Beyond Deep Learning :Learning+Reasoning

Lisa Amini

Director, IBM Research Cambridge,Acting Director, MIT-IBM Watson AI Lab

MIT 6.S191 Intro to Deep Learning2011, IBM Watson computerwins human champions atJeopardy!

2017, IBM demonstrates 95%
scaling efficiency on Caffe deeplearning framework
2017, World’s first 50 qubit
quantum computer

Leading corporate
institution for high-quality
science2017, quantum algo efficiently computes
lowest energy state of small molecules.

IBM Research
3000 creative, scientific and technical minds worldwide

6Nobel Laureates

10National Medals ofTechnology

5National Medals ofScience

6Turing Awards

The MIT-IBM Watson AI Lab$240M 10 year commitment to jointly create the future of artificial intelligence

Fundamental advances in AI algorithms

Physics of AI
AI Transforming Industries:
Healthcare, Life Sciences & Cybersecurity
Advancing Shared prosperity through AI
http://mitibmwatsonailab.mit.edu/

3

Moments in Time

Landmark 1 Million video dataset to transform AI Vision
Pushing

CarryingMIT-IBM Team•••

Three seconds events
http://moments.csail.mit.edu/
Open access
Goal: Recognizing and understanding actions in video

Recent successes in Deep Learning are awe-inspiring,but epic breakthroughs are still needed for Machine Intelligence• Humans learn without a lot of labeled data per task•

Why can’t machines?

• People learn continuously throughout their lives, remembering what they’ve
learned and leveraging it for new tasks•

Current algorithms suffer from catastrophic forgetting and are unable to recognize andgeneralize to analogous situations or tasks• To interact sensibly with humans, machines must be able to remember,reason, explain, and seek to fill knowledge gaps
• Learning+reasoning5

Making Language Computational• Word Embeddings••

Represent words as a real-valued vector
in some abstract space
Goal: representations that capture
multiple degrees of similarity

Skip-gram model
Distributed representations of words and phrases and theircompositionality, Mikolov, et al, 2013

Maximize the average log probabilityof predicting surrounding words

Don't count, predict! A systematic comparison of context-counting
vs. context-predicting semantic vectors, Baroni, Dinu, Kruszewski,
2014
FastText – Open library for unsupervised learning of wordembeddings. http://fasttext.cc

6

Embeddings Impact on Automated Knowledgebase Construction (AKBC)

Formalknowledge
representation

Reasoning with Neural Tensor Networks for Knowledge
Base Completion, Socher, et al, 2013
Distant Supervision for Relation Extraction with an
Incomplete KB, Min, Grishman, Wan, Wang, Gondek, 2013

Compositional Vector Space Models for Knowledge BaseCompletion, Neelakantan, Roth, McCallum, 20157

Embeddings Impact on Automated Knowledgebase Construction (AKBC)

Predicting Drug-Drug Interactions Through Large-Scale
Similarity-based Link Prediction, Fokoue, et al 2016

Relation prediction with confidence, leveraging disparate
structured and unstructured data
Socrates: Deep Relational Knowledge Induction, Glass, et al,2017
8!st place winner: ISWC Semantic Web Challenge on AKBC

How to create differentiable machines to reason
leveraging learned external knowledge bases?

9

Example Task: Question Answering with Long-term Memories

Towards AI-Complete Question Answering: A set of prerequisite toy tasks, Weston, et al , 201510

Question Answering with External Memories
Supervision(direct orreward-based)

ExampleInputs

OutputMemory Module

addressin

Controller module

readmgreadm

Memoryvectors

addressingInput

q

Jointly trained with Inputs (I èx è m),Questions (Qèq), Answer (u è o)Internal state
Vector (initially: query)

Memory Networks, Weston, et al , 2015
Memory Networks for Language Understanding, ICML
Tutorial, Weston, et al , 201611

Question Answering with External Memories
Supervision(direct orreward-based)

OutputMemory Module

addressin

Controller module

readm

g

readm

Memoryvectors

addressing

Input

q
Internal state
Vector (initially: query)

Memory Networks, Weston, et al , 2015
Memory Networks for Language Understanding, ICML
Tutorial, Weston, et al , 2016

End-to-End Memory Networks, Sukhbaatar, et al , 2015
12

Want to Learn More?
•Improved detection of key relations KBQA

Improved Neural Relation Detection for Knowledge Base
Question Answering, Yu, et al 2016

•Simulator to generate challenge questions
from ambiguous texts

Learning to Query, Reason, and Answer Questions on
Ambiguous Texts, Guo et al, 2017•

Bringing commonsense knowledge intovector space

Lifted Rule Injection for Relation Embeddings, Demeester,Rocktaschel, Riedel, 2016

•Learning to represent and executeprograms

Neural Program Interpreters, Reed, et al, 2015

•Learning representations to induce logicalrules and perform multi-hop reasoning

End-to-end Differentiable Proving, Rocktaschel, Riedel,
2017

13

Want to do more?
Watson Developer Cloud

Language Natural Language Classifier  Personality Insights
 Tone Analyzer
 Language Translator Natural Language Understanding

DataInsights

 Conversation – build chatbots and virtual agents across anychannel and domain
Data Insights

Language

Speech Discovery – aggregate and organize massive amounts ofenterprise data, and answer questions in context

SpeechVision

 Speech to Text – transcribe audio and take action
 Text to Speech – verbalize written text into understandable audio
Vision Visual Recognition – help people understand and take action
from visual data

For the latest view of Watson API’s available, go to: www/ibm.com/watsondevelopercloud

IBM14

Thank you!

15