talk-data.com

Fairness and inclusivity are critical challenges as AI systems influence decisions in healthcare, finance, and everyday life. Yet, most fairness frameworks are developed in limited contexts, often overlooking the data diversity needed for global reliability.

In this talk, Tito Osadebey shares lessons from his research on bias in computer vision models to highlight where fairness efforts often fall short and how data professionals can address these gaps. He’ll outline practical principles for building and evaluating inclusive AI systems, discuss pitfalls that lead to hidden biases, and explore what “fairness” really means in practice.

Tito Osadebey is an AI researcher and data scientist whose work focuses on fairness, inclusivity, and ethical representation in AI systems. He recently published a paper on bias in computer vision models using Nigerian food images, which examines how underrepresentation of the Global South affects model performance and trust.

Tito has contributed to research and industry projects spanning computer vision, NLP, GenAI and data science with organisations including Keele University, Synectics Solutions, and Unify. His work has been featured on BBC Radio, and he led a team from Keele University which secured 3rd place globally at the 2025 IEEE MetroXraine Forensic Handwritten Document Analysis Challenge.

He is passionate about making AI systems more inclusive, context-aware, and equitable bridging the gap between technical innovation and human understanding.

Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event!

When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern

Register for the Zoom:

https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/

Interpretable AI Models in Radiology

AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.

About the Speaker

Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.

Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology

Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine.

About the Speakers

Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications.

Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good.

Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models

In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks.

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Exploring Instance Imbalance in Medical Semantic Segmentation

Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation.

About the Speaker

Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.

Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event!

When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern

Register for the Zoom:

https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/

Interpretable AI Models in Radiology

AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.

About the Speaker

Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.

Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology

Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine.

About the Speakers

Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications.

Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good.

Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models

In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks.

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Exploring Instance Imbalance in Medical Semantic Segmentation

Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation.

About the Speaker

Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.

Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event!

When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern

Register for the Zoom:

https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/

Interpretable AI Models in Radiology

AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.

About the Speaker

Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.

Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology

Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine.

About the Speakers

Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications.

Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good.

Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models

In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks.

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Exploring Instance Imbalance in Medical Semantic Segmentation

Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation.

About the Speaker

Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.

Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event!

When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern

Register for the Zoom:

https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/

Interpretable AI Models in Radiology

AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.

About the Speaker

Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.

Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology

Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine.

About the Speakers

Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications.

Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good.

Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models

In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks.

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Exploring Instance Imbalance in Medical Semantic Segmentation

Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation.

About the Speaker

Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.

When May 8, 2024 – 10:00 AM Pacific / 1:00 PM Eastern

Where Virtual / Zoom: https://voxel51.com/computer-vision-events/may-8-2024-ai-machine-learning-data-science-meetup/

To Infer or To Defer: Hazy Oracles in Human+AI Collaboration

This talk explores the evolving dynamics of human+AI collaboration, focusing on the concept of the human as a “hazy oracle” rather than an infallible source. It outlines the journey of integrating AI systems more deeply into practical applications through human+AI cooperation, discussing the potential value and challenges. The discussion includes the modeling of interaction errors and the strategic choices between immediate AI inference or seeking additional human input, supported by results from a user study on optimizing these collaborations.

About the Speaker

Jason Corso is a Professor of Robotics, Electrical Engineering, and Computer Science at the University of Michigan, and Co-Founder / Chief Scientist at AI startup Voxel51. His research spans computer vision, robotics, and AI, with over 150 peer-reviewed publications.

From Research to Industry: Bridging Real-World Applications with Anomalib at the CVPR VAND Challenge

This talk highlights the role of Anomalib, an open-source deep learning framework, in advancing anomaly detection within AI systems, particularly showcased at the upcoming CVPR Visual Anomaly and Novelty Detection (VAND) workshop. Anomalib integrates advanced algorithms and tools to facilitate both academic research and practical applications in sectors like manufacturing, healthcare, and security. It features capabilities such as experiment tracking, model optimization, and scalable deployment solutions. Additionally, the discussion will include Anomalib’s participation in the VAND challenge, focusing on robust real-world applications and few-shot learning for anomaly detection.

About the Speaker

Samet Akcay, an AI research engineer and a tech lead, specializes in semi/self-supervised, zero/few-shot anomaly detection, and multi-modality. He is recently known for his open-source contributions to the ML/DL community. He is the lead author of anomalib, a major open-source anomaly detection library. He also maintains the OpenVINO Training Extensions, a low-code transfer learning framework for building computer vision models.

Learning Robot Perception and Control using Vision with Action

To achieve general utility, robots must continue to learn in unstructured environments. In this talk, I describe how our mobile manipulation robot uses vision with action to 1) learn visual control, 2) annotate its own training data, and 3) learn to estimate depth for new objects and the environment. Using these techniques, I describe how I led a small group to win consecutive robot competitions against teams from Stanford, MIT, and other Universities.

About the Speaker

Brent Griffin is the Perception Lead at Agility Robotics and was previously an assistant research scientist at the University of Michigan conducting research at the intersection of computer vision, control, and robot learning. He is lead author on publications in all of the top IEEE conferences for computer vision, robotics, and control, and his work has been featured in Popular Science, in IEEE Spectrum, and on the Big Ten Network.

Anomaly Detection with Anomalib and FiftyOne

Most anomaly detection techniques are unsupervised, meaning that anomaly detection models are trained on unlabeled non-anomalous data. Developing the highest-quality dataset and data pipeline is essential to training robust anomaly detection models.

In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.

About the Speaker

Jacob Marks is a Senior Machine Learning Engineer and Researcher at Voxel51, where he leads open source efforts in vector search, semantic search, and generative AI for the FiftyOne data-centric AI toolkit. Prior to joining Voxel51, Jacob worked at Google X, Samsung Research, and Wolfram Research.

When May 8, 2024 – 10:00 AM Pacific / 1:00 PM Eastern

Where Virtual / Zoom: https://voxel51.com/computer-vision-events/may-8-2024-ai-machine-learning-data-science-meetup/

To Infer or To Defer: Hazy Oracles in Human+AI Collaboration

This talk explores the evolving dynamics of human+AI collaboration, focusing on the concept of the human as a “hazy oracle” rather than an infallible source. It outlines the journey of integrating AI systems more deeply into practical applications through human+AI cooperation, discussing the potential value and challenges. The discussion includes the modeling of interaction errors and the strategic choices between immediate AI inference or seeking additional human input, supported by results from a user study on optimizing these collaborations.

About the Speaker

Jason Corso is a Professor of Robotics, Electrical Engineering, and Computer Science at the University of Michigan, and Co-Founder / Chief Scientist at AI startup Voxel51. His research spans computer vision, robotics, and AI, with over 150 peer-reviewed publications.

From Research to Industry: Bridging Real-World Applications with Anomalib at the CVPR VAND Challenge

This talk highlights the role of Anomalib, an open-source deep learning framework, in advancing anomaly detection within AI systems, particularly showcased at the upcoming CVPR Visual Anomaly and Novelty Detection (VAND) workshop. Anomalib integrates advanced algorithms and tools to facilitate both academic research and practical applications in sectors like manufacturing, healthcare, and security. It features capabilities such as experiment tracking, model optimization, and scalable deployment solutions. Additionally, the discussion will include Anomalib’s participation in the VAND challenge, focusing on robust real-world applications and few-shot learning for anomaly detection.

About the Speaker

Samet Akcay, an AI research engineer and a tech lead, specializes in semi/self-supervised, zero/few-shot anomaly detection, and multi-modality. He is recently known for his open-source contributions to the ML/DL community. He is the lead author of anomalib, a major open-source anomaly detection library. He also maintains the OpenVINO Training Extensions, a low-code transfer learning framework for building computer vision models.

Learning Robot Perception and Control using Vision with Action

To achieve general utility, robots must continue to learn in unstructured environments. In this talk, I describe how our mobile manipulation robot uses vision with action to 1) learn visual control, 2) annotate its own training data, and 3) learn to estimate depth for new objects and the environment. Using these techniques, I describe how I led a small group to win consecutive robot competitions against teams from Stanford, MIT, and other Universities.

About the Speaker

Brent Griffin is the Perception Lead at Agility Robotics and was previously an assistant research scientist at the University of Michigan conducting research at the intersection of computer vision, control, and robot learning. He is lead author on publications in all of the top IEEE conferences for computer vision, robotics, and control, and his work has been featured in Popular Science, in IEEE Spectrum, and on the Big Ten Network.

Anomaly Detection with Anomalib and FiftyOne

Most anomaly detection techniques are unsupervised, meaning that anomaly detection models are trained on unlabeled non-anomalous data. Developing the highest-quality dataset and data pipeline is essential to training robust anomaly detection models.

In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.

About the Speaker

Jacob Marks is a Senior Machine Learning Engineer and Researcher at Voxel51, where he leads open source efforts in vector search, semantic search, and generative AI for the FiftyOne data-centric AI toolkit. Prior to joining Voxel51, Jacob worked at Google X, Samsung Research, and Wolfram Research.

When May 8, 2024 – 10:00 AM Pacific / 1:00 PM Eastern

Where Virtual / Zoom: https://voxel51.com/computer-vision-events/may-8-2024-ai-machine-learning-data-science-meetup/

To Infer or To Defer: Hazy Oracles in Human+AI Collaboration

This talk explores the evolving dynamics of human+AI collaboration, focusing on the concept of the human as a “hazy oracle” rather than an infallible source. It outlines the journey of integrating AI systems more deeply into practical applications through human+AI cooperation, discussing the potential value and challenges. The discussion includes the modeling of interaction errors and the strategic choices between immediate AI inference or seeking additional human input, supported by results from a user study on optimizing these collaborations.

About the Speaker

Jason Corso is a Professor of Robotics, Electrical Engineering, and Computer Science at the University of Michigan, and Co-Founder / Chief Scientist at AI startup Voxel51. His research spans computer vision, robotics, and AI, with over 150 peer-reviewed publications.

From Research to Industry: Bridging Real-World Applications with Anomalib at the CVPR VAND Challenge

This talk highlights the role of Anomalib, an open-source deep learning framework, in advancing anomaly detection within AI systems, particularly showcased at the upcoming CVPR Visual Anomaly and Novelty Detection (VAND) workshop. Anomalib integrates advanced algorithms and tools to facilitate both academic research and practical applications in sectors like manufacturing, healthcare, and security. It features capabilities such as experiment tracking, model optimization, and scalable deployment solutions. Additionally, the discussion will include Anomalib’s participation in the VAND challenge, focusing on robust real-world applications and few-shot learning for anomaly detection.

About the Speaker

Samet Akcay, an AI research engineer and a tech lead, specializes in semi/self-supervised, zero/few-shot anomaly detection, and multi-modality. He is recently known for his open-source contributions to the ML/DL community. He is the lead author of anomalib, a major open-source anomaly detection library. He also maintains the OpenVINO Training Extensions, a low-code transfer learning framework for building computer vision models.

Learning Robot Perception and Control using Vision with Action

To achieve general utility, robots must continue to learn in unstructured environments. In this talk, I describe how our mobile manipulation robot uses vision with action to 1) learn visual control, 2) annotate its own training data, and 3) learn to estimate depth for new objects and the environment. Using these techniques, I describe how I led a small group to win consecutive robot competitions against teams from Stanford, MIT, and other Universities.

About the Speaker

Brent Griffin is the Perception Lead at Agility Robotics and was previously an assistant research scientist at the University of Michigan conducting research at the intersection of computer vision, control, and robot learning. He is lead author on publications in all of the top IEEE conferences for computer vision, robotics, and control, and his work has been featured in Popular Science, in IEEE Spectrum, and on the Big Ten Network.

Anomaly Detection with Anomalib and FiftyOne

Most anomaly detection techniques are unsupervised, meaning that anomaly detection models are trained on unlabeled non-anomalous data. Developing the highest-quality dataset and data pipeline is essential to training robust anomaly detection models.

In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.

About the Speaker

Jacob Marks is a Senior Machine Learning Engineer and Researcher at Voxel51, where he leads open source efforts in vector search, semantic search, and generative AI for the FiftyOne data-centric AI toolkit. Prior to joining Voxel51, Jacob worked at Google X, Samsung Research, and Wolfram Research.

When May 8, 2024 – 10:00 AM Pacific / 1:00 PM Eastern

Where Virtual / Zoom: https://voxel51.com/computer-vision-events/may-8-2024-ai-machine-learning-data-science-meetup/

To Infer or To Defer: Hazy Oracles in Human+AI Collaboration

This talk explores the evolving dynamics of human+AI collaboration, focusing on the concept of the human as a “hazy oracle” rather than an infallible source. It outlines the journey of integrating AI systems more deeply into practical applications through human+AI cooperation, discussing the potential value and challenges. The discussion includes the modeling of interaction errors and the strategic choices between immediate AI inference or seeking additional human input, supported by results from a user study on optimizing these collaborations.

About the Speaker

Jason Corso is a Professor of Robotics, Electrical Engineering, and Computer Science at the University of Michigan, and Co-Founder / Chief Scientist at AI startup Voxel51. His research spans computer vision, robotics, and AI, with over 150 peer-reviewed publications.

From Research to Industry: Bridging Real-World Applications with Anomalib at the CVPR VAND Challenge

This talk highlights the role of Anomalib, an open-source deep learning framework, in advancing anomaly detection within AI systems, particularly showcased at the upcoming CVPR Visual Anomaly and Novelty Detection (VAND) workshop. Anomalib integrates advanced algorithms and tools to facilitate both academic research and practical applications in sectors like manufacturing, healthcare, and security. It features capabilities such as experiment tracking, model optimization, and scalable deployment solutions. Additionally, the discussion will include Anomalib’s participation in the VAND challenge, focusing on robust real-world applications and few-shot learning for anomaly detection.

About the Speaker

Samet Akcay, an AI research engineer and a tech lead, specializes in semi/self-supervised, zero/few-shot anomaly detection, and multi-modality. He is recently known for his open-source contributions to the ML/DL community. He is the lead author of anomalib, a major open-source anomaly detection library. He also maintains the OpenVINO Training Extensions, a low-code transfer learning framework for building computer vision models.

Learning Robot Perception and Control using Vision with Action

To achieve general utility, robots must continue to learn in unstructured environments. In this talk, I describe how our mobile manipulation robot uses vision with action to 1) learn visual control, 2) annotate its own training data, and 3) learn to estimate depth for new objects and the environment. Using these techniques, I describe how I led a small group to win consecutive robot competitions against teams from Stanford, MIT, and other Universities.

About the Speaker

Brent Griffin is the Perception Lead at Agility Robotics and was previously an assistant research scientist at the University of Michigan conducting research at the intersection of computer vision, control, and robot learning. He is lead author on publications in all of the top IEEE conferences for computer vision, robotics, and control, and his work has been featured in Popular Science, in IEEE Spectrum, and on the Big Ten Network.

Anomaly Detection with Anomalib and FiftyOne

Most anomaly detection techniques are unsupervised, meaning that anomaly detection models are trained on unlabeled non-anomalous data. Developing the highest-quality dataset and data pipeline is essential to training robust anomaly detection models.

In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.

About the Speaker

Jacob Marks is a Senior Machine Learning Engineer and Researcher at Voxel51, where he leads open source efforts in vector search, semantic search, and generative AI for the FiftyOne data-centric AI toolkit. Prior to joining Voxel51, Jacob worked at Google X, Samsung Research, and Wolfram Research.

We are happy to welcome you to 28th edition of PyData Trójmiasto! We are going to listen to Kreshnaa Raam talking about a proper approach to AutoML and Grzegorz Jacenków talking about Video-Text retrieval methods. If you have previously signed up to early access to zerve.ai platform for data scientists, then it is a great opportunity to get to know the creators!

When: 21st November, at 18:00 CET Where: Gdańsk Science-Technology Park, Building C, Room ABC

Registration: The event is free to enter. 100 seats available. Let us know you're coming via meetup.

Agenda: 18:00 - 18:05 - Meeting boarding 18:05 - 18:10 - A few words about PyData 18:10 - 18:15 - Aga Myśliwczyk will spread a news about her initiatives! 18:15 - 19:00 - AutoML as it should have always been by Kreshnaa Raam 19:00 - 19:05 - A quick break 19:05 - 19:50 - Bridging Text and Video: An Affordable Landscape of Video-Text Retrieval Methods by Grzegorz Jacenków 19:50 - Pizza & Networking

About "AutoML as it should have always been": A new modular opensource approach to AutoML and ML pipeline generation in python for different use case/target types. We will go through in-depth understanding of emerging data science platform https://www.zerve.ai/. PyData community received limited access to the platform earlier this year. Now you will have an opportunity to watch the progress and ask all the necessary questions to help us shape the future of data science tooling.

About Kreshnaa Raam: Kreshnaa is a Lead Data Scientist at Zerve.ai. He works on developing python packages, engineers solutions and conducts general research of the product. Prior to his current experience he worked as McKinsey's analytics practice contultant and as a data scientist at DataRobot in AI Execution team.

About "Bridging Text and Video: An Affordable Landscape of Video-Text Retrieval Methods": Video-Text Retrieval (VTR) has emerged as a pivotal domain in the realm of multimedia analysis and understanding. This presentation delves into the multifaceted applications of VTR, spanning video search engines, content recommendations, video annotations, medical imaging, and beyond. We aim to provide a comprehensive overview of the VTR landscape, encapsulating datasets, models, and benchmarking strategies. Our primary focus revolves around the pivotal issue of affordability, a critical consideration in contemporary VTR research. Recent advancements within the field have predominantly relied on resource-intensive large-scale models, demanding extensive GPU power and extensive datasets. By scrutinising the latest innovations and their economic implications, we aim to shed light on the balance between computational demands and real-world applicability in VTR.

About Grzegorz Jacenków: Currently a Data Scientist at Ring, Grzegorz Jacenków specialises in multimodal learning research and large language models (LLMs). Prior to joining Amazon, he was a PhD student in Healthcare AI at The University of Edinburgh, where he also earned an MSc in Artificial Intelligence. His academic foundation was laid with a BSc in Computer Science with Business and Management from The University of Manchester. Notably, Grzegorz contributed to CERN as a technical student, addressing author disambiguation at Inspire-HEP. His research interests encompass multimodal alignment, low-resource learning, and leveraging knowledge graphs.

Notes: The event will not be live-streamed nor recorded.

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. Join us for an enlightening exploration of how IBM Quantum is transforming healthcare. In this presentation, we'll delve into compelling use-cases that showcase the groundbreaking potential of quantum computing in the healthcare and life sciences sectors.

Whether you're a healthcare professional, a tech enthusiast, or simply curious about the future of healthcare, this presentation promises insights that could shape the future of medicine and healthcare.

Note that this is part 2 of a 4-session series "Quantum Horizon: Exploring the Frontier in Quantum Computing Careers" on Oct 25/26, Nov 8/9, Nov 29/30, Dec 6/7. The sessions are not prerequisites for each other, and are not recorded. We will provide reference links and do quick recaps of previous content as required, so if you miss an earlier session, you can still get value from subsequent sessions.

Presenters: Aarushi Karki, Wiktor Mazin

Aarushi Karki's background in Physics, specialising in Biophysics and medical research procedures, has paved the way for her deep interest in quantum technologies for healthcare applications. With over 5+ years of experience in laboratory environments, she has cultivated a passion for bridging scientific theory with real-world solutions. As a new member of IBM's Healthcare and Life Sciences (HCLS) Quantum Guild, she is excited to explore the exciting potential of quantum computing in healthcare and beyond. In her day role, she is a data engineer developing efficient and reliable data pipelines. These pipelines automate the seamless movement of data from source to destination while performing necessary and efficient transformations along the way.

Wiktor Mazin is a Quantum Industry Application Consultant in Healthcare and Life Sciences in IBM Quantum and a Qiskit Advocate. Prior to joining IBM, Wiktor worked more than 10 years with data science and ML in biotech and healthcare, with biomarker analysis and personalized medicine in oncology and psychiatric diseases.

Wiktor holds a M.Sc. in Engineering, a Ph.D. in Biophysics and an eMBA from the Technical University of Denmark (DTU).

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/rf62f15604f3a3a2959e18db039402a4d

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. Join us for an enlightening exploration of how IBM Quantum is transforming healthcare. In this presentation, we'll delve into compelling use-cases that showcase the groundbreaking potential of quantum computing in the healthcare and life sciences sectors.

Whether you're a healthcare professional, a tech enthusiast, or simply curious about the future of healthcare, this presentation promises insights that could shape the future of medicine and healthcare.

Note that this is part 2 of a 4-session series "Quantum Horizon: Exploring the Frontier in Quantum Computing Careers" on Oct 25/26, Nov 8/9, Nov 29/30, Dec 6/7. The sessions are not prerequisites for each other, and are not recorded. We will provide reference links and do quick recaps of previous content as required, so if you miss an earlier session, you can still get value from subsequent sessions.

Presenters: Aarushi Karki, Wiktor Mazin

Aarushi Karki's background in Physics, specialising in Biophysics and medical research procedures, has paved the way for her deep interest in quantum technologies for healthcare applications. With over 5+ years of experience in laboratory environments, she has cultivated a passion for bridging scientific theory with real-world solutions. As a new member of IBM's Healthcare and Life Sciences (HCLS) Quantum Guild, she is excited to explore the exciting potential of quantum computing in healthcare and beyond. In her day role, she is a data engineer developing efficient and reliable data pipelines. These pipelines automate the seamless movement of data from source to destination while performing necessary and efficient transformations along the way.

Wiktor Mazin is a Quantum Industry Application Consultant in Healthcare and Life Sciences in IBM Quantum and a Qiskit Advocate. Prior to joining IBM, Wiktor worked more than 10 years with data science and ML in biotech and healthcare, with biomarker analysis and personalized medicine in oncology and psychiatric diseases.

Wiktor holds a M.Sc. in Engineering, a Ph.D. in Biophysics and an eMBA from the Technical University of Denmark (DTU).

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/rf62f15604f3a3a2959e18db039402a4d

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. Join us for an enlightening exploration of how IBM Quantum is transforming healthcare. In this presentation, we'll delve into compelling use-cases that showcase the groundbreaking potential of quantum computing in the healthcare and life sciences sectors.

Whether you're a healthcare professional, a tech enthusiast, or simply curious about the future of healthcare, this presentation promises insights that could shape the future of medicine and healthcare.

Note that this is part 2 of a 4-session series "Quantum Horizon: Exploring the Frontier in Quantum Computing Careers" on Oct 25/26, Nov 8/9, Nov 29/30, Dec 6/7. The sessions are not prerequisites for each other, and are not recorded. We will provide reference links and do quick recaps of previous content as required, so if you miss an earlier session, you can still get value from subsequent sessions.

Presenters: Aarushi Karki, Wiktor Mazin

Aarushi Karki's background in Physics, specialising in Biophysics and medical research procedures, has paved the way for her deep interest in quantum technologies for healthcare applications. With over 5+ years of experience in laboratory environments, she has cultivated a passion for bridging scientific theory with real-world solutions. As a new member of IBM's Healthcare and Life Sciences (HCLS) Quantum Guild, she is excited to explore the exciting potential of quantum computing in healthcare and beyond. In her day role, she is a data engineer developing efficient and reliable data pipelines. These pipelines automate the seamless movement of data from source to destination while performing necessary and efficient transformations along the way.

Wiktor Mazin is a Quantum Industry Application Consultant in Healthcare and Life Sciences in IBM Quantum and a Qiskit Advocate. Prior to joining IBM, Wiktor worked more than 10 years with data science and ML in biotech and healthcare, with biomarker analysis and personalized medicine in oncology and psychiatric diseases.

Wiktor holds a M.Sc. in Engineering, a Ph.D. in Biophysics and an eMBA from the Technical University of Denmark (DTU).

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/rf62f15604f3a3a2959e18db039402a4d

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. Join us for an enlightening exploration of how IBM Quantum is transforming healthcare. In this presentation, we'll delve into compelling use-cases that showcase the groundbreaking potential of quantum computing in the healthcare and life sciences sectors.

Whether you're a healthcare professional, a tech enthusiast, or simply curious about the future of healthcare, this presentation promises insights that could shape the future of medicine and healthcare.

Note that this is part 2 of a 4-session series "Quantum Horizon: Exploring the Frontier in Quantum Computing Careers" on Oct 25/26, Nov 8/9, Nov 29/30, Dec 6/7. The sessions are not prerequisites for each other, and are not recorded. We will provide reference links and do quick recaps of previous content as required, so if you miss an earlier session, you can still get value from subsequent sessions.

Presenters: Aarushi Karki, Wiktor Mazin

Aarushi Karki's background in Physics, specialising in Biophysics and medical research procedures, has paved the way for her deep interest in quantum technologies for healthcare applications. With over 5+ years of experience in laboratory environments, she has cultivated a passion for bridging scientific theory with real-world solutions. As a new member of IBM's Healthcare and Life Sciences (HCLS) Quantum Guild, she is excited to explore the exciting potential of quantum computing in healthcare and beyond. In her day role, she is a data engineer developing efficient and reliable data pipelines. These pipelines automate the seamless movement of data from source to destination while performing necessary and efficient transformations along the way.

Wiktor Mazin is a Quantum Industry Application Consultant in Healthcare and Life Sciences in IBM Quantum and a Qiskit Advocate. Prior to joining IBM, Wiktor worked more than 10 years with data science and ML in biotech and healthcare, with biomarker analysis and personalized medicine in oncology and psychiatric diseases.

Wiktor holds a M.Sc. in Engineering, a Ph.D. in Biophysics and an eMBA from the Technical University of Denmark (DTU).

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/rae96783bbdb2d73231c9a43a4a1750a0

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. Join us for an enlightening exploration of how IBM Quantum is transforming healthcare. In this presentation, we'll delve into compelling use-cases that showcase the groundbreaking potential of quantum computing in the healthcare and life sciences sectors.

Whether you're a healthcare professional, a tech enthusiast, or simply curious about the future of healthcare, this presentation promises insights that could shape the future of medicine and healthcare.

Note that this is part 2 of a 4-session series "Quantum Horizon: Exploring the Frontier in Quantum Computing Careers" on Oct 25/26, Nov 8/9, Nov 29/30, Dec 6/7. The sessions are not prerequisites for each other, and are not recorded. We will provide reference links and do quick recaps of previous content as required, so if you miss an earlier session, you can still get value from subsequent sessions.

Presenters: Aarushi Karki, Wiktor Mazin

Aarushi Karki's background in Physics, specialising in Biophysics and medical research procedures, has paved the way for her deep interest in quantum technologies for healthcare applications. With over 5+ years of experience in laboratory environments, she has cultivated a passion for bridging scientific theory with real-world solutions. As a new member of IBM's Healthcare and Life Sciences (HCLS) Quantum Guild, she is excited to explore the exciting potential of quantum computing in healthcare and beyond. In her day role, she is a data engineer developing efficient and reliable data pipelines. These pipelines automate the seamless movement of data from source to destination while performing necessary and efficient transformations along the way.

Wiktor Mazin is a Quantum Industry Application Consultant in Healthcare and Life Sciences in IBM Quantum and a Qiskit Advocate. Prior to joining IBM, Wiktor worked more than 10 years with data science and ML in biotech and healthcare, with biomarker analysis and personalized medicine in oncology and psychiatric diseases.

Wiktor holds a M.Sc. in Engineering, a Ph.D. in Biophysics and an eMBA from the Technical University of Denmark (DTU).

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/rae96783bbdb2d73231c9a43a4a1750a0

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. Join us for an enlightening exploration of how IBM Quantum is transforming healthcare. In this presentation, we'll delve into compelling use-cases that showcase the groundbreaking potential of quantum computing in the healthcare and life sciences sectors.

Whether you're a healthcare professional, a tech enthusiast, or simply curious about the future of healthcare, this presentation promises insights that could shape the future of medicine and healthcare.

Note that this is part 2 of a 4-session series "Quantum Horizon: Exploring the Frontier in Quantum Computing Careers" on Oct 25/26, Nov 8/9, Nov 29/30, Dec 6/7. The sessions are not prerequisites for each other, and are not recorded. We will provide reference links and do quick recaps of previous content as required, so if you miss an earlier session, you can still get value from subsequent sessions.

Presenters: Aarushi Karki, Wiktor Mazin

Aarushi Karki's background in Physics, specialising in Biophysics and medical research procedures, has paved the way for her deep interest in quantum technologies for healthcare applications. With over 5+ years of experience in laboratory environments, she has cultivated a passion for bridging scientific theory with real-world solutions. As a new member of IBM's Healthcare and Life Sciences (HCLS) Quantum Guild, she is excited to explore the exciting potential of quantum computing in healthcare and beyond. In her day role, she is a data engineer developing efficient and reliable data pipelines. These pipelines automate the seamless movement of data from source to destination while performing necessary and efficient transformations along the way.

Wiktor Mazin is a Quantum Industry Application Consultant in Healthcare and Life Sciences in IBM Quantum and a Qiskit Advocate. Prior to joining IBM, Wiktor worked more than 10 years with data science and ML in biotech and healthcare, with biomarker analysis and personalized medicine in oncology and psychiatric diseases.

Wiktor holds a M.Sc. in Engineering, a Ph.D. in Biophysics and an eMBA from the Technical University of Denmark (DTU).

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/rae96783bbdb2d73231c9a43a4a1750a0

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A Mission to Save Lives in Africa and beyond - Eleni Stamatelou

Outline:

• TBA

About the speaker: I'm Elena Stamatelou, a machine learning researcher and educator passionate about using Data Science to improve healthcare and save human lives. My expertise includes signal processing, deep learning, and data-driven design.

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Why design matters in data products is a question that, at first glance, may not be easily answered for some until they see users try to use ML models and analytics to make decisions. For Bill Báez, a data scientist and VP of Strategy at Ascend Innovations, realizing that design and UX matters in this context was a realization that grew over the course of a few years. Bill’s origins in the Air Force, and his transition to Ascend Innovations, instilled lessons about the importance of using design thinking with both clients and users. 

After observing solutions built in total isolation with zero empathy and knowledge of how they were being perceived in the wild, Bill realized the critical need to bring developers “upstairs” to actually observe the people using the solutions that were being built. 

Currently, Ascend Innovation’s consulting is primarily rooted in healthcare and community services, and in this episode, Bill provides some real-world examples where their machine learning and analytics solutions were informed by approaching the problems from a human-centered design perspective. Bill also dives in to where he is on his journey to integrate his UX and data science teams at Ascend so they can create better value for their clients and their client’s constituents. 

Highlights in this episode include:

What caused Bill to notice design for the first time and its importance in data products (03:12) Bridging the gap between data science, UX, and the client’s needs at Ascend (08:07) How to deal with the “presenting problem” and working with feedback (16:00) Bill’s advice for getting designers, UX, and clients on the same page based on his experience to date (23:56) How Bill provides unity for his UX and data science teams   (32:40) The effects of UX in medicine (41:00)

Quotes from Today’s Episode “My journey into Design Thinking started in earnest when I started at Ascend, but I didn’t really have the terminology to use. For example, Design Thinking and UX were actually terms I was not personally aware of until last summer. But now that I know and have been exposed to it and have learned more about it, I realize I’ve been doing a lot of that type of work in earnest since 2018. - Bill (03:37)

“Ascend Innovations has always been product-focused, although again, services is our main line of business. As we started hiring a more dedicated UX team, people who’ve been doing this for their whole career, it really helped me to understand what I had experienced prior to coming to Ascend. Part of the time I was here at Ascend that UX framework and that Design Thinking lens, it really brings a lot more firepower to what data science is trying to achieve at the end of the day.” - Bill (08:29) “Clients were surprised that we were asking such rudimentary questions.  They’ll say ‘Well, we’ve already talked about that,’ or, ‘It should be obvious.’ or ‘Well, why are you asking me such a simple question?’ And we had to explain to them that we wanted to start at the bottom to move to the top. We don’t want to start somewhere midway and get the top. We want to make sure that we are all in alignment with what we’re trying to do, so we want to establish that baseline of understanding. So, we’re going to start off asking very simple questions and work our way up from there...” - Bill (21:09)

“We’re building a thing, but the thing only has value if it creates a change in the world. The world being, in the mind of the stakeholder, in the minds of the users, maybe some third parties that are affected by that stuff, but it’s the change that matters. So what is the better state we want in the future for our client or for our customers and users? That’s the thing we’re trying to create. Not the thing; the change from the thing is what we want, and getting to that is the hard part.” - Brian (@rhythmspice) (26:33)

“This is a gift that you’re giving to [stakeholders] to save time, to save money, to avoid building something that will never get used and will not provide value to them. You do need to push back against this and if they say no, that’s fine. Paint the picture of the risk, though, by not doing design. It’s very easy for us to build a ML model. It’s hard for us to build a model that someone will actually use to make the world better. And in this case, it’s healthcare or support, intervention support for addicts. “Do you really want a model, or do you want an improvement in the lives of these addicts? That’s ultimately where we’re going with this, and if we don’t do this, the risk of us pushing out an output that doesn’t get used is high. So, design is a gift, not a tax...” - Brian (@rhythmspice) (34:34)

“I’d say to anybody out there right now who’s currently working on data science efforts: the sooner you get your people comfortable with the idea of doing Design Thinking, get them implemented into the projects that are currently going on. [...] I think that will be a real game-changer for your data scientists and your organization as a whole...” - Bill  (42:19)