1. Azure AI provides updates on advances in AI capabilities such as object recognition reaching human parity in 2016 and machine translation reaching human parity in 2018.
2. Responsible AI practices at Microsoft include interpretability, fairness, and privacy tools to ensure AI systems are understandable, unbiased, and protect user data.
3. Differential privacy and homomorphic encryption techniques allow training models and performing inferences on encrypted user data to enable private and confidential machine learning.
1. Azure AI –
Build 2020 Updates
SATO Naoki (Neo)
Senior Software Engineer
Microsoft
2.
3. Fueled by breakthrough research
2016
Object recognition
human parity
2017
Speech recognition
human parity
2018
Reading comprehension
human parity
2018
Machine translation
human parity
2018
Speech synthesis
near-human parity
2019
General Language
Understanding human parity
2020
Document summary at
human parity
4. 1.8M
Hours of meetings
transcribed in real-time
1B
PowerPoint Designer
slides used
Tested at scale in Microsoft solutions
80M
Personalized experiences
delivered daily
Machine translation
human parity
Object detection
human parity
Switchboard
Switchboard cellular
Meeting
speech
IBM Switchboard
Broadcast speech
Speech recognition
human parity
Conversational Q&A
human parity
First FPGA deployed
in a datacenter
5. Power Platform
Power BI Power Apps Power Automate Power Virtual Agents
Azure
58 Regions 90+ Compliance Offerings $1B Security investment per year95% Fortune 500 use Azure
Azure AI
ML platform
Customizable models
Vision, Speech, Language, Decision
Scenario-specific services
Cognitive Services
Azure Machine Learning
Data Platform App Dev Platform & tools Compute
Cognitive SearchBot Service Form Recognizer Video Indexer
8. PracticesPrinciples Tools
AETHER committee
The Partnership on AI
Guidelines for
Human-AI Design
Homomorphic Encryption
Interpret ML
Differential Privacy
Data Drift
Secure MPC
Guidelines for
Conversational AI
Fairness
Reliability
Privacy
Inclusivity
Accountability
Transparency
12. Loan Application Decisions
Azure Machine Learning
How does it decide who
to accept or reject?
Is my model fair?
Create a model for loan
application acceptance
13. Loan Application Decisions
Azure Machine Learning
How does it decide who
to accept or reject?
Is my model fair?
Create a model for loan
application acceptance
14. Fairness in AI
There are many ways that an AI system can behave unfairly
A voice recognition system
might fail to work as well for
women as it does for men
A model for screening loan or job
application might be much better at
picking good candidates among white
men than among other groups
Avoiding negative outcomes of AI systems for different groups of people
15. Assessing unfairness in your model
https://github.com/fairlearn/fairlearn
Fairness
assessment:
Usecommonfairness metrics
andaninteractive dashboardto
assess which groups of peoplemay
benegatively impacted
Model formats:
Python models using scikit predict
convention, Scikit, Tensorflow,
Pytorch, Keras
Metrics:
15+ common group fairness metrics
Model types:
Classification, Regression
Fairness mitigation:
Use state-of-the-art algorithms
to mitigate unfairness in your
classificationandregressionmodels
18. Loan Application Decisions
Azure Machine Learning
How does it decide who
to accept or reject?
Is my model fair?
Create a model for loan
application acceptance
19. Understand and debug your model
Interpret
Glassbox and blackbox
interpretability methods
for tabular data
Interpret-
community
Additional interpretability
techniques for tabular data
Interpret-text
Interpretability
methods for text data
DiCE
Diverse Counterfactual
Explanations
Blackbox models:
Model formats:
Python models using scickit predict
convention, Scikit, Tensorflow, Pytorch, Keras
Explainers:
SHAP, LIME, Global Surrogate,
Feature Permutation
Glassbox Models:
Model types:
Linear Models, Decision Trees, Decision Rules,
Explainable Boosting Machines
AzurML-interpret
AzureML SDK wrapper for Interpret
and Interpret-community
https://github.com/interpretml
25. Differential Privacy for Machine Learning and Analytics
https://github.com/opendifferentialprivacy
Native Runtime
C,C++, Python, R
Validator
Automatically stress test DP
algorithms
Data Source Connectivity
Data Lakes, SQL Server, Postgres,
Apache Spark, Apache Presto and
CSV files
Privacy Budget
Control queries by users
26. WhiteNoise
Privacy Module
Report
Budget Store
BUDGET
User Private
Dataset
Submits a query
Receives a
differentially
private report
Mechanism adds
noise
Private data
Dataset checks
budget and access
credentials
Checks
budget and
private
compute
Credentials to
access the data
https://github.com/opendifferentialprivacy
28. Homomorphic Encryption
Decrypt(Encrypt(A) + Encrypt(B)) = A + B
Decrypt(Encrypt(A) * Encrypt(B)) = A * B
Privacy
Barrier
Homomorphic encryption allows
certain computations to be done on
encrypted data, without requiring
any decryption in the process:
Different from classical encryption
like AES or RSA:
29. Microsoft SEAL
Open-source homomorphic encryption
library
Developed actively since 2015
Recently released v3.5
Available at GitHub.com/Microsoft/SEAL
Supports Windows, Linux, macOS, Android,
FreeBSD
Written in C++; includes .NET Standard
wrappers for public API
From open source community:
PyHeal (Python wrappers from Accenture)
node-seal (JavaScript wrappers)
nGraph HE Transformer (from Intel)
30. Private Prediction
on Encrypted Data
Through a trained machine
learning model, private prediction
enables inferencing on encrypted
data without revealing the content
of the data to anyone.
Microsoft SEAL can be deployed in
a variety of applications to protect
users personal and private data
Privacy Barrier
[cryptographic]
Medical prediction
31. Responsible ML Resources
Microsoft Responsible AI Resource Center
https://aka.ms/RAIresources
Azure Machine Learning
https://azure.microsoft.com/en-
us/services/machine-learning/
https://docs.microsoft.com/en-
us/azure/machine-learning/concept-
responsible-ml
OpenDP
http://opendp.io/
https://twitter.com/opendp_io
WhiteNoise
https://github.com/opendifferentialprivacy
https://docs.microsoft.com/azure/machine-
learning/concept-differential-privacy
https://docs.microsoft.com/azure/machine-
learning/how-to-differential-privacy
https://aka.ms/WhiteNoiseWhitePaper
SEAL
https://github.com/Microsoft/SEAL
https://docs.microsoft.com/azure/machine-
learning/how-to-homomorphic-encryption-
seal
https://aka.ms/SEALonAML
33. Next-gen AI capabilities
To accelerate the change we needed,
we took advantage of three trends
Transfer
learning
Large pre-trained self
supervised networks
Culture
shift
34. Microsoft Turing NLG
5 b
7.5 b
10 b
12.5 b
15 b
17.5 b
Spring ‘18 Summer ‘18 Autumn ‘18 Winter ‘19 Spring ‘19 Summer ‘19 Autumn ‘19 Winter ‘20
2.5 b
ELMo
94m
GPT
110m
BERT - large
340 m
Transformer
ELMo
465m
GPT-2
1.5b
MT-DNN
330m
XLNET
340m
XLM 665m
Grover-Mega
1.5b
RoBERTa
355m DistilBERT
66m
MegatronLM
8.3b
T-NLG
17b
Numberofparameters
41. Collaboration with OpenAI
Hosted in Azure
285,000 CPU cores, 10,000
GPUs, 400 Gbps for each
GPU server
Top 5 in Top 500 SCs
https://blogs.microsoft.co
m/ai/openai-azure-
supercomputer/
42. Learn more:
• AI at Scale introduction: aka.ms/AIatScale
• AI at Scale Deep Dive: aka.ms/AIS-DeepDive
• DeepSpeed library: github.com/microsoft/DeepSpeed
• ONNX Runtime: aka.ms/onnxruntime
• Try T-NLG in the Companion App: aka.ms/Build-AIS
43. Rethinking the AI Stack
NVidia GPUs
Intel FPGAs
NVLink
Infiniband
DeepSpeed allowing
for training models 15x
bigger, 10x faster on
the same infrastructure
ONNX Runtime
Central AI group
coordinating bringing
the best of research
into products
All available on Azure and GitHub for everyone!