The document discusses several topics related to artificial collective intelligence including:
1. Learning to compete through designing game environments, machine bidding in auctions, and creativity learning by generating texts, images, music and poetry.
2. Learning to collaborate through developing AI that can play StarCraft together as a team.
3. Using generative adversarial networks (GANs) to generate realistic data samples by having a generator and discriminator compete against each other. GANs have been applied to generate images and text.
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
Zaikun Xu from the Università della Svizzera Italiana presented this deck at the 2016 Switzerland HPC Conference.
“In the past decade, deep learning as a life-changing technology, has gained a huge success on various tasks, including image recognition, speech recognition, machine translation, etc. Pio- neered by several research groups, Geoffrey Hinton (U Toronto), Yoshua Benjio (U Montreal), Yann LeCun(NYU), Juergen Schmiduhuber (IDSIA, Switzerland), Deep learning is a renaissance of neural network in the Big data era.
Neural network is a learning algorithm that consists of input layer, hidden layers and output layers, where each circle represents a neural and the each arrow connection associates with a weight. The way neural network learns is based on how different between the output of output layer and the ground truth, following by calculating the gradients of this discrepancy w.r.b to the weights and adjust the weight accordingly. Ideally, it will find weights that maps input X to target y with error as lower as possible.”
Watch the video presentation: http://insidehpc.com/2016/03/deep-learning/
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Scene classification using Convolutional Neural Networks - Jayani WithanawasamWithTheBest
Scene Classification is used in Convolutional Neural Networks (CNNs). We seek to redefine computer vision as an AI problem, understand the importance of scene classification as well as challenges, and the difference between traditional machine learning and deep learning. Additionally, we discuss CNNs, using caffe for implementing CNNs and importact reosources to imorove.
CNNs
Jayani Withanawasam
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
Zaikun Xu from the Università della Svizzera Italiana presented this deck at the 2016 Switzerland HPC Conference.
“In the past decade, deep learning as a life-changing technology, has gained a huge success on various tasks, including image recognition, speech recognition, machine translation, etc. Pio- neered by several research groups, Geoffrey Hinton (U Toronto), Yoshua Benjio (U Montreal), Yann LeCun(NYU), Juergen Schmiduhuber (IDSIA, Switzerland), Deep learning is a renaissance of neural network in the Big data era.
Neural network is a learning algorithm that consists of input layer, hidden layers and output layers, where each circle represents a neural and the each arrow connection associates with a weight. The way neural network learns is based on how different between the output of output layer and the ground truth, following by calculating the gradients of this discrepancy w.r.b to the weights and adjust the weight accordingly. Ideally, it will find weights that maps input X to target y with error as lower as possible.”
Watch the video presentation: http://insidehpc.com/2016/03/deep-learning/
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Scene classification using Convolutional Neural Networks - Jayani WithanawasamWithTheBest
Scene Classification is used in Convolutional Neural Networks (CNNs). We seek to redefine computer vision as an AI problem, understand the importance of scene classification as well as challenges, and the difference between traditional machine learning and deep learning. Additionally, we discuss CNNs, using caffe for implementing CNNs and importact reosources to imorove.
CNNs
Jayani Withanawasam
Deep Neural Networks that talk (Back)… with styleRoelof Pieters
Talk at Nuclai 2016 in Vienna
Can neural networks sing, dance, remix and rhyme? And most importantly, can they talk back? This talk will introduce Deep Neural Nets with textual and auditory understanding and some of the recent breakthroughs made in these fields. It will then show some of the exciting possibilities these technologies hold for "creative" use and explorations of human-machine interaction, where the main theorem is "augmentation, not automation".
http://events.nucl.ai/track/cognitive/#deep-neural-networks-that-talk-back-with-style
Slides from Portland Machine Learning meetup, April 13th.
Abstract: You've heard all the cool tech companies are using them, but what are Convolutional Neural Networks (CNNs) good for and what is convolution anyway? For that matter, what is a Neural Network? This talk will include a look at some applications of CNNs, an explanation of how CNNs work, and what the different layers in a CNN do. There's no explicit background required so if you have no idea what a neural network is that's ok.
AI&BigData Lab. Артем Чернодуб "Распознавание изображений методом Lazy Deep ...GeeksLab Odessa
23.05.15 Одесса. Impact Hub Odessa. Конференция AI&BigData Lab
Артем Чернодуб (Computer Vision Team, ZZ Wolf)
"Распознавание изображений методом Lazy Deep Learning в фото-органайзере ZZ Photo"
В докладе рассматривается проблема распознавания изображений методами машинного зрения. Проводится краткий обзор существующих подзадач в этой области (детекция обьектов, классификация сцен, ассоциативный поиск в базах изображений, распознавание лиц и др.) и современных методов их решения с акцентом на глубокое обучение (Deep Learning).
Подробнее:
http://geekslab.co/
https://www.facebook.com/GeeksLab.co
https://www.youtube.com/user/GeeksLabVideo
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Lecture by Xavier Giro-i-Nieto (UPC) at the Master in Computer Vision Barcelona (March 30, 2016).
http://pagines.uab.cat/mcv/
This lecture provides an overview of computer vision analysis of images at a global scale using deep learning techniques. The session is structure in two blocks: a first one addressing end to end learning, and a second one focusing on applications that use off-the-shelf features.
Please submit your feedback as comments on the GDrive source slides:
https://docs.google.com/presentation/d/1ms9Fczkep__9pMCjxtVr41OINMklcHWc74kwANj7KKI/edit?usp=sharing
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Deep Reinforcement Leaning In Machine LearningInterCon
A session by Dr Ganapathi Pulipaka, Chief Data Scientist, Accenture on the topic of 'Deep Reinforcement Leaning In Machine Learning' at InterCon USA 2019, held at Caesars Palace, Las Vegas on 18-20 June, 2019.
Deep Neural Networks that talk (Back)… with styleRoelof Pieters
Talk at Nuclai 2016 in Vienna
Can neural networks sing, dance, remix and rhyme? And most importantly, can they talk back? This talk will introduce Deep Neural Nets with textual and auditory understanding and some of the recent breakthroughs made in these fields. It will then show some of the exciting possibilities these technologies hold for "creative" use and explorations of human-machine interaction, where the main theorem is "augmentation, not automation".
http://events.nucl.ai/track/cognitive/#deep-neural-networks-that-talk-back-with-style
Slides from Portland Machine Learning meetup, April 13th.
Abstract: You've heard all the cool tech companies are using them, but what are Convolutional Neural Networks (CNNs) good for and what is convolution anyway? For that matter, what is a Neural Network? This talk will include a look at some applications of CNNs, an explanation of how CNNs work, and what the different layers in a CNN do. There's no explicit background required so if you have no idea what a neural network is that's ok.
AI&BigData Lab. Артем Чернодуб "Распознавание изображений методом Lazy Deep ...GeeksLab Odessa
23.05.15 Одесса. Impact Hub Odessa. Конференция AI&BigData Lab
Артем Чернодуб (Computer Vision Team, ZZ Wolf)
"Распознавание изображений методом Lazy Deep Learning в фото-органайзере ZZ Photo"
В докладе рассматривается проблема распознавания изображений методами машинного зрения. Проводится краткий обзор существующих подзадач в этой области (детекция обьектов, классификация сцен, ассоциативный поиск в базах изображений, распознавание лиц и др.) и современных методов их решения с акцентом на глубокое обучение (Deep Learning).
Подробнее:
http://geekslab.co/
https://www.facebook.com/GeeksLab.co
https://www.youtube.com/user/GeeksLabVideo
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Lecture by Xavier Giro-i-Nieto (UPC) at the Master in Computer Vision Barcelona (March 30, 2016).
http://pagines.uab.cat/mcv/
This lecture provides an overview of computer vision analysis of images at a global scale using deep learning techniques. The session is structure in two blocks: a first one addressing end to end learning, and a second one focusing on applications that use off-the-shelf features.
Please submit your feedback as comments on the GDrive source slides:
https://docs.google.com/presentation/d/1ms9Fczkep__9pMCjxtVr41OINMklcHWc74kwANj7KKI/edit?usp=sharing
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Deep Reinforcement Leaning In Machine LearningInterCon
A session by Dr Ganapathi Pulipaka, Chief Data Scientist, Accenture on the topic of 'Deep Reinforcement Leaning In Machine Learning' at InterCon USA 2019, held at Caesars Palace, Las Vegas on 18-20 June, 2019.
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.
This is the keynote I gave at the 2018 ACM SIGGRAPH Conference on Motion, Interaction and Games.
Title: Toward a Science of Game Design
Abstract:
Game development is costly, technically challenging, and poorly understood. Increased demand for games as a form of entertainment has motivated research into technology to help ameliorate the burden involved in development. This technology unfortunately has the potential to create more problems than it solves. In this talk, I will argue that this increased demand should motivate more research into human-centered game design, involving both artifact and person. This research requires computationally modeling our human intelligence, as part of an agenda that seeks to codify the precise interplay between a person’s cognition (an inner environment), the game’s controls (an interface), and a fictional universe (an outer environment); the interplay is concerned with attaining design goals by adapting the inner environment to the outer environment. I will present examples of this agenda as embodied through my own work and identify key challenges that I think the MIG community is well-poised to address in service of establishing what Herb Simon might have called a “science of game design.”
GDC2019 - SEED - Towards Deep Generative Models in Game DevelopmentElectronic Arts / DICE
Deep learning is becoming ubiquitous in Machine Learning (ML) research, and it's also finding its place in industry-related applications. Specifically, deep generative models have proven incredibly useful at generating and remixing realistic content from scratch, making themselves a very appealing technology in the field of AI-enhanced content authoring. As part of this year's Machine Learning Tutorial at the Game Developers Conference 2019 (GDC), Jorge Del Val from SEED will cover in an accessible manner the fundamentals of deep generative modeling, including some common algorithms and architectures. He will also discuss applications to game development and explore some recent advances in the field.
The attendee will gain basic understanding of the fundamentals of generative models and how to implement them. Also, attendees will grasp potential applications in the field of game development to inspire their work and companies. This talk does not require a mathematical or machine learning background, although previous knowledge on either of those is beneficial.
Jane Hsu is a professor and department chair of Computer Science and Information Engineering at National Taiwan University. Her research interests include multi-agent systems, intelligent data analysis, commonsense knowledge, and context-aware computing. Prof. Hsu is the director of the Intel-NTU Connected Context Computing Center, featuring global research collaboration among NTU, Intel, and the National Science Council of Taiwan. She serves on the editorial board of Journal of Information Science and Engineering (2010-), International Journal of Service Oriented Computing and Applications (Springer, 2007-2009) and Intelligent Data Analysis (Elsevier/IOS Press, 1997-2002). She is actively involved in many key international AI conferences as organizers and members of the program committee. In addition to serving as the President of Taiwanese Association for Artificial Intelligence (2013-2014), Prof. Hsu has been a member of AAAI, IEEE, ACM, Phi Tau Phi, and an executive committee member of the IEEE Technical Committee on E-Commerce (2000) and TAAI (2004-current).
machine learning in the age of big data: new approaches and business applicat...Armando Vieira
Presentation at University of Lisbon on Machine Learning and big data.
Deep learning algorithms and applications to credit risk analysis, churn detection and recommendation algorithms
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.
A grand challenge of AI has fallen - a decade earlier than "experts" predicted. But should we care?
What made AlphaGo, the AI built by DeepMind, so unique?
Dive into AlphaGo's system of deep learning, evaluation, and search algorithms that combined to defeat the reigning Go world champion, and draw your own conclusions.
Tim Riser presented an analysis of "Mastering the Game of Go with Deep Neural Networks & Tree Search", a paper by Google DeepMind to the Boston/Cambridge chapter of Papers We Love, a computer science discussion group on June 28, 2016.
AI+
AI
即將從棋盤,走入我們的真實世界
AI + 社會科學的開始
New Wave of AI Application Example – Digital Ads
AI+ “Misbehaving”
Q & A
1. New Wave of
AI 應用例 — 數位廣告
你的點擊,
反映你的選擇決策
Digital Advertising Revenues Hit $19.6 B in Q1 2017, Climbing 23% Year-Over-Year
Internet Ads > TV 全球網路廣告花費已大於電視
Winner Takes All 網路廣告贏者通吃
Google & Facebook Ads Examples
搓合與優化配置
Internet as a mass media
網路廣告優化方程式
七大族群的精準行銷?
十五大族群的精準行銷?
Common Data Categories
大數據分析找到更多潛在客群
Advertiser Utility: The Value Funnel
Range
Ads Optimization Formula
Data Science
The Revolution of Big Data
Models Cases
Models Cases
Optimization Perspective
Gradient Descent
“New” Wave of Machine Learning
“Deep” Learning AI
(Big) Data-driven
More tolerance for “state-of-the-art” empirical evidence
Ensemble with Reinforcement-learning & other methods
World, Model & Theory
Model?!
Artificial Power Artificial Intelligence 體力 腦力 的第四次工業革命
2.
AI 與 “不當行為”
以人為中心
2017 年諾貝爾經濟學獎揭曉,行為經濟學出線
人是自私和理性的
“The Theory of Moral Sentiments” by Adam Smith
Every man is, no doubt, by nature first and principally recommended to his own care; and he is fitter to care of himself than of every other person…" (1759, 82)
(每個人天生都是為自己活著的,並且他比其他任何人都更有能力為自己精打細算)
市場是有效率的(尤其是金融市場)
正是由於每個人自私自利的天性,Adam Smith 提出的Invisible Hands(看不見的手)才可能最有效的發揮其作用,讓市場在供需影響下達到最有效的狀態。
Loss Aversion & Endowment Effect
「97% 贏得 100 美元」 vs. 「37% 贏得 300 美元」 ?
如果送你一個賭注,你會願意多少錢轉賣出去?
Loss Aversion & Endowment Effect
A: 這項治療法可治 200 人
B: 這項治療法有 1/3 的機會拯救 600 人, 2/3 的機會無人得救
Loss Aversion & Endowment Effect
「97% 贏得 100 美元」 vs. 「37% 贏得 300 美元」 ?
如果送你一個賭注,你會願意多少錢轉賣出去?
the Coase Theorem Works at Tokens 寇斯定理對於明確價值的交易可行
the Coase Theorem did not Work in Practice 寇斯定理在實務上不可行
「損失的痛苦」是「獲得的快樂」之 2 倍
The Behavioral Economics of BitCoin
The Behavioral Economics of Cryptokitties
「損失的痛苦」是「獲得的快樂」之 2 倍
Simon’s Bounded Rationality
人真的是理性/非理性的嗎?
Paul Krugman – Nobel Price(2008)
Home DNA Test
How Many Kinds of People in the World? 人有幾種?
Know-What, Know-Why, Know-How and Decision Making
Kinds of Human in the World? 人有幾種?
Ads Optimization <-> Economic Decision
AlphaGo, Master to Zero
AlphaZero
AlphaZero
AlphaGo/Master/Zero, AlphaZero
全宇宙原子數大約為 10^80
以全宇宙可見物質總質量(1.45×10^53) / 氫原子質量(1.67×10^−27)
圍棋的排列組合總數 10^171
AlphaGo 的運算能力
早期的 AlphaGo Fan 使用 176 個 GPU
AlphaGo Lee 使用了 48 個 TPU
AlphaGo Master 與 AlphaGo Zero 皆只使用 4 個 TPU。
Computation Economics
“New” Wave of Machine Learning
“Deep” Learning AI
(Big) Data-driven
More tolerance for “state-of-the-art” empirical
The first part of the talk will cover our latest research on 1-million-agent reinforcement learning and its potential applications. Our findings show that the dynamics of the population from AI agents, driven by reinforcement learning and self-interest, share a similar pattern as those found in Nature. At the second part of the talk I shall move to a reinforcement learning setting where the game environment is strategic and designable. We present a simple case on how to design a difficult Maze, but the techniques can be used for various applications where the system level objectives are inconsistent with agents’ goals. I will finally conclude the talk by pointing out the future direction on this exciting field of AI.
RTB tutorial Version 2.
In display and mobile advertising, the most significant development in recent years is the Real-Time Bidding (RTB), which allows selling and buying in real-time one ad impression at a time. Since then, RTB has fundamentally changed the landscape of the digital marketing by scaling the buying process across a large number of available inventories. The demand for automation, integration and optimisation in RTB brings new research opportunities in the IR/DM/ML fields. However, despite its rapid growth and huge potential, many aspects of RTB remain unknown to the research community for many reasons. In this tutorial, together with invited distinguished speakers from online advertising industry, we aim to bring the insightful knowledge from the real-world systems to bridge the gaps and provide an overview of the fundamental infrastructure, algorithms, and technical and research challenges of this new frontier of computational advertising. We will also introduce to researchers the datasets, tools, and platforms which are publicly available thus they can get hands-on quickly.
This tutorial aims to provide not only a comprehensive and systematic introduction to RTB and computational advertising in general, but also the emerging research challenges and research tools and datasets in order to facilitate the research. Compared to previous Computational Advertising tutorials in relevant top-tier conferences, this tutorial takes a fresh, neutral, and the latest look of the field and focuses on the fundamental changes brought by RTB. We expect the audience, after attending the tutorial, to understand the real-time online advertising mechanisms and the state of the art techniques, as well as to grasp the research challenges in this field. Our motivation is to help the audience acquire domain knowledge and obtain relevant datasets, and to promote research activities in RTB and computational advertising in general.
Weinan Zhang's KDD15 Talk: Statistical Arbitrage Mining for Display AdvertisingJun Wang
We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.
Computational Advertising has recently emerged as a new scientific sub-discipline, bridging the gap among the areas such as information retrieval, data mining, machine learning, economics, and game theory. In this tutorial, I shall present a number of challenging issues by analogy with financial markets. The key vision is that display opportunities are regarded as raw material “commodities” similar to petroleum and natural gas – for a particular ad campaign, the effectiveness (quality) of a display opportunity shouldn’t rely on where it is brought and whom it belongs, but it should depend on how good it will benefit the campaign (e.g., the underlying web users’ satisfactions or respond rates). With this vision in mind, I will go through the recently emerged real-time advertising, aka Real-Time Bidding (RTB), and provide the first empirical study of RTB on an operational ad exchange. We show that RTB, though suffering its own issue, has the potential of facilitating a unified and interconnected ad marketplace, making it one step closer to the properties in financial markets. At the latter part of this talk, I will talk about Programmatic Premium, i.e., a counterpart to RTB to make display opportunities in future time accessible. For that, I will present a new type of ad contracts, ad options, which have the right, but no obligation to purchase ads. With the option contracts, advertisers have increased certainty about their campaign costs, while publishers could raise the advertisers’ loyalty. I show that our proposed pricing model for the ad option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keywords) and is also multi-exercisable (multi-clicks). Experimental results on real advertising data verify our pricing model and demonstrate that advertising options can benefit both advertisers and search engines.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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
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/
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
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.
2. Deep Reinforcement learning
• Computerised agent: Learning what to do
– How to map situations (states) to actions so as to
maximise a numerical reward signal
Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 1998.
5. What is next?
• All above are single AI unit
• But, true human intelligence
embraces social and collective
wisdom
– collective efforts would solve the
problem otherwise unthinkable e.g., esp
game. Crowdsourcing
• A next grand challenge of AI
– How large-scale multiple AI agents
could learn human-level collaborations
(or competitions) from their experiences?
6. What is next?
• All above are single AI unit
• But, true human intelligence
embraces social and collective
wisdom
– collective efforts would solve the
problem otherwise unthinkable e.g., esp
game. Crowdsourcing
• A next grand challenge of AI
– How large-scale multiple AI agents
could learn human-level collaborations
(or competitions) from their experiences?
Artificial Collective Intelligence
7. Artificial Collective Intelligence
• Huge applications space
– Trading robots gaming on the stock
markets,
– Ad bidding agents competing with each
other over online advertising exchanges
– E-commerce collaborative filtering
recommenders predicting user interests
through the wisdom of the crowd
– Traffic control
– Self-driving car
– Creativity learning (generative txts,
images, music, poetry)
– …
8. Summary
• Learning to compete
– Designing game environment
– Machine Bidding in auction
– Creativity learning (generating texts, images,
music, poetry)
• Learning to collaborate
– AI plays StarCraft game
9. Summary
• Learning to compete
– Designing game environment
– Machine Bidding in auction
– Creativity learning (generating texts, images,
music, poetry)
• Learning to collaborate
– AI plays StarCraft game
11. Controllable Environments
• We consider the environment is controllable
and strategic
• A mini-max game between the agent and the
environment
Haifeng, Zhang, et al, Learning to Generate (Adversarial) Environments in Deep
Reinforcement Learning, under submission, 2017
1. Generate
Environments
2. Each environment
trains an agent
3. Operate in the
environments with
4. Agent return
G ...G1 6
Agent
πµ
θ
θ
A
Environment
Generator
M
ϕw
θ1
A
M θ2
A
M θ3
A
M
θ4
A
M θ5
A
M θ6
A
M
respectively...πϕ1
πϕ6
generator update
guide the
1: Framework dealing with non-differentiable transitions. Generator generates environmen
ter ✓. For each ✓, agents are trained until optimal policies are obtained. Then agents are teste
esponding environments and returns are observed, which finally guide the generator to updat
olution for Undifferentiable Transition
gh we have proved the equivalence between the transition optimization and the policy o
In this paper, we consider a particular objective of MDP that the MDP acts as an83
environment minimizing the expected return of the agent, i.e. O(H) =
P1
t=1
t
84
Thus, the objective function is formulated as:85
✓⇤
= arg min
✓
max E[G|⇡ ; M✓ = hS, A, P✓, R, i].
This adversarial objective can be applied to design environments to analyse the weakness86
and its policy learning algorithms.87
13. Design Maze: Results
Haifeng, Zhang, et al, Learning to Generate (Adversarial) Environments in Deep Reinforcement Learning, under submission, 2017
DFS
DQNOptimal
RHS
14. Summary
• Learning to compete
– Designing game environment
– Machine Bidding in auction
– Creativity learning (generating texts, images,
music, poetry)
• Learning to collaborate
– AI plays StarCraft game
18. Online Advertising + Artificial Intelligence
• Design learning algorithms to make the best match
between the advertisers and Internet users with
economic constraints
•Transformed from a low-tech process to highly optimized, mathematical, computer-centric (Wall
Street-like) process
• Key directions: operations research, estimating CTR/AR; auction systems; machine learning
algorithms; behavioral targeting; fighting spam (click fraud)
21. Can we have a dynamic model?
Bidding in RTB as an RL problem
Advertiser
with ad budget
Environment
auction result,
user response
bid request
xt+1
bid request xt bid price at
• From the perspective of an advertiser with budget, sequentially bidding
in RTB is a reinforcement learning (RL) problem.
• The goal is to maximize the user responses on the displayed ads.
Cai, H., K. Ren, W. Zhag, K. Malialis, and J. Wang. "Real-Time Bidding by Reinforcement Learning in Display Advertising."
In The Tenth ACM International Conference on Web Search and Data Mining (WSDM). ACM, 2017.
22. MDP Formulation of RTB
Environment
[s] left auction 𝑻
[s] left budget 𝑩 𝑻
2. [a]
bid 𝒂
1. [s] bid
request 𝒙 𝑻
3. [p] auction result
3. [r] user response
[s] left auction 𝑻 − 𝟏
[s] left budget 𝑩 𝑻'𝟏
[s] left auction 𝟎
[s] left budget 𝑩 𝟎
next episode
• Consider bidding in RTB as an episodic process.
[s] state [a] action [p] state transition [r] reward
Cai, H., K. Ren, W. Zhag, K. Malialis, and J. Wang. "Real-Time Bidding by Reinforcement Learning in Display Advertising."
In The Tenth ACM International Conference on Web Search and Data Mining (WSDM). ACM, 2017.
23. Summary
• Learning to compete
– Designing game environment
– Machine bidding in auction
– Creativity learning (generating texts, images,
music, poetry)
• Learning to collaborate
– AI plays StarCraft game
26. Generative Adversarial Nets (GANs)
• Minimax game between a discriminator & a generator:
– Discriminator (D) tries to correctly distinguish the true data and the
fake model-generated data
– Generator (G) tries to generate high-quality data to fool discriminator
• G & D can be implemented via neural networks
• Ideally, when D cannot distinguish the true and generated data,
G nicely fits the true underlying data distribution
[Goodfellow I, Pouget-Abadie J, Mirza M, et al. 2014. Generative adversarial nets. In NIPS 2014.]
29. GAN with Activation Maximisation
[Zhiming Zhou, Shu Rong, Han Cai, Weinan Zhang, Yong Yu, Jun Wang Generative Adversarial Nets with Labeled Data by Activation Maximization, 2017 ]
ed Data by Activation Maximization
Class 1 Class 2
Generated Sample
Final Gradient
for G
Gradient 1 Gradient 2
Figure 1. The problem of overlayed gradient of LabGAN (Sal-
imans et al., 2016) from multi-mode real data. We assume the
logit is built based on the distance between the gradient sample
and the class center.
where
↵lab
k (x) =
(Dk(x)
Dr(x)
k 2 {1, . . . , K}
1 k = K+1
. (8)
From the formulation, we see that the overall gradient w.r.t
generated example x is (1 Dr(x)). This is consistent
with the original GAN (Goodfellow et al., 2014) when no
Generative Adversarial Nets with Labeled Data by Activation Maximization
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5 1.5-1.0 1.0-0.5 0.5-0.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5 1.5-1.0 1.0-0.5 0.5-0.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5 1.5-1.0 1.0-0.5 0.5-0.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5 1.5-1.0 1.0-0.5 0.5-0.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5 1.5-1.0 1.0-0.5 0.5-0.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5 1.5-1.0 1.0-0.5 0.5-0.0
LabGAN Iteration:50k NLL:17.86 LabGAN Iteration:150k NLL:17.11 LabGAN Iteration:200k NLL:16.71
SAM-GAN Iteration:50k NLL:17.66 SAM-GAN Iteration:150k NLL:15.94 SAM-GAN Iteration:200k NLL:15.79
Real data p.d.f.
Gen. data
Figure 2. The generated examples along with the true density
distribution on synthetic data.
Figure 3. Training iterations on the synthetic data measured with
NNL by Oracle.
Iterations
truck
ship
hourse
frog
dog
deer
cat
bird
automobile
airplane
5,000
5.79
8.31 8.55 8.74 8.84 9.20 9.29
6.90 7.74 8.01 8.17
10,000 15,000 30,000 150,000 300,000
Inception
AM score
score 8.34
Figure 4. CIFAR-10 progress results.
Generative Adversarial Nets with Labeled Data by Activation Maximization
(a) Real Images (b) Generated Images
Figure 5. MNIST results.
30. SeqGAN – Sequence generation
• Generator is a reinforcement learning policy generating a sequence
– decide the next word to generate (action) given the previous ones as
the state
• Discriminator provides the reward (i.e. the probability of being true
data) for the whole sequence
Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. AAAI 2017.
33. Obama Speech Text Generation
• i stood here today i have one and
most important thing that not on
violence throughout the horizon
is OTHERS american fire and
OTHERS but we need you are a
strong source
• for this business leadership will
remember now i can’t afford to
start with just the way our
european support for the right
thing to protect those american
story from the world and
• i want to acknowledge you were
going to be an outstanding job
times for student medical
education and warm the
republicans who like my times if
he said is that brought the
• When he was told of this
extraordinary honor that he
was the most trusted man in
America
• But we also remember and
celebrate the journalism that
Walter practiced -- a standard
of honesty and integrity and
responsibility to which so many
of you have committed your
careers. It's a standard that's a
little bit harder to find today
• I am honored to be here to pay
tribute to the life and times of
the man who chronicled our
time.
Human Machine
34. Summary
• Learning to compete
– Machine Bidding in auction
– Creativity learning (generating texts, images,
music, poetry)
• Learning to collaborate
– AI plays StarCraft game
35. AI plays StarCraft
• One of the most difficult games for computers
• At least 101685 possible states (for reference, the game of Go has about
10170 states)!
• how large-scale multiple AI agents could learn human-level
collaborations, or competitions, from their experiences?
36. Bidirectional-Coordinated nets (BiCNet)
Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, Jun Wang, Multiagent Bidirectionally-
Coordinated Nets for Learning to Play StarCraft Combat Games, 2017
39. “Hit and Run” tactics
combat 3 Marines (ours) vs. 1 Zealot (enemy)
Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, Jun Wang, Multiagent Bidirectionally-
Coordinated Nets for Learning to Play StarCraft Combat Games, 2017
(a) Early stage of training (b) Early stage of training (c) Well-trained (d) Well-trained
Figure 2: Coordinated moves without collision in combat 3 Marines (ours) vs. 1 Super Zergling
(enemy). The first two (a) and (b) illustrate that the collision happens when the agents are close by
during the early stage of the training; the last two (c) and (d) illustrate coordinated moves over the
well-trained agents.
(a) time step 1: run when
attacked
(b) time step 2: fight back
when safe
(c) time step 3: run again
Attack
Move
Enemy
(d) time step 4: fight back
again
Figure 3: Hit and Run tactics in combat 3 Marines (ours) vs. 1 Zealot (enemy).
efficiently propagated through the entire networks. Yet, unlike CommNet [20], our communication is
not fully symmetric, and we maintain certain social conventions and roles by fixing the order of the
agents that join the RNN. This would help solving any possible tie between multiple optimal joint
actions [35, 36].
The structure of our bidirectionally-coordinated net (BiCNet) is illustrated in Fig. 1. It consists of
40. Coordinated moves without collision
Combat 3 Marines (ours) vs. 1 Zergling (enemy)
Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, Jun Wang, Multiagent Bidirectionally-
Coordinated Nets for Learning to Play StarCraft Combat Games, 2017
(a) time step 1 (b) time step 2 (c) time step 3
Attack
Move
Enemy
(d) time step 4
Figure 4: Coordinated cover attack in combat 3 Marines (ours) vs. 1 Zergling (enemy).
Table 1: Winning rate against difficulty settings by hit points (HP) and damage. Training steps:
100k/200k/300k.
Difficulty
Damage=4 Damage=3
41. Focus fire
combat 15 Marines (ours) vs. 16 Marines (enemy)
Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, Jun Wang, Multiagent Bidirectionally-
Coordinated Nets for Learning to Play StarCraft Combat Games, 2017
(a) time step 1 (b) time step 2 (c) time step 3
Attack
Move
(d) time step 4
Figure 5: "focus fire" in combat 15 Marines (ours) vs. 16 Marines (enemy).
42. Coordinated heterogeneous agents
combat 2 Dropships and 2 tanks vs. 1 Ultralisk
Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, Jun Wang, Multiagent Bidirectionally-
Coordinated Nets for Learning to Play StarCraft Combat Games, 2017
(a) time step 1 (b) time step 2 (c) time step 3 (d) time step 4
Figure 5: "focus fire" in combat 15 Marines (ours) vs. 16 Marines (enemy).
(a) time step 1
Attack
Enemy
Load
Unload
(b) time step 2
igure 6: Coordinated heterogeneous agents in combat 2 Dropships and 2 tanks vs. 1 Ultralisk
ver way. Neither scattering over all enemies nor focusing on one enemy (wasting attacking fi
lso called overkill) are desired. The grouping design in the policy network serves as the
or for BiCNet to learn “focus fire without overkill”. In our experiments, we dynamically gro
agents based on agents’ geometric locations. Based on the grouping inputs, BiCNet manage