Technology-driven trends will revolutionize how industry players respond to changing consumer behavior, develop partnerships, and drive transformational change.
In this deck from the HPC User Forum, Rick Stevens from Argonne presents: AI for Science.
"Artificial Intelligence (AI) is making strides in transforming how we live. From the tech industry embracing AI as the most important technology for the 21st century to governments around the world growing efforts in AI, initiatives are rapidly emerging in the space. In sync with these emerging initiatives including U.S. Department of Energy efforts, Argonne has launched an “AI for Science” initiative aimed at accelerating the development and adoption of AI approaches in scientific and engineering domains with the goal to accelerate research and development breakthroughs in energy, basic science, medicine, and national security, especially where we have significant volumes of data and relatively less developed theory. AI methods allow us to discover patterns in data that can lead to experimental hypotheses and thus link data driven methods to new experiments and new understanding."
Watch the video: https://wp.me/p3RLHQ-kQi
Learn more: https://www.anl.gov/topic/science-technology/artificial-intelligence
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
Explainable Artificial Intelligence (XAI)
Presented at Lightning Talk session at ICACCI'18 on 20th September 208
An Explainable AI (XAI) or Transparent AI is an artificial intelligence (AI) whose actions can be easily understood by humans. It contrasts with the concept of the "black box" in machine learning, meaning the "interpretability" of the workings of complex algorithms, where even their designers cannot explain why the AI arrived at a specific decision.
https://en.wikipedia.org/wiki/Explainable_Artificial_Intelligence
AI Governance and Ethics - Industry StandardsAnsgar Koene
Presentation on the potential for Ethics based Industry Standards to function as vehicle to address socio-technical challenges from AI.
Presentation given at the the 1st Austrian IFIP forum ono "AI and future society".
AI for Manufacturing (Machine Vision, Edge AI, Federated Learning)byteLAKE
This is the extended presentation about byteLAKE's and Lenovo's Artificial Intelligence solutions for Manufacturing.
Topics covered: AI strategy for manufacturing, Edge AI, Federated Learning and Machine Vision.
It's the first publication in the upcoming series: AI for Manufacturing. Highlights: AI-assisted quality monitoring automation, AI-assisted production line monitoring and issues detection, AI-assisted measurements, Intelligent Cameras and many more. Reach out to us to learn more: welcome@byteLAKE.com.
Presented during the world's first Federated Learning conference (Jun'20). Recording: https://youtu.be/IMqRIi45dDA
Related articles:
- Revolution in factories: Industry 4.0.
https://medium.com/@marcrojek/revolution-in-factories-industry-4-0-conference-made-in-wroclaw-2020-translation-ae96e5e14d55
- Cognitive Automation helps where RPAs fall short.
https://medium.com/@marcrojek/cognitive-automation-helps-where-rpas-fall-short-a1c5a01a66f8
- Machine Vision, how AI brings value to industries.
https://medium.com/@marcrojek/machine-vision-how-ai-brings-value-to-industries-e6a4f8e56f42
Learn more:
- https://www.bytelake.com/en/cognitive-services/
- https://www.lenovo.com/ai
- https://federatedlearningconference.com/
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
In this deck from the HPC User Forum, Rick Stevens from Argonne presents: AI for Science.
"Artificial Intelligence (AI) is making strides in transforming how we live. From the tech industry embracing AI as the most important technology for the 21st century to governments around the world growing efforts in AI, initiatives are rapidly emerging in the space. In sync with these emerging initiatives including U.S. Department of Energy efforts, Argonne has launched an “AI for Science” initiative aimed at accelerating the development and adoption of AI approaches in scientific and engineering domains with the goal to accelerate research and development breakthroughs in energy, basic science, medicine, and national security, especially where we have significant volumes of data and relatively less developed theory. AI methods allow us to discover patterns in data that can lead to experimental hypotheses and thus link data driven methods to new experiments and new understanding."
Watch the video: https://wp.me/p3RLHQ-kQi
Learn more: https://www.anl.gov/topic/science-technology/artificial-intelligence
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
Explainable Artificial Intelligence (XAI)
Presented at Lightning Talk session at ICACCI'18 on 20th September 208
An Explainable AI (XAI) or Transparent AI is an artificial intelligence (AI) whose actions can be easily understood by humans. It contrasts with the concept of the "black box" in machine learning, meaning the "interpretability" of the workings of complex algorithms, where even their designers cannot explain why the AI arrived at a specific decision.
https://en.wikipedia.org/wiki/Explainable_Artificial_Intelligence
AI Governance and Ethics - Industry StandardsAnsgar Koene
Presentation on the potential for Ethics based Industry Standards to function as vehicle to address socio-technical challenges from AI.
Presentation given at the the 1st Austrian IFIP forum ono "AI and future society".
AI for Manufacturing (Machine Vision, Edge AI, Federated Learning)byteLAKE
This is the extended presentation about byteLAKE's and Lenovo's Artificial Intelligence solutions for Manufacturing.
Topics covered: AI strategy for manufacturing, Edge AI, Federated Learning and Machine Vision.
It's the first publication in the upcoming series: AI for Manufacturing. Highlights: AI-assisted quality monitoring automation, AI-assisted production line monitoring and issues detection, AI-assisted measurements, Intelligent Cameras and many more. Reach out to us to learn more: welcome@byteLAKE.com.
Presented during the world's first Federated Learning conference (Jun'20). Recording: https://youtu.be/IMqRIi45dDA
Related articles:
- Revolution in factories: Industry 4.0.
https://medium.com/@marcrojek/revolution-in-factories-industry-4-0-conference-made-in-wroclaw-2020-translation-ae96e5e14d55
- Cognitive Automation helps where RPAs fall short.
https://medium.com/@marcrojek/cognitive-automation-helps-where-rpas-fall-short-a1c5a01a66f8
- Machine Vision, how AI brings value to industries.
https://medium.com/@marcrojek/machine-vision-how-ai-brings-value-to-industries-e6a4f8e56f42
Learn more:
- https://www.bytelake.com/en/cognitive-services/
- https://www.lenovo.com/ai
- https://federatedlearningconference.com/
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
How Azure helps to build better business processes and customer experiences w...Maxim Salnikov
Artificial Intelligence is not the future, it is NOW. Cloud technology empowers developers and technology leaders to benefit from AI effectively and responsibly with the models and tools they need. In this session, we go through the portfolio of Azure AI services and run some demos to showcase how AI can improve daily life, safety, productivity, accessibility, and business outcomes.
Artificial Intelligence Bill of Rights: Impacts on AI GovernanceTrustArc
Artificial Intelligence (AI) is increasingly being used to make decisions that impact individuals and society as a whole. As the use of AI continues to grow, there is a need to establish guidelines and regulations to ensure that it is being used responsibly and ethically.
In October 2022, the White House Office of Science and Technology Policy (OSTP) published a Blueprint for an AI Bill of Rights (“Blueprint”), which shared a nonbinding roadmap for the responsible use of artificial intelligence (AI). In this webinar, we will examine the key principles that underpin the bill, such as transparency, accountability, and fairness, and discuss how they can help ensure that the use of AI aligns with the values and rights of individuals.
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more. This class is freshly updated for 2023 and also includes a section on the bias inherent in AI, which impacts the kind of treatment that patients receive.
The numbers tell the story: 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics?
To answer that question, Accenture conducted a landmark global study involving 1,500 C-suite executives from organizations across 16 industries. The aim: Help companies progress on their AI journey, from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.
Read the full report:
http://www.accenture.com/AI-Built-to-Scale-Slideshare
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
Connected & Autonomous vehicles: cybersecurity on a grand scale v1Bill Harpley
A presentation which was given at 'How the Internet of Things is Changing Cyber Security - an event organised by Optimise Hub (Portsmouth University) on January 26th 2017 at Havant.
- This talk describes the issues relating to cybersecurity of Connected Cars and Autonomous Vehicles. It begins with an introduction to technology and standards. It then looks at the key security challenges and asks how prepared we are to deal with the future risks.
- It is a perfect case study in the challenge of achieving cybersecurity on a massive scale.
Artificial Intelligence In The Automotive Industry - M&A Trend AnalysisNetscribes
Artificial Intelligence (AI) is redefining the automotive industry. Organizations in the automotive industry are realizing the need to leverage advanced algorithms and computational structures, innovative testing and validation platforms, integrated cockpit solutions, and 5G network adoption and application deployment for building their next generation mobility services. As a result, mergers and acquisitions focused on acquiring AI capabilities is on the rise in auto sector.
The report provides a detailed analysis of more than 60 AI-focused deals in the auto sector over the last 10 years. Understand the specific AI technologies and capabilities that are high in demand, deal sizes, and the strategies driving those partnerships.
To purchase the full report, write to us at info@netscribes.com
Tech Trends 2024 and Beyond - AI and VR and MOreBrian Pichman
Join Brian Pichman, the tech geek from the Evolve Project, in a
jolly tech-filled sleigh ride through the hottest trends that'll make
this holiday season merrier for librarians! From digital AI elves
to magical augmented reality, this fun-packed presentation will
unwrap the tech wonders that'll keep libraries ahead of the
game in the North Pole of innovation. Don't miss out on the
holiday cheer and the chance to sprinkle some digital snow on
your library's future!
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
Automotive Revolution Perspective Towards 2030Stradablog
McKinsey & Company’s comprehensive research into the disruptions most likely to affect the automotive space through 2030 identifies eight key trends that could trigger an industry revolution. From innovative mobility plays to new industry entrants, these trends signal a changing of the guard that could shatter current business models and ways of doing things.
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
How Azure helps to build better business processes and customer experiences w...Maxim Salnikov
Artificial Intelligence is not the future, it is NOW. Cloud technology empowers developers and technology leaders to benefit from AI effectively and responsibly with the models and tools they need. In this session, we go through the portfolio of Azure AI services and run some demos to showcase how AI can improve daily life, safety, productivity, accessibility, and business outcomes.
Artificial Intelligence Bill of Rights: Impacts on AI GovernanceTrustArc
Artificial Intelligence (AI) is increasingly being used to make decisions that impact individuals and society as a whole. As the use of AI continues to grow, there is a need to establish guidelines and regulations to ensure that it is being used responsibly and ethically.
In October 2022, the White House Office of Science and Technology Policy (OSTP) published a Blueprint for an AI Bill of Rights (“Blueprint”), which shared a nonbinding roadmap for the responsible use of artificial intelligence (AI). In this webinar, we will examine the key principles that underpin the bill, such as transparency, accountability, and fairness, and discuss how they can help ensure that the use of AI aligns with the values and rights of individuals.
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more. This class is freshly updated for 2023 and also includes a section on the bias inherent in AI, which impacts the kind of treatment that patients receive.
The numbers tell the story: 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics?
To answer that question, Accenture conducted a landmark global study involving 1,500 C-suite executives from organizations across 16 industries. The aim: Help companies progress on their AI journey, from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.
Read the full report:
http://www.accenture.com/AI-Built-to-Scale-Slideshare
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
Connected & Autonomous vehicles: cybersecurity on a grand scale v1Bill Harpley
A presentation which was given at 'How the Internet of Things is Changing Cyber Security - an event organised by Optimise Hub (Portsmouth University) on January 26th 2017 at Havant.
- This talk describes the issues relating to cybersecurity of Connected Cars and Autonomous Vehicles. It begins with an introduction to technology and standards. It then looks at the key security challenges and asks how prepared we are to deal with the future risks.
- It is a perfect case study in the challenge of achieving cybersecurity on a massive scale.
Artificial Intelligence In The Automotive Industry - M&A Trend AnalysisNetscribes
Artificial Intelligence (AI) is redefining the automotive industry. Organizations in the automotive industry are realizing the need to leverage advanced algorithms and computational structures, innovative testing and validation platforms, integrated cockpit solutions, and 5G network adoption and application deployment for building their next generation mobility services. As a result, mergers and acquisitions focused on acquiring AI capabilities is on the rise in auto sector.
The report provides a detailed analysis of more than 60 AI-focused deals in the auto sector over the last 10 years. Understand the specific AI technologies and capabilities that are high in demand, deal sizes, and the strategies driving those partnerships.
To purchase the full report, write to us at info@netscribes.com
Tech Trends 2024 and Beyond - AI and VR and MOreBrian Pichman
Join Brian Pichman, the tech geek from the Evolve Project, in a
jolly tech-filled sleigh ride through the hottest trends that'll make
this holiday season merrier for librarians! From digital AI elves
to magical augmented reality, this fun-packed presentation will
unwrap the tech wonders that'll keep libraries ahead of the
game in the North Pole of innovation. Don't miss out on the
holiday cheer and the chance to sprinkle some digital snow on
your library's future!
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
Automotive Revolution Perspective Towards 2030Stradablog
McKinsey & Company’s comprehensive research into the disruptions most likely to affect the automotive space through 2030 identifies eight key trends that could trigger an industry revolution. From innovative mobility plays to new industry entrants, these trends signal a changing of the guard that could shatter current business models and ways of doing things.
Get Automotive Smart - Automotive Futuresemmersons1
The automotive industry is ramping up to a period of transformation. But what does the future look like, and what do the predicted changes mean for existing players?
With increasingly autonomous capabilities and a declining interest in ownership, the auto industry needs to focus on in-transit innovation, purpose-driven design and a transition to a service-based business model.
Cars with access to the Internet, also known as connected cars, are gaining popularity in the automobile industry. Download the Special Report by Aranca here!
Connected cars a rising trend in the global automobile sectorAranca
Connected cars a rising trend in the global automobile sector.Find special reports on industries, latest innovations & technology trends, business analysis, intellectual property & patent industry & other knowledge reports created by Aranca, a global provider of outsourced research & analytics services firm & a trusted research partner for various global clients.
No Hands: The Autonomous Future of TruckingCognizant
The impacts of autonomous trucking will reverberate far beyond the trucking industry. As members of the workforce, public policy proponents, technology strategists and business leaders grapple with the technological, economic and cultural fall-out of self-driving trucks, what happens next could serve as a template for other fields influenced by AI.
Connected and Self-Driving Vehicles Spark Industry ConvergenceRay Pun
We live in a connected world where industries and organizations are partnering to serve the consumer. Organizations are responding to major trends including the Internet of Things, urban mobility and the automotive revolution that will lead to:
• More than 190 million connected vehicles by 2021.
• Massive growth in big data.
• Ready or not, self-driving vehicles are coming.
As part of a J.D. Power and Acxiom research study, consumers were asked to share their opinions about self-driving and connected vehicles.
Transportation 2050 | The future of personal mobilityIdeafarms
A Delphi white paper in collaboration with Ideafarm s. Transportation 2050 presents a radical view of how personal mobility and Mobility as a Service (MaaS) will alter the landscape for transportation in ways that will be as disruptive to the automobile as the automobile was to the horse and buggy.
From electric cars to autonomous vehicles; and from entertainment on-the-go to vehicles that are semi-legal entities, Transportation 2050 provides a future view of mobility that offers a sustainable model for industry and the planet, while changing some of the most fundamental notions we have of the automobile's role, the business model for personal transportation, automobile ownership, and personal mobility.
Study HERE SBD - How autonomous vehicles could relieve or worsen traffic cong...Ludovic Privat
The utopian vision of autonomous cars and a world where traffic issues are null is a generation away, according to this whitepaper from HERE and SBD research, which also asserts that advancement in autonomous vehicles will be gridlocked without the cooperation of all stakeholders.
In the past decade, auto manufacturers have installed various technologies designed to make cars safer, more responsive, and more pleasurable to drive. From the hands-free cellphone, to iPod berths, to satellite radio, to automated parking—not to mention Google’s self-driving vehicle—the automobile is undergoing an electronic overhaul that promises to transform its role for consumers. What once was perceived as personal transportation is fast evolving into a new mobile device, merging with the digital world into an all-encompassing communications environment.
This ongoing transformation is poised to shift into high gear as cars display still greater connectivity and broader capabilities than ever. What makes this shift different from the way automobiles adopted new technologies in the past is that this time, automakers may have to consider how they can quickly merge consumer electronics and software with their traditional automotive systems.
Autonomous vehicles: Plotting a route to the driverless futureAccenture Insurance
How will roadways dominated by high or fully automated vehicles impact future industries, economies and populations? What shifts in leverage and underlying business models are imminent? What new pathways for ecosystem innovation might arise from the data explosion that comes with AV proliferation?
The answers to these questions can be revealed by examining the immediate impact of AV adoption on three industry segments: automotive sales and service; logistics and supply chains; and auto insurance.
WHAT IS THE COX AUTOMOTIVE INSIGHT REPORT?
How will the new and used car markets perform during the rest of this year? What will become the future fuel of choice? What are the barriers as we drive towards Mobility as a Service (MaaS)?
In this first annual Insight Report from Cox Automotive and Grant Thornton, we go beyond the headlines to provide our view on the future of our market, and what it means for us all.
Similar to Disruptive Trends That Will Transform The Automotive Industry (20)
Automotive companies have started to respond to changing customer buying behavior by piloting new online business models. However, most current initiatives are still removed from what customers expect.
Human drivers have always been an essential requirement in the operation of a motor vehicle. At the same
time, research has repeatedly demonstrated that driver error plays a role in more than 90% of road crashes
(NHTSA 2008; Blanco et al. 2016). As such, in the past two decades, vehicle manufacturers have designed new
and increasingly sophisticated features that provide more assistance to drivers to help mitigate such errors. Such
features are an important precursor to the development of automated vehicles and, currently, expectations are
high that the advent of semi- or fully- automated vehicles will dramatically reduce road crashes
The race is on
Clearly, Canadian executives are feeling that the race is on; but it remains to be seen whether they act quickly enough and with the right focus to effectively transform and evolve. Among our findings:
75 percent of CEOs agree that the next three years will be more critical to their industry than the previous 50 years;
74 percent of CEOs believe their company will remain largely the same in the next 3 years;
98 percent are concerned about the loyalty of customers;
13 percent feel confident that they are fully prepared for a cyber-event.
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𝘼𝙣𝙩𝙞𝙦𝙪𝙚 𝙋𝙡𝙖𝙨𝙩𝙞𝙘 𝙏𝙧𝙖𝙙𝙚𝙧𝙨 𝙞𝙨 𝙫𝙚𝙧𝙮 𝙛𝙖𝙢𝙤𝙪𝙨 𝙛𝙤𝙧 𝙢𝙖𝙣𝙪𝙛𝙖𝙘𝙩𝙪𝙧𝙞𝙣𝙜 𝙩𝙝𝙚𝙞𝙧 𝙥𝙧𝙤𝙙𝙪𝙘𝙩𝙨. 𝙒𝙚 𝙝𝙖𝙫𝙚 𝙖𝙡𝙡 𝙩𝙝𝙚 𝙥𝙡𝙖𝙨𝙩𝙞𝙘 𝙜𝙧𝙖𝙣𝙪𝙡𝙚𝙨 𝙪𝙨𝙚𝙙 𝙞𝙣 𝙖𝙪𝙩𝙤𝙢𝙤𝙩𝙞𝙫𝙚 𝙖𝙣𝙙 𝙖𝙪𝙩𝙤 𝙥𝙖𝙧𝙩𝙨 𝙖𝙣𝙙 𝙖𝙡𝙡 𝙩𝙝𝙚 𝙛𝙖𝙢𝙤𝙪𝙨 𝙘𝙤𝙢𝙥𝙖𝙣𝙞𝙚𝙨 𝙗𝙪𝙮 𝙩𝙝𝙚 𝙜𝙧𝙖𝙣𝙪𝙡𝙚𝙨 𝙛𝙧𝙤𝙢 𝙪𝙨.
Over the 10 years, we have gained a strong foothold in the market due to our range's high quality, competitive prices, and time-lined delivery schedules.
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Disruptive Trends That Will Transform The Automotive Industry
1. 1/9 http://autoassembly.mckinsey.com McKinsey & Company, Inc.
Disruptive Trends that will transform the
Automotive Industry
Technology-driven trends will revolutionize how industry players respond
to changing consumer behavior, develop partnerships, and drive
transformational change.
by Paul Gao, Hans-Werner Kaas, Detlev Mohr, and Dominik Wee
Today’s economies are changing dramatically, triggered by development in
emerging markets, the accelerated rise of new technologies, sustainability
policies, and changing consumer preferences around the ownership of everything
from homes to cars. Digitization, increasing automation, and new business
models have revolutionized other industries, and automotive will be no exception.
These forces are giving rise to four disruptive technology-driven trends in the
automotive sector: diverse mobility, autonomous driving, electrification, and
connectivity.
Defining eight key industry disruptions
Most industry players and experts agree that the four trends will reinforce and
accelerate one another, and that the automotive industry is ripe for disruption.
Given the widespread understanding that game-changing disruption is already on
the horizon, there is still no integrated perspective on how these trends will
change the industry in 10 to 15 years.
To that end, the following eight key perspectives on the “2030 automotive
revolution” provide scenarios regarding the kinds of changes ahead, and their
effect on traditional vehicle manufacturers and suppliers, potential new players,
regulators, consumers, markets, and the automotive value chain. This study aims
to make the imminent changes more tangible. The forecasts represent a
projection of the most probable assumptions across all four trends, based on
current understanding. They should help industry players to prepare for
uncertainties better by revealing potential future states.
1) Driven by shared mobility, connectivity services, and feature upgrades,
new business models could expand automotive revenue pools by about 30
percent, adding up to $1.5 trillion in value.
2. 2/9 http://autoassembly.mckinsey.com McKinsey & Company, Inc.
The automotive revenue pool will significantly increase and diversify toward on-
demand mobility and data-driven services. This could create up to $1.5 trillion—
or 30 percent more—in additional revenue potential in 2030, compared with
about $5.2 trillion from traditional car sales and aftermarket products and
services, which itself is 50 percent increase from the approximately $3.5 trillion
the industry generated in 2015 (Exhibit 1).
Exhibit 1: Automotive revenues will expand into mobility and data services
Connectivity, and later autonomous technology, will increasingly turn the
automobile into a platform for drivers and passengers to use their time in transit
to consume infotainment services and engage in other personal activities. The
increasing speed of innovation, especially in software-based systems, will make
upgradability a key feature of future cars. As shared mobility solutions with
shorter life cycles become commonplace, consumers will constantly seek
technological advances, which will further increase demand for upgradability in
privately used cars as well.
3. 3/9 http://autoassembly.mckinsey.com McKinsey & Company, Inc.
2) Despite a shift toward shared mobility, vehicle unit sales will continue to
grow, but likely at a lower rate of about 2 percent per year.
Overall, global car sales will continue to grow, but the annual growth rate will
likely drop from the 3.6 percent over the last five years to around 2 percent by
2030. Macroeconomic factors will largely drive this decline, along with the rise of
new mobility services such as car sharing and e-hailing.
A detailed analysis suggests that dense areas with a large, established vehicle
base are fertile ground for new mobility services, and many cities and suburbs in
Europe and North America fit this profile. New mobility services may result in a
decline of private-vehicle sales, but this decline is likely to be offset by increased
sales in shared vehicles that need to be replaced more often due to higher
utilization and related wear and tear.
The remaining driver of growth in global car sales is the overall positive
macroeconomic development, including the rise of the global consumer middle
class. With established markets slowing their expansion, however, growth will
continue to rely on emerging economies, particularly China, while product-mix
differences will explain different development of revenues.
3) Consumer mobility behavior is changing, with the potential for one in ten
cars sold in 2030 being a shared vehicle and the subsequent rise of a
market for fit-for-purpose mobility solutions.
Changing consumer preferences, tightening regulation, and technological
breakthroughs add up to a fundamental shift in individual mobility behavior.
Consumers increasingly use multiple modes of transportation to complete their
journey; consumers have goods and services delivered rather than picking them
up personally. As a result, the traditional car sales business model will become
but one of a range of diverse, on-demand mobility solutions, especially in dense
urban environments that proactively discourage private-car use.
Consumers today use their cars as all-purpose vehicles, whether they are
commuting alone to work or taking the whole family to the beach. In the future,
they may want the flexibility to choose the best solution for a specific purpose, on
demand and via their smartphones. Early signs are already emerging that the
importance of private-car ownership is declining. In the United States, for
example, the share of young people (16 to 24 years of age) who hold a driver’s
license dropped from 76 percent in 2000 to 71 percent in 2013. At the same time,
the number of car-sharing members in North America and Germany has
increased by more than 30 percent annually over the last five years.
4. 4/9 http://autoassembly.mckinsey.com McKinsey & Company, Inc.
Consumers’ new habit of using tailored solutions for each purpose will lead to
new segments of specialized vehicles designed for very specific needs. For
example, the market for a car specifically built for e-hailing services—that is, a
car designed for high utilization, robustness, additional mileage, and passenger
comfort—would already be millions of units today, and this is just the beginning.
Because of this shift to diverse mobility solutions, up to one out of ten new cars
sold in 2030 will likely be a shared vehicle, which could reduce the sales of
private-use vehicles. This would mean that more than 30 percent of the miles
driven in new cars sold could be from shared mobility. On this trajectory, one out
of three new cars sold could potentially be a shared vehicle as soon as 2050.
4) City type will replace country or region as the most relevant
segmentation dimension that determines mobility behavior and, thus, the
speed and scope of the automotive revolution.
Understanding where future business opportunities lie requires a more granular
view of mobility markets than ever before. Specifically, marketers need to
segment these markets by city types based primarily on their population density,
economic development, and prosperity. Across those segments, consumer
preferences, policy and regulation, and the availability and price of new business
models will strongly diverge. In megacities such as London, for example, car
ownership is already becoming a burden for many due to congestion fees, a lack
of parking, traffic jams, and other issues. By contrast, in rural areas such as the
state of Iowa in the United States, private-car usage will remain the preferred
means of transport by far.
The type of city will thus become the key indicator for mobility behavior, replacing
the traditional regional perspective on the mobility market. By 2030, the car
market in New York will likely have much more in common with the market in
Shanghai than the one in Kansas.
5) Once technological and regulatory issues have been resolved, up to 15
percent of new cars sold in 2030 could be fully autonomous.
Fully autonomous vehicles are unlikely to be commercially available before 2020.
Meanwhile, advanced driver-assistance systems (ADAS) will play a crucial role in
preparing regulators, consumers, and corporations for the medium-term reality of
cars taking over control from drivers.
The market introduction of ADAS has shown that the primary challenges
impeding faster market penetration are pricing, consumer understanding, and
safety/security issues. Regarding technological readiness, tech players and start-
5. 5/9 http://autoassembly.mckinsey.com McKinsey & Company, Inc.
ups will likely also play an important role in the development of autonomous
vehicles. Regulation and consumer acceptance may represent additional hurdles
for autonomous vehicles. However, once the industry addresses these
challenges, autonomous vehicles will offer tremendous value for consumers (for
example, the ability to work while commuting, or the convenience of using social
media or watching movies while traveling).
A progressive scenario would see fully autonomous cars accounting for up to 15
percent of passenger vehicles sold worldwide in 2030 (Exhibit 2).
Exhibit 2: The percentage of fully autonomous cars in 2030
6. 6/9 http://autoassembly.mckinsey.com McKinsey & Company, Inc.
6) Electrified vehicles are becoming viable and competitive; however, the
speed of their adoption will vary strongly at the local level.
Stricter emission regulations, lower battery costs, more widely available charging
infrastructure, and increasing consumer acceptance will create new and strong
momentum for the penetration of “electrified vehicles” (e.g., hybrid, plug-in,
battery electric, and fuel cell) in the coming years. The speed of adoption will
depend on the interaction of consumer pull (partially driven by total cost of
ownership) and regulatory push, which will vary strongly at the regional and local
level.
In 2030, the share of electrified vehicles could range from 10 percent to 50
percent of new-vehicle sales. Adoption rates will be highest in developed,
densely populated cities with strict emission regulations and consumer incentives
(e.g., tax breaks, special parking and driving privileges, and discounted electricity
pricing). Sales penetration will be slower in small towns and rural areas with
lower levels of charging infrastructure and higher dependency on driving range.
Through continuous improvements in battery technology and cost, local
differences will diminish, and electrified vehicles should gain more market share
from conventional vehicles. With battery costs potentially decreasing to $150 to
$200 per kilowatt-hour over the next decade, electrified vehicles will achieve cost
competitiveness with conventional vehicles, creating the most significant catalyst
for market penetration. At the same time, electrified vehicles include a large
portion of hybrid electrics, which means that even beyond 2030, the internal-
combustion engine will remain very relevant.
7) Within a more complex and diversified mobility-industry landscape,
incumbent players will compete simultaneously on multiple fronts and
cooperate with competitors.
While other industries, such as telecommunications or mobile phones/handsets,
have already been disrupted, the automotive industry has seen very little change
and consolidation so far. For example, only two new players have appeared on
the list of the top-15 automotive original-equipment manufacturers (OEMs) in the
last 15 years, compared with ten new players in the handset industry.
A paradigm shift to mobility as a service, along with new entrants, will inevitably
force traditional car manufacturers to compete on multiple fronts. Mobility
providers, tech giants, and specialty OEMs increase the complexity of the
competitive landscape. Traditional automotive players that are under continuous
7. 7/9 http://autoassembly.mckinsey.com McKinsey & Company, Inc.
pressure to reduce costs, improve fuel efficiency, reduce emissions, and become
more capital-efficient will feel the squeeze, likely leading to shifting market
positions in the evolving automotive and mobility industries, potentially driving
consolidation or new forms of partnerships among incumbent players.
In another game-changing development, software competence is increasingly
becoming one of the most important differentiating factors for various domain
areas, including ADAS/active safety, connectivity, and infotainment. Going
forward, as cars increasingly join the connected world, automakers will have no
choice but to participate in the new mobility ecosystems that emerge because of
technological and consumer trends.
8) New market entrants will likely initially target only specific, economically
attractive segments and activities along the value chain before potentially
exploring other opportunities.
Diverging markets will open opportunities for new players, which will initially focus
on a few selected steps along the value chain and target only specific,
economically attractive market segments—and then expand from there. While a
number of current high-tech player are currently generating significant amounts
of interest, they probably represent just the tip of the iceberg. Many additional
new players are likely to enter the market, especially cash-rich high-tech
companies and start-ups. These new entrants from outside the industry are also
wielding more influence with consumers and regulators (that is, generating
interest around new mobility forms and lobbying for favorable regulation of new
technologies). Similarly, some Chinese car manufacturers, with impressive sales
growth recently, might leverage the ongoing disruptions to play an important role
globally.
Preparing for change
Automotive incumbents can’t predict the future of the industry with certainty.
However, they can make strategic moves now to shape its evolution. To get
ahead of inevitable disruptions, incumbent players need to implement a four-
pronged strategic approach:
1. Prepare for uncertainty. Success in 2030 will require automotive players
to shift to a continuous process of anticipating new market trends,
exploring alternatives and complements to the traditional business model,
and exploring new mobility business models and their economic and
consumer viability. This will require a sophisticated degree of scenario
planning and agility to identify and scale new attractive business models.