A rapid tour through some of the most exciting areas of machine learning, presenting the author's own efforts at training a computer to master Super Mario Bros.
(In)convenient truths about applied machine learningMax Pagels
This document provides observations and recommendations for reconciling machine learning with business needs. Some key points made include:
- In many cases, machine learning is not needed to solve a problem and simpler solutions like collecting missing data can work better.
- The data companies already have is sometimes useless for machine learning problems. Domain expertise alone also often means less than expected.
- Not understanding technical constraints can cause machine learning projects to fail. Always create a proof-of-concept first before full development.
- It is important to establish causality through proper testing like A/B testing, as this validates models and addresses financial risks of implementations.
- Framing learning problems is challenging due to issues like lous
Reinforcement learning is a machine learning technique where an agent learns to act by interacting with an environment. The agent takes actions and receives rewards, with the goal of maximizing total reward over time. Real-world reinforcement learning is challenging due to large state spaces and delayed rewards. However, it can be made more tractable by framing problems as contextual bandits, where rewards are immediate and state does not depend on past actions. Contextual bandits can then be solved using supervised learning techniques by addressing the partial information problem inherent to reinforcement learning.
This document provides an introduction to deep learning. It begins with a refresher on machine learning, covering classification, regression, supervised learning, unsupervised learning, and reinforcement learning. It then discusses neural networks and their basic components like layers, nodes, and weights. An example of unsupervised learning is given about learning Chinese. Deep learning is introduced as using large neural networks to learn complex feature hierarchies from large amounts of data. Key aspects of deep learning covered include representation learning, layer-wise training, and using unsupervised pre-training before supervised fine-tuning. Applications and impact areas of deep learning are also mentioned.
This document discusses best practices for setting up development and test sets for machine learning models. It recommends that the dev and test sets:
1) Should reflect the actual data distribution you want your model to perform well on, rather than just being a random split of your training data.
2) Should come from the same data distribution. Having mismatched dev and test sets makes progress harder to measure.
3) The dev set should be large enough, typically thousands to tens of thousands of examples, to detect small performance differences as models are improved. The test set size depends on desired confidence in overall performance.
This document provides an introduction to deep learning, including:
1. It discusses several applications of deep learning like image captioning, machine translation, virtual assistants, and self-driving cars.
2. It explains that deep learning can automatically extract complex patterns from large amounts of data using neural networks with many layers, unlike traditional machine learning which relies on human-designed features.
3. It outlines some of the challenges of training deep neural networks like overfitting and vanishing gradients, and how techniques like dropout, batch normalization, and improved optimization algorithms help address these issues.
Video Game HUDs - Information Presentation and Spatial ImmersionJames Babu
This document is a thesis submitted by James Babu in partial fulfillment of the requirements for a Master of Science degree in Human-Computer Interaction from Rochester Institute of Technology. The thesis explores how feelings of immersion are affected by diegetic versus non-diegetic methods of presenting a player's status information in video games. It reviews literature on evaluating game usability and interface design, specifically the use of heads-up displays. An experiment was conducted comparing immersion ratings and eye tracking data between players experiencing a diegetic and non-diegetic game. The results found no significant difference in immersion ratings but increased fixation duration for non-immersive experiences, suggesting players spend more time processing information.
This document outlines a presentation on the impacts of video games on players. It begins with an introduction that establishes the research question of how gaming shapes players negatively. It then provides background on the early history of video games and the modern gaming industry. The presentation discusses both benefits of gaming such as improved problem solving skills and risks of excessive gaming like addiction and increased aggression. It concludes that video games can be beneficial if played in moderation.
Video Games: Advantages and DisadvantagesMohsin Ahamed
This document discusses video games and gaming consoles. It notes that while games can provide social, intellectual, and educational benefits like improved coordination, reaction time, and problem solving skills, they also have disadvantages like potential isolation, overuse leading to physical problems, and being an expensive hobby. The document also lists the PlayStation 3 and Xbox 360 as commonly used consoles and acknowledges that new motion-sensing games require more physical activity.
(In)convenient truths about applied machine learningMax Pagels
This document provides observations and recommendations for reconciling machine learning with business needs. Some key points made include:
- In many cases, machine learning is not needed to solve a problem and simpler solutions like collecting missing data can work better.
- The data companies already have is sometimes useless for machine learning problems. Domain expertise alone also often means less than expected.
- Not understanding technical constraints can cause machine learning projects to fail. Always create a proof-of-concept first before full development.
- It is important to establish causality through proper testing like A/B testing, as this validates models and addresses financial risks of implementations.
- Framing learning problems is challenging due to issues like lous
Reinforcement learning is a machine learning technique where an agent learns to act by interacting with an environment. The agent takes actions and receives rewards, with the goal of maximizing total reward over time. Real-world reinforcement learning is challenging due to large state spaces and delayed rewards. However, it can be made more tractable by framing problems as contextual bandits, where rewards are immediate and state does not depend on past actions. Contextual bandits can then be solved using supervised learning techniques by addressing the partial information problem inherent to reinforcement learning.
This document provides an introduction to deep learning. It begins with a refresher on machine learning, covering classification, regression, supervised learning, unsupervised learning, and reinforcement learning. It then discusses neural networks and their basic components like layers, nodes, and weights. An example of unsupervised learning is given about learning Chinese. Deep learning is introduced as using large neural networks to learn complex feature hierarchies from large amounts of data. Key aspects of deep learning covered include representation learning, layer-wise training, and using unsupervised pre-training before supervised fine-tuning. Applications and impact areas of deep learning are also mentioned.
This document discusses best practices for setting up development and test sets for machine learning models. It recommends that the dev and test sets:
1) Should reflect the actual data distribution you want your model to perform well on, rather than just being a random split of your training data.
2) Should come from the same data distribution. Having mismatched dev and test sets makes progress harder to measure.
3) The dev set should be large enough, typically thousands to tens of thousands of examples, to detect small performance differences as models are improved. The test set size depends on desired confidence in overall performance.
This document provides an introduction to deep learning, including:
1. It discusses several applications of deep learning like image captioning, machine translation, virtual assistants, and self-driving cars.
2. It explains that deep learning can automatically extract complex patterns from large amounts of data using neural networks with many layers, unlike traditional machine learning which relies on human-designed features.
3. It outlines some of the challenges of training deep neural networks like overfitting and vanishing gradients, and how techniques like dropout, batch normalization, and improved optimization algorithms help address these issues.
Video Game HUDs - Information Presentation and Spatial ImmersionJames Babu
This document is a thesis submitted by James Babu in partial fulfillment of the requirements for a Master of Science degree in Human-Computer Interaction from Rochester Institute of Technology. The thesis explores how feelings of immersion are affected by diegetic versus non-diegetic methods of presenting a player's status information in video games. It reviews literature on evaluating game usability and interface design, specifically the use of heads-up displays. An experiment was conducted comparing immersion ratings and eye tracking data between players experiencing a diegetic and non-diegetic game. The results found no significant difference in immersion ratings but increased fixation duration for non-immersive experiences, suggesting players spend more time processing information.
This document outlines a presentation on the impacts of video games on players. It begins with an introduction that establishes the research question of how gaming shapes players negatively. It then provides background on the early history of video games and the modern gaming industry. The presentation discusses both benefits of gaming such as improved problem solving skills and risks of excessive gaming like addiction and increased aggression. It concludes that video games can be beneficial if played in moderation.
Video Games: Advantages and DisadvantagesMohsin Ahamed
This document discusses video games and gaming consoles. It notes that while games can provide social, intellectual, and educational benefits like improved coordination, reaction time, and problem solving skills, they also have disadvantages like potential isolation, overuse leading to physical problems, and being an expensive hobby. The document also lists the PlayStation 3 and Xbox 360 as commonly used consoles and acknowledges that new motion-sensing games require more physical activity.
This document provides an introduction to deep learning. It begins by discussing modeling human intelligence with machines and the history of neural networks. It then covers concepts like supervised learning, loss functions, and gradient descent. Deep learning frameworks like Theano, Caffe, Keras, and Torch are also introduced. The document provides examples of deep learning applications and discusses challenges for the future of the field like understanding videos and text. Code snippets demonstrate basic network architecture.
Deep learning uses neural networks with multiple hidden layers between the input and output layers to learn representations of data with multiple levels of abstraction. It can learn these representations on its own without being programmed by humans. Deep learning has achieved great success in tasks like image recognition and natural language processing. While deep learning is gaining popularity, challenges remain in applying it to new problems like understanding videos and text. Researchers hope that advances in deep learning over the next five years will allow systems to comprehend YouTube videos and tell stories about what happened.
Sippin: A Mobile Application Case Study presented at Techfest LouisvilleDawn Yankeelov
"Sippin: A Mobile Application Case Study," was presented at Techfest Louisville 2017 hosted by the Technology Association of Louisville Kentucky on Aug. 16th-17th.
This document discusses testing practices for deep learning models. It covers various types of testing including unit testing, integrated testing, black box testing, and smoke testing. It also discusses adversarial examples and how they can be used to test models. The document emphasizes that writing good tests is important for finding bugs early, iterating quickly, debugging easily, and designing better code. It recommends starting testing by focusing on a single functionality, using available tools, and writing tests early.
Distributed Deep Learning: Methods and Resources
This document discusses distributed deep learning methods and resources. It provides an overview of deep learning and stochastic gradient descent (SGD), and how they can be parallelized using data and model parallelism. It describes Neuromation, a platform developing a worldwide marketplace for knowledge mining using distributed computational resources. Neuromation will use blockchain and its TokenAI token to combine synthetic data generation, distributed training of neural networks, and payment for computational work into a single decentralized platform.
This document discusses artificial intelligence, machine learning, deep learning, and data science. It defines each term and explains the relationships between them. AI is the overarching field, while machine learning and deep learning are subsets of AI. Machine learning allows machines to improve performance over time without human intervention by learning from examples, and deep learning uses artificial neural networks with many layers to closely mimic the human brain. The document provides an example of a fruit detection system using deep learning that trains a neural network to detect ripe fruit for automated harvesting.
This document provides an overview and introduction to deep learning. It discusses motivations for deep learning such as its powerful learning capabilities. It then covers deep learning basics like neural networks, neurons, training processes, and gradient descent. It also discusses different network architectures like convolutional neural networks and recurrent neural networks. Finally, it describes various deep learning applications, tools, and key researchers and companies in the field.
Edge AI allows devices like self-driving cars to make decisions immediately using on-device processing rather than cloud-based processing, which introduces latency. Edge AI processes data and inferences locally on IoT and sensor devices. This enables applications like self-driving cars using computer vision to detect humans and stop in real-time. While Edge AI provides benefits like lower latency, security, and data privacy, it also faces limitations in processing power and operational complexity compared to cloud-based AI.
Introduction to Deep Learning | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/goQxnL )
This CloudxLab Deep Learning tutorial helps you to understand Deep Learning in detail. Below are the topics covered in this tutorial:
1) What is Deep Learning
2) Deep Learning Applications
3) Artificial Neural Network
4) Deep Learning Neural Networks
5) Deep Learning Frameworks
6) AI vs Machine Learning
Machine learning para tertulianos, by javier ramirez at teowakijavier ramirez
Would you like to use machine learning in your projects but you think you don't know enough? I'll tell you why machine learning is relevant, how machines learn, and which ready-made algorithms you can use if you don't know much maths but you still want to take advantage of ML
Deep Learning: concepts and use cases (October 2018)Julien SIMON
An introduction to Deep Learning theory
Neurons & Neural Networks
The Training Process
Backpropagation
Optimizers
Common network architectures and use cases
Convolutional Neural Networks
Recurrent Neural Networks
Long Short Term Memory Networks
Generative Adversarial Networks
Getting started
MDEC Data Matters Series: machine learning and Deep Learning, A PrimerPoo Kuan Hoong
The document provides an overview of machine learning and deep learning. It discusses the history and development of neural networks, including deep belief networks, convolutional neural networks, and recurrent neural networks. Applications of deep learning in areas like computer vision, natural language processing, and robotics are also covered. Finally, popular platforms, frameworks and libraries for developing deep learning models are presented, along with examples of pre-trained models that are available.
Team knowledge sharing presentation covering topics of decision trees, XGBoost, logistic regression, neural networks, and deep learning using scikit-learn, statsmodels, and Keras over TensorFlow in python within PowerBI, Azure Notebooks, AWS SageMaker notebooks, and Google Colab notebooks
Hacking Predictive Modeling - RoadSec 2018HJ van Veen
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
Artificial Intelligence Workshop, Collegio universitario Bertoni, Milano, 20 May 2017.
Audience of the workshop: undergraduate students without neural networks background.
Summary:
- Deep Learning Showcase
- What is deep learning and how it works
- How to start with deep learning
- Live demo: image recognition with Nvidia DIGITS
- Playground
Duration: 2 hours.
Deep learning techniques have achieved great success in recent years, but still lack general intelligence. Several approaches may lead to artificial general intelligence (AGI):
1. Scaling up current deep learning methods like supervised learning using vast amounts of labeled data, or unsupervised learning with huge generative models. However, it is unclear if these narrow techniques can develop general intelligence without other changes.
2. Formal approaches like AIXI aim to mathematically define optimal intelligent behavior, but suffer from computational intractability in practice.
3. Brain simulation attempts to reverse engineer the human brain, but faces challenges in modeling its high-dimensional state and dynamics at an appropriate level of abstraction.
4. Artificial life
This document provides an overview of artificial neural networks. It begins with an introduction and overview of neural networks and how the brain processes information. It then explains how a neural network works using a feedforward network design and describing how information flows from the input to hidden and output layers. The document also discusses backpropagation for neural network learning and some applications such as text translation, face identification, handwriting recognition, and speech recognition. It concludes with some key advantages of neural networks like their ability to learn non-linear relationships and generalize to unseen data through continuous learning.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
More Related Content
Similar to Teaching Your Computer To Play Video Games
This document provides an introduction to deep learning. It begins by discussing modeling human intelligence with machines and the history of neural networks. It then covers concepts like supervised learning, loss functions, and gradient descent. Deep learning frameworks like Theano, Caffe, Keras, and Torch are also introduced. The document provides examples of deep learning applications and discusses challenges for the future of the field like understanding videos and text. Code snippets demonstrate basic network architecture.
Deep learning uses neural networks with multiple hidden layers between the input and output layers to learn representations of data with multiple levels of abstraction. It can learn these representations on its own without being programmed by humans. Deep learning has achieved great success in tasks like image recognition and natural language processing. While deep learning is gaining popularity, challenges remain in applying it to new problems like understanding videos and text. Researchers hope that advances in deep learning over the next five years will allow systems to comprehend YouTube videos and tell stories about what happened.
Sippin: A Mobile Application Case Study presented at Techfest LouisvilleDawn Yankeelov
"Sippin: A Mobile Application Case Study," was presented at Techfest Louisville 2017 hosted by the Technology Association of Louisville Kentucky on Aug. 16th-17th.
This document discusses testing practices for deep learning models. It covers various types of testing including unit testing, integrated testing, black box testing, and smoke testing. It also discusses adversarial examples and how they can be used to test models. The document emphasizes that writing good tests is important for finding bugs early, iterating quickly, debugging easily, and designing better code. It recommends starting testing by focusing on a single functionality, using available tools, and writing tests early.
Distributed Deep Learning: Methods and Resources
This document discusses distributed deep learning methods and resources. It provides an overview of deep learning and stochastic gradient descent (SGD), and how they can be parallelized using data and model parallelism. It describes Neuromation, a platform developing a worldwide marketplace for knowledge mining using distributed computational resources. Neuromation will use blockchain and its TokenAI token to combine synthetic data generation, distributed training of neural networks, and payment for computational work into a single decentralized platform.
This document discusses artificial intelligence, machine learning, deep learning, and data science. It defines each term and explains the relationships between them. AI is the overarching field, while machine learning and deep learning are subsets of AI. Machine learning allows machines to improve performance over time without human intervention by learning from examples, and deep learning uses artificial neural networks with many layers to closely mimic the human brain. The document provides an example of a fruit detection system using deep learning that trains a neural network to detect ripe fruit for automated harvesting.
This document provides an overview and introduction to deep learning. It discusses motivations for deep learning such as its powerful learning capabilities. It then covers deep learning basics like neural networks, neurons, training processes, and gradient descent. It also discusses different network architectures like convolutional neural networks and recurrent neural networks. Finally, it describes various deep learning applications, tools, and key researchers and companies in the field.
Edge AI allows devices like self-driving cars to make decisions immediately using on-device processing rather than cloud-based processing, which introduces latency. Edge AI processes data and inferences locally on IoT and sensor devices. This enables applications like self-driving cars using computer vision to detect humans and stop in real-time. While Edge AI provides benefits like lower latency, security, and data privacy, it also faces limitations in processing power and operational complexity compared to cloud-based AI.
Introduction to Deep Learning | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/goQxnL )
This CloudxLab Deep Learning tutorial helps you to understand Deep Learning in detail. Below are the topics covered in this tutorial:
1) What is Deep Learning
2) Deep Learning Applications
3) Artificial Neural Network
4) Deep Learning Neural Networks
5) Deep Learning Frameworks
6) AI vs Machine Learning
Machine learning para tertulianos, by javier ramirez at teowakijavier ramirez
Would you like to use machine learning in your projects but you think you don't know enough? I'll tell you why machine learning is relevant, how machines learn, and which ready-made algorithms you can use if you don't know much maths but you still want to take advantage of ML
Deep Learning: concepts and use cases (October 2018)Julien SIMON
An introduction to Deep Learning theory
Neurons & Neural Networks
The Training Process
Backpropagation
Optimizers
Common network architectures and use cases
Convolutional Neural Networks
Recurrent Neural Networks
Long Short Term Memory Networks
Generative Adversarial Networks
Getting started
MDEC Data Matters Series: machine learning and Deep Learning, A PrimerPoo Kuan Hoong
The document provides an overview of machine learning and deep learning. It discusses the history and development of neural networks, including deep belief networks, convolutional neural networks, and recurrent neural networks. Applications of deep learning in areas like computer vision, natural language processing, and robotics are also covered. Finally, popular platforms, frameworks and libraries for developing deep learning models are presented, along with examples of pre-trained models that are available.
Team knowledge sharing presentation covering topics of decision trees, XGBoost, logistic regression, neural networks, and deep learning using scikit-learn, statsmodels, and Keras over TensorFlow in python within PowerBI, Azure Notebooks, AWS SageMaker notebooks, and Google Colab notebooks
Hacking Predictive Modeling - RoadSec 2018HJ van Veen
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
Artificial Intelligence Workshop, Collegio universitario Bertoni, Milano, 20 May 2017.
Audience of the workshop: undergraduate students without neural networks background.
Summary:
- Deep Learning Showcase
- What is deep learning and how it works
- How to start with deep learning
- Live demo: image recognition with Nvidia DIGITS
- Playground
Duration: 2 hours.
Deep learning techniques have achieved great success in recent years, but still lack general intelligence. Several approaches may lead to artificial general intelligence (AGI):
1. Scaling up current deep learning methods like supervised learning using vast amounts of labeled data, or unsupervised learning with huge generative models. However, it is unclear if these narrow techniques can develop general intelligence without other changes.
2. Formal approaches like AIXI aim to mathematically define optimal intelligent behavior, but suffer from computational intractability in practice.
3. Brain simulation attempts to reverse engineer the human brain, but faces challenges in modeling its high-dimensional state and dynamics at an appropriate level of abstraction.
4. Artificial life
This document provides an overview of artificial neural networks. It begins with an introduction and overview of neural networks and how the brain processes information. It then explains how a neural network works using a feedforward network design and describing how information flows from the input to hidden and output layers. The document also discusses backpropagation for neural network learning and some applications such as text translation, face identification, handwriting recognition, and speech recognition. It concludes with some key advantages of neural networks like their ability to learn non-linear relationships and generalize to unseen data through continuous learning.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
Similar to Teaching Your Computer To Play Video Games (20)
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Infrastructure Challenges in Scaling RAG with Custom AI models
Teaching Your Computer To Play Video Games
1. Teaching Your Computer To Play
Video Games
A Presentation For The Bainbridge BARN
September 18, 2016
2. About Me
Tech enthusiast; hardware and software
hacker; particular interest in machine learning
Pros:
This presentation is free of charge!
Cons:
No training in computer science, embedded
systems design, electrical engineering,
software development
3. What Is Machine Learning?
● A way for computers to learn without being
explicitly programmed
● It allows machines to make predictions
about the future after studying examples
from the past
● Forms the basis of artificial intelligence
● One of the hottest areas of computer
science today!
4. Why Video Games?
● They are easy to set up and let you control
every aspect of the learning environment
● They are fun
● They can be directly compared against
human performance
Here’s one of my favorite examples
6. Consider Spam Filtering...
● It is impossible to predict every possible way a
spam email could be written...
● You could try programming a bunch of rules:
○ “Cheap Meds From Canada” -> SPAM
○ “Your Medication Has Shipped -> NOT SPAM
● This rapidly becomes intractable - likely to get too
many false positives and false negatives
7. Consider Spam Filtering...
● A better way is to show the machine a bunch of
human-labeled examples, and let it generalize a
way to identify spam from these
● This is called Supervised Learning, because we
train our system on a bunch of examples
● Basis for most spam-filtering systems today
8. So How Does It Work?
● There are many types of algorithms that are used
for learning
● These have colorful names:
○ Naive Baysian Classifiers
○ Support Vector Machines
○ Random Forest Trees
● But we’ll focus here on Neural Networks since they
are currently some of the most widely used and are
so cool
9. Neural Networks
● Neural networks were
inspired from studying how
our brains work
● These consist of multiple
layers of interconnected
nodes (like neurons)
● They take an input (like a
video image), pass it
through, and yield an output
(like a label)
14. How Neural Networks Learn
At each step, you compare the actual output (ie - 78%
chance it’s a cat) with the expected output (ie - yes,
it’s a cat)
......
NOT CAT
CAT
Pixel
Value
183
22
78
15. How Neural Networks Learn
The weights are then adjusted to bring the actual
output closer to the expect output. Rinse and repeat...
......
NOT CAT
CAT
Weights
183
20
80
16. Neural Network Learning
● Adjusting these weights is how the network learns
● Real life networks may have millions of weights
spread over many layers
● This process allows the network to learn complex
behaviors and, we hope, an ability to generalize
concepts beyond what it was explicitly taught
17. Neural Network Topology
● There are multiple ways of connecting the nodes in
a neural network
● All of these seek to minimize the number of weights
you need and to combat the central problem of
machine learning: overfitting
● Overfitting means your network performs great so
long as it’s working with data it’s already seen. But
it fails miserably when it needs to generalize to data
it hasn’t seen
18. Neural Networks
● Recently, a type called convolutional neural
networks has been achieving amazing results,
particularly for problems that involve classifying
images or video
● Moreover, when you incorporate many layers (5, 6,
7, and more), the power of these networks is
astounding
● This is where the phrase deep learning comes
from, since these networks have many layers
19. Stanford’s Image Classifier
● Here’s a deep convolutional neural network in
action from a 2014 competition!
● This network was trained on 1.2 million images,
each labeled with one of 1000 categories
● Then was tested on images it had not seen
before…
● And achieved an error rate of only 5.1% compared
with how humans would classify the images
Check it out!
20. Text Generation
● Another type of neural network (called a recurrent
neural network) is great for sequential problems,
like predicting the next word in a sentence: “There
are so many clouds in the ____.”
● A fun trick with these is to train them on a body of
text (like the Bible or the complete works of
Shakespeare) and see what they spit out...
21. Computer-Generated Bible and
Shakespeare Verses
● 1 Chronicles 4:7 Then came them out of the
house of brass; and in the midst is to him, and was
done with him with the new moon: for in the city of
Jeshua ye shall put him speed, as the horn of me
plagued among them that hath need.
● Second Senator: They are away this miseries,
produced upon my soul, Breaking and strongly
should be buried, when I perish The earth and
thoughts of many states.
Source: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
22. Image Captioning
● An even more challenging machine learning task is
automatically generating captions to images
● This often combines deep convolutional networks
with recurrent neural networks
● Here’s an example of Google’s work on this
subject...
25. Unsupervised Learning
● Most examples so far have been of Supervised
Learning, where the machine is trained on a bunch
of human-labeled examples; Unsupervised
Learning is where the human does not provide any
guidance
● Specifically, Reinforcement Learning simply
provides an environment for the machine to play in,
and it is given rewards and penalties based on its
actions
● It’s up to the machine to figure out the best
strategy...
26. Learning To Play Video Games
● This is how we can teach a machine to play video
games:
○ The score is the reward
○ The machine gets to press any buttons it wants
Here’s a video demonstrating Google’s Atari project
(from 9:25)
27. How Reinforcement Learning
Works
● A neural network is at the heart of Reinforcement
Learning
● For video games, the input is the screen itself at
each frame, and the output is an estimate of the
value of each possible move (right, up, jump, etc.)
● The machine records its experiences at each point
in time: the screen, the action it took, the reward it
received, and the resulting screen afterwards
28. How Reinforcement Learning
Works (cont.)
● The machine then compares its prediction of the
reward it will get given a screen and given a
particular move, and compares this with the actual
result it received
● The network’s weights are adjusted to bring the two
closer
● Rinse and repeat
29. Reinforcement Learning
● Many concepts of Reinforcement Learning are
analogous to how our own minds work:
○ Learning Rate: how fast the network should
adapt to new information
○ Explore v. Exploit: how much to try new things
versus simply maxing out the best strategy
you’ve found so far
30. Reinforcement Learning
● Many concepts of Reinforcement Learning are
analogous to how our own minds work:
○ Memory Size: how long should we maintain our
memory of past experiences
○ Discount Rate: how much should we discount
future rewards over immediate rewards
31. Super Mario Bros.
● My own project was to apply Google’s methods to
play Super Mario Bros.
Here’s how it started…
And here’s how it was doing after about 72 hours...
32. The State Of The Art
● The next step for Mario - why run a single game
when you can run eight!
33. The State Of The Art
● Advances in machine learning are happening
extremely fast!
○ More powerful machines
○ The proliferation of open-source tools
○ The availability of tasks (like video games) we
can use to measure our progress
34. Where To Learn More
● Google Atari Project, and the paper in Nature
● My fork of this project to play Super Mario Bros.
● A text generator using recurrent neural nets
● The latest-and-greatest A3C algorithm for training
Atari
● The latest (free) tools of machine learning: Theano,
Torch, TensorFlow, and Chainer