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Keynotes
Sigrid Eldh (Ericsson)
Testing software in the DevOps and AI/ML era
Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and ‘open’ information. Localized models can make your company’s information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?
In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction – which shows the difficulty of determining a precise and correct result.
Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation – integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development. Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that’s being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.
Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She’s passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She’s also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she’s the editor-in-chief for IEEE Software.
Robert Engels (Capgemini AI Lab)
a guide to not getting fooled by AI
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Martin van den Brink (ASML)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Confirmed speakers
Wytse Oortwijn (TNO-ESI) & Sjoerd Zwart (VDL ETG)
Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and ‘open’ information. Localized models can make your company’s information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?
In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction – which shows the difficulty of determining a precise and correct result.
Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation – integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development. Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that’s being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.
Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She’s passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She’s also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she’s the editor-in-chief for IEEE Software.
Maaike van Leuken (TNO)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Laurie Bax (VBTI)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Mahnaz Shokrpour (Canon Production Printing)
Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and ‘open’ information. Localized models can make your company’s information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?
In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction – which shows the difficulty of determining a precise and correct result.
Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation – integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development. Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that’s being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.
Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She’s passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She’s also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she’s the editor-in-chief for IEEE Software.
Artiom van den Broek (Ministry of Defense)
Filip Slijkhuis (Thales)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Bas Beuting (Cordis) & Niels Brouwers (Capgemini Engineering)
Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and ‘open’ information. Localized models can make your company’s information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?
In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction – which shows the difficulty of determining a precise and correct result.
Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation – integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development. Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that’s being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.
Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She’s passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She’s also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she’s the editor-in-chief for IEEE Software.
Nan Yang (TNO-ESI)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Klaus Lambertz (Verifysoft)
Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and ‘open’ information. Localized models can make your company’s information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?
In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction – which shows the difficulty of determining a precise and correct result.
Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation – integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development. Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that’s being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.
Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She’s passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She’s also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she’s the editor-in-chief for IEEE Software.
Oliver Dengiz (Hitex)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Elise van der Wielen (Alten)
Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and ‘open’ information. Localized models can make your company’s information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?
In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction – which shows the difficulty of determining a precise and correct result.
Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation – integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development. Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that’s being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.
Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She’s passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She’s also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she’s the editor-in-chief for IEEE Software.
Annekoos Schaap (Itility)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Arlette van Wissen (Philips)
Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and ‘open’ information. Localized models can make your company’s information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?
In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction – which shows the difficulty of determining a precise and correct result.
Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation – integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development. Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that’s being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.
Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She’s passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She’s also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she’s the editor-in-chief for IEEE Software.
Ana-Lucia Varbanescu (UT)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Sezen Acur & Bram van der Sanden (TNO-ESI)
Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and ‘open’ information. Localized models can make your company’s information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?
In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction – which shows the difficulty of determining a precise and correct result.
Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation – integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development. Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that’s being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.
Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She’s passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She’s also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she’s the editor-in-chief for IEEE Software.
Leon Bouwmeester (Signify)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Shari Finner & Thomas Rooijakkers (TNO)
Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and ‘open’ information. Localized models can make your company’s information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?
In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction – which shows the difficulty of determining a precise and correct result.
Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation – integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development. Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that’s being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.
Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She’s passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She’s also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she’s the editor-in-chief for IEEE Software.
Steven van der Vlugt (Astron) & Tawfeeq Ahmad (iWave)
AI is like a teenager – it’s always promising more than it can deliver. But as someone who’s been in the AI game for three decades, I’ve learned to separate the hype from the reality. I’ve seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I’ve seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I’ve also seen the incredible potential of AI to transform our lives and businesses.
In this talk, I’ll give you a no-nonsense look at what AI can and can’t do, and what we can expect from the future. I’ll share my hard-won insights on what works, what doesn’t and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let’s cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.
Robert Engels is an AI veteran who’s been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he’s spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he’s not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he’s not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he’s the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.
Program
- Generative AI
- Machine learning
- Software quality
- System architecture
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- 12 tickets (stand crew included)
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- Floor space of 8 m2 (4×2 m) + high table
- 8 tickets (stand crew included)
- Participant list
- Your logo on event promotions (advertisement, website, mailings)*
- Workstation (high table + max. 1 banner) of 4 m2 (2×2 m)
- 4 tickets (stand crew included)
- Participant list
- Your logo on event promotions (advertisement, website, mailings)*
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