SIOC Post profile for "AI3:::Adaptive Information"
type: Document
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primary topic: Knowledge-based Artificial Intelligence
generator agent: http://rdfs.org/sioc/wp-sioc.php

Knowledge-based Artificial Intelligence
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2014-11-17T15:56:49Z
content: What Goes Around, Comes Around, Only Now with Real Knowledge Download PDF A recent interview with a noted researcher, IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley, provided a downplayed view of recent AI hype. Jordan was particularly critical of AI metaphors to real brain function and took the air out of the balloon about algorithm advances, pointing out that most current methods have roots that are decades long [1]. In fact, the roots of knowledge-based artificial intelligence (KBAI), the subject of this article, also extend back decades. Yet the real point, only briefly touched upon by Jordan in his lauding of Amazon's recommendation service, is that the dynamo in recent AI progress has come from advances in the knowledge and statistical bases driving these algorithms. The improved digital knowledge bases behind KBAI have been the power behind these advances. Knowledge bases are finally being effectively combined with AI, a dynamic synergy that is only now being recognized, let alone leveraged. As this realization increases, many forms of useful information structure in the wild will begin to be mapped to these knowledge bases, which will further extend the benefits we are are now seeing from KBAI. Knowledge-based artificial intelligence, or KBAI, is the use of large statistical or knowledge bases to inform feature selection for machine-based learning algorithms used in AI. The use of knowledge bases to train the features of AI algorithms improves the accuracy, recall and precision of these methods. This improvement leads to perceptibly better results to information queries, including pattern recognition. Further, in a virtuous circle, KBAI techniques can also be applied to identify additional possible facts within the knowledge bases themselves, improving them further still for KBAI purposes. It is thus, in my view, the combination of KB + AI that has led to the notable AI breakthroughs of the past, …
encoded: <img decoding="async" style="border: 0px solid; width: 240px; height: 242px; float: left; margin-right: 10px;" title="Manifold Learning Methods, from http://www.skybluetrades.net/blog/posts/2011/10/30/machine-learning/" src="https://www.mkbergman.com/wp-content/themes/ai3/images/2014Posts/test-swiss-roll_240.png" alt="Manifold Learning Methods, from http://www.skybluetrades.net/blog/posts/2011/10/30/machine-learning/" align="left"/> What Goes Around, Comes Around, Only Now with Real Knowledge Download PDF A recent interview with a noted researcher, IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley, provided a downplayed view of recent AI hype. Jordan was particularly critical of AI metaphors to real brain function and took the air out of the balloon about algorithm advances, pointing out that most current methods have roots that are decades long [1]. In fact, the roots of knowledge-based artificial intelligence (KBAI), the subject of this article, also extend back decades. Yet the real point, only briefly touched upon by Jordan in his lauding of Amazon’s recommendation service, is that the dynamo in recent AI progress has come from advances in the knowledge and statistical bases driving these algorithms. The improved digital knowledge bases behind KBAI have been the power behind these advances. Knowledge bases are finally being effectively combined with AI, a dynamic synergy that is only now being recognized, let alone leveraged. As this realization increases, many forms of useful information structure in the wild will begin to be mapped to these knowledge bases, which will further extend the benefits we are are now seeing from KBAI. Knowledge-based artificial intelligence, or KBAI, is the use of large statistical or knowledge bases to inform feature selection for machine-based learning algorithms used in AI. The use of knowledge bases to train the features of AI algorithms improves the accuracy, recall and …
topic: Artificial Intelligence
topic: Big Structure
topic: Enterprise-scale Semantic Systems
topic: Knowledge-based Artificial Intelligence
topic: Ontologies
topic: Semantic Web
has reply: comment-506540
has reply: comment-506541
has reply: comment-506542
links to: Download PDF
links to: Michael I. Jordan
links to: [1]
links to: feature
links to: machine-based learning algorithms
links to: recall and precision
links to: Structured Dynamics
links to: Dendral
links to: Edward Feigenbaum
links to: expert system
links to: knowledge-based systems
links to: Wikipedia
links to: knowledge engineers
links to: knowledge-based engineering
links to: CAD
links to: CASE
links to: [2]
links to: [3]
links to: Indiana University
links to: SUNY Buffalo
links to: Georgia Tech
links to: [4]
links to: [5]
links to: Knowledge models
links to: Reasoning systems
links to: knowledge bases
links to: APIs
links to: [6]
links to: [7]
links to: Apple
links to: Siri
links to: [8]
links to: Google Now
links to: pattern recognition
links to: natural language processing
links to: [16]
links to: Watson
links to: [9]
links to: Knowledge Graph
links to: [10]
links to: Wolfram Alpha
links to: data visualization
links to: language translation
links to: [11]
links to: recommendation system
links to: [12]
links to: [42]
links to: [13]
links to: [14]
links to: [15]
links to: Freebase
links to: Metaweb
links to: lists of text corpora
links to: many other things
links to: Web 1T 5-gram
links to: [17]
links to: Franz Josef Och
links to: text corpus
links to: [18]
links to: NELL
links to: [19]
links to: [20]
links to: BabelNet

Mike Bergman
type: User

Mike Bergman
type: Person

test-swiss-roll.png
label: <img style="border: 0px solid; width: 240px; height: 242px; float: left; margin-right: 10px;" title="Manifold Learning Methods, from http://www.skybluetrades.net/blog/posts/2011/10/30/machine-learning/" src="http://www.mkbergman.com/wp-content/themes/ai3/images/2014Posts/test-swiss-roll_240.png" alt="Manifold Learning Methods, from http://www.skybluetrades.net/blog/posts/2011/10/30/machine-learning/" align="left"/>

language translation
label: http://www.spiegel.de/international/europe/google-translate-has-ambitious-goals-for-machine-translation-a-921646.html.

Web 1T 5-gram
label: Linguistic Data Corporation

[21]

[22]

ConceptNet

[23]

Cyc

[24]

DBpedia

[25]

[26]

[27]

Freebase

[28]

GeoNames

[29]

[30

KnowItAll

[31]

Knowledge Vault

[32]

Learning About Anything

[43]

Probase

[33]

UMBEL

[34]

Wikidata

[35]

[36]

[37]

WordNet

[38]

YAGO

[39]

mereology

[40]

[41]

Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts

IEEE Spectrum

us5142469
label: "Method for converting a programmable logic controller hardware configuration and corresponding control program for use on a first programmable logic controller to use on a second programmable logic controller,"

SWEETpedia

Wikipedia in Academic Studies

This is Watson

intro to Watson video

Introducing the Knowledge Graph: Things, not Strings

The Unreasonable Effectiveness of Data

Amazon. com Recommendations: Item-to-item Collaborative Filtering

Microsoft Research Shows off Advances in Artificial Intelligence with Project Adam

Interview: Inside Google Brain Founder Andrew Ng's Plans To Transform Baidu

How Semantic Web Tech Can Make Big Data Smarter

"Statistical Machine Translation: Foundations and Recent Advances,"

Toward an Architecture for Never-Ending Language Learning

Knowledge Bases in the Age of Big Data Analytics,

BabelNet: The Automatic Construction, Evaluation and Application of a Wide-coverage Multilingual Semantic Network

Biperpedia: An Ontology for Search Applications

Representing General Relational Knowledge in ConceptNet 5

DBpedia: A Nucleus for a Web of Open Data

DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference

Statistical Entity Extraction From the Web

Freebase: a Collaboratively Created Graph Database for Structuring Human Knowledge

ImageNet: A Large-scale Hierarchical Image Database

Unsupervised Named-entity Extraction from the Web: An Experimental Study

Knowledge Vault: a Web-scale Approach to Probabilistic Knowledge Fusion

Probase: A Probabilistic Taxonomy for Text Understanding

"UMBEL: A Subject Concepts Reference Layer for the Web"

Wikidata: A Free Collaborative Knowledgebase

Transforming Wikipedia into a Large Scale Multilingual Concept Network

Ponzetto, and Michael Strube, 2007. "Deriving a Large Scale Taxonomy from Wikipedia

YAGO2: a Spatially and Temporally Enhanced Knowledge Base from Wikipedia

YAGO: a Core of Semantic Knowledge

Big Structure and Data Interoperability

The Value of Connecting Things – Part II: The Viking Algorithm

Facebook Taps 'Deep Learninig' Giant for New AI Lab

Learning Everything about Anything: Webly-Supervised Visual Concept Learning

CVPR 2014