The document provides an overview of deep learning based object detection models. It discusses early approaches like R-CNN, Fast R-CNN, and Faster R-CNN, as well as more recent single-shot detectors like YOLO, SSD, RetinaNet, and CenterNet. It covers performance metrics like mean average precision (mAP) and compares the speed and accuracy of different models. The document concludes by outlining general guidelines for choosing an object detection model based on priorities like accuracy, speed, model size, and portability.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/understanding-selecting-and-optimizing-object-detectors-for-edge-applications-a-presentation-from-walmart-global-tech/
Md Nasir Uddin Laskar, Staff Machine Learning Engineer at Walmart Global Tech, presents the “Understanding, Selecting and Optimizing Object Detectors for Edge Applications” tutorial at the May 2023 Embedded Vision Summit.
Object detectors count objects in a scene and determine their precise locations, while also labeling them. Object detection plays a crucial role in many vision applications, from autonomous driving to smart appliances. In many of these applications, it’s necessary or desirable to implement object detection at the edge.
In this presentation, Laskar explores the evolution of object detection algorithms, from traditional approaches to deep learning-based methods and transformer-based architectures. He delves into widely used approaches for object detection, such as two-stage R-CNNs and one-stage YOLO algorithms, and examines their strengths and weaknesses. And he provides guidance on how to evaluate and select an object detector for an edge application.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/understanding-selecting-and-optimizing-object-detectors-for-edge-applications-a-presentation-from-walmart-global-tech/
Md Nasir Uddin Laskar, Staff Machine Learning Engineer at Walmart Global Tech, presents the “Understanding, Selecting and Optimizing Object Detectors for Edge Applications” tutorial at the May 2023 Embedded Vision Summit.
Object detectors count objects in a scene and determine their precise locations, while also labeling them. Object detection plays a crucial role in many vision applications, from autonomous driving to smart appliances. In many of these applications, it’s necessary or desirable to implement object detection at the edge.
In this presentation, Laskar explores the evolution of object detection algorithms, from traditional approaches to deep learning-based methods and transformer-based architectures. He delves into widely used approaches for object detection, such as two-stage R-CNNs and one-stage YOLO algorithms, and examines their strengths and weaknesses. And he provides guidance on how to evaluate and select an object detector for an edge application.
Computer Vision Landscape : Present and FutureSanghamitra Deb
Millions of people all around the world Learn with Chegg. Education at Chegg is powered by the depth and diversity of the content that we have. A huge part of our content is in form of images. These images could be uploaded by students or by content creators. Images contain text that is extracted using a transcription service. Very often uploaded images are noisy. This leads to irrelevant characters or words in the transcribed text. Using object detection techniques we develop a service that extracts the relevant parts of the image and uses a transcription service to get clean text. In the first part of the presentation, I will talk about building an object detection model using YOLO for cropping and masking images to obtain a cleaner text from transcription. YOLO is a deep learning object detection and recognition modeling framework that is able to produce highly accurate results with low latency. In the next part of my presentation, I will talk about the building the Computer Vision landscape at Chegg. Starting from images on academic materials that are composed of elements such as text, equations, diagrams we create a pipeline for extracting these image elements. Using state of the art deep learning techniques we create embeddings for these elements to enhance downstream machine learning models such as content quality and similarity.
Deep learning fundamental and Research project on IBM POWER9 system from NUSGanesan Narayanasamy
Moving object recognition (MOR) corresponds to the localisation and classification of moving objects in videos. Discriminating moving objects from static objects and background in videos is an essential task for many computer vision applications. MOR has widespread applications in intelligent visual surveillance, intrusion detection, anomaly detection and monitoring, industrial sites monitoring, detection-based tracking, autonomous vehicles, etc. In this session, Murari is going to talk about the deep learning algorithms to identify both locations and corresponding categories of moving objects with a convolutional network. The challenges in developing such algorithms will be discussed. The discourse will also include the implementation details of these models in both conventional and UAV videos.
NIT Silchar ML Hackathon 2019 Session on Computer Vision with Deep Learning.
Targeted Audience: Pre-requisite: Basic knowledge on Machine Learning and Deep Learning
Recent Progress on Object Detection_20170331Jihong Kang
This slide provides a brief summary of recent progress on object detection using deep learning.
The concept of selected previous works(R-CNN series/YOLO/SSD) and 6 recent papers (uploaded to the Arxiv between Dec/2016 and Mar/2017) are introduced in this slide.
Most papers are focusing on improving the performance of small object detection.
Scratch to Supercomputers: Bottoms-up Build of Large-scale Computational Lens...inside-BigData.com
In this deck from the 2018 Swiss HPC Conference, Gilles Fourestey from EPFL presents: Scratch to Supercomputers: Bottoms-up Build of Large-scale Computational Lensing Software.
"LENSTOOL is a gravitational lensing software that models mass distribution of galaxies and clusters. It was developed by Prof. Kneib, head of the LASTRO lab at EPFL, et al., starting from 1996. It is used to obtain sub-percent precision measurements of the total mass in galaxy clusters and constrain the dark matter self-interaction cross-section, a crucial ingredient to understanding its nature.
However, LENSTOOL lacks efficient vectorization and only uses OpenMP, which limits its execution to one node and can lead to execution times that exceed several months. Therefore, the LASTRO and the EPFL HPC group decided to rewrite the code from scratch and in order to minimize risk and maximize performance, a bottom-up approach that focuses on exposing parallelism at hardware and instruction levels was used. The result is a high performance code, fully vectorized on Xeon, Xeon Phis and GPUs that currently scales up to hundreds of nodes on CSCS’ Piz Daint, one of the fastest supercomputers in the world."
Watch the video: https://wp.me/p3RLHQ-ili
Learn more: https://infoscience.epfl.ch/record/234382/files/EPFL_TH8338.pdf?subformat=pdfa
and
http://www.hpcadvisorycouncil.com/events/2018/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/deploying-large-models-on-the-edge-success-stories-and-challenges-a-presentation-from-qualcomm/
Vinesh Sukumar, Senior Director of Product Management at Qualcomm Technologies, presents the “Deploying Large Models on the Edge: Success Stories and Challenges” tutorial at the May 2024 Embedded Vision Summit.
In this talk, Dr. Sukumar explains and demonstrates how Qualcomm has been successful in deploying large generative AI and multimodal models on the edge for a variety of use cases in consumer and enterprise markets. He examines key challenges that must be overcome before large models at the edge can reach their full commercial potential. He also highlights how Qualcomm is addressing these challenges through upgraded processor hardware, improved developer tools and a comprehensive library of fully optimized AI models in the Qualcomm AI Hub.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/scaling-vision-based-edge-ai-solutions-from-prototype-to-global-deployment-a-presentation-from-network-optix/
Maurits Kaptein, Chief Data Scientist at Network Optix and Professor at the University of Eindhoven, presents the “Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment” tutorial at the May 2024 Embedded Vision Summit.
The Embedded Vision Summit brings together innovators in silicon, devices, software and applications and empowers them to bring computer vision and perceptual AI into reliable and scalable products. However, integrating recent hardware, software and algorithm innovations into prime-time-ready products is quite challenging. Scaling from a proof of concept—for example, a novel neural network architecture performing a valuable task efficiently on a new piece of silicon—to an AI vision system installed in hundreds of sites requires surmounting myriad hurdles.
First, building on Network Optix’s 14 years of experience, Professor Kaptein details how to overcome the networking, fleet management, visualization and monetization challenges that come with scaling a global vision solution. Second, Kaptein discusses the complexities of making vision AI solutions device-agnostic and remotely manageable, proposing an open standard for AI model deployment to edge devices. The proposed standard aims to simplify market entry for silicon manufacturers and enhance scalability for solution developers. Kaptein outlines the standard’s core components and invites collaborative contributions to drive market expansion.
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Computer Vision Landscape : Present and FutureSanghamitra Deb
Millions of people all around the world Learn with Chegg. Education at Chegg is powered by the depth and diversity of the content that we have. A huge part of our content is in form of images. These images could be uploaded by students or by content creators. Images contain text that is extracted using a transcription service. Very often uploaded images are noisy. This leads to irrelevant characters or words in the transcribed text. Using object detection techniques we develop a service that extracts the relevant parts of the image and uses a transcription service to get clean text. In the first part of the presentation, I will talk about building an object detection model using YOLO for cropping and masking images to obtain a cleaner text from transcription. YOLO is a deep learning object detection and recognition modeling framework that is able to produce highly accurate results with low latency. In the next part of my presentation, I will talk about the building the Computer Vision landscape at Chegg. Starting from images on academic materials that are composed of elements such as text, equations, diagrams we create a pipeline for extracting these image elements. Using state of the art deep learning techniques we create embeddings for these elements to enhance downstream machine learning models such as content quality and similarity.
Deep learning fundamental and Research project on IBM POWER9 system from NUSGanesan Narayanasamy
Moving object recognition (MOR) corresponds to the localisation and classification of moving objects in videos. Discriminating moving objects from static objects and background in videos is an essential task for many computer vision applications. MOR has widespread applications in intelligent visual surveillance, intrusion detection, anomaly detection and monitoring, industrial sites monitoring, detection-based tracking, autonomous vehicles, etc. In this session, Murari is going to talk about the deep learning algorithms to identify both locations and corresponding categories of moving objects with a convolutional network. The challenges in developing such algorithms will be discussed. The discourse will also include the implementation details of these models in both conventional and UAV videos.
NIT Silchar ML Hackathon 2019 Session on Computer Vision with Deep Learning.
Targeted Audience: Pre-requisite: Basic knowledge on Machine Learning and Deep Learning
Recent Progress on Object Detection_20170331Jihong Kang
This slide provides a brief summary of recent progress on object detection using deep learning.
The concept of selected previous works(R-CNN series/YOLO/SSD) and 6 recent papers (uploaded to the Arxiv between Dec/2016 and Mar/2017) are introduced in this slide.
Most papers are focusing on improving the performance of small object detection.
Scratch to Supercomputers: Bottoms-up Build of Large-scale Computational Lens...inside-BigData.com
In this deck from the 2018 Swiss HPC Conference, Gilles Fourestey from EPFL presents: Scratch to Supercomputers: Bottoms-up Build of Large-scale Computational Lensing Software.
"LENSTOOL is a gravitational lensing software that models mass distribution of galaxies and clusters. It was developed by Prof. Kneib, head of the LASTRO lab at EPFL, et al., starting from 1996. It is used to obtain sub-percent precision measurements of the total mass in galaxy clusters and constrain the dark matter self-interaction cross-section, a crucial ingredient to understanding its nature.
However, LENSTOOL lacks efficient vectorization and only uses OpenMP, which limits its execution to one node and can lead to execution times that exceed several months. Therefore, the LASTRO and the EPFL HPC group decided to rewrite the code from scratch and in order to minimize risk and maximize performance, a bottom-up approach that focuses on exposing parallelism at hardware and instruction levels was used. The result is a high performance code, fully vectorized on Xeon, Xeon Phis and GPUs that currently scales up to hundreds of nodes on CSCS’ Piz Daint, one of the fastest supercomputers in the world."
Watch the video: https://wp.me/p3RLHQ-ili
Learn more: https://infoscience.epfl.ch/record/234382/files/EPFL_TH8338.pdf?subformat=pdfa
and
http://www.hpcadvisorycouncil.com/events/2018/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Similar to “Understanding DNN-Based Object Detectors,” a Presentation from Au-Zone Technologies (20)
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/deploying-large-models-on-the-edge-success-stories-and-challenges-a-presentation-from-qualcomm/
Vinesh Sukumar, Senior Director of Product Management at Qualcomm Technologies, presents the “Deploying Large Models on the Edge: Success Stories and Challenges” tutorial at the May 2024 Embedded Vision Summit.
In this talk, Dr. Sukumar explains and demonstrates how Qualcomm has been successful in deploying large generative AI and multimodal models on the edge for a variety of use cases in consumer and enterprise markets. He examines key challenges that must be overcome before large models at the edge can reach their full commercial potential. He also highlights how Qualcomm is addressing these challenges through upgraded processor hardware, improved developer tools and a comprehensive library of fully optimized AI models in the Qualcomm AI Hub.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/scaling-vision-based-edge-ai-solutions-from-prototype-to-global-deployment-a-presentation-from-network-optix/
Maurits Kaptein, Chief Data Scientist at Network Optix and Professor at the University of Eindhoven, presents the “Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment” tutorial at the May 2024 Embedded Vision Summit.
The Embedded Vision Summit brings together innovators in silicon, devices, software and applications and empowers them to bring computer vision and perceptual AI into reliable and scalable products. However, integrating recent hardware, software and algorithm innovations into prime-time-ready products is quite challenging. Scaling from a proof of concept—for example, a novel neural network architecture performing a valuable task efficiently on a new piece of silicon—to an AI vision system installed in hundreds of sites requires surmounting myriad hurdles.
First, building on Network Optix’s 14 years of experience, Professor Kaptein details how to overcome the networking, fleet management, visualization and monetization challenges that come with scaling a global vision solution. Second, Kaptein discusses the complexities of making vision AI solutions device-agnostic and remotely manageable, proposing an open standard for AI model deployment to edge devices. The proposed standard aims to simplify market entry for silicon manufacturers and enhance scalability for solution developers. Kaptein outlines the standard’s core components and invites collaborative contributions to drive market expansion.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/whats-next-in-on-device-generative-ai-a-presentation-from-qualcomm/
Jilei Hou, Vice President of Engineering and Head of AI Research at Qualcomm Technologies, presents the “What’s Next in On-device Generative AI” tutorial at the May 2024 Embedded Vision Summit.
The generative AI era has begun! Large multimodal models are bringing the power of language understanding to machine perception, and transformer models are expanding to allow machines to understand using multiple types of sensors. This new wave of approaches is poised to revolutionize user experiences, disrupt industries and enable powerful new capabilities. For generative AI to reach its full potential, however, we must deploy it on edge devices, providing improved latency, pervasive interaction and enhanced privacy.
In this talk, Hou shares Qualcomm’s vision of the compelling opportunities enabled by efficient generative AI at the edge. He also identifies the key challenges that the industry must overcome to realize the massive potential of these technologies. And he highlights research and product development work that Qualcomm is doing to lead the way via an end-to-end system approach—including techniques for efficient on-device execution of LLMs, LVMs and LMMs, methods for orchestration of large models at the edge and approaches for adaptation and personalization.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/learning-compact-dnn-models-for-embedded-vision-a-presentation-from-the-university-of-maryland-at-college-park/
Shuvra Bhattacharyya, Professor at the University of Maryland at College Park, presents the “Learning Compact DNN Models for Embedded Vision” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Bhattacharyya explores methods to transform large deep neural network (DNN) models into effective compact models. The transformation process that he focuses on—from large to compact DNN form—is referred to as pruning. Pruning involves the removal of neurons or parameters from a neural network. When performed strategically, pruning can lead to significant reductions in computational complexity without significant degradation in accuracy. It is sometimes even possible to increase accuracy through pruning.
Pruning provides a general approach for facilitating real-time inference in resource-constrained embedded computer vision systems. Bhattacharyya provides an overview of important aspects to consider when applying or developing a DNN pruning method and presents details on a recently introduced pruning method called NeuroGRS. NeuroGRS considers structures and trained weights jointly throughout the pruning process and can result in significantly more compact models compared to other pruning methods.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/introduction-to-computer-vision-with-cnns-a-presentation-from-mohammad-haghighat/
Independent consultant Mohammad Haghighat presents the “Introduction to Computer Vision with Convolutional Neural Networks” tutorial at the May 2023 Embedded Vision Summit.
This presentation covers the basics of computer vision using convolutional neural networks. Haghighat begins by introducing some important conventional computer vision techniques and then transition to explaining the basics of machine learning and convolutional neural networks (CNNs) and showing how CNNs are used in visual perception.
Haghighat illustrates the building blocks and computational elements of neural networks through examples. This session provides an overview of how modern computer vision algorithms are designed, trained and used in real-world applications.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/selecting-tools-for-developing-monitoring-and-maintaining-ml-models-a-presentation-from-yummly/
Parshad Patel, Data Scientist at Yummly, presents the “Selecting Tools for Developing, Monitoring and Maintaining ML Models” tutorial at the May 2023 Embedded Vision Summit.
With the boom in tools for developing, monitoring and maintaining ML models, data science teams have many options to choose from. Proprietary tools provided by cloud service providers are enticing, but teams may fear being locked in—and may worry that these tools are too costly or missing important features when compared with alternatives from specialized providers.
Fortunately, most proprietary, fee-based tools have an open-source component that can be integrated into a home-grown solution at low cost. This can be a good starting point, enabling teams to get started with modern tools without making big investments and leaving the door open to evolve tool selection over time. In this talk, Patel presents a step-by-step process for creating an MLOps tool set that enables you to deliver maximum value as a data scientist. He shares how Yummly built pipelines for model development and put them into production using open-source projects.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/building-accelerated-gstreamer-applications-for-video-and-audio-ai-a-presentation-from-wave-spectrum/
Abdo Babukr, Accelerated Computing Consultant at Wave Spectrum, presents the “Building Accelerated GStreamer Applications for Video and Audio AI,” tutorial at the May 2023 Embedded Vision Summit.
GStreamer is a popular open-source framework for creating streaming media applications. Developers often use GStreamer to streamline the development of computer vision and audio perception applications. Since perceptual algorithms are often quite demanding in terms of processing performance, in many cases developers need to find ways to accelerate key GStreamer building blocks, taking advantage of specialized features of their target processor or co-processor.
In this talk, Babukr introduces GStreamer and shows how to use it to build computer vision and audio perception applications. He also shows how to create efficient, high-performance GStreamer applications that utilize specialized hardware features.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/introduction-to-modern-lidar-for-machine-perception-a-presentation-from-the-university-of-ottawa/
Robert Laganière, Professor at the University of Ottawa and CEO of Sensor Cortek, presents the “Introduction to Modern LiDAR for Machine Perception” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Laganière provides an introduction to light detection and ranging (LiDAR) technology. He explains how LiDAR sensors work and their main advantages and disadvantages. He also introduces different approaches to LiDAR, including scanning and flash LiDAR.
Laganière explores the types of data produced by LiDAR sensors and explains how this data can be processed using deep neural networks. He also examines the synergy between LiDAR and cameras, and the concept of pseudo-LiDAR for detection.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/vision-language-representations-for-robotics-a-presentation-from-the-university-of-pennsylvania/
Dinesh Jayaraman, Assistant Professor at the University of Pennsylvania, presents the “Vision-language Representations for Robotics” tutorial at the May 2023 Embedded Vision Summit.
In what format can an AI system best present what it “sees” in a visual scene to help robots accomplish tasks? This question has been a long-standing challenge for computer scientists and robotics engineers. In this presentation, Jayaraman provides insights into cutting-edge techniques being used to help robots better understand their surroundings, learn new skills with minimal guidance and become more capable of performing complex tasks.
Jayaraman discusses recent advances in unsupervised representation learning and explains how these approaches can be used to build visual representations that are appropriate for a controller that decides how the robot should act. In particular, he presents insights from his research group’s recent work on how to represent the constituent objects and entities in a visual scene, and how to combine vision and language in a way that permits effectively translating language-based task descriptions into images depicting the robot’s goals.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/adas-and-av-sensors-whats-winning-and-why-a-presentation-from-techinsights/
Ian Riches, Vice President of the Global Automotive Practice at TechInsights, presents the “ADAS and AV Sensors: What’s Winning and Why?” tutorial at the May 2023 Embedded Vision Summit.
It’s clear that the number of sensors per vehicle—and the sophistication of these sensors—is growing rapidly, largely thanks to increased adoption of advanced safety and driver assistance features. In this presentation, Riches explores likely future demand for automotive radars, cameras and LiDARs.
Riches examines which vehicle features will drive demand out to 2030, how vehicle architecture change is impacting the market and what sorts of compute platforms these sensors will be connected to. Finally, he shares his firm’s vision of what the landscape could look like far beyond 2030, considering scenarios out to 2050 for automated driving and the resulting sensor demand.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/computer-vision-in-sports-scalable-solutions-for-downmarkets-a-presentation-from-sportlogiq/
Mehrsan Javan, Co-founder and CTO of Sportlogiq, presents the “Computer Vision in Sports: Scalable Solutions for Downmarket Leagues” tutorial at the May 2023 Embedded Vision Summit.
Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this requires a fully automated, robust end-to-end pipeline, spanning from visual input, to player and group activities, to player and team evaluation to planning. Despite major advancements in computer vision and machine learning, today sports analytics solutions are limited to top leagues and are not widely available for downmarket leagues and youth sports.
In this talk, Javan explains how his company has developed scalable and robust computer vision solutions to democratize sport analytics and offer pro-league-level insights to leagues with modest resources, including youth leagues. He highlights key challenges—such as the requirement for low-cost, low-latency processing and the need for robustness despite variations in venues. He discusses the approaches Sportlogiq tried and how it ultimately overcame these challenges, including the use of transformers and fusion of multiple type of data streams to maximize accuracy.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/detecting-data-drift-in-image-classification-neural-networks-a-presentation-from-southern-illinois-university/
Spyros Tragoudas, Professor and School Director at Southern Illinois University Carbondale, presents the “Detecting Data Drift in Image Classification Neural Networks” tutorial at the May 2023 Embedded Vision Summit.
An unforeseen change in the input data is called “drift,” and may impact the accuracy of machine learning models. In this talk, Tragoudas presents a novel scheme for diagnosing data drift in the input streams of image classification neural networks. His proposed method for drift detection and quantification uses a threshold dictionary for the prediction probabilities of each class in the neural network model.
The method is applicable to any drift type in images such as noise and weather effects, among others. Tragoudas shares experimental results on various data sets, drift types and neural network models to show that his proposed method estimates the drift magnitude with high accuracy, especially when the level of drift significantly impacts the model’s performance.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/deep-neural-network-training-diagnosing-problems-and-implementing-solutions-a-presentation-from-sensor-cortek/
Fahed Hassanat, Chief Operating Officer and Head of Engineering at Sensor Cortek, presents the “Deep Neural Network Training: Diagnosing Problems and Implementing Solutions” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Hassanat delves into some of the most common problems that arise when training deep neural networks. He provides a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score.
Hassanat then explores training challenges that arise from problems with hyperparameters, inappropriately sized models, inadequate models, poor-quality datasets, imbalances within training datasets and mismatches between training and testing datasets. To help detect and diagnose training problems, he also covers techniques such as understanding performance curves, recognizing overfitting and underfitting, analyzing confusion matrices and identifying class interaction issues.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/ai-start-ups-the-perils-of-fishing-for-whales-war-stories-from-the-entrepreneurial-front-lines-a-presentation-from-seechange-technologies/
Tim Hartley, Vice President of Product for SeeChange Technologies, presents the “AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepreneurial Front Lines)” tutorial at the May 2023 Embedded Vision Summit.
You have a killer idea that will change the world. You’ve thought through product-market fit and differentiation. You have seed funding and a world-beating team. Best of all, you’ve caught the attention of major players in your industry. You’ve reached peak “start-up”—that point of limitless possibility—when you go to bed with the same level of energy and enthusiasm you had when you woke. And then the first proof of concept starts…
In this talk, Hartley lays out some of the pitfalls that await those building the next big thing. Using real examples, he shares some of the dos and don’ts, particularly when dealing with that big potential first customer. Hartley discusses the importance of end-to-end design, ensuring your product solves real-world problems. He explores how far the big companies will tell you to jump—and then jump again—for free. And, most importantly, how to build long-term partnerships with major corporations without relying on over-promising sales pitches.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/a-computer-vision-system-for-autonomous-satellite-maneuvering-a-presentation-from-scout-space/
Andrew Harris, Spacecraft Systems Engineer at SCOUT Space, presents the “Developing a Computer Vision System for Autonomous Satellite Maneuvering” tutorial at the May 2023 Embedded Vision Summit.
Computer vision systems for mobile autonomous machines experience a wide variety of real-world conditions and inputs that can be challenging to capture accurately in training datasets. Few autonomous systems experience more challenging conditions than those in orbit. In this talk, Harris describes how SCOUT Space has designed and trained satellite vision systems using dynamic and physically informed synthetic image datasets.
Harris describes how his company generates synthetic data for this challenging environment and how it leverages new real-world data to improve our datasets. In particular, he explains how these synthetic datasets account for and can replicate real sources of noise and error in the orbital environment, and how his company supplements them with in-space data from the first SCOUT-Vision system, which has been in orbit since 2021.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/bias-in-computer-vision-its-bigger-than-facial-recognition-a-presentation-from-santa-clara-university/
Susan Kennedy, Assistant Professor of Philosophy at Santa Clara University, presents the “Bias in Computer Vision—It’s Bigger Than Facial Recognition!” tutorial at the May 2023 Embedded Vision Summit.
As AI is increasingly integrated into various industries, concerns about its potential to reproduce or exacerbate bias have become widespread. While the use of AI holds the promise of reducing bias, it can also have unintended consequences, particularly in high-stakes computer vision applications such as facial recognition. However, even seemingly low-stakes computer vision applications such as identifying potholes and damaged roads can also present ethical challenges related to bias.
This talk explores how bias in computer vision often poses an ethical challenge, regardless of the stakes involved. Kennedy discusses the limitations of technical solutions aimed at mitigating bias, and why “bias-free” AI may not be achievable. Instead, she focuses on the importance of adopting a “bias-aware” approach to responsible AI design and explores strategies that can be employed to achieve this.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/sensor-fusion-techniques-for-accurate-perception-of-objects-in-the-environment-a-presentation-from-sanborn-map-company/
Baharak Soltanian, Vice President of Research and Development for the Sanborn Map Company, presents the “Sensor Fusion Techniques for Accurate Perception of Objects in the Environment” tutorial at the May 2023 Embedded Vision Summit.
Increasingly, perceptual AI is being used to enable devices and systems to obtain accurate estimates of object locations, speeds and trajectories. In demanding applications, this is often best done using a heterogeneous combination of sensors (e.g., vision, radar, LiDAR). In this talk, Soltanian introduces techniques for combining data from multiple sensors to obtain accurate information about objects in the environment.
Soltanian briefly introduces the roles played by Kalman filters, particle filters, Bayesian networks and neural networks in this type of fusion. She then examines alternative fusion architectures, such as centralized and decentralized approaches, to better understand the trade-offs associated with different approaches to sensor fusion as used to enhance the ability of machines to understand their environment.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/updating-the-edge-ml-development-process-a-presentation-from-samsara/
Jim Steele, Vice President of Embedded Software at Samsara, presents the “Updating the Edge ML Development Process” tutorial at the May 2023 Embedded Vision Summit.
Samsara (NYSE:IOT) is focused on digitizing the world of operations. The company helps customers across many industries—including food and beverage, utilities and energy, field services and government—get information about their physical operations into the cloud, so they can operate more safely, efficiently and sustainably. Samsara’s sensors collect billions of data points per day and on-device processing is instrumental to its success. The company is constantly developing, improving and deploying ML models at the edge.
Samsara has found that the traditional development process—where ML scientists create models and hand them off to firmware engineers for embedded implementation—is slow and often produces difficult-to-resolve differences between the original model and the embedded implementation. In this talk, Steele presents an alternative development process that his company has adopted with good results. In this process, firmware engineers develop a general framework that ML scientists use to design, develop and deploy their models. This enables quick iterations and fewer confounding bugs.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/combating-bias-in-production-computer-vision-systems-a-presentation-from-red-cell-partners/
Alex Thaman, Chief Architect at Red Cell Partners, presents the “Combating Bias in Production Computer Vision Systems” tutorial at the May 2023 Embedded Vision Summit.
Bias is a critical challenge in predictive and generative AI that involves images of humans. People have a variety of body shapes, skin tones and other features that can be challenging to represent completely in training data. Without attention to bias risks, ML systems have the potential to treat people unfairly, and even to make humans more likely to do so.
In this talk, Thaman examines the ways in which bias can arise in visual AI systems. He shares techniques for detecting bias and strategies for minimizing it in production AI systems.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/developing-an-embedded-vision-ai-powered-fitness-system-a-presentation-from-peloton-interactive/
Sanjay Nichani, Vice President for Artificial Intelligence and Computer Vision at Peloton Interactive, presents the “Developing an Embedded Vision AI-powered Fitness System” tutorial at the May 2023 Embedded Vision Summit.
The Guide is Peloton’s first strength-training product that runs on a physical device and also the first that uses AI technology. It turns any TV into an interactive personal training studio. Members have access to Peloton’s instructors, who lead classes that use dumbbells and body weight. The Guide uses an innovative combination of hardware, software and user interface; it is one of only a handful of consumer products in this market that use real-time embedded computer vision, and Peloton’s members love it!
In this talk, Nichani shares insights into how the computer vision technology behind the Guide was built. He focuses on data, models and processes. The AI and Computer Vision team at Peloton worked through numerous obstacles by using synthetic data, advanced algorithms and field-testing iteration to create an affordable AI-powered device that enables at-home fitness for everyone.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!