Ant colony optimization is an example of taking inspiration from nature for AI. It is inspired by how ants find the shortest path between their colony and a food source. Individual ants deposit pheromones along the paths they follow; other ants are more likely to follow a path with a stronger pheromone concentration and less likely to follow one with a weaker concentration, with the result that the shortest path is identified and reinforced through positive feedback over multiple ant trips between the colony and food source. This decentralized process was abstracted and applied to solve combinatorial optimization problems in computer science.
What Is Machine Learning? | What Is Machine Learning And How Does It Work? | ...Simplilearn
This presentation on Machine Learning will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning is a core sub-area of artificial intelligence. Machine Learning is a technique which uses statistical methods enabling machines to learn from their past data. it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data has been gaining momentum over the last several years. Now, let us get started and understand the concept of Machine Learning in detail.
Below topics are explained in this "What is Machine Learning?" presentation:
1. Machine Learning
- What is Machine Learning
2. Artificial intelligence vs Machine Learning vs Deep Learning
3. How does Machine Learning work?
4. Types of Machine Learning
5. Machine Learning pre-requisites
6. Applications of Machine Learning
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers
2. Information Architects
3. Analytics Professionals
4. Graduates
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
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
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
Overview of artificial intelligence, its definition and classification, its history and historical development, as well as several theories and concepts.
What Is Machine Learning? | What Is Machine Learning And How Does It Work? | ...Simplilearn
This presentation on Machine Learning will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning is a core sub-area of artificial intelligence. Machine Learning is a technique which uses statistical methods enabling machines to learn from their past data. it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data has been gaining momentum over the last several years. Now, let us get started and understand the concept of Machine Learning in detail.
Below topics are explained in this "What is Machine Learning?" presentation:
1. Machine Learning
- What is Machine Learning
2. Artificial intelligence vs Machine Learning vs Deep Learning
3. How does Machine Learning work?
4. Types of Machine Learning
5. Machine Learning pre-requisites
6. Applications of Machine Learning
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers
2. Information Architects
3. Analytics Professionals
4. Graduates
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
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
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
Overview of artificial intelligence, its definition and classification, its history and historical development, as well as several theories and concepts.
Human intelligence is the intellectual powers of humans, Learning
Decision Making
Solve Problems
Feelings(Love,Happy,Angry)
Understand
Apply logic
Experience
making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Robots are autonomous or semi-autonomous machines meaning that they can act independently of external commands. Artificial intelligence is software that learns and self-improves.
Why Artificial Intelligence?
• Computers can do computations, by fixed programmed rules
• A.I machines perform tedious tasks efficiently & reliably.
• computers can’t understanding & adapting to new situations.
• A.I aims to improve machine to do such complex tasks.
Advantages of A.I:
Error Reduction
Difficult Exploration(mining & exploration processes)
Daily Application(Siri, Cortana)
Digital Assistants(interact with users)
Medical Applications(Radiosurgery)
Repetitive Jobs(monotonous)
No Breaks
Some disadvantages of A.I:
High Cost
Unemployment
Weaponization
No Replicating Humans
No Original Creativity
No Improvement with Experience
Safety/Privacy Issues
Artificial intelligence will be a Greatest invention Until Machines under the human control. Otherwise The new ERA will be There…..!
This presentation is the part of the webinar conducted by CloudxLab. This was the free session on Machine Learning.
Cloudxlab conducts such webinars very frequently and to make sure you never miss the future webinar update, please see the 'Events' section at CloudxLab.com
*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
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
This collection of slides are meant as a starting point and tutorial for the ones who want to understand AI Ethics and in particular the challenges around bias and fairness. Furthermore, I have also included studies on how we as humans perceive AI influence in our private as well as working lives.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
This PPT gives you more than enough introduction to artificial intelligence and makes you to learn yourself artificial intelligence creating interest upon it
Artificial Intelligence and Machine Learning Aditya Singh
Presented By JBIMS Marketting Batch (2017-2020).
Application Artificial Intelligence in MIS(Management Information System). Presented By Trilok Prabhakaran , Aditya Singh , Shashi Yadav, Vaibhav Rokade. Presentation have live cases of two different industry.
Human intelligence is the intellectual powers of humans, Learning
Decision Making
Solve Problems
Feelings(Love,Happy,Angry)
Understand
Apply logic
Experience
making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Robots are autonomous or semi-autonomous machines meaning that they can act independently of external commands. Artificial intelligence is software that learns and self-improves.
Why Artificial Intelligence?
• Computers can do computations, by fixed programmed rules
• A.I machines perform tedious tasks efficiently & reliably.
• computers can’t understanding & adapting to new situations.
• A.I aims to improve machine to do such complex tasks.
Advantages of A.I:
Error Reduction
Difficult Exploration(mining & exploration processes)
Daily Application(Siri, Cortana)
Digital Assistants(interact with users)
Medical Applications(Radiosurgery)
Repetitive Jobs(monotonous)
No Breaks
Some disadvantages of A.I:
High Cost
Unemployment
Weaponization
No Replicating Humans
No Original Creativity
No Improvement with Experience
Safety/Privacy Issues
Artificial intelligence will be a Greatest invention Until Machines under the human control. Otherwise The new ERA will be There…..!
This presentation is the part of the webinar conducted by CloudxLab. This was the free session on Machine Learning.
Cloudxlab conducts such webinars very frequently and to make sure you never miss the future webinar update, please see the 'Events' section at CloudxLab.com
*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
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
This collection of slides are meant as a starting point and tutorial for the ones who want to understand AI Ethics and in particular the challenges around bias and fairness. Furthermore, I have also included studies on how we as humans perceive AI influence in our private as well as working lives.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
This PPT gives you more than enough introduction to artificial intelligence and makes you to learn yourself artificial intelligence creating interest upon it
Artificial Intelligence and Machine Learning Aditya Singh
Presented By JBIMS Marketting Batch (2017-2020).
Application Artificial Intelligence in MIS(Management Information System). Presented By Trilok Prabhakaran , Aditya Singh , Shashi Yadav, Vaibhav Rokade. Presentation have live cases of two different industry.
AI is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for the use of information), reasoning (using the rules to reach approximate or final conclusions) and self-correction. Particular applications of the AI include expert system speech recognition and artificial vision.
What do you need to think about before bringing advanced technology into your community, library or organization? How do you introduce it to staff? Will they worry about being replaced or losing their jobs? And how do you get machines to operate at optimal efficiency? Machines need to learn to be effective, whether it’s Siri, Alexa, or Watson. And people have to adapt to the machines. Join us and learn more!
AI - Artificial Intelligence - Implications for LibrariesBrian Pichman
What does the world of AI (artificial intelligence) mean for libraries? Can AI replace library services or how can libraries leverage the technology for more streamlined services. From Smart Houses, to Robots, to technology yet to be mainstreamed, this session will cover it all to help you better prepare and plan for the future.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. The goal of AI is to develop computer systems capable of performing tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, learning from experience, and making decisions.
There are various types of AI, including narrow AI and general AI. Narrow AI, also known as weak AI, is designed to perform specific tasks or solve particular problems, such as speech recognition, image recognition, or playing chess. General AI, also known as strong AI or artificial general intelligence (AGI), refers to AI systems that possess the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence.
AI algorithms and techniques can be categorized into several subfields, including:
1. Machine Learning: Machine learning is a subset of AI that focuses on the development of algorithms that enable computers to learn from data and improve their performance over time without being explicitly programmed. This includes supervised learning, unsupervised learning, and reinforcement learning.
2. Deep Learning: Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to model complex patterns in large amounts of data. Deep learning has been particularly successful in tasks such as image recognition, speech recognition, and natural language processing.
3. Natural Language Processing (NLP): NLP is a field of AI that focuses on the interaction between computers and humans through natural language. NLP enables computers to understand, interpret, and generate human language, allowing for applications such as language translation, sentiment analysis, and chatbots.
4. Computer Vision: Computer vision is a field of AI that enables computers to interpret and understand visual information from the real world, such as images and videos. Computer vision algorithms can be used for tasks such as object detection, image classification, and facial recognition.
5. Robotics: Robotics combines AI with mechanical engineering to create machines that can perform tasks autonomously or semi-autonomously. AI-powered robots are used in various industries, including manufacturing, healthcare, and agriculture, to automate repetitive tasks and improve efficiency.
AI has a wide range of applications across various industries, including healthcare, finance, transportation, retail, and entertainment. Some examples of AI applications include virtual assistants like Siri and Alexa, autonomous vehicles, recommendation systems like those used by Netflix and Amazon, and medical diagnosis systems.
While AI has the potential to bring about significant benefits and advancements, it also raises ethical and societal concerns, such as job displacement, algorithmic bias, privacy issues, and the potential for misuse or abuse of AI te
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Given above is a PowerPoint Presentation on Artificial Intelligence (AI). Ideal for activities, school projects, essays etc. Hope it is accommodating.
Thank you.
You can also find out my other presentations on Technology.
The links are given below-
https://www.slideshare.net/GursheenKaurChawla/all-about-the-internetpptx and https://www.slideshare.net/GursheenKaurChawla/impact-of-online-gamespptx
PowerPoint Presentation on the topic "Artificial Intelligence" including the brief history,information about the founders and pioneers of the concept and the varied applications and future of Artificial Intelligence.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
Introduction to Artificial Intelligence and Machine Learning
1. Introduction Artificial Intelligence and Machine Learning
Assoc. Prof. Emad Nabil
Faculty of computer and information system, Islamic University of Madinah, KSA
Faculty of Computers and Artificial Intelligence, Cairo, Egypt
11 /11/2019
2. What is the definition of human intelligence and
artificial intelligence
4. The human intelligence
• Oxford Dictionary: The ability to acquire and apply
knowledge and skills.
5. The human intelligence
• Edwin Boring Professor of Psychology at Harvard
University
The human Intelligence is what we can measure
using IQ Tests
6. The human intelligence
Intelligence is the term that includes the
following mental abilities:
• Analysis,
• planning,
• solving problems,
• building conclusions,
• Abstract thinking,
• Learning languages,
• speed of learning,
• Ability to feel and express feelings
• Understanding the feelings of others.
8. The Artificial intelligence
• The term artificial intelligence was coined by
the American scientist John McCarthy in 1956
• He defined it as the science and engineering
of making intelligent machines.
9. The Artificial intelligence
• AI research aims to create AI that can
replicate or mimic intelligent humans.
Some abilities of humans are:
• Writing poetry
• Drawing
• Solving complex mathematical problems
• Translation
10. Portrait of Edmond de Belamy (2018), The first AI-Generated Portrait Sells for $432,500 in an Auction
11. AI Types
Weak AI
• Weak AI is the use of software to study or
accomplish a solution to a particular problem and
does not include all human cognitive abilities.
• Ex: Deep Blue: The famous chess program .. It
solves certain issues but does not have the
capabilities of human cognition such as
awareness.
DEEP BLUE - A super chess computer with the addition of specific circuits
time-A prominent three.IBMproduced by,s1990developed in the early
1989who was defeated in the firstGary Kasparov,world chess champion
and the second in 1996 with a score of 2-4 and then won the revenge
match of six innings on May 11, 1997
12. Types of AI
Strong AI is the case when the machine can:
* Approach human intelligence
* has some degree cognitive abilities.
13. The Robot Sofia
• Appeared in 2016.
• Sofia is a human-like robot designed by Hanson Robotics in Hong
Kong.
• The robot made headlines in the world as the first robot in the
world to become a legitimate citizen by obtaining Saudi
citizenship.
• Sofia has some features of humans, like:
• Expressing her feelings
• Having sense of humor
• Speaking and communicating in an understandable language
15. The Robot Sofia
• David Hanson designed the robot to be:
• A suitable companion for the elderly
• To help people in large events, public
squares (airports or train stations)
17. The Robot Sofia
Granting Sofia citizenship has led to several
questions
• The right to vote
• The right to marry (Bicentennial Man movie)
• If disabling its system is a murder.
19. When do we call the machine intelligent?
In 1950, the English mathematician Alan Turing
designed a test if passed by the machine we
consider intelligent.
20. When do we call the machine intelligent?
Using Turing test
As explained in the image in LHS, the
goal From the test to determine who
is the man and who is the machine
by asking questions if the judge could
not differentiate between them, we
say the machine is intelligent.
21. Is there a machine that has
passed the Turing test?
22. Eugene Goostman
• Eugene Goostman is an artificial intelligence software program that conducts online conversations as a 13-
year-old Ukrainian child.
• In 2014, at an event at the Royal Society in London, organized by the University of Reading, Eugene
Gustman passed the Turing test, convincing 33% of the judges that he was a human.
• Unfortunately the chatbot is not available online, which raises doubts about that claim.
24. Sources of inspiration for IA
Today AI not only mimic humans, but also can mimic other species and
phenomenon in nature.
Neural network, deep learning
Biology Nature Physics
Ant colony optimization Quantum-Mechanics-Based Algorithms
27. AI Languages
27
Python
very simple and can be easily learnt.
Easier than Java, C++ or Ruby.
There are plenty of libraries and frameworks in python
https://hackernoon.com/top-8-python-libraries-for-machine-learning-and-artificial-intelligence-y08id3031
28. AI Languages
R :
R is one of the most effective language and environment for analyzing
and manipulating the data for statistical purposes.
29. AI Languages
Lisp
• one of the oldest and the most suited languages for the development
in AI.
•
• It was invented by John McCarthy, the father of Artificial Intelligence
in 1958.
• It has the capability of processing the symbolic information
effectively.
30. AI Languages
Prolog: The features provided by Prolog include
• efficient pattern matching,
• tree-based data structuring and
• automatic backtracking.
31. AI Languages
Java: Java can also be considered as a good choice for AI development.
• easy use, debugging ease, package services,
• simplified work with large-scale projects,
• graphical representation of data and better user interaction.
33. The Super Doctor
By the help of AI, doctors can diagnose
serious diseases such as cancer, for example,
before it is too late. It analyzes images to
diagnose any possible early signs.
34.
35. The Super Doctor
• The ability to analyze medical images and
notice the finer details that doctors may not
notice.
• Ex: Stanford has produced an algorithm that
can better detect pneumonia الرئوي اﻻلتهاب than
radiologists.
36. The Super Doctor
Google Deep Mind is used in the UK
National Health Services to detect
potential health hazards by collecting
data from a mobile application.
37. Cleveland Clinic and IBM , Cleveland Clinic is ranked as one of the best
hospitals in the United States
40. Self-driving cars
Tesla One of the first automotive brands that launched a self-driving vehicle.
Google, Audi, Cadillac and Volvo are also developing their own model
41. Self-driving cars
Uber also made the first 50,000 orders by a self-driving truck.
https://www.wired.com/2016/10/ubers-self-driving-truck-makes-first-delivery-50000-beers/
43. Things To Consider About
Self-driving Cars
Police: Driving license please
Me : I don't drive
Police: 🤔🤔
44. Things To Consider About
Self-driving Cars
Officer: Sir have you been drinking?
Me :“Of course, but not the car”
Officer : 🤔🤔
45. Things To Consider About
Self-driving Cars
What about accidents!
who is responsible ???
46. Automatic Programming
• A higher goal of Automatic programming is to produce a smart
program that can produce a program by itself.
• i.e. give the program the details of the problem to design and it
will produce the program.
51. AI Applications that you can try
Deep Art Effects
https://www.deeparteffects.com/
transforms your photos and videos into works using artistic style transfer of famous artists.
53. Characteristics of artificial intelligence programs
The ability to learn
The ability to learn is one of the
characteristics of intelligent behavior
learning in humans is through
observation (Supervised Learning)
or take advantage of the mistakes of
the past (Reinforcement Learning)
54. Characteristics of artificial intelligence programs
The ability to learn
• The difference between deep learning
and reinforcement learning
• Deep learning is learning from a
training group and then applying that
learning to a new data set
• Reinforcement learning dynamically
learns by adjusting actions based on
continuous feedback to maximize
reward.
https://jisrlabs.com/%D9%85%D8%A7-%D8%A7%D9%84%D9%81%D8%B1%D9%82-%D8%A8%D9%8A%D9%86-%D8%A7%D9%84%D8%AA%D8%B9%D9%84%D9%85-%D8%A7%D9%84%D8%B9%D9%85%D9%8A%D9%82-%D9%88%D8%A7%D9%84%D8%AA%D8%B9%D9%84%D9%85-%D8%A7%D9%84%D9%85/
56. Fears of artificial intelligence
Significant advances in AI may lead to
the humans' end.
57. Fears of artificial intelligence
The argument is supported by the hypothesis:
• humans dominate other creatures because they
have a brain with distinctive abilities
• so if AI outperforms human brains and in turn
becomes super-intelligent
• it will be powerful and difficult to control.
58. Fears of artificial intelligence
• Programming the machines with values similar
to human values is very difficult
• AI devices may try to stop their shutting down
• AI has a lot for humanity
• AI machines will not have the desire to keep
themselves running.
59. Fears of artificial intelligence
Amazon Killed an AI Recruitment System
Because It Couldn’t Stop the Tool from
Discriminating Against Women
https://fortune.com/2018/10/10/amazon-ai-recruitment-bias-women-sexist/?fbclid=IwAR1up6bTmEfV6Fi9BLCEouIKeFJqPGECMbEHmC8D9RvghDNHbQev1iXUbmo
61. Q1: Neural Network is not a new AI
technique, why is it booming nowadays?
• ANN needs learning which is a time-consuming task, in the past
hardware was poor, but now the advances in hardware make learning
NN an easy task.
• ANN accuracy depends on the amount of training data, nowadays
there are a tremendous amount of data in almost every field.