Beyond Keywords: The Revolution in Search

SLA Contributed Papers, Session 1885

June 15, 2015

Joe Buzzanga, Senior Product Manager, IEEE

Kurzweil's Law of Accelerating Returns

"An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense “intuitive linear” view".http://www.kurzweilai.net/the-law-of-accelerating-returns

What is Search?

Search: Basic Model

The Dream of Search

Yet, in many ways, we’re a million miles away from creating the search engine of my dreams, one that gets you just the right information at the exact moment you need it with almost no effort.  

Larry Page, 2013 Google Founders Letter, https://investor.google.com/corporate/2013/founders-letter.html

Recipe for a Dream

AI (especially machine learning) +

Knowledge Bases +

Intelligent Personal Assistants

Achieving the Dream

Search = AIAI-->Machine Learning

Machine Learning-->Deep Neural Networks

Machine Learning (ML)

Kulkarni, Parag. "Introduction to reinforcement and systemic machine learning."Reinforcement and Systemic Machine Learning for Decision Making (2012)

Machine Learning (ML) is EverywhereBaidu’s Artificial-Intelligence Supercomputer Beats Google at Image RecognitionMay 13, 2015 http://www.technologyreview.com/

Scientists See Promise in Deep-Learning ProgramsNov. 23, 2012 http://www.nytimes.com/2012/11/24/science/

A Google Computer Can Teach Itself GamesFeb. 25, 2015 http://bits.blogs.nytimes.com/

Amazon's Fire Phone recognizes everything around you with 'Firefly'By Chris Welch on June 18, 2014http://www.theverge.com

Facebook Launches Advanced AI Effort to Find Meaning in Your PostsSept. 2013 http://www.technologyreview.com

Google Acquires British Artificial Intelligence DeveloperJan. 27, 2014 http://dealbook.nytimes.com

Amazon Launches Machine Learning-As-A-ServiceApril 10, 2015 http://www.informationweek.com

ML is Everywhere

"Google using ML in 47 products" Jeff Dean, 2015 NVIDIA GPU Technology Conference

ML is Everywhere

ML (Neural Nets) for Text

This article demonstrates that we can apply deep learning to text understanding from character level inputs all the way up to abstract text concepts, using temporal convolutional networks(LeCun et al., 1998) (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.

arXiv:1502.01710v2 [cs.LG] 7 Apr 2015

Text Understanding from Scratch

ML (Neural Nets) for Text

Natural Language Processing (Almost) from ScratchWe 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…. our system learns internal representations on thebasis of vast amounts of mostly unlabeled training data.

Journal of Machine Learning Research 12 (2011) 2461-2505 Submitted 1/10; Revised 11/10; Published 8/11

Using ML in Search: Patent Search Example

In this invention, we propose to utilize analog behaviors of off-the-shelf Flash memory to enable hardware-based security functions in a wide range of electronic devices without requiring custom hardware. More specifically, we show that a standard Flash memory interface can be used to generate true random numbers from quantum and thermal noises and to produce device fingerprints based on manufacturing variations. The techniques can be applied to any floating-gate non-volatile memory in general, and does not require any hardware modifications to today's Flash memory chips, allowing them to be widely deployed.

ML--Topic Models

Knowledge Bases Emerge

Knowledge Base: Application

• Understand query

• Understand content (web pages, documents, images…)

• Return answers

• Inference?"We’ve gone up a level from just talking about the words to talking about what the thing actually is. In crawling and indexing documents we can now have an understanding of what the document is about. If the document is about famous tennis players we actually know it’s about sport and tennis. Every piece of information that we crawl, index, or search is analyzed in the context of Knowledge Graph. That’s not the same as completely understanding the text as you and I might do but it’s a step towards it.--John Giannandrea, Google VP, Jan. 2014 http://www.technologyreview.com

Knowledge Base Comparison

Name Entities Facts Source

DBpedia 4.6M 583M Wikipedia

YAGO 10M 120M Wikipedia, WordNet, GeoNames

NELL 5.2M 50M Candidates, 2.4M Confident

Machine learning, crawls open web

Knowledge Graph (Google)

500M 3.5B Wikipedia, Freebase, and other sources

Knowledge Vault (Google)

45M 271M Machine learning, crawls open web

 

Knowledge Base: Examples

Knowledge Base: Examples

Intelligent Personal Assistants

Intelligent Personal Assistants

How well do they perform?One recent report tested the question answering ability of Google Now, Siri and Cortana, based on over 3,000 queries. Google Now returned a direct, correct answer to 51% of the queries, while Siri managed to answer 15% correctly and Cortana only 8%.

Vendor Product Initial Release Scope

Apple Siri October, 2011 General

Google Google Now July, 2012 General

Microsoft Cortana April, 2014 General

Samsung S Voice May, 2012 General

Nuance Ask Nina August, 2012Task specific – Customer care

x.ai x.ai Restricted betaTask specific – Scheduling assistant

Viv Viv In development General

Conclusions

• User expectations are rising

• Seize opportunities to add value beyond the traditional

• Seek out Data Science expertise

• Partner with IT

• Look for ML-savvy vendors