TCAV is a method for interpreting machine learning models by quantitatively measuring the importance of user-chosen concepts for a model's predictions, even if those concepts were not part of the model's training data or input features. It does this by learning concept activation vectors (CAVs) that represent concepts and using the CAVs to calculate a model's sensitivity or importance to each concept via directional derivatives. TCAV was shown to validate ground truths from sanity check experiments, uncover geographical biases in widely used models, and match domain expert concepts for diabetic retinopathy versus those a model may use, helping ensure models' values and knowledge are properly aligned and reflected.
【論文紹介】Spatial Temporal Graph Convolutional Networks for Skeleton-Based Acti...ddnpaa
(参考文献)Sijie Yan, Yuanjun Xiong, Dahua Lin.Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. Association for the Advancement of Artificial Intelligence (AAAI)2018
【論文紹介】Spatial Temporal Graph Convolutional Networks for Skeleton-Based Acti...ddnpaa
(参考文献)Sijie Yan, Yuanjun Xiong, Dahua Lin.Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. Association for the Advancement of Artificial Intelligence (AAAI)2018
論文紹介:DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object D...Toru Tamaki
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heung-Yeung Shum, "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection" arXiv2022
https://arxiv.org/abs/2203.03605
論文紹介:DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object D...Toru Tamaki
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heung-Yeung Shum, "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection" arXiv2022
https://arxiv.org/abs/2203.03605
What are machines learning? How might that impact design?Andreas Wolters
Machines have picked up astonishing skills: they beat us at chess and Go, they have learnt to pilot a drone or how to create oil paintings. These impressive feats are pushing the boundaries, they show us what is possible. But machines have picked up skills that are less noteworthy, but way more useful: they have learnt to understand what we say, they can figure out which series we might want to binge on next or how to write compelling news articles.
Machines have learnt many things that are finding their ways into digital products. In this talk, I will give a bird’s-eye view of these developments — and I’ll dare to make some predictions about how these might impact the way we design products.
I gave this talk in Zürich on the 31st of October 2019.
Knowledge graphs generation is outpacing the ability to intelligently use the information that they contain. Octavian's work is pioneering Graph Artificial Intelligence to provide the brains to make knowledge graphs useful.
Our neural networks can take questions and knowledge graphs and return answers. Imagine:
a google assistant that reads your own knowledge graph (and actually works)
a BI tool reads your business' knowledge graph
a legal assistant that reads the graph of your case
Taking a neural network approach is important because neural networks deal better with the noise in data and variety in schema. Using neural networks allows people to ask questions of the knowledge graph in their own words, not via code or query languages.
Octavian's approach is to develop neural networks that can learn to manipulate graph knowledge into answers. This approach is radically different to using networks to generate graph embeddings. We believe this approach could transform how we interact with databases.
This presentation goes over Data Mining the City, a course taught at Columbia University GSAPP. This lecture also covers, complexity, cybernetics and agent based modeling.
Slide presentasi ini dibawakan oleh Imron Zuhri dalam acara Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
Human-Centered AI: Scalable, Interactive Tools for Interpretation and Attribu...polochau
Artificial intelligence and machine learning models are growing increasingly available, but many models offer predictions that are difficult to understand, evaluate and ultimately act upon. We present how scalable interactive visualization can be used to amplify people’s ability to understand and interact with large-scale data and complex models. We sample from projects where interactive visualization has provided key leaps of insight, from increased model explorability with models trained on millions of instances (ActiVis deployed with Facebook), increased usability for non-experts about state-of-the-art AI (GAN Lab open-sourced with Google Brain; went viral!), and our latest work Summit, the first interactive system that scalably summarizes and visualizes what features a deep learning model has learned and how those features interact to make predictions. We conclude by highlighting the next visual analytics research frontiers in AI.
World Usability Day, 2018
AI is becoming a greater part of the systems and products we design, yet algorithms have been shown time and time again to be imbued with unintentional racism, sexism, and other -isms. As design and AI fields converge can how researchers, designers, and developers work together to ensure that our powers are used for good, and not for accidental evil?
Chat bots been have popping up everywhere for silly things, but what if they can help us make the world more safe and secure? The work of designing secure systems often involves iterating over designs with a team but what if you don’t have a team? What if you could iterate over system design and analysis in a chat window and have a design document with safety constraints as the end product? This talk will present an original chat bot that will do just that
Continuous Automated Testing - Cast conference workshop august 2014Noah Sussman
CAST 2014 New York: The Art and Science of Testing
The Association for Software Testing www.associationforsoftwaretesting.org
COURSE DESCRIPTION
Automated tools provide test professionals with the capability to make relevant observations even in the fastest-paced environments. Automated testing is also a powerful tool for improving communication between software engineers. This is important because good communication is a prerequisite for growing a great software engineering organization.
This workshop will explore the continuous testing of software systems. Special focus will be given to the situation where the engineering team is deploying code to production so frequently that it is not possible to perform deep regression testing before each release.
People who participate in this course will learn pragmatic automated testing strategies like:
* Data analysis on the command line with find, grep and wc.
* Network analysis with Chrome Inspector, Charles and netcat.
* Using code churn to predict hotspots where bugs may occur.
* Putting stack traces in context with automated SCM blame emails.
* Using statsd to instrument a whole application.
* Testing in production.
* Monitoring-as-testing.
Technical level: participants should have some familiarity with the command line and with editing code using a text editor or IDE. Familiarity with Git, SVN or another version control system is helpful but not required. Likewise some knowledge of Web servers is helpful but not required. It is desirable for participants to bring laptops.
BIO
From 2010 to 2012 Noah was a Test Architect at Etsy. He helped build Etsy's continuous integration system, and has helped countless other engineers develop successful automated testing strategies.These days Noah is an independent consultant in New York. He is passionate about helping engineers understand and use automated tools as they work to scale their applications more effectively.
On Inherent Complexity of Computation, by Attila SzegediZeroTurnaround
The system you just recently deployed is likely an application processing some data, likely relying on some configuration, maybe using some plugins, certainly relying on some libraries, using services of an operating system running on some physical hardware. The previous sentence names 7 categories into which we compartmentalise various parts of a computation process that’s in the end going on in a physical world. Where do you draw the line of functionality between categories? From what vantage points do these distinctions become blurry? Finally, how does it all interact with the actual physical world in which the computation takes place? (What is the necessary physical minimum required to perform a computation, anyway?) Let’s make a journey from your AOP-assembled, plugin-injected, YAML-configured, JIT compiled, Hotspot-executed, Linux-on-x86 hosted Java application server talking JSON-over-HTTP-over-TCP-over-IP-over-Ethernet all the way down to electrons. And then back. Recorded at GeekOut 2013.
In this Lunch & Learn session, Chirag Jain gives us a friendly & gentle introduction to Machine Learning & walks through High-Level Learning frameworks using Linear Classifiers.
Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...MLconf
Understanding Human Impact: Social and Equity Assessments for AI Technologies
Social and Equity Impact Assessments have broad applications but can be a useful tool to explore and mitigate for Machine Learning fairness issues and can be applied to product specific questions as a way to generate insights and learnings about users, as well as impacts on society broadly as a result of the deployment of new and emerging technologies.
In this presentation, my goal is to advocate for and highlight the need to consult community and external stakeholder engagement to develop a new knowledge base and understanding of the human and social consequences of algorithmic decision making and to introduce principles, methods and process for these types of impact assessments.
Ted Willke - The Brain’s Guide to Dealing with Context in Language UnderstandingMLconf
The Brain’s Guide to Dealing with Context in Language Understanding
Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity.
In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.
Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...MLconf
Applying Computer Vision to Reduce Contamination in the Recycling Stream
With China’s recent refusal of most foreign recyclables, North American waste haulers are scrambling to figure out how to make on-shore recycling cost-effective in order to continue providing recycling services. Recyclables that were once being shipped to China for manual sorting are now primarily being redirected to landfills or incinerators. Without a solution, a nearly $5 billion annual recycling market could come to a halt.
Purity in the recycling stream is key to this effort as contaminants in the stream can increase the cost of operations, damage equipment and reduce the ability to create pure commodities suitable for creating recycled goods. This market disruption as a result of China’s new regulations, however, provides us the chance to re-examine and improve our current disposal & collection habits with modern monitoring & artificial intelligence technology.
Using images from our in-dumpster cameras, Compology has developed an ML-based process that helps identify, measure and alert for contaminants in recycling containers before they are picked-up, helping keep the recycling stream clean.
Our convolutional neural network flags potential instances of contamination inside a dumpster, enabling garbage haulers to know which containers have the wrong type of material inside. This allows them to provide targeted, timely education, and when appropriate, assess fines, to improve recycling compliance at the businesses and residences they serve, helping keep recycling services financially viable.
In this presentation, we will walk through our ML-based contamination measurement and scoring process by showing how Waste Management, a national waste hauler, has experienced 57% contamination reduction in nearly 2,000 containers over six months, This progress shows significant strides towards financially viable recycling services.
Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold RushMLconf
Quantum Computing: a Treasure Hunt, not a Gold Rush
Quantum computers promise a significant step up in computational power over conventional computers, but also suffer a number of counterintuitive limitations --- both in their computational model and in leading lab implementations. In this talk, we review how quantum computers compete with conventional computers and how conventional computers try to hold their ground. Then we outline what stands in the way of successful quantum ML applications.
Josh Wills - Data Labeling as Religious ExperienceMLconf
Data Labeling as Religious Experience
One of the most common places to deploy a production machine learning systems is as a replacement for a legacy rules-based system that is having a hard time keeping up with new edge cases and requirements. I'll be walking through the process and tooling we used to help us design, train, and deploy a model to replace a set of static rules we had for handling invite spam at Slack, talk about what we learned, and discuss some problems to solve in order to make these migrations easier for everyone.
Vinay Prabhu - Project GaitNet: Ushering in the ImageNet moment for human Gai...MLconf
Project GaitNet: Ushering in the ImageNet moment for human Gait kinematics
The emergence of the upright human bipedal gait can be traced back 4 to 2.8 million years ago, to the now extinct hominin Australopithecus afarensis. Fine grained analysis of gait using the modern MEMS sensors found on all smartphones not just reveals a lot about the person’s orthopedic and neuromuscular health status, but also has enough idiosyncratic clues that it can be harnessed as a passive biometric. While there were many siloed attempts made by the machine learning community to model Bipedal Gait sensor data, these were done with small datasets oft collected in restricted academic environs. In this talk, we will introduce the ImageNet moment for human gait analysis by presenting 'Project GaitNet', the largest ever planet-sized motion sensor based human bipedal gait dataset ever curated. We’ll also present the associated state-of-the-art results in classifying humans harnessing novel deep neural architectures and the related success stories we have enjoyed in transfer-learning into disparate domains of human kinematics analysis.
Jekaterina Novikova - Machine Learning Methods in Detecting Alzheimer’s Disea...MLconf
Machine Learning Methods in Detecting Alzheimer’s Disease from Speech and Language
Alzheimer's disease affects millions of people worldwide, and it is important to predict the disease as early and as accurate as possible. In this talk, I will discuss development of novel ML models that help classifying healthy people from those who develop Alzheimer's, using short samples of human speech. As an input to the model, features of different modalities are extracted from speech audio samples and transcriptions: (1) syntactic measures, such as e.g. production rules extracted from syntactic parse trees, (2) lexical measures, such as e.g. features of lexical richness and complexity and lexical norms, and (3) acoustic measures, such as e.g. standard Mel-frequency cepstral coefficients. I will present the ML model that detects cognitive impairment by reaching agreement among modalities. The resulting model is able to achieve state of the art performance in both supervised and semi-supervised manner, using manual transcripts of human speech. Additionally, I will discuss potential limitations of any fully-automated speech-based Alzheimer's disease detection model, focusing mostly on the analysis of the impact of a not-so-accurate automatic speech recognition (ASR) on the classification performance. To illustrate this, I will present the experiments with controlled amounts of artificially generated ASR errors and explain how the deletion errors affect Alzheimer's detection performance the most, due to their impact on the features of syntactic and lexical complexity.
Meghana Ravikumar - Optimized Image Classification on the CheapMLconf
Optimized Image Classification on the Cheap
In this talk, we anchor on building an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning -fine tuning and feature extraction- and the impact of hyperparameter optimization on these techniques. Once we define the most performant transfer learning technique for Stanford Cars, we will double the size of the dataset through image augmentation to boost the classifier’s performance. We will use Bayesian optimization to learn the hyperparameters associated with image transformations using the downstream image classifier’s performance as the guide. In conjunction with model performance, we will also focus on the features of these augmented images and the downstream implications for our image classifier.
To both maximize model performance on a budget and explore the impact of optimization on these methods, we apply a particularly efficient implementation of Bayesian optimization to each of these architectures in this comparison. Our goal is to draw on a rigorous set of experimental results that can help us answer the question: how can resource-constrained teams make trade-offs between efficiency and effectiveness using pre-trained models?
Noam Finkelstein - The Importance of Modeling Data CollectionMLconf
The Importance of Modeling Data Collection
Data sets used in machine learning are often collected in a systematically biased way - certain data points are more likely to be collected than others. We call this "observation bias". For example, in health care, we are more likely to see lab tests when the patient is feeling unwell than otherwise. Failing to account for observation bias can, of course, result in poor predictions on new data. By contrast, properly accounting for this bias allows us to make better use of the data we do have.
In this presentation, we discuss practical and theoretical approaches to dealing with observation bias. When the nature of the bias is known, there are simple adjustments we can make to nonparametric function estimation techniques, such as Gaussian Process models. We also discuss the scenario where the data collection model is unknown. In this case, there are steps we can take to estimate it from observed data. Finally, we demonstrate that having a small subset of data points that are known to be collected at random - that is, in an unbiased way - can vastly improve our ability to account for observation bias in the rest of the data set.
My hope is that attendees of this presentation will be aware of the perils of observation bias in their own work, and be equipped with tools to address it.
The Uncanny Valley of ML
Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human decision systems a similar effect can be measured in increased response time and decreased accuracy. These systems include radiology, judicial assignments, bus schedules, housing prices, power grids and a growing variety of applications. Unfortunately, the Uncanny Valley of ML can be hard to detect in these systems and can lead to degraded system performance when ML is introduced, at great expense. Here, we'll introduce key design principles for introducing ML into human decision systems to navigate around the Uncanny Valley and avoid its pitfalls.
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection TasksMLconf
Deep Learning Architectures for Semantic Relation Detection Tasks
Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for creating labelled datasets required for training deep learning models for classifying semantic relations between phrases. I will further present various neural network architectures that integrate morphological features into integrated path-based and distributional relation detection algorithms and demonstrate that this model outperforms state-of-the-art models in distinguishing semantic relations and is capable of efficiently handling multi-word expressions.
Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...MLconf
Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model
At Netflix, our main goal is to maximize our members’ enjoyment of the selected show by minimizing the amount of time it takes for them to find it. We try to achieve this goal by personalizing almost all the aspects of our product -- from what shows to recommend, to how to present these shows and construct their home-pages to what images to select per show, among many other things. Everything is recommendations for us and as an applied Machine Learning group, we spend our time building models for personalization that will eventually increase the joy and satisfaction of our members. In this talk we will primarily focus our attention on a) making a global deep learned recommender model that is regional tastes and popularity aware and b) adapting this model to changing taste preferences as well as dynamic catalog availability.
We will first go through some standard recommender system models that use Matrix Factorization and Topic Models and then compare and contrast them with more powerful and higher capacity deep learning based models such as sequence models that use recurrent neural networks. We will show what it entails to build a global model that is aware of regional taste preferences and catalog availability. We will show how models that are built on simple Maximum Likelihood principle fail to do that. We will then describe one solution that we have employed in order to enable the global deep learned models to focus their attention on capturing regional taste preferences and changing catalog.In the latter half of the talk, we will discuss how we do incremental learning of deep learned recommender system models. Why do we need to do that ? Everything changes with time. Users’ tastes change with time. What’s available on Netflix and what’s popular also change over time. Therefore, updating or improving recommendation systems over time is necessary to bring more joy to users. In addition to how we apply incremental learning, we will discuss some of the challenges we face involving large-scale data preparation, infrastructure setup for incremental model training as well as pipeline scheduling. The incremental training enables us to serve fresher models trained on fresher and larger amounts of data. This helps our recommender system to nicely and quickly adapt to catalog and users’ taste changes, and improve overall performance.
Vito Ostuni - The Voice: New Challenges in a Zero UI WorldMLconf
Vito Ostuni - The Voice: New Challenges in a Zero UI World
The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a daily delightful listening experience for millions of users. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic, and broad open-ended. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query.
We will also present the differences and challenges regarding evaluation of voice powered recommendation systems. Since pure voice interfaces do not contain visual UI elements, relevance labels need to be inferred through implicit actions such as play time, query reformulations or other types of session level information. Another difference is that while the typical recommendation task corresponds to recommending a ranked list of items, a voice play request translates into a single item play action. Thus, some considerations about closed feedback loops need to be made. In summary, improving the quality of voice interactions in music services is a relatively new challenge and many exciting opportunities for breakthroughs still remain. There are many new aspects of recommendation system interfaces to address to bring a delightful and effortless experience for voice users. We will share a few open challenges to solve for the future.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
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!
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
1. Interpretability beyond feature attribution:
Testing with Concept Activation Vectors
TCAV
Been Kim
Presenting work with a lot of awesome people inside and outside of Google:
Marten Wattenberg, Julius Adebayo, Justin Gilmer, Carrie Cai, James Wexler,
Fernanda Viegas, Ian Goodfellow, Mortiz Hardt, Rory Sayres
2. My goal
interpretability
!2
To use machine learning responsibly
we need to ensure that
1. our values are aligned
2. our knowledge is reflected
for everyone.
http://blogs.teradata.com/
Machine
Learning
Models
Human
3. My goal
interpretability
!3
To use machine learning responsibly
we need to ensure that
1. our values are aligned
2. our knowledge is reflected
for everyone.
http://blogs.teradata.com/
Machine
Learning
Models
Human
13. Agenda
1. Revisiting existing methods:
Saliency maps
2. Making explanations using the way humans think:
Testing with concept activation vectors (TCAV)
Post-training explanations
14. Agenda
Sanity Checks for Saliency Maps
Joint work with Adebayo, Gilmer, Goodfellow, Hardt, [NIPS 18]
1. Revisiting existing methods:
Saliency maps
2. Making explanations using the way humans think:
Testing with concept activation vectors (TCAV)
Post-training explanations
16. One of the most popular interpretability methods for images:
Saliency maps
!16
SmoothGrad [Smilkov, Thorat, K., Viégas, Wattenberg ’17]
Integrated gradient [Sundararajan, Taly, Yan ’17]
picture credit: @sayres
Caaaaan do! We’ve got
saliency maps to measure
importance of each pixel!
a logit
pixel i,j
17. One of the most popular interpretability methods for images:
Saliency maps
!17
SmoothGrad [Smilkov, Thorat, K., Viégas, Wattenberg ’17]
Integrated gradient [Sundararajan, Taly, Yan ’17]
widely used
for images
local
undestandingNN
humans’
subjective
judgement
picture credit: @sayres
18. One of the most popular interpretability methods for images:
Saliency maps
!18
Sanity check:
If I change M a lot, will human
perceive that E has changed a lot?
SmoothGrad [Smilkov, Thorat, K., Viégas, Wattenberg ’17]
Integrated gradient [Sundararajan, Taly, Yan ’17]
widely used
for images
local
undestandingNN
humans’
subjective
judgement
picture credit: @sayres
19. Some confusing behaviors of saliency maps.
Saliency map
Sanity Checks for Saliency Maps
Joint work with Adebayo, Gilmer, Goodfellow, Hardt, [NIPS 18]
20. Some confusing behaviors of saliency maps.
Randomized weights!
Network now makes garbage prediction.
Saliency map
Sanity Checks for Saliency Maps
Joint work with Adebayo, Gilmer, Goodfellow, Hardt, [NIPS 18]
21. Some confusing behaviors of saliency maps.
Saliency map
Randomized weights!
Network now makes garbage prediction.
!!!!!???!?
Sanity Checks for Saliency Maps
Joint work with Adebayo, Gilmer, Goodfellow, Hardt, [NIPS 18]
22. Some saliency maps look similar
when we randomize the network
(= making the network completely useless)
Before After
Guided
Backprop
Integrated
Gradient
Sanity Checks for Saliency Maps
Joint work with Adebayo, Gilmer, Goodfellow, Hardt, [NIPS 18]
23. • Potential human confirmation bias: Just because it
“makes sense” to humans, doesn’t mean they reflect
evidence for the prediction.
• Our discovery is consistent with other findings.
[Nie, Zhang, Patel ’18] [Ulyanov, Vedaldi, Lempitsky ’18]
• Some of these methods have been shown to be useful for
humans. Why? More studies needed.
What can we learn from this?
Sanity Checks for Saliency Maps
Joint work with Adebayo, Gilmer, Goodfellow, Hardt, [NIPS 18]
26. Agenda
TCAV [ICML’18]
Joint work with Wattenberg, Gilmer, Cai, Wexler, Viegas, Sayres
1. Revisiting existing methods:
Saliency maps
2. Making explanations using the way humans think:
Testing with concept activation vectors (TCAV)
Post-training explanations
30. prediction:
Cash machine
https://pair-code.github.io/saliency/
SmoothGrad [Smilkov, Thorat, K., Viégas, Wattenberg ’17]
What we really want to ask…
!30
Were there more pixels on the cash
machine than on the person?
Which concept mattered more?
Is this true for all other cash
machine predictions?
Did the ‘human’ concept matter?
Did the ‘wheels’ concept matter?
31. prediction:
Cash machine
https://pair-code.github.io/saliency/
SmoothGrad [Smilkov, Thorat, K., Viégas, Wattenberg ’17]
What we really want to ask…
!31
Oh no! I can’t express these concepts
as pixels!!
They weren’t my input features either!
Were there more pixels on the cash
machine than on the person?
Which concept mattered more?
Is this true for all other cash
machine predictions?
Did the ‘human’ concept matter?
Did the ‘wheels’ concept matter?
32. prediction:
Cash machine
https://pair-code.github.io/saliency/
SmoothGrad [Smilkov, Thorat, K., Viégas, Wattenberg ’17]
What we really want to ask…
!32
Were there more pixels on the cash
machine than on the person?
Which concept mattered more?
Is this true for all other cash
machine predictions?
Wouldn’t it be great if we can
quantitatively measure how
important any of these
user-chosen concepts are?
Did the ‘human’ concept matter?
Did the ‘wheels’ concept matter?
33. Quantitative explanation: how much a concept (e.g., gender, race)
was important for a prediction in a trained model.
…even if the concept was not part of the training.
Goal of TCAV:
Testing with Concept Activation Vectors
!33 ICML 2018
34. Goal of TCAV:
Testing with Concept Activation Vectors
!34
Doctor-ness
A trained
machine learning model
(e.g., neural network)
vactruth.com healthcommunitiesproviderservices
35. Goal of TCAV:
Testing with Concept Activation Vectors
!35
Doctor-ness
A trained
machine learning model
(e.g., neural network)
Was gender concept important
to this doctor image classifier?
vactruth.com healthcommunitiesproviderservices
36. Goal of TCAV:
Testing with Concept Activation Vectors
!36
Doctor-ness
TCAV score for
womennot women
Doctor
A trained
machine learning model
(e.g., neural network)
vactruth.com healthcommunitiesproviderservices
Was gender concept important
to this doctor image classifier?
37. Goal of TCAV:
Testing with Concept Activation Vectors
!37
Doctor-ness
TCAV score for
womennot women
Doctor
A trained
machine learning model
(e.g., neural network)
vactruth.com healthcommunitiesproviderservices
Was gender concept important
to this doctor image classifier?
TCAV provides
quantitative importance of
a concept if and only if your
network learned about it.
38. Goal of TCAV:
Testing with Concept Activation Vectors
!38
zebra-ness
A trained
machine learning model
(e.g., neural network)
Was striped concept important
to this zebra image classifier?
TCAV score for
not stripedstriped
Zebra
TCAV provides
quantitative importance of
a concept if and only if your
network learned about it.
39. TCAV
TCAV:
Testing with Concept Activation Vectors
!39
zebra-ness
A trained
machine learning model
(e.g., neural network)
Was striped concept important
to this zebra image classifier?
1. Learning CAVs
1. How to define
concepts?
40. Defining concept activation vector (CAV)
Inputs:
!40
Random
images
Examples of
concepts
A trained network under investigation
and
Internal tensors
41. !41
Inputs:
Train a linear classifier to
separate activations.
CAV ( ) is the vector
orthogonal to the decision
boundary.
[Smilkov ’17, Bolukbasi ’16 , Schmidt ’15]
Defining concept activation vector (CAV)
42. TCAV
TCAV:
Testing with Concept Activation Vectors
!42
zebra-ness
A trained
machine learning model
(e.g., neural network)
Was striped concept important
to this zebra image classifier?
1. Learning CAVs 2. Getting TCAV score
2. How are the CAVs
useful to get
explanations?
43. TCAV core idea:
Derivative with CAV to get prediction sensitivity
!43
TCAV score
Directional derivative with CAV
45. TCAV
TCAV:
Testing with Concept Activation Vectors
!45
zebra-ness
A trained
machine learning model
(e.g., neural network)
Was striped concept important
to this zebra image classifier?
1. Learning CAVs 2. Getting TCAV score
46. TCAV
TCAV:
Testing with Concept Activation Vectors
!46
zebra-ness
A trained
machine learning model
(e.g., neural network)
Was striped concept important
to this zebra image classifier?
1. Learning CAVs 2. Getting TCAV score 3. CAV validation
Qualitative
Quantitative
51. Check the distribution of
is statistically
different from random
using t-test
TCAV score
random
……
Zebra
Quantitative validation:
Guarding against spurious CAV
!51
*
52. Recap TCAV:
Testing with Concept Activation Vectors
!52
1. Learning CAVs 2. Getting TCAV score 3. CAV validation
Qualitative
Quantitative
TCAV provides
quantitative importance of
a concept if and only if your
network learned about it.
Even if your training data wasn’t
tagged with the concept
Even if your input feature did
not include the concept
53. Results
1. Sanity check experiment
2. Biases in Inception V3 and GoogleNet
3. Domain expert confirmation from Diabetic Retinopathy
!53
54. Results
1. Sanity check experiment
2. Biases from Inception V3 and GoogleNet
3. Domain expert confirmation from Diabetic Retinopathy
!54
57. Sanity check experiment setup
!57
An image
+
Potentially noisy Caption
image
concept
models can use either
image or caption
concept for
classification.
caption
concept
58. Sanity check experiment setup
!58 Caption noise level in training set
An image
+
Potentially noisy Caption
image
concept
models can use either
image or caption
concept for
classification.
caption
concept
59. Sanity check experiment setup
!59
Test accuracy
with
no caption image
=
Importance of
image concept
Caption noise level in training set
image
concept
caption
concept
models can use either
image or caption
concept for
classification.
62. Can saliency maps communicate
the same information?
!62
Ground truth
Image
concept
Image
concept
Image
concept
Image
concept
Image
with caption
63. Human subject experiment:
Can saliency maps communicate the same
information?
• 50 turkers are
• asked to judge importance of
image vs. c. ept given saliency
maps.
• asked to indicate their confidence
• shown 3 classes (cab, zebra,
cucumber) x 2 saliency maps for
one model
!63
image caption
64. !64
• Random chance: 50%
• Human performance with
saliency map: 52%
• Humans can’t agree: more
than 50% no significant
consensus
• Humans are very confident
even when they are wrong.
Human subject experiment:
Can saliency maps communicate the same
information?
65. Human subject experiment:
Can saliency maps communicate the same
information?
• Random chance: 50%
• Human performance with
saliency map: 52%
• Humans can’t agree: more
than 50% no significant
consensus
• Humans are very confident
even when they are wrong.
!65
66. Results
1. Sanity check experiment
2. Biases from Inception V3 and GoogleNet
3. Domain expert confirmation from Diabetic Retinopathy
!66
68. TCAV in
Two widely used image prediction models
!68
Geographical
bias!
http://www.abc.net.au
69. TCAV in
Two widely used image prediction models
!69
Quantitative
confirmation to
previously
qualitative
findings
[Stock & Cisse,
2017]
Geographical
bias?
70. TCAV in
Two widely used image prediction models
!70
Quantitative
confirmation to
previously
qualitative
findings
[Stock & Cisse,
2017]
Geographical
bias?
Goal of interpretability:
To use machine learning responsibly
we need to ensure that
1. our values are aligned
2. our knowledge is reflected
71. Results
1. Sanity check experiment
2. Biases Inception V3 and GoogleNet
3. Domain expert confirmation from Diabetic Retinopathy
!71
72. Diabetic Retinopathy
• Treatable but sight-threatening conditions
• Have model to with accurate prediction of DR (85%)
[Krause et al., 2017]
!72
Concepts the ML model uses
Vs
Diagnostic Concepts human doctors use
73. Collect human doctor’s knowledge
!73
PRP
PRH/VH
NV/FP
VB
MA HMA
DR level 4
DR level 1
Concepts
belong to
this level
Concepts do not
belong to
this level
74. TCAV for Diabetic Retinopathy
!74
PRP PRH/VH NV/FP VB
Green: domain expert’s label on concepts belong to the level
Red: domain expert’s label on concepts does not belong to the level
Prediction
class
DR level 4
Prediction
accuracy
High
Example
TCAV scores TCAV shows the
model is consistent
with doctor’s
knowledge when
model is accurate
75. PRP PRH/VH NV/FP VB
Green: domain expert’s label on concepts belong to the level
Red: domain expert’s label on concepts does not belong to the level
Prediction
class
DR level 4
Prediction
accuracy
High
Example
TCAV scores TCAV shows the
model is consistent
with doctor’s
knowledge when
model is accurate
TCAV shows the
model is inconsistent
with doctor’s
knowledge for classes
when model is less
accurate
DR level 1 Med
TCAV for Diabetic Retinopathy
!75
MA HMA
76. PRP PRH/VH NV/FP VB
Green: domain expert’s label on concepts belong to the level
Red: domain expert’s label on concepts does not belong to the level
Prediction
class
DR level 4
Prediction
accuracy
High
Example
TCAV scores TCAV shows the
model is consistent
with doctor’s
knowledge when
model is accurate
Level 1 was often confused to level 2.
DR level 1 Low
TCAV shows the
model is inconsistent
with doctor’s
knowledge for classes
when model is less
accurate
TCAV for Diabetic Retinopathy
!76
MA HMA
Goal of interpretability:
To use machine learning responsibly
we need to ensure that
1. our values are aligned
2. our knowledge is reflected
77. Summary:
Testing with Concept Activation Vectors
!77
stripes concept (score: 0.9)
was important to zebra class
for this trained network.
PRP PRH/VH NV/FP VB
Our values Our knowledge
TCAV provides
quantitative importance of
a concept if and only if your
network learned about it.
Joint work with Wattenberg, Gilmer, Cai, Wexler, Viegas, Sayres
ICML 2018