SlideShare a Scribd company logo
1 of 15
Download to read offline
IEEE Computer Society
2024 Technology Predictions Update
Hironori (Hiro) Washizaki
IEEE Computer Society 2025 President
Waseda University, Professor
FM+SE SUMMIT 2024, March 28th 2024
IEEE Computer Society: Empowering Computer Science and Engineering
Professionals to Fuel Continued Advancement
IEEE CS Technology Prediction
Team (Chair: Dejan Milojicic)
IEEE CS Technology Predictions 2023
4
IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/2023-top-technology-predictions
Chance of success higher
than impact on humanity
Impact on humanity higher than
chance of success (worth investing in)
Remote Healthcare
Wearables
Generative AI
Disinformation
detection/correction
AI-assisted
DevOps
Artificial General
Intelligence (AGI)
5
IEEE Computer Society (CS) Global Scientists and Engineers Rank 2023
Technology Trend Predictions
https://www.computer.org/press-room/scientists-and-engineers-rank-2023-
technology-trend-predictions
Remote Healthcare &
Wearables
Generative AI
Disinformation
detection/correction
AI-assisted
DevOps
Artificial General
Intelligence (AGI)
IEEE CS Technology 2023
Prediction vs. Assessment
• How original grades
stood the one-year test,
as reflected in A/B
grading
• High growth of generative
AI, slowdown of COVID-
related technologies
Prediction
Jan 2023
Assessment
Dec 2023
IEEE CS Technology Prediction
Team (Chair: Dejan Milojicic)
7
Megatrends in IEEE Future Direction 2023 and IEEE-CS Technology Predictions 2024
IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
• AGI technologies are
deeply entangled with
socio, economic, and
ecological aspects.
Next Gen AI
Generative AI
applications
Metaverse
Low power AI
accelerator
IEEE CS Technology Predictions 2024
Chance of success higher
than impact on humanity
Impact on humanity
higher than chance of
success (worth investing in)
IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
Generative AI
• Provides flexibility for new challenges and
adaptive responses demand.
• Safety and security: Need to build safeguards
against misuses and generated harmful content,
such as deep fakes.
• Lacking Robustness, Reliability, Control, and
Explainability, necessitating transparent
techniques and consistent AI models. This is a
major issue for agents and trusted apps.
• Bias and data quality issues in large datasets call
for better curation.
• High computational costs limit model training to
an oligarchy of very few players who can afford to
train a foundation model.
• Evolving regulatory landscapes, especially
regarding data privacy and use, demand for
ensuring legal and ethical compliance.
• Enhanced creativity in arts and
design, accelerated design.
• Generative AI-based
revolutionized personalized
medicine, from drug discovery to
tailored treatment plans.
• Personalized education and
marketing boost productivity.
• Improved customer support
through natural interactions-
conversation, problem solving,
detailed product knowledge.
• Accelerated scientific discovery
and 3D modeling.
Problems/demand Opportunities
IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
• Helps content creators and designers to be
more productive.
• Helps businesses improve their digital
channels and marketing.
• Time-To-Market significantly decreased.
• Better accessibility: image-to-text, audio
transcripts, translations (including to sign
language).
• Personalized assistants (coding, editing,
teaching, etc.) increase productivity and
efficiency.
• Displacement of repetitive jobs requires
reskilling/upskilling.
• Spreading of much higher quality
misinformation requires source checking
and critical thinking.
• More environmentally sustainable practices
in various industries through optimization of
resources and reduction of waste.
• Significantly changing traditional
Industries—like manufacturing, agriculture,
and transportation.
• Generative AI may decrease the need for processing-
intensive training.
• Support for no/low-code.
• Generative AI will increase AI adoption and create new
revenue streams.
• Global cooperation in standardization and best practices to
address challenges like intellectual property, cybersecurity,
and ethical norms.
• Public-Private Partnerships: tighter collaborations between
government, academia, and industry.
• Increased significance of Edge Computing in processing data
closer to the source, reducing latency, and improving response
times.
• Enablers: New ML approaches, affordable AI tools, open
models, access to large curated datasets, AI-integrated agents
for automation.
• Inhibitors: Threat to content creators and IP holders, difficulty
in distinguishing human created content versus machine
created, adversarial applications, Ethical questions of GAI
versus human content generation, lack of interoperability,
closed models, low quality and biased datasets, high
compute costs, lack of trust in AI, regulatory burdens and
resistance to change.
Impact Sustainable Solution/Business Opportunity
IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
Knowledge Area
Topic Topic
Reference
Material
Body of Knowledge Skills Competencies Jobs / Roles
SWEBOK
Software Engineering Professional Certifications
SWECOM
EITBOK
Learning courses
11
Guide to the Software Engineering Body of Knowledge (SWEBOK)
https://www.computer.org/education/bodies-of-knowledge/software-engineering
• Guiding researchers and practitioners to identify and have
common understanding on “generally-accepted-knowledge”
in software engineering
• Foundations for certifications and educational curriculum
• ‘01 v1, ‘04 v2, ‘05 ISO adoption, ‘14 v3, ’24 v4 soon!
SWEBOK Guide V4
(Editor: Hironori Washizaki)
Mainframe
70’s –
Early 80’s
Late 80’s -
Early 90’s
Late 90’s -
Early 00’s
Late 00’s -
Early 10’s
PC,
Client &
server
Internet
Ubiquitous
computing
Late 10’s -
Early 20’s
IoT,
Big data,
AI
Structured
programming
Waterfall
Formalization
Design
Program
generation
Maturity
Management
Object-oriented
Req. eng.
Modeling
Verification
Reuse
Model-driven
Product-line
Global & open
Value-based
Systems eng.
Agile
Iterative &
incremental
DevOps
Empirical
Data-driven
Continuous
SE and IoT
SE and AI
SWEBOK V1
SWEBOK V2
SWEBOK V3
SWEBOK V4
SWEBOK Evolution from V3 to V4
• Modern engineering, practice update, BOK grows and recently developed areas
Requirements
Design
Construction
Testing
Maintenance
Configuration Management
Engineering Management
Process
Models and Methods
Quality
Professional Practice
Economics
Computing Foundations
Mathematical Foundations
Engineering Foundations
Requirements
Architecture
Design
Construction
Testing
Operations
Maintenance
Configuration Management
Engineering Management
Process
Models and Methods
Quality
Security
Professional Practice
Economics
Computing Foundations
Mathematical Foundations
Engineering Foundations
V3 V4
Agile,
DevOps
AI for SE,
SE for AI Software
engineering
AI
AI for SE
SE for AI
Mainframe
70’s –
Early 80’s
Late 80’s -
Early 90’s
Late 90’s -
Early 00’s
Late 00’s -
Early 10’s
PC,
Client &
server
Internet
Ubiquitous
computing
Late 10’s -
Early 20’s
IoT,
Big data,
AI
GenAI, FM,
Autonomous,
Quantum,
Continuum
Late 20’s
Structured
programming
Waterfall
Formalization
Design
Program
generation
Maturity
Management
Object-oriented
Req. eng.
Modeling
Verification
Reuse
Model-driven
Product-line
Global & open
Value-based
Systems eng.
Agile
Iterative &
incremental
DevOps
Empirical
Data-driven
Continuous
SE and IoT
SE and AI
SE and GenAI
SE and QC
Sustainability
SE for
autonomous
and continuum
AI-assisted
DevOps/OpsDev
SWEBOK V1
SWEBOK V2
SWEBOK V3
SWEBOK V4
8
Megatrends in IEEE Future Direction 2023 and IEEE-CS Technology Predictions 2024
IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
• We have four core AGI technology
predictions compared to eight
sustainability-related and nine
digital transformation-related
predictions.
• AGI technologies are deeply
entangled with socio, economic,
and ecological aspects.
IEEE CS Technology Predictions 2024
Chance of success
higher than impact
on humanity
Impact on humanity higher
than chance of success
(worth investing in)
IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
Generative AI
• Provides flexibility for new challenges and adaptive
responses demand.
• Safety and security: Need to build safeguards
against misuses and generated harmful content,
such as deep fakes.
• Lacking Robustness, Reliability, Control, and
Explainability, necessitating transparent techniques
and consistent AI models.
• This is a major issue for agents and trusted apps.
• Bias and data quality issues in large datasets call
for better curation.
• High computational costs limit model training to an
oligarchy of very few players who can afford to
train a foundation model.
• Evolving regulatory landscapes, especially
regarding data privacy and use, demand for
ensuring legal and ethical compliance.
• Enhanced creativity in arts and
design, accelerated design.
• Generative AI-based
revolutionized personalized
medicine, from drug discovery to
tailored treatment plans.
• Personalized education and
marketing boost productivity.
• Improved customer support
through natural interactions-
conversation, problem solving,
detailed product knowledge.
• Accelerated scientific discovery
and 3D modeling.
Problems/demand Opportunities
Mainframe
70’s –
Early 80’s
Late 80’s -
Early 90’s
Late 90’s -
Early 00’s
Late 00’s -
Early 10’s
PC,
Client &
server
Internet
Ubiquitous
computing
Late 10’s -
Early 20’s
IoT,
Big data,
AI
GenAI, FM,
Autonomous,
Quantum,
Continuum
Late 20’s
Structured
programming
Waterfall
Formalization
Design
Program
generation
Maturity
Management
Object-oriented
Req. eng.
Modeling
Verification
Reuse
Model-driven
Product-line
Global & open
Value-based
Systems eng.
Agile
Iterative &
incremental
DevOps
Empirical
Data-driven
Continuous
SE and IoT
SE and AI
SE and GenAI
SE and QC
Sustainability
SE for
autonomous
and continuum
AI-assisted
DevOps
SWEBOK V1
SWEBOK V2
SWEBOK V3
SWEBOK V4

More Related Content

What's hot

強化学習の基礎と深層強化学習(東京大学 松尾研究室 深層強化学習サマースクール講義資料)
強化学習の基礎と深層強化学習(東京大学 松尾研究室 深層強化学習サマースクール講義資料)強化学習の基礎と深層強化学習(東京大学 松尾研究室 深層強化学習サマースクール講義資料)
強化学習の基礎と深層強化学習(東京大学 松尾研究室 深層強化学習サマースクール講義資料)
Shota Imai
 

What's hot (20)

ナレッジグラフとオントロジー
ナレッジグラフとオントロジーナレッジグラフとオントロジー
ナレッジグラフとオントロジー
 
強化学習の基礎と深層強化学習(東京大学 松尾研究室 深層強化学習サマースクール講義資料)
強化学習の基礎と深層強化学習(東京大学 松尾研究室 深層強化学習サマースクール講義資料)強化学習の基礎と深層強化学習(東京大学 松尾研究室 深層強化学習サマースクール講義資料)
強化学習の基礎と深層強化学習(東京大学 松尾研究室 深層強化学習サマースクール講義資料)
 
Slurmのジョブスケジューリングと実装
Slurmのジョブスケジューリングと実装Slurmのジョブスケジューリングと実装
Slurmのジョブスケジューリングと実装
 
【DL輪読会】Efficiently Modeling Long Sequences with Structured State Spaces
【DL輪読会】Efficiently Modeling Long Sequences with Structured State Spaces【DL輪読会】Efficiently Modeling Long Sequences with Structured State Spaces
【DL輪読会】Efficiently Modeling Long Sequences with Structured State Spaces
 
先端技術とメディア表現1 #FTMA15
先端技術とメディア表現1 #FTMA15先端技術とメディア表現1 #FTMA15
先端技術とメディア表現1 #FTMA15
 
Docker Compose 徹底解説
Docker Compose 徹底解説Docker Compose 徹底解説
Docker Compose 徹底解説
 
TLS, HTTP/2演習
TLS, HTTP/2演習TLS, HTTP/2演習
TLS, HTTP/2演習
 
ベイジアンディープニューラルネット
ベイジアンディープニューラルネットベイジアンディープニューラルネット
ベイジアンディープニューラルネット
 
機械学習の定番プラットフォームSparkの紹介
機械学習の定番プラットフォームSparkの紹介機械学習の定番プラットフォームSparkの紹介
機械学習の定番プラットフォームSparkの紹介
 
本当は恐ろしい分散システムの話
本当は恐ろしい分散システムの話本当は恐ろしい分散システムの話
本当は恐ろしい分散システムの話
 
remote Docker over SSHが熱い
remote Docker over SSHが熱いremote Docker over SSHが熱い
remote Docker over SSHが熱い
 
PlaySQLAlchemy: SQLAlchemy入門
PlaySQLAlchemy: SQLAlchemy入門PlaySQLAlchemy: SQLAlchemy入門
PlaySQLAlchemy: SQLAlchemy入門
 
SQL大量発行処理をいかにして高速化するか
SQL大量発行処理をいかにして高速化するかSQL大量発行処理をいかにして高速化するか
SQL大量発行処理をいかにして高速化するか
 
【DL輪読会】Hyena Hierarchy: Towards Larger Convolutional Language Models
【DL輪読会】Hyena Hierarchy: Towards Larger Convolutional Language Models【DL輪読会】Hyena Hierarchy: Towards Larger Convolutional Language Models
【DL輪読会】Hyena Hierarchy: Towards Larger Convolutional Language Models
 
SGD+α: 確率的勾配降下法の現在と未来
SGD+α: 確率的勾配降下法の現在と未来SGD+α: 確率的勾配降下法の現在と未来
SGD+α: 確率的勾配降下法の現在と未来
 
テスト文字列に「うんこ」と入れるな
テスト文字列に「うんこ」と入れるなテスト文字列に「うんこ」と入れるな
テスト文字列に「うんこ」と入れるな
 
いまどきの組込みOSの​ ZephyrRTOSと​ OpenThreadを​ Arduino環境で遊んでみる
いまどきの組込みOSの​ ZephyrRTOSと​ OpenThreadを​ Arduino環境で遊んでみるいまどきの組込みOSの​ ZephyrRTOSと​ OpenThreadを​ Arduino環境で遊んでみる
いまどきの組込みOSの​ ZephyrRTOSと​ OpenThreadを​ Arduino環境で遊んでみる
 
入門 Kubeflow ~Kubernetesで機械学習をはじめるために~ (NTT Tech Conference #4 講演資料)
入門 Kubeflow ~Kubernetesで機械学習をはじめるために~ (NTT Tech Conference #4 講演資料)入門 Kubeflow ~Kubernetesで機械学習をはじめるために~ (NTT Tech Conference #4 講演資料)
入門 Kubeflow ~Kubernetesで機械学習をはじめるために~ (NTT Tech Conference #4 講演資料)
 
性能測定道 事始め編
性能測定道 事始め編性能測定道 事始め編
性能測定道 事始め編
 
大規模データ処理の定番OSS Hadoop / Spark 最新動向 - 2021秋 -(db tech showcase 2021 / ONLINE 発...
大規模データ処理の定番OSS Hadoop / Spark 最新動向 - 2021秋 -(db tech showcase 2021 / ONLINE 発...大規模データ処理の定番OSS Hadoop / Spark 最新動向 - 2021秋 -(db tech showcase 2021 / ONLINE 発...
大規模データ処理の定番OSS Hadoop / Spark 最新動向 - 2021秋 -(db tech showcase 2021 / ONLINE 発...
 

Similar to IEEE Computer Society 2024 Technology Predictions Update

Generative AI .pptx.....................
Generative AI .pptx.....................Generative AI .pptx.....................
Generative AI .pptx.....................
hanamshettyvani
 
Resume.20110926
Resume.20110926Resume.20110926
Resume.20110926
RobertMars
 

Similar to IEEE Computer Society 2024 Technology Predictions Update (20)

Department of Computer Science - MIT SOE, MIT-ADT University, Pune
Department of Computer Science - MIT SOE, MIT-ADT University, PuneDepartment of Computer Science - MIT SOE, MIT-ADT University, Pune
Department of Computer Science - MIT SOE, MIT-ADT University, Pune
 
Towards the Industrialization of AI
Towards the Industrialization of AITowards the Industrialization of AI
Towards the Industrialization of AI
 
Smart SE: Recurrent Education Program of IoT and AI for Business
Smart SE: Recurrent Education Program of IoT and AI for BusinessSmart SE: Recurrent Education Program of IoT and AI for Business
Smart SE: Recurrent Education Program of IoT and AI for Business
 
Se research update
Se research updateSe research update
Se research update
 
Various industry trends and career opportunities for engineering graduates in...
Various industry trends and career opportunities for engineering graduates in...Various industry trends and career opportunities for engineering graduates in...
Various industry trends and career opportunities for engineering graduates in...
 
Visualization for Software Analytics
Visualization for Software AnalyticsVisualization for Software Analytics
Visualization for Software Analytics
 
Patterns for New Software Engineering: Machine Learning and IoT Engineering P...
Patterns for New Software Engineering: Machine Learning and IoT Engineering P...Patterns for New Software Engineering: Machine Learning and IoT Engineering P...
Patterns for New Software Engineering: Machine Learning and IoT Engineering P...
 
IEEE Projects
IEEE ProjectsIEEE Projects
IEEE Projects
 
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...
Testing with Fewer Resources:  Toward Adaptive Approaches for Cost-effective ...Testing with Fewer Resources:  Toward Adaptive Approaches for Cost-effective ...
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...
 
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...
Testing with Fewer Resources:  Toward Adaptive Approaches for Cost-effective ...Testing with Fewer Resources:  Toward Adaptive Approaches for Cost-effective ...
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...
 
IEEE Computer Society’s Strategic Activities and Products including SWEBOK Guide
IEEE Computer Society’s Strategic Activities and Products including SWEBOK GuideIEEE Computer Society’s Strategic Activities and Products including SWEBOK Guide
IEEE Computer Society’s Strategic Activities and Products including SWEBOK Guide
 
Generative AI .pptx.....................
Generative AI .pptx.....................Generative AI .pptx.....................
Generative AI .pptx.....................
 
Ai open powermeetupmarch25th
Ai open powermeetupmarch25thAi open powermeetupmarch25th
Ai open powermeetupmarch25th
 
Ai open powermeetupmarch25th
Ai open powermeetupmarch25thAi open powermeetupmarch25th
Ai open powermeetupmarch25th
 
Ai open powermeetupmarch25th
Ai open powermeetupmarch25thAi open powermeetupmarch25th
Ai open powermeetupmarch25th
 
Recognizing the Future of Systems Engineering in a Changing World
Recognizing the Future of Systems Engineering in a Changing WorldRecognizing the Future of Systems Engineering in a Changing World
Recognizing the Future of Systems Engineering in a Changing World
 
Career guidance talk it makaut_ppt_sabyasachi mukhopadhyay
Career guidance talk it makaut_ppt_sabyasachi mukhopadhyayCareer guidance talk it makaut_ppt_sabyasachi mukhopadhyay
Career guidance talk it makaut_ppt_sabyasachi mukhopadhyay
 
Solnet dev secops meetup
Solnet dev secops meetupSolnet dev secops meetup
Solnet dev secops meetup
 
Resume.20110926
Resume.20110926Resume.20110926
Resume.20110926
 
Internet of Things: Trends and challenges for future
Internet of Things: Trends and challenges for futureInternet of Things: Trends and challenges for future
Internet of Things: Trends and challenges for future
 

More from Hironori Washizaki

More from Hironori Washizaki (20)

Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their Engineering
 
鷲崎弘宜, "国際規格ISO/IEC 24773とその意義", 情報処理学会 第86回全国大会
鷲崎弘宜, "国際規格ISO/IEC 24773とその意義", 情報処理学会 第86回全国大会鷲崎弘宜, "国際規格ISO/IEC 24773とその意義", 情報処理学会 第86回全国大会
鷲崎弘宜, "国際規格ISO/IEC 24773とその意義", 情報処理学会 第86回全国大会
 
TISO/IEC JTC1におけるソフトウェア工学知識体系、技術者認証および品質の標準化と研究・教育他への活用
TISO/IEC JTC1におけるソフトウェア工学知識体系、技術者認証および品質の標準化と研究・教育他への活用TISO/IEC JTC1におけるソフトウェア工学知識体系、技術者認証および品質の標準化と研究・教育他への活用
TISO/IEC JTC1におけるソフトウェア工学知識体系、技術者認証および品質の標準化と研究・教育他への活用
 
アジャイル品質のパターンとメトリクス Agile Quality Patterns and Metrics (QA2AQ) 20240225
アジャイル品質のパターンとメトリクス Agile Quality Patterns and Metrics (QA2AQ) 20240225アジャイル品質のパターンとメトリクス Agile Quality Patterns and Metrics (QA2AQ) 20240225
アジャイル品質のパターンとメトリクス Agile Quality Patterns and Metrics (QA2AQ) 20240225
 
Joseph Yoder : Being Agile about Architecture
Joseph Yoder : Being Agile about ArchitectureJoseph Yoder : Being Agile about Architecture
Joseph Yoder : Being Agile about Architecture
 
世界標準のソフトウェア工学知識体系SWEBOK Guide最新第4版を通じた開発アップデート
世界標準のソフトウェア工学知識体系SWEBOK Guide最新第4版を通じた開発アップデート世界標準のソフトウェア工学知識体系SWEBOK Guide最新第4版を通じた開発アップデート
世界標準のソフトウェア工学知識体系SWEBOK Guide最新第4版を通じた開発アップデート
 
SWEBOK Guide Evolution and Its Emerging Areas including Machine Learning Patt...
SWEBOK Guide Evolution and Its Emerging Areas including Machine Learning Patt...SWEBOK Guide Evolution and Its Emerging Areas including Machine Learning Patt...
SWEBOK Guide Evolution and Its Emerging Areas including Machine Learning Patt...
 
デジタルトランスフォーメーション(DX)におけるソフトウェアの側面とダイバーシティ・インクルーシブに関する研究実践動向
デジタルトランスフォーメーション(DX)におけるソフトウェアの側面とダイバーシティ・インクルーシブに関する研究実践動向デジタルトランスフォーメーション(DX)におけるソフトウェアの側面とダイバーシティ・インクルーシブに関する研究実践動向
デジタルトランスフォーメーション(DX)におけるソフトウェアの側面とダイバーシティ・インクルーシブに関する研究実践動向
 
SQuBOKガイドV3概説 ~IoT・AI・DX時代のソフトウェア品質とシステム監査~
SQuBOKガイドV3概説 ~IoT・AI・DX時代のソフトウェア品質とシステム監査~SQuBOKガイドV3概説 ~IoT・AI・DX時代のソフトウェア品質とシステム監査~
SQuBOKガイドV3概説 ~IoT・AI・DX時代のソフトウェア品質とシステム監査~
 
人生100年・60年カリキュラム時代のDX人材育成: スマートエスイー 2021年度成果および2022年度募集
人生100年・60年カリキュラム時代のDX人材育成: スマートエスイー 2021年度成果および2022年度募集人生100年・60年カリキュラム時代のDX人材育成: スマートエスイー 2021年度成果および2022年度募集
人生100年・60年カリキュラム時代のDX人材育成: スマートエスイー 2021年度成果および2022年度募集
 
スマートエスイーコンソーシアムの概要と2021年度成果紹介
スマートエスイーコンソーシアムの概要と2021年度成果紹介スマートエスイーコンソーシアムの概要と2021年度成果紹介
スマートエスイーコンソーシアムの概要と2021年度成果紹介
 
DXの推進において企業内に求められる人材やデジタル人材の育て方
DXの推進において企業内に求められる人材やデジタル人材の育て方DXの推進において企業内に求められる人材やデジタル人材の育て方
DXの推進において企業内に求められる人材やデジタル人材の育て方
 
対応性のある運用のパターン
対応性のある運用のパターン対応性のある運用のパターン
対応性のある運用のパターン
 
モデル訓練のパターン
モデル訓練のパターンモデル訓練のパターン
モデル訓練のパターン
 
パターンのつながりとAI活用成熟度
パターンのつながりとAI活用成熟度パターンのつながりとAI活用成熟度
パターンのつながりとAI活用成熟度
 
データ表現のパターン
データ表現のパターンデータ表現のパターン
データ表現のパターン
 
機械学習デザインパターンの必要性と機械学習ライフサイクル
機械学習デザインパターンの必要性と機械学習ライフサイクル機械学習デザインパターンの必要性と機械学習ライフサイクル
機械学習デザインパターンの必要性と機械学習ライフサイクル
 
青山幹雄先生を偲んで(開拓、理論、実践、コミュニティ&国際)
青山幹雄先生を偲んで(開拓、理論、実践、コミュニティ&国際)青山幹雄先生を偲んで(開拓、理論、実践、コミュニティ&国際)
青山幹雄先生を偲んで(開拓、理論、実践、コミュニティ&国際)
 
Software Engineering Patterns for Machine Learning Applications
Software Engineering Patterns for Machine Learning ApplicationsSoftware Engineering Patterns for Machine Learning Applications
Software Engineering Patterns for Machine Learning Applications
 
機械学習デザインパターンおよび機械学習システムの品質保証の取り組み
機械学習デザインパターンおよび機械学習システムの品質保証の取り組み機械学習デザインパターンおよび機械学習システムの品質保証の取り組み
機械学習デザインパターンおよび機械学習システムの品質保証の取り組み
 

Recently uploaded

Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Recently uploaded (20)

TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
 
Simplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptxSimplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptx
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
Stronger Together: Developing an Organizational Strategy for Accessible Desig...
Stronger Together: Developing an Organizational Strategy for Accessible Desig...Stronger Together: Developing an Organizational Strategy for Accessible Desig...
Stronger Together: Developing an Organizational Strategy for Accessible Desig...
 
Modernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using BallerinaModernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using Ballerina
 
Quantum Leap in Next-Generation Computing
Quantum Leap in Next-Generation ComputingQuantum Leap in Next-Generation Computing
Quantum Leap in Next-Generation Computing
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
API Governance and Monetization - The evolution of API governance
API Governance and Monetization -  The evolution of API governanceAPI Governance and Monetization -  The evolution of API governance
API Governance and Monetization - The evolution of API governance
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and Insight
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Choreo: Empowering the Future of Enterprise Software Engineering
Choreo: Empowering the Future of Enterprise Software EngineeringChoreo: Empowering the Future of Enterprise Software Engineering
Choreo: Empowering the Future of Enterprise Software Engineering
 
Introduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDMIntroduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDM
 
How to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cfHow to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cf
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 

IEEE Computer Society 2024 Technology Predictions Update

  • 1. IEEE Computer Society 2024 Technology Predictions Update Hironori (Hiro) Washizaki IEEE Computer Society 2025 President Waseda University, Professor FM+SE SUMMIT 2024, March 28th 2024
  • 2. IEEE Computer Society: Empowering Computer Science and Engineering Professionals to Fuel Continued Advancement
  • 3. IEEE CS Technology Prediction Team (Chair: Dejan Milojicic)
  • 4. IEEE CS Technology Predictions 2023 4 IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/2023-top-technology-predictions Chance of success higher than impact on humanity Impact on humanity higher than chance of success (worth investing in)
  • 5. Remote Healthcare Wearables Generative AI Disinformation detection/correction AI-assisted DevOps Artificial General Intelligence (AGI) 5 IEEE Computer Society (CS) Global Scientists and Engineers Rank 2023 Technology Trend Predictions https://www.computer.org/press-room/scientists-and-engineers-rank-2023- technology-trend-predictions Remote Healthcare & Wearables Generative AI Disinformation detection/correction AI-assisted DevOps Artificial General Intelligence (AGI) IEEE CS Technology 2023 Prediction vs. Assessment • How original grades stood the one-year test, as reflected in A/B grading • High growth of generative AI, slowdown of COVID- related technologies Prediction Jan 2023 Assessment Dec 2023
  • 6. IEEE CS Technology Prediction Team (Chair: Dejan Milojicic)
  • 7. 7 Megatrends in IEEE Future Direction 2023 and IEEE-CS Technology Predictions 2024 IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions • AGI technologies are deeply entangled with socio, economic, and ecological aspects. Next Gen AI Generative AI applications Metaverse Low power AI accelerator
  • 8. IEEE CS Technology Predictions 2024 Chance of success higher than impact on humanity Impact on humanity higher than chance of success (worth investing in) IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
  • 9. Generative AI • Provides flexibility for new challenges and adaptive responses demand. • Safety and security: Need to build safeguards against misuses and generated harmful content, such as deep fakes. • Lacking Robustness, Reliability, Control, and Explainability, necessitating transparent techniques and consistent AI models. This is a major issue for agents and trusted apps. • Bias and data quality issues in large datasets call for better curation. • High computational costs limit model training to an oligarchy of very few players who can afford to train a foundation model. • Evolving regulatory landscapes, especially regarding data privacy and use, demand for ensuring legal and ethical compliance. • Enhanced creativity in arts and design, accelerated design. • Generative AI-based revolutionized personalized medicine, from drug discovery to tailored treatment plans. • Personalized education and marketing boost productivity. • Improved customer support through natural interactions- conversation, problem solving, detailed product knowledge. • Accelerated scientific discovery and 3D modeling. Problems/demand Opportunities IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
  • 10. • Helps content creators and designers to be more productive. • Helps businesses improve their digital channels and marketing. • Time-To-Market significantly decreased. • Better accessibility: image-to-text, audio transcripts, translations (including to sign language). • Personalized assistants (coding, editing, teaching, etc.) increase productivity and efficiency. • Displacement of repetitive jobs requires reskilling/upskilling. • Spreading of much higher quality misinformation requires source checking and critical thinking. • More environmentally sustainable practices in various industries through optimization of resources and reduction of waste. • Significantly changing traditional Industries—like manufacturing, agriculture, and transportation. • Generative AI may decrease the need for processing- intensive training. • Support for no/low-code. • Generative AI will increase AI adoption and create new revenue streams. • Global cooperation in standardization and best practices to address challenges like intellectual property, cybersecurity, and ethical norms. • Public-Private Partnerships: tighter collaborations between government, academia, and industry. • Increased significance of Edge Computing in processing data closer to the source, reducing latency, and improving response times. • Enablers: New ML approaches, affordable AI tools, open models, access to large curated datasets, AI-integrated agents for automation. • Inhibitors: Threat to content creators and IP holders, difficulty in distinguishing human created content versus machine created, adversarial applications, Ethical questions of GAI versus human content generation, lack of interoperability, closed models, low quality and biased datasets, high compute costs, lack of trust in AI, regulatory burdens and resistance to change. Impact Sustainable Solution/Business Opportunity IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
  • 11. Knowledge Area Topic Topic Reference Material Body of Knowledge Skills Competencies Jobs / Roles SWEBOK Software Engineering Professional Certifications SWECOM EITBOK Learning courses 11 Guide to the Software Engineering Body of Knowledge (SWEBOK) https://www.computer.org/education/bodies-of-knowledge/software-engineering • Guiding researchers and practitioners to identify and have common understanding on “generally-accepted-knowledge” in software engineering • Foundations for certifications and educational curriculum • ‘01 v1, ‘04 v2, ‘05 ISO adoption, ‘14 v3, ’24 v4 soon! SWEBOK Guide V4 (Editor: Hironori Washizaki)
  • 12. Mainframe 70’s – Early 80’s Late 80’s - Early 90’s Late 90’s - Early 00’s Late 00’s - Early 10’s PC, Client & server Internet Ubiquitous computing Late 10’s - Early 20’s IoT, Big data, AI Structured programming Waterfall Formalization Design Program generation Maturity Management Object-oriented Req. eng. Modeling Verification Reuse Model-driven Product-line Global & open Value-based Systems eng. Agile Iterative & incremental DevOps Empirical Data-driven Continuous SE and IoT SE and AI SWEBOK V1 SWEBOK V2 SWEBOK V3 SWEBOK V4
  • 13. SWEBOK Evolution from V3 to V4 • Modern engineering, practice update, BOK grows and recently developed areas Requirements Design Construction Testing Maintenance Configuration Management Engineering Management Process Models and Methods Quality Professional Practice Economics Computing Foundations Mathematical Foundations Engineering Foundations Requirements Architecture Design Construction Testing Operations Maintenance Configuration Management Engineering Management Process Models and Methods Quality Security Professional Practice Economics Computing Foundations Mathematical Foundations Engineering Foundations V3 V4 Agile, DevOps AI for SE, SE for AI Software engineering AI AI for SE SE for AI
  • 14. Mainframe 70’s – Early 80’s Late 80’s - Early 90’s Late 90’s - Early 00’s Late 00’s - Early 10’s PC, Client & server Internet Ubiquitous computing Late 10’s - Early 20’s IoT, Big data, AI GenAI, FM, Autonomous, Quantum, Continuum Late 20’s Structured programming Waterfall Formalization Design Program generation Maturity Management Object-oriented Req. eng. Modeling Verification Reuse Model-driven Product-line Global & open Value-based Systems eng. Agile Iterative & incremental DevOps Empirical Data-driven Continuous SE and IoT SE and AI SE and GenAI SE and QC Sustainability SE for autonomous and continuum AI-assisted DevOps/OpsDev SWEBOK V1 SWEBOK V2 SWEBOK V3 SWEBOK V4
  • 15. 8 Megatrends in IEEE Future Direction 2023 and IEEE-CS Technology Predictions 2024 IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions • We have four core AGI technology predictions compared to eight sustainability-related and nine digital transformation-related predictions. • AGI technologies are deeply entangled with socio, economic, and ecological aspects. IEEE CS Technology Predictions 2024 Chance of success higher than impact on humanity Impact on humanity higher than chance of success (worth investing in) IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions Generative AI • Provides flexibility for new challenges and adaptive responses demand. • Safety and security: Need to build safeguards against misuses and generated harmful content, such as deep fakes. • Lacking Robustness, Reliability, Control, and Explainability, necessitating transparent techniques and consistent AI models. • This is a major issue for agents and trusted apps. • Bias and data quality issues in large datasets call for better curation. • High computational costs limit model training to an oligarchy of very few players who can afford to train a foundation model. • Evolving regulatory landscapes, especially regarding data privacy and use, demand for ensuring legal and ethical compliance. • Enhanced creativity in arts and design, accelerated design. • Generative AI-based revolutionized personalized medicine, from drug discovery to tailored treatment plans. • Personalized education and marketing boost productivity. • Improved customer support through natural interactions- conversation, problem solving, detailed product knowledge. • Accelerated scientific discovery and 3D modeling. Problems/demand Opportunities Mainframe 70’s – Early 80’s Late 80’s - Early 90’s Late 90’s - Early 00’s Late 00’s - Early 10’s PC, Client & server Internet Ubiquitous computing Late 10’s - Early 20’s IoT, Big data, AI GenAI, FM, Autonomous, Quantum, Continuum Late 20’s Structured programming Waterfall Formalization Design Program generation Maturity Management Object-oriented Req. eng. Modeling Verification Reuse Model-driven Product-line Global & open Value-based Systems eng. Agile Iterative & incremental DevOps Empirical Data-driven Continuous SE and IoT SE and AI SE and GenAI SE and QC Sustainability SE for autonomous and continuum AI-assisted DevOps SWEBOK V1 SWEBOK V2 SWEBOK V3 SWEBOK V4