The document discusses how to test AI models, including defining test data through automated and manual collection of FAQs, evaluating models using metrics like precision and recall, and analyzing results by preprocessing output, calculating metrics, and visualizing performance. It also provides myths and facts about AI and chatbots, and demonstrates testing an FAQ model through collecting data, training a model, running tests, and analyzing the results.
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
This presentation educates you about Artificial intelligence, How does AI work?, 3 Cognitive Skills, Why is artificial intelligence important?, Advantages and Disadvantages, Strong AI vs. weak AI, 4 Types of artificial intelligence and applications of AI.
For topics stay tuned with Learnbay.
Introduction to Artificial IntelligenceSanjay Kumar
This presentation talks about what is Artificial Intelligence, what are key Algorithms (CNN, RNN, Reinforcement Learning), their applications. AI use cases such as detecting fish species and Spoting Distracted Driver
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
This presentation educates you about Artificial intelligence, How does AI work?, 3 Cognitive Skills, Why is artificial intelligence important?, Advantages and Disadvantages, Strong AI vs. weak AI, 4 Types of artificial intelligence and applications of AI.
For topics stay tuned with Learnbay.
Introduction to Artificial IntelligenceSanjay Kumar
This presentation talks about what is Artificial Intelligence, what are key Algorithms (CNN, RNN, Reinforcement Learning), their applications. AI use cases such as detecting fish species and Spoting Distracted Driver
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.
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!
THIS PPT IS CREATED BY MYSELF ROHIT YEMUL,
RUTWIK DOSHI,ONKAR KUDALE,AND SANKALP KOTHARI.
IF YOU WANT THIS PPT EMAIL US ON rohityemul2067@gmail.com.
or you can DM on instagram id = thisisrohi268 (rohit yemul).
The State of Artificial Intelligence in 2018: A Good Old Fashioned ReportNathan Benaich
Artificial intelligence (AI) is a multidisciplinary field of science whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world.
This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Talent: Supply, demand and concentration of talent working in the field.
Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.
Collaboratively produced in East London, UK by:
- Nathan Benaich, Founder of Air Street Capital (www.airstreet.com) and RAAIS (www.raais.co).
- Ian Hogarth, Visiting Professor at UCL's IIPP (https://www.twitter.com/IIPP_UCL) and angel investor.
15 Pros and 5 Cons of Artificial Intelligence in the ClassroomLiveTiles
Technology has provided us with many new ways to learn. In the classroom, there are both pros and cons of the artificial intelligence that technology offers.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open
access peer-reviewed journal that publishes articles which contribute new results in all areas of
the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for
professionals and researchers in all fields of AI for researchers, programmers, and software and
hardware manufacturers. The journal also aims to publish new attempts in the form of special
issues on emerging areas in Artificial Intelligence and applications.
Presentation at the HEA-funded workshop 'Exploring innovative approaches to experiential teaching and learning in management decision making education'
This one day workshop provided a platform to critically examine various innovative approaches to experiential teaching/learning in Management Decision Making in order to provoke and stimulate educators. The workshop consisted of invited speeches, participants’ presentations, group debate and discussion, and panel Q&A. There were also opportunities for professional networking and socialising.
This presentation is part of a related blog post that provides an overview of the event:
For further details of the HEA's work on active and experiential learning in the Social Sciences, please see: http://bit.ly/17NwgKX
AI, Machine Learning and Deep Learning - The OverviewSpotle.ai
The deck takes you into a fascinating journey of Artificial Intelligence, Machine Learning and Deep Learning, dissect how they are connected and in what way they differ. Supported by illustrative case studies, the deck is your ready reckoner on the fundamental concepts of AI, ML and DL.
Explore more videos, masterclasses with global experts, projects and quizzes on https://spotle.ai/learn
Artificial intelligence and its impact on jobs and employmentafp11saurabhj
This presentation outlines the impact of AI on employment and jobs. which jobs will get obsolete faster and how the education system should change to reap the benefits of AI developments.
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
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 presentation discusses:
1. Background on Artificial Intelligence (AI)
2. How is reshaping Human Resources practices and processes, with emphasis on talent acquisition and learning & development.
3. New skills that HR professionals need in this new ERA
When we hear the Word Machine Learning we think of Self Driving Car and Advanced Medical Solutions. This brings the awe-inspiring of Huge and Complex Data, Advanced Statistics, Algebra and Sophisticated Solutions & we get scared to Build Solutions in Machine Learning.
Machine Learning solutions are not that Hard to develop and the same time not that easy to make them perfect. This slide decks will provide insight and demos of How a Software Engineer can start Developing Machine Learning Solutions easily and Eventually master the Knowledge of Machine Learning.
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.
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!
THIS PPT IS CREATED BY MYSELF ROHIT YEMUL,
RUTWIK DOSHI,ONKAR KUDALE,AND SANKALP KOTHARI.
IF YOU WANT THIS PPT EMAIL US ON rohityemul2067@gmail.com.
or you can DM on instagram id = thisisrohi268 (rohit yemul).
The State of Artificial Intelligence in 2018: A Good Old Fashioned ReportNathan Benaich
Artificial intelligence (AI) is a multidisciplinary field of science whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world.
This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Talent: Supply, demand and concentration of talent working in the field.
Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.
Collaboratively produced in East London, UK by:
- Nathan Benaich, Founder of Air Street Capital (www.airstreet.com) and RAAIS (www.raais.co).
- Ian Hogarth, Visiting Professor at UCL's IIPP (https://www.twitter.com/IIPP_UCL) and angel investor.
15 Pros and 5 Cons of Artificial Intelligence in the ClassroomLiveTiles
Technology has provided us with many new ways to learn. In the classroom, there are both pros and cons of the artificial intelligence that technology offers.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open
access peer-reviewed journal that publishes articles which contribute new results in all areas of
the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for
professionals and researchers in all fields of AI for researchers, programmers, and software and
hardware manufacturers. The journal also aims to publish new attempts in the form of special
issues on emerging areas in Artificial Intelligence and applications.
Presentation at the HEA-funded workshop 'Exploring innovative approaches to experiential teaching and learning in management decision making education'
This one day workshop provided a platform to critically examine various innovative approaches to experiential teaching/learning in Management Decision Making in order to provoke and stimulate educators. The workshop consisted of invited speeches, participants’ presentations, group debate and discussion, and panel Q&A. There were also opportunities for professional networking and socialising.
This presentation is part of a related blog post that provides an overview of the event:
For further details of the HEA's work on active and experiential learning in the Social Sciences, please see: http://bit.ly/17NwgKX
AI, Machine Learning and Deep Learning - The OverviewSpotle.ai
The deck takes you into a fascinating journey of Artificial Intelligence, Machine Learning and Deep Learning, dissect how they are connected and in what way they differ. Supported by illustrative case studies, the deck is your ready reckoner on the fundamental concepts of AI, ML and DL.
Explore more videos, masterclasses with global experts, projects and quizzes on https://spotle.ai/learn
Artificial intelligence and its impact on jobs and employmentafp11saurabhj
This presentation outlines the impact of AI on employment and jobs. which jobs will get obsolete faster and how the education system should change to reap the benefits of AI developments.
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
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 presentation discusses:
1. Background on Artificial Intelligence (AI)
2. How is reshaping Human Resources practices and processes, with emphasis on talent acquisition and learning & development.
3. New skills that HR professionals need in this new ERA
When we hear the Word Machine Learning we think of Self Driving Car and Advanced Medical Solutions. This brings the awe-inspiring of Huge and Complex Data, Advanced Statistics, Algebra and Sophisticated Solutions & we get scared to Build Solutions in Machine Learning.
Machine Learning solutions are not that Hard to develop and the same time not that easy to make them perfect. This slide decks will provide insight and demos of How a Software Engineer can start Developing Machine Learning Solutions easily and Eventually master the Knowledge of Machine Learning.
[DSC Europe 22] On the Aspects of Artificial Intelligence and Robotic Autonom...DataScienceConferenc1
Autonomy in targeting is a function that could be applied to any intelligent system, in particular the rapidly expanding array of robotic systems, in the air, on land and at sea – including swarms of small robots. This is an area of significant investment and emphasis for many armed forces, and the question is not so much whether we will see more intelligent robots, but whether and by what means they will remain under human control. Today’s remote-controlled weapons could become tomorrow’s autonomous weapons with just a software upgrade. The central element of any future autonomous weapon system will be the software. Military powers are investing in AI for a wide range of applications10 and significant efforts are already underway to harness developments in image, facial and behavior recognition using AI and machine learning techniques for intelligence gathering and “automatic target recognition” to identify people, objects or patterns. Although not all autonomous weapon systems incorporate AI and machine learning, this software could form the basis of future autonomous weapon systems.
[QE 2018] Paul Gerrard – Automating Assurance: Tools, Collaboration and DevOpsFuture Processing
The Digital Transformation is real. It is having a profound effect on how business is done and the nature of the systems required to deliver productive customer experiences and consequent business benefits. The demand for flexible and rapid delivery of software and systems is there. Software development teams can deliver if they adopt the disciplines of Continuous Delivery, DevOps and in-production experimentation. The barrier to achieving success in the software delivery process is likely to be the inability of testers to align testing and automated testing in particular to the development processes. Our track record in test automation is not good enough. In order to succeed a new way of thinking about testing is required, and the New Model of Testing offers a way of identifying the elements of the test process that must be ‘shifted left’. This does not necessarily mean testers move, but rather that the thinking processes must move.
During this lecture, Paul has shown that it is possible that users, BAs, and developers take some responsibility in this area. The New Model applies to all testing, whether performed in development, integration, system or user testing, by people or tools.
NLP & Machine Learning - An Introductory Talk Vijay Ganti
An Introductory talk with the goal of getting people started on the NLP/ML journey. A practitioner's perspective. Code that makes it real and accessible.
Reinforcement Learning In AI Powerpoint Presentation Slide Templates Complete...SlideTeam
Showcase how machines are built to perform intelligent tasks by using our content-ready Reinforcement Learning In AI PowerPoint Presentation Slide Templates Complete Deck. Take advantage of these artificial intelligence PowerPoint visuals, and describe how machine learning models are trained to make sequences of decisions in a complex environment. Showcase the types of artificial intelligence such as deep learning, machine learning. Explain the concept of machine learning which delivers predictive models based on the data fed into machine learning algorithms. Take the assistance of our visually attention-grabbing reinforcement learning PowerPoint templates and discuss the effective uses of artificial intelligence in various areas such as supply chain, human resources, fraud detection, knowledge creation, research, and development, etc. You can also present the usage of AI in healthcare. This includes treatment, diagnosis, training and research, early detection, etc. Explain the working of machine learning by downloading our attention-grabbing supervised learning PowerPoint presentation. https://bit.ly/3kQBnEZ
Leading and leaning-in on Ai in Recruitment
● What is Ai and why does it matter?
● What value does Ai add to the recruitment life cycle?
● What risks should you be aware of?
● Key questions to ask to evaluate and mitigate risks
● The FAIR™ Framework
● The Power of intelligent chat to Hire with Heart
Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.
Similar to [DevDay2019] How do I test AI models? - By Minh Hoang, Senior QA Engineer at KMS (20)
[DevDay2019] Lean UX - By Bryant Castro, Bryant Castro at WizelineDevDay.org
Lean UX helps teams build the minimal product necessary to validate risky assumptions and minimize the time to market with the right product. On this lecture, Lean UX principles and its value to the product cycle will be introduced. Also, the methods and tools that will help you get feedback from users and learn rapidly will be discussed. This session is geared towards those who are interested in UX but have no much experience, those looking for new methods to improve their current product processes, and anyone interested in design, business, and user centered design.
[DevDay2019] Why you'll lose without UX Design - By Szilard Toth, CTO at e·pi...DevDay.org
UX Design is on a radical rise. The most successful companies like Google or Uber know that great UX is no longer a nice-to-have but a key business driver. Szilard Toth (CTO e·pilot) and Nicolas Python (Head of Design KLARA) talk about their own experience of UX Design in modern engineering environments. Whether you're a business leader or an engineer, learn why you'll lose without UX Design.
[DevDay2019] Things i wish I knew when I was a 23-year-old Developer - By Chr...DevDay.org
Christophe will talk about what he's learned from his almost 20 years of experience in the IT industry, and his career and training advice for the upcoming generation. This include his personal experiences, what motivates him everyday, and hopefully may help you define your path to “success”. This is not about any specific technology.
[DevDay2019] Designing design teams - Christopher Nguyen, UX Manager at WizelineDevDay.org
We'll discover what it takes to build an effective Design Team. We'll dive into some of the examples and experiments that you can try with your own design teams.
[DevDay2019] Growth Hacking - How to double the benefits of your startup with...DevDay.org
What is growth hacking? Why do all startup need it? Examples of Growth Hack with 10 Classic (Facebook, Dropbox, Airbnb, etc.). How to create robot to automatize your task. How to find clients automatically in 5 minutes. 6 SEO hacks to grow up super fast on Google.
[DevDay2019] Collaborate or die: The designers’ guide to working with develop...DevDay.org
Collaboration and open communication tend to be categorized as “soft skills” and are often overlooked in organizations. In this session, he is going to discuss how to develop an effective strategy in bridging the gap between product, design, and engineering teams. He will also share some tips for including developers in different stages of design — from planning features to usability testing.
[DevDay2019] How AI is changing the future of Software Testing? - By Vui Nguy...DevDay.org
Artificial intelligence (AI) has been changing the way software is tested and how humans interact with technology. AI predicts, prevents and automates the entire process of testing using algorithms. It will not only support and improve the models and test cases but also provide more sophisticated and refined form of text recognition and better code generators. Using AI will help to save time for testing and ensure a better quality software.
[DevDay2019] Hands-on Machine Learning on Google Cloud Platform - By Thanh Le...DevDay.org
By recent release on Google Cloud Platform, Google focus on the era of AI/ML technological change, it lets us bring the powerful machine learning features to the mobile application whether it is for Android/iOS and whether experienced/beginner machine learning developer. The purpose of this topic is to share our use case on how to make your model as serving by bringing it to the cloud.
Microservices is a well-known term for recently year. But the truth is that it mostly focused on backends site while the frontend is still a monolithic application. This presentation intends to provide the necessary tooling to achieve independent apps loaded separately and run on different parts on a single web page in complete isolation which is officially called micro-frontends.
[DevDay2019] Power of Test Automation and DevOps combination - One click savi...DevDay.org
Test Automation is becomming a MUST in software development life cycle now. DevOps has been an emerging trend, and it's no longer new. Remebering the old days, when you have to stand-up the test servers, get the builds from developers, deploy it, start-up agent machines, run your tests, collect reports, shutdown all resources you have just started, and spend days to analyze the failures. Now it's time to bring DevOps into this game and let it streamline all of these processes then you can save your days for other greater jobs of software testing.
[DevDay2019] How to quickly become a Senior Engineer - By Tran Anh Minh, CEO ...DevDay.org
Many graduated students do not have clear orientation to become a Senior Engineer as quickly as possible. His topic will discuss and recommend some useful methods for students to help you become a Senior Engineer.
[Devday2019] Dev start-up - By Le Trung, Founder & CEO at Hifiveplus and Edu...DevDay.org
In this talk, Trung will convey his experience and discuss business start-up issues from the perspective of a developer. This position has many advantages to start a business in the technological age. It also allows us to learn, so we can reduce possible risks.
[DevDay2019] Web Development In 2019 - A Practical Guide - By Hoang Nhu Vinh,...DevDay.org
This is the step-by-step guide to becoming a web developer in 2019. We will look at nearly all aspects of web technology including the necessities as well as some of the new trends for 2019.
[DevDay2019] Opportunities and challenges for human resources during the digi...DevDay.org
The term "digital transformation" is mentioned a lot recently and is considered as the first platform to access and apply technologies in the 4th industrial revolution. So what are the opportunities and challenges for human resources during this period? With many years working and researching in human resource training for the software industry, he hopes these sharing will be helpful to you.
[DevDay2019] Do you dockerize? Are your containers safe? - By Pham Hong Khanh...DevDay.org
Docker containers are a fast-growing technology that has become hugely popular in the software industry nowadays. It offers amazing benefits but also presents the developer with lots of security challenges. This talk will give you an introduction to Docker as well basic security best practices. But don’t worry, we will also do some live hacking :).
[DevDay2019] Develop a web application with Kubernetes - By Nguyen Xuan Phong...DevDay.org
Kubernetes is a platform used to automate the management, to scale and to deploy applications in the form of containers. Kubernetes is also called Container orchestration engine.
[DevDay2019] Paradigm shift towards effective Scrum - By Tam Doan, Agile Coac...DevDay.org
Scrum has become one of the most popular Agile frameworks in IT, as its lightweight and easy to understand. But why is it so difficult to apply? One of the challenges of effectively applying Scrum comes from the basic understanding of why Scrum was initially created in the first place. Having this paradigm shift will significantly enhance becoming an effective Scrum Team member.
[DevDay2019] JAM Stack - By Ngo Thi Ni, Web Developer at Agility IODevDay.org
JAM Stack is modern web development architecture based on client-side JavaScript, reusable APIs, an prebuilt Markup. You can check it here: jamstack.org
[DevDay2019] Layering GraphQL on top of existing infrastructure - By Phan Tha...DevDay.org
This is a demonstration of how to layering GraphQL on top of existing infrastructure without rewriting any data layer. In this demonstration, you and me will build a simple GraphQL endpoint then try to layer it on top of several types of data access layer like Mysql DAL, ORM, Rest API, etc.
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
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
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.
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 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
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
4. Agenda
1 • What is machine learning
• Myths & Facts about AI
• Myths & Facts about Chatbot
ABOUT A.I
3 TAKE AWAY
2
• The right metrics for evaluating the
ML model
• How we test FAQ model
• Demo
HOW I TEST THE AI MODEL
4 REFERENCES
• Tools & Libraries
6. What Is Machine Learning?
Machine learning is the subfield of
computer science that gives
computers the ability to learn without
being explicitly programmed.
7. Myths And Facts About A.I
MYTH FACT
Artificial intelligence and machine learning will wipe out
all the jobs.
A.I is no different from other technological advances in
that it helps humans become more effective and
processes more efficient.
“Cognitive AI” technologies are able to understand and
solve new problems the way the human brain can.
“Cognitive” technologies can’t solve problems they
weren’t designed to solve.
You need a PH.D. to work in machine learning & data
science.
Nowadays, a lot of documents and tutorial on the Internet
can help people step by step approach machine learning
world.
8. v
What Is Chatbot?
A computer program designed to
simulate conversation with human
users, especially over the Internet.
9. Myths And Facts About Chatbot
MYTH FACT
Chatbot have only been around for a short while.
ELIZA is one of the most well-known Chatbot
therapists and the bot was created about 50 years
ago.
Texts or voice is the only way to interact with Bots.
Actually Chatbot platforms allows users to interact
with them via graphical interfaces or graphical
widgets, and recent Chatbot platforms follow this
development approach.
All Chatbot platforms use AI.
Not all Chatbot platforms use AI. Most Chatbot
platforms are rule-based which follow a simple,
autonomous process, something along the lines of a
decision tree.
11. Regression
• MSPE
• MSAE
• R Square
• Adjusted R Square
Classification
• Precision – Recall
• ROC-AUC
• Accuracy
• Log-Loss
Unsupervised Models
• Rand Index
• Mutual
• Information
Others
• CV Error
• Heuristic methods to
find K
• BLEU Score (NLP)
The Right Metric For Evaluating
Ml Models
12. Actual positive Actual negative
Predicted positive True positive
False positive
(Type I errors)
Predicted negative
False negative
(Type II errors)
True negative
Confusion Matrix
Commonly Used Metrics In Classification
13. Accuracy:
• Percentage of total items classified correctly
• Formula:
Commonly Used Metrics In Classification
14. Recall/Sensitivity/TPR (True Positive Rate):
• Number of items correctly identified as
positive out of total true positives
• Formula:
Commonly Used Metrics In Classification
Actual positive Actual negative
Predicted positive True positive
False positive
(Type I errors)
Predicted negative
False negative
(Type II errors)
True negative
15. Precision
• Number of items correctly identified as
positive out of total items identified as
positive
• Formula:
Commonly Used Metrics In Classification
Actual positive Actual negative
Predicted positive True positive
False positive
(Type I errors)
Predicted negative
False negative
(Type II errors)
True negative
16. Precision
• It is a harmonic mean of precision and recall
• Formula:
Commonly Used Metrics In Classification
Precision Recall F1
1 1 1
0.1 0.1 0.1
0.5 0.5 0.5
1 0.1 0.182
0.3 0.8 0.36
0.8 0.3 0.436
18. Prepare test
data
•Crawl FAQ data
•Generate question
from FAQ data
Run test
•Train model with FAQ
data
•Run test
Analyze
result
•Pre-process the raw
result
•Calculate metrics to
evaluate the AI model
in classification
•Visualize the metrics
Model
Result
•Select the threshold
value
The Process To Test FAQ Model?
19. • Collect FAQ questions data (Manual and
Automate)
• Use NLTK to generate new question data
(NLG)
• Self-defined question data
How We Define Test Data
Set?
20. Train with domain X and run the test defined for domain X.
How We Evaluate The AI Model?
21. • Pre-process the raw result.
• Calculate metrics to evaluate the AI model
in classification.
• Visually metrics.
How We Analyze The
Result?
24. Take Away
• Know main metrics for evaluating ML model.
• Know how to test the classification AI model.
• It is up to your self-learning skills and adaptability to decide whether working on
___ projects (AI, blockchain, VR, etc.) is difficult.
• Use Automation to reduce time and effort to prepare test data
Artificial intelligence and machine learning will wipe out all the jobs:
Technology has been threatening jobs and displacing jobs throughout history. Telephone switching technology replaced human operators. Automatic call directors replaced receptionists. Word processing and voicemail replaced secretaries, email replaced inter-office couriers. Call center technology innovation has added efficiency and effectiveness at various stages of standing up customer service capabilities—from recruiting new reps using machine learning to screen resumes, to selecting the right training program based on specific learning styles, to call routing based on sentiment of the caller and disposition of the rep, to integration of various information sources and channels of communication. In each of these processes, technology augmentation enhanced the capabilities of humans. Were some jobs replaced? Perhaps, but more jobs were created, albeit requiring different skills.
The use of AI-driven chatbots and virtual assistants is another iteration of this ongoing evolution. It needs to be thought of as augmentation rather than complete automation and replacement. Humans engage, machines simplify. There will always be the need for humans in the loop to interact with humans at some level.
Bots and digital workers will enable the “super CSR” of the future and enable increasing levels of service with declining costs. At the same time, the information complexity of our world is increasing and prompting the need for human judgment. Some jobs will be lost, but the need and desire for human interaction at critical decision points will increase, and the CSR’s role will change from answering rote questions to providing better customer service at a higher level, especially for interactions requiring emotional engagement and judgment.
“Cognitive AI” technologies are able to understand and solve new problems the way the human brain can:
Cognitive AI simulates how a human might deal with ambiguity and nuance; however, we are a long way from AI that can extend learning to new problem areas. AI is only as good as the data on which it is trained, and humans still need to define the scenarios and use cases under which it will operate. Within those scenarios, cognitive AI offers significant value, but AI cannot define new scenarios in which it can successfully operate. This capability is referred to as “general AI” and there is much debate about when, if ever, it will emerge. For computers to answer broad questions and approach problems the way that humans do will require technological breakthroughs that are not yet on the horizon.
RMSE (Root Mean Square Error)
MAE is the average of the absolute difference between the predicted values and observed value.
BLEU (Bilingual Evaluation Understudy)
Recall or Sensitivity or TPR (True Positive Rate): Number of items correctly identified as positive out of total true positives- TP/(TP+FN) : được định nghĩa là tỉ lệ số điểm true positive trong số những điểm thực sự là positive.
Specificity or TNR (True Negative Rate): Number of items correctly identified as negative out of total negatives- TN/(TN+FP)
Precision: Number of items correctly identified as positive out of total items identified as positive- TP/(TP+FP): được định nghĩa là tỉ lệ số điểm true positive trong số những điểm được phân loại là positive.
False Positive Rate or Type I Error: Number of items wrongly identified as positive out of total true negatives- FP/(FP+TN)
False Negative Rate or Type II Error: Number of items wrongly identified as negative out of total true positives- FN/(FN+TP)
Recall or Sensitivity or TPR (True Positive Rate): Number of items correctly identified as positive out of total true positives- TP/(TP+FN) : được định nghĩa là tỉ lệ số điểm true positive trong số những điểm thực sự là positive. Hay còn gọi là tỉ lệ dự đoán chính xác giá trị positive của model
Precision: Number of items correctly identified as positive out of total items identified as positive- TP/(TP+FP): được định nghĩa là tỉ lệ số điểm true positive trong số những điểm được phân loại là positive. Hay còn gọi là khả năng phân loại Positive chính xác của model
Precision: Number of items correctly identified as positive out of total items identified as positive- TP/(TP+FP): được định nghĩa là tỉ lệ số điểm true positive trong số những điểm được phân loại là positive. Hay còn gọi là khả năng phân loại Positive chính xác của model
Recall or Sensitivity or TPR (True Positive Rate): Number of items correctly identified as positive out of total true positives- TP/(TP+FN) : được định nghĩa là tỉ lệ số điểm true positive trong số những điểm thực sự là positive. Hay còn gọi là tỉ lệ dự đoán chính xác giá trị positive của model (tỉ lệ bỏ sót positive data)
Mô hình 1: lý tưởng
Mô hình 2: tệ vì dự doán chính xác giá trị positive thấp cũng như bỏ sót giá tị là positive
Mô hình 3: balance
Mô hình 4: tỉ lệ dự đoán chính xác giá trị positive chính xác tuyết đối nhưng tỉ lệ tìm ra positive thấp. Ví dụ: tập data có 100 giá trị positive nhưng model chỉ dự đoán đuọc đúng 1 giá trị là positive data và giá trị đó được dự đoán đúng là positive
Mô hình 5: tỉ lệ dự đóán chính xác giá trị positive thấp nhưng tỉ lệ tìm ra positive cao. Ví dụ: tập data có 100 giá trị positive, model dự đoán 80 giá tị positive nhưng chỉ có 10 trong số đó là positive
Mô hình 5: tỉ lệ dự đóán chính xác giá trị positive cao nhưng tỉ lệ tìm ra positive thấp. Ví dụ: tập data có 100 giá trị positive, model dự đoán 30 giá tị positive và trong 20 giá trị trong số đó là positive