A joint report between EY and LSE with contribution from Seldon. This report describes research undertaken by The London School of Economics and Political Science on behalf of EY Financial Services to investigate the use of Artificial Intelligence and Machine Learning and to provide one use case for each of the following sectors; Insurance, Banking & Capital Markets, and Wealth & Asset Management.
A joint report between EY and LSE with contribution from Seldon. This report describes research undertaken by The London School of Economics and Political Science on behalf of EY Financial Services to investigate the use of Artificial Intelligence and Machine Learning and to provide one use case for each of the following sectors; Insurance, Banking & Capital Markets, and Wealth & Asset Management.
In this new Accenture Finance & Risk presentation we explore machine learning as a solution to some of the most important challenges faced by the banking sector today. To learn more, read our blog on Machine Learning in Banking: https://accntu.re/2oTVJiX
Loan default prediction with machine language Aayush Kumar
Deafult-Loan-Prediction-Project-Using-Random-Forest-and-Decision-Tree
Deafult Loan Prediction Project Using Random Forest and Decision Tree, In This Project we use loan data from Leanding Club Random Forest Project - Deafult Loan Prediction For this project we will be exploring publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.
Deep Credit Risk Ranking with LSTM with Kyle GroveDatabricks
Find out how Teradata and some of world’s largest financial institutions are innovating credit risk ranking with deep learning techniques and AnalyticOps. With the AnalyticOps framework, these organization have built models with increased accuracy to drive more profitable lending decisions, while being explainable to regulators.
Join us for a live session and learn about:
A machine learning ensemble including LSTM that achieves 90%+ accuracy at predicting delinquency/default, exceeding conventional credit risk methods by more than 20%.
A model management accelerator that is used to build and deploy the models in an integrated cloud platform, based on TensorFlow and Spark, and supports Keras, DeepLearning4J and SparkML models.
An innovative technique for model interpretability that obviates LIME’s need to generate synthetic examples.
Regulating Generative AI - LLMOps pipelines with TransparencyDebmalya Biswas
The growing adoption of Gen AI, esp. LLMs, has re-ignited the discussion around AI Regulations — to ensure that AI/ML systems are responsibly trained and deployed. Unfortunately, this effort is complicated by multiple governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms.
Rather than trying to understand and regulate all types of AI, we recommend a different (and practical) approach in this talk based on AI Transparency —
to transparently outline the capabilities of the AI system based on its training methodology and set realistic expectations with respect to what it can (and cannot) do.
We outline LLMOps architecture patterns and show how the proposed approach can be integrated at different stages of the LLMOps pipeline capturing the model's capabilities. In addition, the AI system provider also specifies scenarios where (they believe that) the system can make mistakes, and recommends a ‘safe’ approach with guardrails for those scenarios.
Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to get started with Artificial Intelligence on the AWS Cloud. In particular, we will explore AWS cloud-native machine learning and deep learning technologies that address a range of different use cases and needs. These include AWS Lex, which provides natural language understanding (NLU) and automatic speech recognition (ASR); Amazon Rekognition, which provides visual search and image recognition capabilities; Amazon Polly for text-to-speech (TTS) capabilities; and Amazon Machine Learning tools. The session will also cover the AWS Deep Learning AMI, which lets you run deep learning in the cloud at any scale.
If you're based in South East Asia, join us for upcoming AWS Webinar Series https://aws.amazon.com/events/asean/webinars/
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
Measuring and Managing Credit Risk With Machine Learning and Artificial Intel...accenture
In recent years, technological developments have undergone in-depth analysis among banks, but we are still far from attaining mature levels both at the methodological and at the credit granting, monitoring and control process levels. Banks should equip themselves with new and more structured Model Risk frameworks to manage new Machine Learning model validation paradigms. Learn more from Accenture Finance & Risk: https://accntu.re/2qGUUMx
Managing and Versioning Machine Learning Models in PythonSimon Frid
Practical machine learning is becoming messy, and while there are lots of algorithms, there is still a lot of infrastructure needed to manage and organize the models and datasets. Estimators and Django-Estimators are two python packages that can help version data sets and models, for deployment and effective workflow.
ZIGRAM is a high impact organization which operates in the Data Asset space.
Our team is made up of professionals from varied domains like data science, technology, sales, financial services, research and business consulting.
Our aim is to deliver value to clients by Building and Managing Data Assets across use cases - thereby boosting revenues and reducing the cost of doing business, in a data driven world.
This slide deck is a compilation of slides from various sources that stitches together a gentle introduction to Artificial Intelligence, Machine Learning and Deep Learning.
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
Credit Card Fraud Detection Using ML In DatabricksDatabricks
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
Customer Churn Prediction Using Machine Learning Techniques: the case of Lion...IIJSRJournal
The growth of an insurance company is measured by the number of policies purchased by customers. To keep the company growing and having more customers, the customer churn prediction model is crucial to maintain its competitiveness. Even if the company has good service delivery, it is important to identify the customer’s behavior and be able to predict the future churners. The main contribution to our work is the development of a predictive model that can proactively predict the customer who will leave the insurance company. The model developed in this study uses machine learning techniques on lion insurance data. Another main contribution of this study is the labeling of the data using an unsupervised algorithm on 12007 rows with 9 features from which 2 clusters were generated using the K-means++ algorithm. As the cluster results found are imbalanced, the synthetic minority oversampling technique was applied to the training dataset. The Deep Neural Network algorithm turns out to be a very effective model for predicting customer churn, reaching an accuracy of 98.81%. The two years of customer data were obtained from lion insurance and used to train test, and evaluate the model. The Randomized optimization technique was selected for each algorithm. However, the best results were obtained by a deep neural network with a structure of (9-55-55-55-55-55-1). This algorithm was selected for classification in this churn prediction study.
GenerativeAI and Automation - IEEE ACSOS 2023.pptxAllen Chan
Generative AI has been rapidly evolving, enabling different and more sophisticated interactions with Large Language Models (LLMs) like those available in IBM watsonx.ai or Meta Llama2. In this session, we will take a use case based approach to look at how we can leverage LLMs together with existing automation technologies like Workflow, Content Management, and Decisions to enable new solutions.
Artificial Intelligence in the Financial IndustriesGerardo Salandra
As Artificial Intelligence makes its way into our lives, many financial institutions are faced with the difficult question “Should AI be embraced?”. While the eagerness to integrate AI into the financial sector has waxed and waned over the past few decades, it now appears that Fintech is ready to dive head-first into AI as a standard for handling customer transactions, financial risk assessment, industry regulatory compliance and reduced institutional costs.
There is no doubt that AI can be invaluable for the financial industry, but it comes at a price. We expect to witness both success stories and tragic failures over the course of the next few years. With any first-generation technology, there are going to be bugs to solve, and a learning curve before intimate industry familiarity with AI is obtained.
AI is not only going to revolutionize the financial industry but become the industry itself.
Pixels.camp - Machine Learning: Building Successful Products at ScaleAntónio Alegria
See video at: https://www.youtube.com/watch?v=p7s1lcaeoZk
How to build Machine Learning products that scale and autonomously evolve using open source technologies like Spark, Cassandra, Hadoop and many others.
While data technologies have been exploding and becoming commoditized, using them effectively to build a product that delivers real value to users can be a mysterious art. A lot of companies still use "gather data, think about it later" but then fail to put that data to work.
Let’s demystify machine learning system’s Data Science lifecycle (from data to production to a continuously evolvable system). Explore the fundamental recipe to build data-learning products that put data to work and provide experiences that are, ironically, more human.
In this new Accenture Finance & Risk presentation we explore machine learning as a solution to some of the most important challenges faced by the banking sector today. To learn more, read our blog on Machine Learning in Banking: https://accntu.re/2oTVJiX
Loan default prediction with machine language Aayush Kumar
Deafult-Loan-Prediction-Project-Using-Random-Forest-and-Decision-Tree
Deafult Loan Prediction Project Using Random Forest and Decision Tree, In This Project we use loan data from Leanding Club Random Forest Project - Deafult Loan Prediction For this project we will be exploring publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.
Deep Credit Risk Ranking with LSTM with Kyle GroveDatabricks
Find out how Teradata and some of world’s largest financial institutions are innovating credit risk ranking with deep learning techniques and AnalyticOps. With the AnalyticOps framework, these organization have built models with increased accuracy to drive more profitable lending decisions, while being explainable to regulators.
Join us for a live session and learn about:
A machine learning ensemble including LSTM that achieves 90%+ accuracy at predicting delinquency/default, exceeding conventional credit risk methods by more than 20%.
A model management accelerator that is used to build and deploy the models in an integrated cloud platform, based on TensorFlow and Spark, and supports Keras, DeepLearning4J and SparkML models.
An innovative technique for model interpretability that obviates LIME’s need to generate synthetic examples.
Regulating Generative AI - LLMOps pipelines with TransparencyDebmalya Biswas
The growing adoption of Gen AI, esp. LLMs, has re-ignited the discussion around AI Regulations — to ensure that AI/ML systems are responsibly trained and deployed. Unfortunately, this effort is complicated by multiple governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms.
Rather than trying to understand and regulate all types of AI, we recommend a different (and practical) approach in this talk based on AI Transparency —
to transparently outline the capabilities of the AI system based on its training methodology and set realistic expectations with respect to what it can (and cannot) do.
We outline LLMOps architecture patterns and show how the proposed approach can be integrated at different stages of the LLMOps pipeline capturing the model's capabilities. In addition, the AI system provider also specifies scenarios where (they believe that) the system can make mistakes, and recommends a ‘safe’ approach with guardrails for those scenarios.
Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to get started with Artificial Intelligence on the AWS Cloud. In particular, we will explore AWS cloud-native machine learning and deep learning technologies that address a range of different use cases and needs. These include AWS Lex, which provides natural language understanding (NLU) and automatic speech recognition (ASR); Amazon Rekognition, which provides visual search and image recognition capabilities; Amazon Polly for text-to-speech (TTS) capabilities; and Amazon Machine Learning tools. The session will also cover the AWS Deep Learning AMI, which lets you run deep learning in the cloud at any scale.
If you're based in South East Asia, join us for upcoming AWS Webinar Series https://aws.amazon.com/events/asean/webinars/
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
Measuring and Managing Credit Risk With Machine Learning and Artificial Intel...accenture
In recent years, technological developments have undergone in-depth analysis among banks, but we are still far from attaining mature levels both at the methodological and at the credit granting, monitoring and control process levels. Banks should equip themselves with new and more structured Model Risk frameworks to manage new Machine Learning model validation paradigms. Learn more from Accenture Finance & Risk: https://accntu.re/2qGUUMx
Managing and Versioning Machine Learning Models in PythonSimon Frid
Practical machine learning is becoming messy, and while there are lots of algorithms, there is still a lot of infrastructure needed to manage and organize the models and datasets. Estimators and Django-Estimators are two python packages that can help version data sets and models, for deployment and effective workflow.
ZIGRAM is a high impact organization which operates in the Data Asset space.
Our team is made up of professionals from varied domains like data science, technology, sales, financial services, research and business consulting.
Our aim is to deliver value to clients by Building and Managing Data Assets across use cases - thereby boosting revenues and reducing the cost of doing business, in a data driven world.
This slide deck is a compilation of slides from various sources that stitches together a gentle introduction to Artificial Intelligence, Machine Learning and Deep Learning.
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
Credit Card Fraud Detection Using ML In DatabricksDatabricks
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
Customer Churn Prediction Using Machine Learning Techniques: the case of Lion...IIJSRJournal
The growth of an insurance company is measured by the number of policies purchased by customers. To keep the company growing and having more customers, the customer churn prediction model is crucial to maintain its competitiveness. Even if the company has good service delivery, it is important to identify the customer’s behavior and be able to predict the future churners. The main contribution to our work is the development of a predictive model that can proactively predict the customer who will leave the insurance company. The model developed in this study uses machine learning techniques on lion insurance data. Another main contribution of this study is the labeling of the data using an unsupervised algorithm on 12007 rows with 9 features from which 2 clusters were generated using the K-means++ algorithm. As the cluster results found are imbalanced, the synthetic minority oversampling technique was applied to the training dataset. The Deep Neural Network algorithm turns out to be a very effective model for predicting customer churn, reaching an accuracy of 98.81%. The two years of customer data were obtained from lion insurance and used to train test, and evaluate the model. The Randomized optimization technique was selected for each algorithm. However, the best results were obtained by a deep neural network with a structure of (9-55-55-55-55-55-1). This algorithm was selected for classification in this churn prediction study.
GenerativeAI and Automation - IEEE ACSOS 2023.pptxAllen Chan
Generative AI has been rapidly evolving, enabling different and more sophisticated interactions with Large Language Models (LLMs) like those available in IBM watsonx.ai or Meta Llama2. In this session, we will take a use case based approach to look at how we can leverage LLMs together with existing automation technologies like Workflow, Content Management, and Decisions to enable new solutions.
Artificial Intelligence in the Financial IndustriesGerardo Salandra
As Artificial Intelligence makes its way into our lives, many financial institutions are faced with the difficult question “Should AI be embraced?”. While the eagerness to integrate AI into the financial sector has waxed and waned over the past few decades, it now appears that Fintech is ready to dive head-first into AI as a standard for handling customer transactions, financial risk assessment, industry regulatory compliance and reduced institutional costs.
There is no doubt that AI can be invaluable for the financial industry, but it comes at a price. We expect to witness both success stories and tragic failures over the course of the next few years. With any first-generation technology, there are going to be bugs to solve, and a learning curve before intimate industry familiarity with AI is obtained.
AI is not only going to revolutionize the financial industry but become the industry itself.
Pixels.camp - Machine Learning: Building Successful Products at ScaleAntónio Alegria
See video at: https://www.youtube.com/watch?v=p7s1lcaeoZk
How to build Machine Learning products that scale and autonomously evolve using open source technologies like Spark, Cassandra, Hadoop and many others.
While data technologies have been exploding and becoming commoditized, using them effectively to build a product that delivers real value to users can be a mysterious art. A lot of companies still use "gather data, think about it later" but then fail to put that data to work.
Let’s demystify machine learning system’s Data Science lifecycle (from data to production to a continuously evolvable system). Explore the fundamental recipe to build data-learning products that put data to work and provide experiences that are, ironically, more human.
Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud ...DataWorks Summit
PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll explain how we employ sophisticated machine learning tools – open source and in-house developed. I will also present results from experiments conducted on a very large graph data set containing millions of edges and vertices.
Webinar: Fighting Fraud with Graph DatabasesDataStax
Modern fraud detection has significant engineering challenges. From managing the ingestion and scale, to the analysis of those patterns in real-time. We'll first take a look at how DataStax Enterprise Graph, powered by the industry’s best version of Apache Cassandra™, can meet those requirements to help you save the day.
How to Create 80% of a Big Data Pilot ProjectGreg Makowski
When evaluating Open Source Software, or other software of a certain size or complexity, organizations frequently want to conduct a Pilot project, or Proof of Concept (POC). This talk describes a process to reduce the length of the Pilot, by leveraging configurations from performance testing to POC starting configurations.
Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...Flink Forward
ING is using Apache Flink for creating streaming analytics ('fast data') solutions. We created a platform with Flink and Kafka that offers high-throughput and low-latency, ideally suited for complex and demanding use cases in the international bank such as customer notifications and fraud detection. These use cases require fast data processing and a business rules engine and/or machine learning evaluation system. Integrating these components together in a always-on, distributed architecture can be challenging. In this talk, we'll start with a brief overview of the use cases. You'll learn why ING chose Flink for these use cases, and see the architecture of the streaming data platform in depth. Finally, we'll share some lessons learned and useful insights for organizations who embark on a similar journey.
Real-Time With AI – The Convergence Of Big Data And AI by Colin MacNaughtonSynerzip
Making AI real-time to meet mission-critical system demands put a new spin on your architecture. To deliver AI-based applications that will scale as your data grows takes a new approach where the data doesn’t become the bottleneck. We all know that the deeper the data the better the results and the lower the risk. However, doing thousands of computations on big data requires new data structures and messaging to be used together to deliver real-time AI. During this session will look at real reference architectures and review the new techniques that were needed to make AI Real-Time.
Python & Serverless: Refactor your monolith piece by pieceGiuseppe Vallarelli
The introduction of the Function as a Service (Serverless) technologies is facilitating the adoption of a microservices based architecture. In this talk we will discuss why this might be useful (scalability / cost opportunities / choosing the right tool for the job) and what strategies we can follow to either extract independent services or add new capabilities using an event driven architecture.
IT organizations adopting agile development often struggle when applying agile to anything other than small, mid-sized, or non-critical applications. Because IT organizations must deal with the myriad business rules, non-functional requirements, industry regulations, and associated audits, the software requirements and resulting user stories can easily become too complex and interrelated. Tony Higgins says that approaches are surfacing which allow complex IT environments to improve upfront scoping, promote reuse, embrace living documentation, and deal with continuous requirements from a testing perspective. Join Tony as he shares his experiences on how requirements and tests can become one, and user stories exist as executable tests using behavior-driven design. See how all this provides testers with what's needed up front and results in better support for agile testing within IT.
IWMW 2000: Self Evident Applications for UniversitiesIWMW
Slides for the plenary talk on "Self Evident Applications for Universities" presented at the IWMW 2000 event held at the University of Bath on 6-8 September 2000.
See http://www.ukoln.ac.uk/web-focus/events/workshops/webmaster-2000/sessions.html#smart
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.
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/
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
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.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
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.
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.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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.