow “Programming Languages” to find Lisp. After all, it says “(0).” What does that usually mean?

Second, the granularity (or lack thereof) of such a resource listing, enables easier navigation, but at the expense of a lack of detail. Surely post-printed text we can create “views” on the fly that serve the need to navigate as well as varying needs for details or different navigations.

Third, and most importantly from my perspective, how to stay aware of new materials and to find old materials at these sites? RSS feeds can help with changes but doesn’t gather similar reports together and certainly doesn’t help with material already posted.

Another rich lode of resources where delivery could be greatly improved.

Hierarchical Temporal Memory related Papers and Books

Filed under: Artificial Intelligence,Hierarchical Temporal Memory (HTM) — Patrick Durusau @ 6:23 pm

Hierarchical Temporal Memory related Papers and Books

From the post:

I’m writing a report about using Hierarchical Temporal Memory to model kids behaviour learning a second Language. I have Googled many times to find related works. But I noticed that there are just some works related to the HTM. I’ll upload them all here to have a quick reference. I didn’t put link to the original materials to have always a copy of the originals and to be affected by web-site changes. Take note that some of the uploaded contents (in special Numenta Inc. published articles) are licensed and must be used according to the respective License.

I haven’t explored the area, yet, but this is as good a starting point as any.

October 11, 2011

Introduction to Artificial Intelligence – Stanford Class Update

Filed under: Artificial Intelligence,Teaching,Topic Maps — Patrick Durusau @ 5:55 pm

The “Introduction to Artificial Intelligence” class at Stanford has begun with over 145,000 students. I remember lecture classes being large but not quite this large. 😉

The first class lecture is up and I am impressed with the delivery mechanisms chosen for the class.

For example, I have seen graphic tablets used in math videos to draw equations, examples and lecture note type materials. I checked the pricing on such tablets.

Guess what they are using in the Stanford classes? Paper and different colored pens. Well, and printed materials, maps and such, that they can draw upon with the pens.

It doesn’t hurt that both of the presenters are world class lecturers but it also validates the notion that very simple tools can be used very effectively.

Not to mention that the longest each segment has been so far is about 3 minutes or so.

Can say a lot in 3 minutes (or less) if: 1) You know what you want to say, and, 2) You say it clearly.

Another nice aspect is that they are using what appear to be cgi-based graphics to embed quizzes (another low tech solution) at the end of videos.

Points for me to remember: Creating educational materials need not wait for equipment that I then have to master (though I will have to practice using a pen) in order to be productive. (It will be nice to have a pack of pens in different colors, cheaper than a graphics tablet too.)

October 9, 2011

Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web

Filed under: Artificial Intelligence,P2P,Semantic Web — Patrick Durusau @ 6:43 pm

Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web by P. Adjiman, P. Chatalic, F. Goasdoue, M. C. Rousset, and L. Simon.

Abstract:

In a peer-to-peer inference system, each peer can reason locally but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary. In this paper, we consider peer-to-peer inference systems in which the local theory of each peer is a set of propositional clauses defined upon a local vocabulary. An important characteristic of peer-to-peer inference systems is that the global theory (the union of all peer theories) is not known (as opposed to partition-based reasoning systems). The main contribution of this paper is to provide the first consequence finding algorithm in a peer-to-peer setting: DeCA. It is anytime and computes consequences gradually from the solicited peer to peers that are more and more distant. We exhibit a sufficient condition on the acquaintance graph of the peer-to-peer inference system for guaranteeing the completeness of this algorithm. Another important contribution is to apply this general distributed reasoning setting to the setting of the Semantic Web through the Somewhere semantic peer-to-peer data management system. The last contribution of this paper is to provide an experimental analysis of the scalability of the peer-to-peer infrastructure that we propose, on large networks of 1000 peers.

Interesting research on its own but I was struck by the phrase: “but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary.

Can we say that our “peers,” share our “mappings”?

That is mappings between terms and our expectation of others with regard to those terms.

Not the mapping between the label and the subject for which it is a label.

Or is the second mapping encompassed in the first? Or merely a partial expression of the first? (That seems more likely.)

Not immediately applicable to anything but may be important in terms of the mappings we are seeking to capture.

October 4, 2011

Adding Machine Learning to a Web App

Filed under: Artificial Intelligence,Machine Learning,Web Applications — Patrick Durusau @ 7:53 pm

Adding Machine Learning to a Web App by Richard Dallaway.

As Richard points out, the example is contrived and I don’t think you will be rushing off to add machine learning to a web app based on these slides.

That said, I think his point that you should pilot the data first is a good one.

If you mis-understand the data, then your results are not going to be very useful. Hmmm, maybe there is an AI/ML axiom in there somewhere. Probably already discovered, let me know if you run across it.

October 3, 2011

Algorithms of the Intelligent Web Review

Algorithms of the Intelligent Web Review by Pearlene McKinley

From the post:

I have always had an interest in AI, machine learning, and data mining but I found the introductory books too mathematical and focused mostly on solving academic problems rather than real-world industrial problems. So, I was curious to see what this book was about.

I have read the book front-to-back (twice!) before I write this report. I started reading the electronic version a couple of months ago and read the paper print again over the weekend. This is the best practical book in machine learning that you can buy today — period. All the examples are written in Java and all algorithms are explained in plain English. The writing style is superb! The book was written by one author (Marmanis) while the other one (Babenko) contributed in the source code, so there are no gaps in the narrative; it is engaging, pleasant, and fluent. The author leads the reader from the very introductory concepts to some fairly advanced topics. Some of the topics are covered in the book and some are left as an exercise at the end of each chapter (there is a “To Do” section, which was a wonderful idea!). I did not like some of the figures (they were probably made by the authors not an artist) but this was only a minor aesthetic inconvenience.

The book covers four cornerstones of machine learning and intelligence, i.e. intelligent search, recommendations, clustering, and classification. It also covers a subject that today you can find only in the academic literature, i.e. combination techniques. Combination techniques are very powerful and although the author presents the techniques in the context of classifiers, it is clear that the same can be done for recommendations — as the Bell Korr team did for the Netflix prize.

Wonder if this will be useful in the Stanford AI course that starts next week with more than 130,000 students? Introduction to Artificial Intelligence – Stanford Class

I am going to order a copy, if for no other reason than to evaluate the reviewer’s claim of explanations “in plain English.” I have seen some fairly clever explanations of AI algorithms and would like to see how these stack up.

September 29, 2011

Human Computation: Core Research Questions and State of the Art

Filed under: Artificial Intelligence,Crowd Sourcing,Human Computation — Patrick Durusau @ 6:33 pm

Human Computation: Core Research Questions and State of the Art by Luis von Ahn and Edith Law. (> 300 slide tutorial) See also: Human Computation by Edith Law and Luis von Ahn.

Abstract from the book:

Human computation is a newand evolving research area that centers around harnessing human intelligence to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms.With the growth of the Web, human computation systems can now leverage the abilities of an unprecedented number of people via the Web to perform complex computation.There are various genres of human computation applications that exist today. Games with a purpose (e.g., the ESP Game) specifically target online gamers who generate useful data (e.g., image tags) while playing an enjoyable game.Crowdsourcing marketplaces (e.g.,Amazon MechanicalTurk) are human computation systems that coordinate workers to perform tasks in exchange for monetary rewards. In identity verification tasks, users perform computation in order to gain access to some online content; an example is reCAPTCHA, which leverages millions of users who solve CAPTCHAs every day to correct words in books that optical character recognition (OCR) programs fail to recognize with certainty.

This book is aimed at achieving four goals: (1) defining human computation as a research area; (2) providing a comprehensive review of existing work; (3) drawing connections to a wide variety of disciplines, including AI, Machine Learning, HCI, Mechanism/Market Design and Psychology, and capturing their unique perspectives on the core research questions in human computation; and (4) suggesting promising research directions for the future.

You may also want to see Luis van Ahn in a Google Techtalk video from about five years ago:

July 26, 2006 Luis von Ahn is an assistant professor in the Computer Science Department at Carnegie Mellon University, where he also received his Ph.D. in 2005. Previously, Luis obtained a B.S. in mathematics from Duke University in 2000. He is the recipient of a Microsoft Research Fellowship. ABSTRACT Tasks like image recognition are trivial for humans, but continue to challenge even the most sophisticated computer programs. This talk introduces a paradigm for utilizing human processing power to solve problems that computers cannot yet solve. Traditional approaches to solving such problems focus on improving software. I advocate a novel approach: constructively channel human brainpower using computer games. For example, the ESP Game, described in this talk, is an enjoyable online game — many people play over 40 hours a week — and when people play, they help label images on the Web with descriptive keywords. These keywords can be used to significantly improve the accuracy of image search. People play the game not because they want to help, but because they enjoy it. I describe other examples of “games with a purpose”: Peekaboom, which helps determine the location of objects in images, and Verbosity, which collects common-sense knowledge. I also explain a general approach for constructing games with a purpose.

A rapidly developing and exciting area of research. Perhaps your next topic map may be authored or maintained by a combination of entities.

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