The aim of "SP Theory of Intelligence" is Simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme
Differences Between Machine Learning Ml Artificial Intelligence Ai And Deep L...SlideTeam
"You can download this product from SlideTeam.net"
Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/325zI9o
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Introduction to Machine Learning and Artificial Intelligence Technologies. Discover the basics surrounding this tech, including business uses and evolution over time.
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
Differences Between Machine Learning Ml Artificial Intelligence Ai And Deep L...SlideTeam
"You can download this product from SlideTeam.net"
Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/325zI9o
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Introduction to Machine Learning and Artificial Intelligence Technologies. Discover the basics surrounding this tech, including business uses and evolution over time.
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
Now is the time to unravel the mysteries of cloud computing. The speaker is going to brief about Generative AI in the session that is related to study Jams 2023
Presenting this set of slides with name - Artificial Intelligence Overview Powerpoint Presentation Slides. This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with thirtyseven slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Artificial Intelligence Overview Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
Introductory presentation to Explainable AI, defending its main motivations and importance. We describe briefly the main techniques available in March 2020 and share many references to allow the reader to continue his/her studies.
Dive into the world of GPT-4, the state-of-the-art AI language model by OpenAI. Learn how to craft effective prompts and unlock the full potential of GPT-4 for a wide range of applications, including content generation.
Keywords:
GPT-4, OpenAI, artificial intelligence, language model, prompting, content generation, machine learning, natural language processing, NLP, deep learning, tokenization, context window, prompt engineering, reinforcement learning, fine-tuning, response quality, API, zero-shot learning, few-shot learning, AI ethics, use cases, best practices, performance optimization, transformer architecture, AI-powered solutions.
Machine Learning in 10 Minutes | What is Machine Learning? | EdurekaEdureka!
YouTube Link: https://youtu.be/qWHi09C3Dq0
** Machine Learning Training with Python: https://www.edureka.co/machine-learning-certification-training**
This Edureka video on 'Machine Learning in 10 Minutes' will help you understand what exactly is Machine Learning and what are the different types of Machine Learning along with some career opportunities that you can achieve through Machine Learning.
Example
What is AI?
What is Machine Learning
Steps for Machine Learning
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Applications of Machine Learning
What can you be with Machine Learning?
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
This was the training session follow up to the general talk on ChatGPT. This talk has a bit more detail on prompt writing along with the power and limitations of ChatGPT for Marketing.
Introduction To Artificial Intelligence PowerPoint Presentation SlidesSlideTeam
Introduction to Artificial Intelligence is for the mid level managers giving information about what is AI, AI levels, types of AI, where AI is used. You can also know the difference between AI vs Machine learning vs Deep learning to understand expert system in a better way for business growth. https://bit.ly/3er7KWI
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Generative AI in Transportation for Connected Future Transport System July 20...Sudha Jamthe
Sudha Jamthe keynote about Generative AI in smart mobility in the future of transportation
Follow Sudha Jamthe at sudhajamthe.com or learn more about Generative AI at generativeaibook.org
AI, Machine Learning, and Data Science ConceptsDan O'Leary
An overview of AI, Machine Learning, and Data Science concepts, contrasting popular conceptions of AI to state-of-the-art methods in Data Science. An introduction to Machine Learning will compare supervised and unsupervised methods, give high-level descriptions of key methods, and discuss current use cases and trends.
Web version of presentation given to the Data Science Society of Auburn, a mix of undergraduate and graduate students interested in Data Science.
The Smart Way to Invest in Artificial Intelligence and Machine Learning: Lisha Li, Amplify Partners
AI and ML are seeping into every startup, at least into every pitch deck. But what does it mean to build an AI/ML company? Some startups do require a closet filled with five PhD’s in data science, but that doesn’t necessarily mean yours does. Building intelligently with AI and ML.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
Now is the time to unravel the mysteries of cloud computing. The speaker is going to brief about Generative AI in the session that is related to study Jams 2023
Presenting this set of slides with name - Artificial Intelligence Overview Powerpoint Presentation Slides. This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with thirtyseven slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Artificial Intelligence Overview Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
Introductory presentation to Explainable AI, defending its main motivations and importance. We describe briefly the main techniques available in March 2020 and share many references to allow the reader to continue his/her studies.
Dive into the world of GPT-4, the state-of-the-art AI language model by OpenAI. Learn how to craft effective prompts and unlock the full potential of GPT-4 for a wide range of applications, including content generation.
Keywords:
GPT-4, OpenAI, artificial intelligence, language model, prompting, content generation, machine learning, natural language processing, NLP, deep learning, tokenization, context window, prompt engineering, reinforcement learning, fine-tuning, response quality, API, zero-shot learning, few-shot learning, AI ethics, use cases, best practices, performance optimization, transformer architecture, AI-powered solutions.
Machine Learning in 10 Minutes | What is Machine Learning? | EdurekaEdureka!
YouTube Link: https://youtu.be/qWHi09C3Dq0
** Machine Learning Training with Python: https://www.edureka.co/machine-learning-certification-training**
This Edureka video on 'Machine Learning in 10 Minutes' will help you understand what exactly is Machine Learning and what are the different types of Machine Learning along with some career opportunities that you can achieve through Machine Learning.
Example
What is AI?
What is Machine Learning
Steps for Machine Learning
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Applications of Machine Learning
What can you be with Machine Learning?
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
This was the training session follow up to the general talk on ChatGPT. This talk has a bit more detail on prompt writing along with the power and limitations of ChatGPT for Marketing.
Introduction To Artificial Intelligence PowerPoint Presentation SlidesSlideTeam
Introduction to Artificial Intelligence is for the mid level managers giving information about what is AI, AI levels, types of AI, where AI is used. You can also know the difference between AI vs Machine learning vs Deep learning to understand expert system in a better way for business growth. https://bit.ly/3er7KWI
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Generative AI in Transportation for Connected Future Transport System July 20...Sudha Jamthe
Sudha Jamthe keynote about Generative AI in smart mobility in the future of transportation
Follow Sudha Jamthe at sudhajamthe.com or learn more about Generative AI at generativeaibook.org
AI, Machine Learning, and Data Science ConceptsDan O'Leary
An overview of AI, Machine Learning, and Data Science concepts, contrasting popular conceptions of AI to state-of-the-art methods in Data Science. An introduction to Machine Learning will compare supervised and unsupervised methods, give high-level descriptions of key methods, and discuss current use cases and trends.
Web version of presentation given to the Data Science Society of Auburn, a mix of undergraduate and graduate students interested in Data Science.
The Smart Way to Invest in Artificial Intelligence and Machine Learning: Lisha Li, Amplify Partners
AI and ML are seeping into every startup, at least into every pitch deck. But what does it mean to build an AI/ML company? Some startups do require a closet filled with five PhD’s in data science, but that doesn’t necessarily mean yours does. Building intelligently with AI and ML.
Team knowledge sharing presentation covering topics of classical statistics vs modern machine learning including linear regression, logistic regression, neural networks, and deep learning using Python and R
This is the lecture delivered at Jadavpur University for the engineering students. The lecture was organised by the JU Entrepreneurship Cell and Alumni Association, Singapore Chapter.
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,Optical character recognition (OCR),learning to rank and computer vision.
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
If Artificial Intelligence (AI) is a black-box, how can a human comprehend and trust the results of Machine Learning (ML) alogrithms? Explainable AI (XAI) tries to shed light into that AI black-box so humans can trust what is going on. Our speaker Meg Dickey-Kurdziolek is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. Recording of the presentation: https://youtu.be/6N2DNN_HDWU
Semantics of the Black-Box: Using knowledge-infused learning approach to make...Amit Sheth
Keynote at the IEEE ICSC Workshop on Semantic Machine Learning (#SML21: https://ist.gmu.edu/~hpurohit/events/sml21/#keynote):
Video of SML21: https://www.youtube.com/watch?v=cx-l0XDk9Tw
The recent series of deep learning innovations have shown enormous potential to impact individuals and society, both positively and negatively. The deep learning models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of deep learning models and their over-reliance on massive amounts of data condensed into labels and dense representations pose challenges for the system’s interpretability and explainability. Furthermore, deep learning methods have not yet been proven in their ability to effectively utilize relevant domain knowledge and experience critical to human understanding. This aspect is missing in early data-focused approaches and necessitated knowledge-infused learning and other strategies to incorporate computational knowledge. Rapid advances in our ability to create and reuse structured knowledge as knowledge graphs make this task viable. In this talk, we will outline how knowledge, provided as a knowledge graph, is incorporated into the deep learning methods using knowledge-infused learning. We then discuss how this makes a fundamental difference in the interpretability and explainability of current approaches and illustrate it with examples relevant to a few domains.
The recent series of innovations in deep learning have shown enormous potential to impact individuals and society, both positively and negatively. The deep learning models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of deep learning models and their over-reliance on massive amounts of data condensed into labels and dense representations pose challenges for the system’s interpretability and explainability. Furthermore, deep learning methods have not yet been proven in their ability to effectively utilize relevant domain knowledge and experience critical to human understanding. This aspect is missing in early data-focused approaches and necessitated knowledge-infused learning and other strategies to incorporate computational knowledge. Rapid advances in our ability to create and reuse structured knowledge as knowledge graphs make this task viable. In this talk, we will outline how knowledge, provided as a knowledge graph, is incorporated into the deep learning methods using knowledge-infused learning. We then discuss how this makes a fundamental difference in the interpretability and explainability of current approaches and illustrate it with examples relevant to a few domains.
Concept computing is the next paradigm for Internet and enterprise software. Concept computing is a:
-- Paradigm shift from information-centric to knowledge-driven patterns of computing.
-- Spectrum of knowledge representation, from search to knowing.
-- Synthesis of AI, semantic, model-driven, mobile, and User interface technologies.
-- Solution Architecture where every aspect of computing is semantic and directly model-driven.
-- Development methodology where Every stage of the solution lifecycle becomes semantic, model-driven & super-productive.
-- New domain where value multiplies.
Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
Cognitive Analytics
Machine Learning
Deep Learning
Reasoning
And next generation artificial intelligence (AI)
And get involved in the discussion with your own questions to present to the panel.
The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Similar to Autonomous robot & sp theory of intelligence (20)
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
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
1. AUTONOMOUS ROBOTS &
SP THEORY OF INTELLIGENCE
PRESENTED BY:
CHRISTY ABRAHAM JOY
CHRISTYPONNATTIL@GMAIL.COM
2. Introduction
Simplify and integrate concepts across artificial intelligence, mainstream computing and human
perception and cognition, with information compression as a unifying theme
Aim of “The SP theory of intelligence ”
SP THEORY OF INTELLIGENCE 27/19/2015
3. SP Theory of Intelligence
• Product of an extensive program of development and testing via the SP computer model.
• Knowledge represented with arrays of atomic symbols in one or two dimensions called “patterns”.
• Processing are done by compressing information.
• Via the matching and unification of patterns.
• Via the building of multiple alignments .
SP THEORY OF INTELLIGENCE 37/19/2015
4. Benefits of the SP Theory
• Conceptual simplicity combined with descriptive and explanatory power across several aspects of
intelligence.
• Simplification of computing systems, including software.
• Deeper insights and better solutions in several areas of application.
• Seamless integration of structures and functions within and between different areas of application
4SP THEORY OF INTELLIGENCE7/19/2015
5. The SP Theory and the SP Machine: A
Summary
• All kinds of knowledge are represented with patterns: arrays of atomic symbols in one or two
dimensions.
• At the heart of the system is compression of information via the matching and unification (merging) of
patterns, and the building of multiple alignments
• The system learns by compressing “New” patterns to create “Old” patterns
SP THEORY OF INTELLIGENCE 57/19/2015
6. Multiple Alignment
• The system aims to find multiple alignments that enable a New pattern to be encoded economically in
terms of one or more Old patterns
• Multiple alignment provides the key to:
1. Versatility in representing different kinds of knowledge.
2. Versatility in different kinds of processing in AI and mainstream computing.
6SP THEORY OF INTELLIGENCE7/19/2015
7. The Best Multiple Alignment
SP THEORY OF INTELLIGENCE 7
The best multiple alignment created by the SP computer model with a store of Old patterns like those in rows
1 to 8 (representing grammatical structures, including words) and a New pattern (representing a sentence to
be parsed) shown in row 0.
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9. Simplification of Computing Systems
Apart from the simplification and integration of concepts in artificial intelligence, mainstream
computing, and human perception and cognition, the SP theory can help to simplify computing
systems, including software.
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11. Benefits of Overall Simplification of
Computing Systems
• Savings in development effort and associated costs. With more intelligence in the CPU there should be
less need for it to be encoded in applications.
• Savings in development time. With a reduced need for hand crafting, applications may be developed
more quickly.
• Savings in storage costs. There may be useful economies in the storage space required for application
code.
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12. Towards HUMAN-LIKE VERSATILITY
In Intelligence
• Versatility in intelligence - a major strength of the SP system-flows from the goal that has been central
in the development of the theory: to combine conceptual simplicity with descriptive and explanatory
power.
• This strength of the SP system chimes well with what is required in any autonomous robot that is to
function effectively in situations where little or no help can be provided by people.
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13. Towards HUMAN-LIKE VERSATILITY
In Intelligence
A. SIMPLIFICATION AND INTEGRATION
B. NATURAL LANGUAGE PROCESSING
C. PATTERN RECOGNITION
D. INFORMATION STORAGE AND RETRIEVAL
E. VISION
F. REASONING
G. PLANNING AND PROBLEM SOLVING
H. SEQUENTIAL AND PARALLEL PROCEDURES
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14. SIMPLIFICATION AND INTEGRATION
1. SIMPLIFICATION OF STRUCTURES AND FUNCTIONS
• The adoption of one simple format - SP patterns - for the representation of all kinds of knowledge.
• One computational framework, with multiple alignment center-stage, for all kinds of processing.
2. INTEGRATION OF STRUCTURES AND FUNCTIONS
• Syntax and Semantics
• Recognition and Learning
• Knowledge Representation and Learning
• Knowledge Representation and Reasoning
3. DEEPER INSIGHTS AND BETTER SOLUTIONS TO PROBLEMS
• Relatively new insights are the ways in which computational effciency may be improved, with
corresponding savings in the use of energy
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15. NATURAL LANGUAGE PROCESSING
1. PARSING OF NATURAL LANGUAGE
2. PRODUCTION OF NATURAL LANGUAGE
3. THE INTEGRATION OF SYNTAX AND SEMANTICS
4. PARALLEL STREAMS OF INFORMATION
• Vowel sounds, for example, may be analyzed into formants, two or more of which may occur
simultaneously. Vowels, and perhaps other elements of speech, may be represented most
naturally with parallel streams of information
• It does not seem right that the syntactic and semantic aspects of natural language should be
forced into the procrustean bed of a single sequence. As with formants in speech, it seems most
natural to regard syntax and semantics as parallel streams of information.
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16. PATTERN RECOGNITION &
INFORMATION STORAGE AND RETRIEVAL
SP THEORY OF INTELLIGENCE 16
PATTERN RECOGNITION
• It can recognize patterns at multiple levels of abstraction, with the integration of class-inclusion
relations and part-whole relations.
• It can model ``family resemblance'' or polythetic categories, meaning that recognition does not depend
on the presence absence of any particular feature or combination of features.
• Recognition is robust in the face of errors of omission, commission or substitution in the New pattern or
patterns.
INFORMATION STORAGE AND RETRIEVAL
• The system lends itself to information retrieval in the manner of query-by-example. There is also
potential for information retrieval via the use of natural language or query languages such as SQL.
• The system supports object-oriented concepts such as class hierarchies and inheritance of attributes,
and it provides for the representation of part-whole hierarchies and their seamless integration with
class hierarchies.
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17. VISION
The main strengths and potential of the SP system are:
• Low level perceptual features such as edges or corners may be identified via the multiple alignment
framework by the extraction of redundancy in uniform areas in the manner of the run-length encoding
technique for information compression
• The system may be applied in the recognition of objects and in scene analysis, with the same strengths
as in pattern recognition
• There is potential for the learning of visual entities and classes of entity and the piecing together of
coherent concepts from fragments
• There is potential, via multiple alignment, for the creation of 3D models of objects and of a robot's
surroundings.
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18. REASONING
The SP system lends itself to several kinds of reasoning:
• One-step `deductive' reasoning.
• Abductive reasoning.
• Reasoning with probabilistic decision networks and decision trees.
• Non-monotonic reasoning and reasoning with default values.
• Reasoning in Bayesian networks, including ``explaining away''.
• Reasoning which is not supported by evidence.
• Inheritance of attributes in an object-oriented class hierarchy or.
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19. TOWARDS HUMAN-LIKE ADAPTABILITY
IN INTELLIGENCE
As with versatility in intelligence, the current generation of robots falls far short of human-like adaptability
in intelligence.
A. PRELIMINARIES
B. UNSUPERVISED LEARNING IN THE SP SYSTEM
C. ONE-TRIAL LEARNING
D. LEARNING LINGUISTIC KNOWLEDGE
E. LEARNING TO SEE
F. HOW A ROBOT MAY BUILD 3D MODELS OF OBJECTS, OF ITSELF, AND OF ITS ENVIRONMENT
G. INTERACTIONS AND OTHER REGULARITIES
H. EXPLORATION, PLAY, AND THE LEARNING OF MINOR SKILLS
I. LEARNING A MAJOR SKILL VIA PRACTICE & DEMONSTRATION
J. CUTTING THE COST OF LEARNING
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20. UNSUPERVISED LEARNING IN THE SP
SYSTEM
In broad terms, the SP70 model processes a set of New patterns (which may be referred to as “I” ) in two
main phases:
1) Create a set of Old patterns that may be used to encode “I”.
2) From the Old patterns created in the first phase, compile one or more alternative grammars for the
patterns in New, in accordance with principles of minimum length encoding
The two phases are described in a little more detail in the following to subsections.
• CREATING CANDIDATE PATTERNS
• COMPILING ALTERNATIVE GRAMMARS
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21. CREATING CANDIDATE PATTERNS
Here, the pattern shown in row 1 is an analogue of something that a child has heard (`t h a t b o y r u n s')
with the addition of code symbols `<', `%1', `9', and `>', while the pattern in row 0 (`t h a t g i r l r u n s') is an
analogue of something that the same child has heard later.
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22. 22
From that multiple alignment, the program derives the patterns `t h a t' and `r u n s' from subsequences
that match each other, and it derives `g i r l' and ‘b o y' from subsequences that don't match. In
addition, the program assigns code symbols to the newly-created patterns so that
`t h a t' becomes `< %7 12 t h a t >',
`r u n s' becomes `< %8 13 r u n s >',
and so on. And, using those code symbols, the program creates an abstract pattern,
`< %10 16 < %7 > < %9 > < %8 > >‘
that records the whole sequence. The overall result in this example is the set of patterns. This is
essentially a simple grammar for sequences of the form
`t h a t g i r l r u n s‘ and `t h a t b o y r u n s'.
Patterns derived from the multiple alignment
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23. HOW A ROBOT MAY BUILD 3D MODELS OF
OBJECTS,OF ITSELF, AND OF ITS ENVIRONMENT
• the multiple alignment framework may be applied in creating models of objects (including robots), and
of a robot's environment
• The basic idea is that partially-overlapping images (from the robot's eyes) may be stitched together to
create a coherent whole, in much the same way that partially-overlapping digital photographs may be
stitched together to create a panorama.
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Plan view of a 3D object, with each of the five lines around it representing a view of the object, as seen from the side
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24. INTERACTIONS AND OTHER REGULARITIES
This difference between learning from a one-dimensional stream of information and learning from parallel
streams of information may be accommodated with three refinements of the SP70 model:
• Represent Parallel Streams of Information With 2D Patterns
• Generalize the Sequence Alignment Process to the Matching of 2D Patterns
• Generalize the Process for Building Multiple Alignments to Accommodate 2D Patterns
SP THEORY OF INTELLIGENCE 24
A multiple alignment produced by the SP computer model showing how two instances of the
pattern `I N F O R M A T I O N' may be detected despite the interpolation of non-matching symbols
throughout both instances.
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25. Deeper Insights and Better Solutions in
Several Areas of Application
1. Applications in the Processing of Natural Language
• Towards the Understanding and Translation of Natural Language
• Natural Language and Information Retrieval
• Interactive Services
• Going Beyond FAQs
2. Towards a Versatile Intelligence for Autonomous Robots
• Potential for the kind of visual analysis needed to assimilate the many configurations of balls,
pockets, and cue
• The versatility of the SP framework in the representation and processing of diverse kinds of
knowledge should facilitate the seamless integration of visual information about the table, balls,
and so on, with information about actions by the player and feedback from muscles and from touch.
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26. Deeper Insights and Better Solutions in
Several Areas of Application
3. Computer Vision
• It has potential to simplify and integrate several areas in computer vision, including feature
detection and alignment, segmentation, deriving structure from motion, stitching of images
together, stereo correspondence, scene analysis, and object recognition
4. A Versatile Model for Intelligent Databases
• The system would provide a means of storing and managing the data that are gathered in such
investigations, often in large amounts.
• It may help in the recognition of features or combinations of features that link a given crime to
other crimes, either current or past—and likewise for suspects.
• The system’s capabilities in pattern recognition may also serve in the scanning of data to recognize
indicators of criminal activity.
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27. Deeper Insights and Better Solutions in
Several Areas of Application
5. Software Engineering
• Procedural Programming, Automatic Programming,
• No Compiling or Interpretation
• Sequential and Parallel Processing
6. Information Compression
7. Medical Diagnosis
8. Managing “Big Data” and Gaining Value from It
9. Other Areas of Application
• Knowledge, Reasoning, and the Semantic Web
• Bioinformatics
• Detection of Computer Viruses
• Data Fusion
• Development of Scientific Theories & New Kinds of Computes
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28. Conclusion
•The SP theory of intelligence and its realization in the SP machine may facilitate the development
of autonomous robots: by increasing the computational efficiency of computers; by facilitating
the development of human-like versatility in intelligence; and likewise for human-like
adaptability in intelligence.
•The SP system has potential for substantial gains in computational efficiency, with corresponding
cuts in energy consumption and in the bulkiness of computing machinery: by reducing the size
of data
•Autonomous robots will require a non-von revolution - perhaps along the lines of SP-neural -
there is plenty that can be done via modelling with von-Neumann-style supercomputers to
explore the potential of new architectures.
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29. References
Ames Gerard Wolff, “Autonomous Robots and the SP Theory of Intelligence”, IEEE
Access/January 21, 2015
Wolff, J.G. The SP theory of intelligence: An overview. Information 2013, 4, 283–341
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