Artificial Intelligence
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AI Overview
Foreword
⚫ Mankind is welcoming the fourth industrial revolution represented by intelligent technology. New
technologies such as AI, IoT, 5G and bioengineering are integrated into all aspects of human society;
driving changes in global macro trends, such as sustainable social development and economic growth.
New kinetic energy, smart city upgrading, industrial digital transformation, consumer experience, etc.
⚫ As the world‘s leading provider of ICT (information and communications) infrastructure and smart
terminals, Huawei actively participates in the transformation of artificial intelligence and proposes
Huawei’s full-stack full-scenario AI strategy. This chapter will mainly introduce AI Overview, Technical
Fields and Application Fields of AI, Huawei's AI Development Strategy, AI Disputes, Future Prospects of
AI.
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Objectives
Upon completion of this course, you will be able to:
Understand basic concepts of AI.
Understand AI technologies and their development history.
Understand the application technologies and application fields of AI.
Know Huawei's AI development strategy.
Know the development trends of AI.
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Contents
1. AI Overview
2. Technical Fields and Application Fields of AI
3. Huawei's AI Development Strategy
4. AI Disputes
5. Future Prospects of AI
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AI in the Eyes of the Society
⚫ People get to know AI through news, movies, and actual applications in daily life. What is
AI in the eyes of the public?
Haidian Park: First AI-themed Park in the World StarCraft
II: AlphaStar Beat Professional Players
AI-created Edmond de Belamy Sold at US$430,000
Demand for AI Programmers:↑ 35 Times! Salary: Top 1!
50% Jobs Will be Replaced by AI in the future
Winter is Coming? AI Faces Challenges
…
The Terminator
2001: A Space Odyssey
The Matrix
I, Robot
Blade Runner
Elle
Bicentennial Man
…
Self-service security check
Spoken language evaluation
Music/Movie recommendation
Smart speaker
…
News
AI Applications
AI industry outlook
Challenges faced by AI
…
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Movies
AI Control over human beings
Fall in love with AI
Self-awareness of AI
…
Applications in daily life
Security protection
Entertainment
Smart Home
Finance
…
AI in the Eyes of Researchers
"I propose to consider the question, 'Can machines think?'"
— Alan Turing 1950
The branch of computer science concerned with making computers behave like humans.
— John McCarthy 1956
The science of making machines do things that would require intelligence if done by men.
— Marvin Minsky
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What Are Intelligences?
⚫ Howard Gardner's Multiple Intelligences
⚫ Human intelligences can be divided into seven categories:
Verbal/Linguistic
Logical/Mathematical
Visual/Spatial
Bodily/Kinesthetic
Musical/Rhythmic
Inter-personal/Social
Intra-personal/Introspective
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What Is AI?
⚫ Artificial Intelligence (AI) is a new technical science that studies and develops theories, methods, techniques,
and application systems for simulating and extending human intelligence. In 1956, the concept of AI was first
proposed by John McCarthy, who defined the subject as "science and engineering of making intelligent
machines, especially intelligent computer program". AI is concerned with making machines work in an
intelligent way, similar to the way that the human mind works. At present, AI has become an interdisciplinary
course that involves various fields.
Computer
science
Philosophy
Brain
science
Cognitive
science
AI
Psychology
Logic
Linguistics
Identification of concepts related to AI and machine learning
AI Development Report 2020
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Relationship of AI, Machine Learning, and Deep Learning
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Relationship of AI, Machine Learning and Deep Learning
⚫ AI: A new technical science that focuses on the research and development of theories, methods,
techniques, and application systems for simulating and extending human intelligence.
⚫ Machine learning: A core research field of AI. It focuses on the study of how computers can obtain
new knowledge or skills by simulating or performing learning behavior of human beings, and
reorganize existing knowledge architecture to improve its performance. It is one of the core
research fields of AI.
⚫ Deep learning: A new field of machine learning. The concept of deep learning originates from the
research on artificial neural networks. The multi-layer perceptron (MLP) is a type a deep learning
architecture. Deep learning aims to simulate the human brain to interpret data such as images,
sounds, and texts.
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Three Major Schools of Thought: Symbolism
⚫ Basic thoughts
The cognitive process of human beings is the process of inference and operation of various
symbols.
A human being is a physical symbol system, and so is a computer. Computers, therefore, can be
used to simulate intelligent behavior of human beings.
The core of AI lies in knowledge representation, knowledge inference, and knowledge
application. Knowledge and concepts can be represented with symbols. Cognition is the process
of symbol processing while inference refers to the process of solving problems by using heuristic
knowledge and search.
⚫ Representative of symbolism:
inference,
including symbolic inference and machine
inference
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Three Major Schools of Thought: Connectionism
⚫ Basic thoughts
The basis of thinking is neurons rather than the process of symbol processing.
Human brains vary from computers. A computer working mode based on connectionism is proposed to
replace the computer working mode based on symbolic operation.
• Representative of connectionism: neural networks and
deep learning
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Three Major Schools of Thought: Behaviorism
⚫ Basic thoughts:
Intelligence depends on perception and action. The perception-action mode of intelligent
behavior is proposed.
Intelligence requires no knowledge, representation, or inference. AI can evolve like human
intelligence. Intelligent behavior can only be demonstrated in the real world through the
constant interaction with the surrounding environment.
⚫ Representative of behaviorism: behavior control, adaptation, and evolutionary computing
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Brief Development History of AI
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1956-1976First period of boomThe concept and development target of AI were determined at the Dartmouth conference.1950s1960s1970s1980s1990s2000s2010s2020s1976-1982First period of low ebbAI suffered from questioning and criticism due to insufficient computing capabilities, high computing complexity, and great difficulty of inference realization.1982-1987Second period of boomExpert system capable of logic rule inference and answering questions of specific fields went popular and fifth-generation computers developed.1987-1997Second period of low ebbTechnical fields faced bottlenecks, people on longer focused on abstract inference, and models based on symbol processing were rejected.1997-2010Period of recoveryComputing performance was improved and Internet technologies got popularized quickly.2010-Period of rapid growthNew-generation information technologies triggered transformation of information environment and data basis. Multi-model data such as massive images, voices, and texts emerged continuously. Computing capabilities were improved.1956: AI was proposed at the Dartmouth Conference.1959: Arthur Samuel proposed machine learning.1976: Due to failure of projects such as machine translation and negative impact of some academic reports, the fund for AI was decreased in general.1985: Decision-making tree models with better visualization effect and multi-layer ANNs which broke through the limit of early perceptron.1987: The market of LISP machines collapsed.1997: Deep Blue defeated the world chess champion Garry Kasparov.2006: Hinton and his students started deep learning.2010: The era of big data came.2014: Microsoft released the first individual intelligent assistant Microsft Cortana in the world.2016 March: AlphaGo defeated the world champion Go player Lee Sedol by 4-1.2017 October: The Deep Mind team released AlphaGo Zero, the strongest version of AlphaGo.Overview of AI Technologies
⚫ AI
technologies are multi-layered, covering the application, algorithm mechanism,
toolchain, device, chip, process, and material layers.
Application
Algorithm
Device
Chip
Process
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Types of AI
⚫ Strong AI
The strong AI view holds that it is possible to create intelligent machines that can really reason
and solve problems. Such machines are considered to be conscious and self-aware, can
independently think about problems and work out optimal solutions to problems, have their
own system of values and world views, and have all the same instincts as living things, such as
survival and security needs. It can be regarded as a new civilization in a certain sense.
⚫ Weak AI
The weak AI view holds that intelligent machines cannot really reason and solve problems.
These machines only look intelligent, but do not have real intelligence or self-awareness.
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Classification of Intelligent Robots
⚫ Currently, there is no unified definition of AI research. Intelligent robots are generally
classified into the following four types:
"Thinking like human beings": weak AI, such as Watson and AlphaGo
"Acting like human beings": weak AI, such as humanoid robot, iRobot, and Atlas of Boston
Dynamics
"Thinking rationally": strong AI (Currently, no intelligent robots of this type have been created
due to the bottleneck in brain science.)
"Acting rationally": strong AI
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AI Industry Ecosystem
⚫ The four elements of AI are data, algorithm, computing power, and scenario. To meet requirements of these
four elements, we need to combine AI with cloud computing, big data, and IoT to build an intelligent society.
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Sub-fields of AI
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AI Development Report 2020
Contents
1. AI Overview
2. Technical Fields and Application Fields of AI
3. Huawei's AI Development Strategy
4. AI Disputes
5. Future Prospects of AI
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Technical Fields and Application Fields of AI
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Global AI Development White
Paper 2020
Distribution of AI Application Technologies in Enterprises Inside
and Outside China
⚫ At present, application directions of AI technologies mainly
include:
Computer vision: a science of how to make computers "see"
Speech processing: a general term for various processing
technologies used to research the voicing process, statistical
features of speech signals, speech recognition, machine-based
speech synthesis, and speech perception
Natural language processing (NLP): a subject that use computer
technologies to understand and use natural language
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Distribution of AI application technologies in
enterprises inside and outside China
China AI Development Report 2018
Voice Processing Application Scenario (1)
⚫ The main topics of voice processing research include voice recognition, voice synthesis, voice wakeup,
voiceprint recognition, and audio-based incident detection. Among them, the most mature technology is voice
recognition. As for near field recognition in a quite indoor environment, the recognition accuracy can reach
96%.
⚫ Application scenarios:
Question Answering Bot (QABot)
Voice navigation
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Voice Processing Application Scenario (2)
Return Visit
Real-time conference
records
⚫ Other applications:
Spoken language evaluation
Diagnostic robot
Voiceprint recognition
Smart sound box
...
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NLP Application Scenario (1)
⚫
The main topics of NLP research include machine translation, text mining, and sentiment analysis. NLP imposes high requirements
on technologies but confronts low technology maturity. Due to high complexity of semantics, it is hard to reach the human
understanding level using parallel computing based on big data and parallel computing only.
⚫
⚫
In future, NLP will achieve more growth: understanding of shallow semantics → automatic extraction of features and understanding
of deep semantics; single-purpose intelligence (ML) → hybrid intelligence (ML, DL, and RL)
Application scenarios:
Public opinion
analysis
Theme
mining
Trend
analysis
Evaluation
analysis
Public opinion
analysis
Emotional
analysis
Hotspot
event
Information
distribution
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NLP Application Scenario (2)
Machine
translation
Text
classification
⚫ Other applications:
Knowledge graph
Intelligent copywriting
Video subtitle
...
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AI Application Field - Intelligent Healthcare
Medicine mining: quick development of personalized medicines by AI assistants
Health management: nutrition, and physical/mental health management
Hospital management: structured services concerning medical records (focus)
Assistance for medical research: assistance for biomedical researchers in research
Virtual assistant: electronic voice medical records, intelligent guidance, intelligent diagnosis, and
medicine recommendation
Medical image: medical image recognition, image marking, and 3D image reconstruction
Assistance for diagnosis and treatment: diagnostic robot
Disease risk forecast: disease risk forecast based on gene sequencing
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AI Application Field - Smart Home
⚫ Based on IoT technologies, a smart home ecosystem is formed with hardware, software, and cloud
platforms, providing users personalized life services and making home life more convenient,
comfortable, and safe.
Okay, the
temperature's set.
Set the temperature to
26 degrees.
Control smart home products with voice
processing such as air conditioning
temperature adjustment, curtain switch
control, and voice control on the lighting
system.
Develop user profiles and recommend
content to users with the help of machine
learning and deep learning technologies
and based on historical records of smart
speakers and smart TVs.
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AI Application Field - Retail
⚫
AI will bring revolutionary changes to the retail industry. A typical symptom is unmanned supermarkets. For example, Amazon Go, unmanned
supermarket of Amazon, uses sensors, cameras, computer vision, and deep learning algorithms to completely cancel the checkout process,
allowing customers to pick up goods and "just walk out".
⚫ One of the biggest challenges for unmanned supermarket is how to charge the right fees to the right customers. So far, Amazon Go is the only
successful business case and even this case involves many controlled factors. For example, only Prime members can enter Amazon Go. Other
enterprises, to follow the example of Amazon, have to build their membership system first.
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AI Application Field - Autonomous Driving
⚫ The Society of Automotive Engineers (SAE) in the U.S. defines 6 levels of driving automation ranging from 0
(fully manual) to 5 (fully autonomous). L0 indicates that the driving of a vehicle completely depends on the
driver's operation. The system above L3 can implement the driver's hand-off operation in specific cases, L5
depends on the system when vehicles are driving in all scenarios.
⚫ Currently, only some commercial passenger vehicle models, such as Audi A8, Tesla, and Cadillac, support L2
and L3 Advanced driver-assistance systems (ADAS). It is estimated that by 2020, more L3 vehicle models will
emerge with the further improvement of sensors and vehicle-mounted processors. L4 and L5 autonomous
driving is expected to be first implemented on commercial vehicles in closed campuses. A wider range of
passenger vehicles require advanced autonomous driving, which requires further
improvement of
technologies, policies, and infrastructure. It is estimated that L4 and L5 autonomous driving will be supported
by common roads in 2025–2030.
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Contents
1. AI Overview
2. Technical Fields and Application Fields of AI
3. Huawei's AI Development Strategy
4. AI Disputes
5. Future Prospects of AI
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Huawei's Full-Stack, All-Scenario AI Portfolio
AI Applications
HiAI Engine
ModelArts
TensorFlow
PyTorch
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PaddlePaddle
MindSpore
Full Stack
CANN
Application
Enablement
Framework
Chip
Enablement
Ascend-Nano
Ascend-Tiny
Ascend-Lite
Ascend
Ascend-Mini
Ascend-Max
IP & Chip
IP and Chip
Atlas
All Scenarios
Application enablement: provides end-to-end
services (ModelArts), layered APIs, and pre-
integrated solutions.
MindSpore: supports the unified training and
inference framework that is independent of the
device, edge, and cloud.
CANN: a chip operator library and highly automated
operator development tool.
Ascend: provides a series of NPU IPs and chips based
on a unified, scalable architecture.
Atlas: enables an all-scenario AI infrastructure solution
that is oriented to the device, edge, and cloud based
on the Ascend series AI processors and various product
forms.
Consumer Device
Public Cloud
Private Cloud
Edge Computing
Industrial IoT Device
Huawei's "all AI scenarios" indicate different deployment scenarios for AI, including public clouds, private
clouds, edge computing in all forms, industrial IoT devices, and consumer devices.
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Full Stack - ModelArts Full-Cycle AI Workflow
EI Intelligent Twins
EI Cognition Service AI Service
AI data
framework
Efficient filtering and
semi-automated
labeling, data
preprocessing
Efficiency improved by
100 times
Data
Algorithm
development
Out-of-the-box
development
environment compatible
with mainstream
frameworks
MoXing library,
simplifying model
development
Built-in model algorithms,
improving development
efficiency
Training
Deployment
Market
Distributed training,
shortening training
period from weeks to
minutes
Wizard-based
AutoLearning, code-
free development,
enabling model
training from scratch
One-click deployment
on device, edge, and
cloud
All-scenario
deployment
Inference on the
Ascend AI processor
AI sharing platform
helps enterprises build
internal and external
AI ecosystems
AI applications
Visualized Workflow Management
Version management, traceable and worry-free development
ModelArts
AI data framework
accelerates data processing
by 100 folds.
Visualized workflow
management
makes development
worry-free.
Distributed training
shortens training from
weeks to minutes.
One-click deployment on
device, edge, and cloud
supports various deployment
scenarios.
Automatic learning
enables you to start
from scratch.
AI sharing platform
builds internal and external
AI ecosystems for
enterprises.
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Full Stack — MindSpore (Huawei AI Computing Framework)
⚫ MindSpore provides automatic parallel capabilities. With MindSpore, senior algorithm engineers and data scientists who
focus on data modeling and problem solving can run algorithms on dozens or even thousands of AI computing nodes with
only a few lines of description.
⚫
The MindSpore framework supports both large-scale and small-scale deployment, adapting to independent deployment in
all scenarios. In addition to the Ascend AI processors, MindSpore also supports other processors such as GPUs and CPUs.
AI application ecosystem for all scenarios
MindSpore
Unified APIs for all scenarios
MindSpore intermediate representation (IR) for computational
graph
On-demand collaborative distributed architecture across device-edge-cloud
(deployment, scheduling, and communications)
Processors: Ascend, GPU, and CPU
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Full Stack — CANN
AI applications
HiAI Service
General APIs
Advanced APIs
Pre-integrated Solutions
HiAI Engine
ModelArts
Application
enablement
Full
stack
MindSpore
TensorFlow
PyTorch
PaddlePaddle
…
Framework
CANN
Processor
enablement
Ascend-
Nano
Ascend-
Tiny
Ascend-
Lite
Ascend
Ascend-
Mini
Ascend-
Max
IP and Chip
Consumer device
Public
cloud
Private
cloud
Edge
computing
Industrial
devices
All scenarios
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CANN:
A chip operators library and highly automated operator
development toolkit
Optimal development efficiency, in-depth optimization of the
common operator library, and abundant APIs
Operator convergence, best matching the performance of the
Ascend chip
CANN
Compute Architecture for Neural Networks
FusionEngine
TBE operator
development tool
CCE Operator
Library
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CCE Compiler
Full Stack — Ascend 310 AI Processor and Da Vinci Core
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Ascend AI Processors: Infusing Superior Intelligence for
Computing
Ascend 310
AI SoC with ultimate
energy efficiency
Ascend-Mini
Architecture: Da Vinci
Half-precision (FP16): 8 TFLOPS
Integer precision (INT8): 16 TOPS
16-channel full-HD video decoder: H.264/265
1-channel full-HD video encoder: H.264/265
FLOPS
256T
125T
90T
45T
4
3
2
1
Ascend 910
Ascend 910
Most powerful AI
processor
Ascend-Max
Architecture: Da Vinci
Half-precision (FP16): 256 TFLOPS
Integer precision (INT8): 512 TOPS
128-channel full HD video decoder: H.264/265
Max. power: 8 W
Max. power: 310 W
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Atlas AI Computing Platform Portfolio
Internet, security, finance, transportation, power, etc.
Application
Enablement
Framework
CANN
Chips &
Hardware
Atlas intelligent edge platform
Industry SDK/Container
engine/Basic service repository
MindSpore
AscendCL
Atlas deep learning platform
Cluster management/Model
management/Data pre-processing
TensorFlow/PyTorch/Caffe/MxNet
Framework Adapter
Framework Adapret
Common
components
Operator/Acceleration/Communication libraries (BLAS, FFT, DNN, Rand, Solver, Sparse, HCCL)
Graph engine for graph optimization
Atlas 200
16 TOPS INT8
Atlas 300 inference
accelerator card
64 TOPS INT8
Runtime
Driver
Atlas 800 AI inference server
512 TOPS INT8
Atlas 500
16 TOPS INT8
Atlas 900
256–1024 PFLOPS FP16
Atlas 200 developer kit
Atlas 300 training card
256 TFLOPS FP16
Atlas 800 AI training server
2 PFLOPS FP16
Ascend 310
Da Vinci
Architecture
Ascend 910
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Contents
1. AI Overview
2. Technical Fields and Application Fields of AI
3. Huawei's AI Development Strategy
4. AI Disputes
5. Future Prospects of AI
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Seeing = Believing?
⚫ With the development of computer vision technologies, reliability of images and videos is decreasing. Fake
images can be produced with technologies such as PS and generative adversarial networks (GAN), making it
Publicité
hard to identify whether images are true or not.
⚫ Example:
A suspect provided fake evidence by forging an image in which the suspect is in a place where he has never been to or
with someone he has never seen using PS technologies.
In advertisements for diet pills, people's appearances before and after weight loss can be changed with PS technologies
to exaggerate the effect of the pills.
Lyrebird, a tool for simulating voice of human beings based on recording samples of minutes, may be used by criminals.
Household images released on rent and hotel booking platforms may be generated through GAN.
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AI Development = Rising Unemployment?
⚫
Looking back, human beings have always been seeking ways to improve efficiency, that is, obtain more with
less resources. We used sharp stones to hunt and collect food more efficiently. We used steam engines to
reduce the need for horses. Every step in achieving automation will change our life and work. In the era of AI,
what jobs will be replaced by AI?
⚫ The answer is repetitive jobs that involve little creativity and social interaction.
Jobs Most Likely to Be Replaced by AI
Jobs Most Unlikely to Be Replaced by AI
Courier
Taxi driver
Soldier
Accounting
Telesales personnel
Customer service
...
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Writer
Management personnel
Software engineers
HR manager
Designer
Activity planner
...
Problems to Be Solved
⚫ Are AI-created works protected by copyright laws?
⚫ Who gives authority to robots?
⚫ What rights shall be authorized to robots?
⚫
...
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Contents
1. AI Overview
2. Technical Fields and Application Fields of AI
3. Huawei's AI Development Strategy
4. AI Disputes
5. Future Prospects of AI
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Development Trends of AI Technologies
⚫ Framework: easier-to-use development framework
⚫ Algorithm: algorithm models with better performance and smaller size
⚫ Computing power: comprehensive development of device-edge-cloud computing
⚫ Data: more comprehensive basic data service industry and more secure data sharing
⚫ Scenario: continuous breakthroughs in industry applications
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Easier-to-Use Development Framework
⚫ Various AI development frameworks are evolving towards ease-of-use and omnipotent,
continuously lowering the threshold for AI development.
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Tensorflow 2.0
⚫ TensorFlow 2.0 has been officially released. It integrates Keras as its high-level API, greatly
improving usability.
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Pytorch vs Tensorflow
⚫ PyTorch is widely recognized by academia for its ease of use.
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Comparison between PyTorch and TensorFlow usage
statistics of top academic conferences
Smaller Deep Learning Models
⚫ A model with better performance usually has a larger quantity of parameters, and a large model
has lower running efficiency in industrial applications. More and more model compression
technologies are proposed to further compress the model size while ensuring the model
performance, meeting the requirements of industrial applications.
Low rank approximation
Network pruning
Network quantification
Knowledge distillation
Compact network design
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Network
architecture
design
Low rank
approximation
Network
pruning
Model
compression
Network
quantification
Knowledge
distillation
Computing Power with Comprehensive Device-Edge-Cloud
Development
⚫ The scale of AI chips applied to the cloud, edge devices, and mobile devices keeps increasing,
further meeting the computing power demand of AI.
Sales revenue (CNY100 million)
Growth rate
China AI Chip Industry Development White Paper 2020
Market Scale and Growth Prediction of AI Chips in China from 2020 to 2021
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More Secure Data Sharing
⚫ Federated learning uses different data sources to train models, further breaking data bottlenecks
while ensuring data privacy and security.
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Federated Learning White Paper V1.0
Continuous Breakthroughs in Application Scenarios
⚫ With the continuous exploration of AI in various verticals, the application scenarios of AI
will be continuously broken through.
Mitigating psychological problems
Automatic vehicle insurance and loss assessment
Office automation
...
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Mitigating Psychological Problems
⚫ AI chat robots help alleviate mental health problems such as autism by combining psychological
knowledge.
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Automatic Vehicle Insurance and Loss Assessment
⚫ AI technologies help insurance companies optimize vehicle insurance claims and complete vehicle
insurance loss assessment using deep learning algorithms such as image recognition.
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Office Automation
⚫ AI is automating management, but the different nature and format of data makes it a challenging
task. While each industry and application has its own unique challenges, different industries are
gradually adopting machine learning-based workflow solutions.
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Summary
⚫ This chapter introduces the definition and development history of AI, describes the
technical
fields and application fields of AI, briefly introduces Huawei's AI
development strategy, and finally discusses the disputes and the development trends
of AI.
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Quiz
1.
(Multiple-answer question) Which of the following are AI application fields?
A. Smart household
B. Smart healthcare
C. Smart city
D. Smart education
2.
(True or False) By "all AI scenarios", Huawei means different deployment scenarios for AI, including
public clouds, private clouds, edge computing in all forms, industrial IoT devices, and consumer
devices.
A. True
B. False
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More Information
Online learning website
https://e.huawei.com/en/talent/#/home
Huawei Knowledge Base
https://support.huawei.com/enterprise/en/knowledge?lang=en
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Thank you.
把数字世界带入每个人、每个家庭、
每个组织，构建万物互联的智能世界。
Bring digital to every person, home, and
organization for a fully connected,
intelligent world.
Copyright©2020 Huawei Technologies Co., Ltd.
All Rights Reserved.
The information in this document may contain predictive
statements including, without limitation, statements regarding
the future financial and operating results, future product
portfolio, new technology, etc. There are a number of factors that
could cause actual results and developments to differ materially
from those expressed or implied in the predictive statements.
Therefore, such information is provided for reference purpose
only and constitutes neither an offer nor an acceptance. Huawei
may change the information at any time without notice.