1) The document discusses Howard Gardner's theory of multiple intelligences and how combining human and artificial intelligences could lead to "hyper-intelligences."
2) It examines challenges around developing general artificial intelligence and ensuring AI is developed and applied safely, ethically and for the benefit of humanity.
3) The document outlines a partnership between Cognizant and Microsoft to jointly develop and apply cognitive services and artificial intelligence.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
Explore the risks and concerns surrounding generative AI in this informative SlideShare presentation. Delve into the key areas of concern, including bias, misinformation, job loss, privacy, control, overreliance, unintended consequences, and environmental impact. Gain valuable insights and examples that highlight the potential challenges associated with generative AI. Discover the importance of responsible use and the need for ethical considerations to navigate the complex landscape of this transformative technology. Expand your understanding of generative AI risks and concerns with this engaging SlideShare presentation.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
Explore the risks and concerns surrounding generative AI in this informative SlideShare presentation. Delve into the key areas of concern, including bias, misinformation, job loss, privacy, control, overreliance, unintended consequences, and environmental impact. Gain valuable insights and examples that highlight the potential challenges associated with generative AI. Discover the importance of responsible use and the need for ethical considerations to navigate the complex landscape of this transformative technology. Expand your understanding of generative AI risks and concerns with this engaging SlideShare presentation.
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
IBM Watson overview presented by Mike Pointer, Watson Sr. Solution Architect, at Penn State's Nittany Watson Challenge Immersion event on January 19-20, 2017.
I will talk about Generative AI and its applications to 2D art production in the gaming industry. We will explore the Stable Diffusion neural net and concepts such as Prompt Engineering, Image-to-Image, ControlNet, and Dreambooth and how they can enhance game development. Moreover, we will compare the pros and cons of Stable Diffusion with Midjourney. As a result, you will better understand the potential benefits of incorporating generative AI into your game development workflow.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
Beyond the Hype: Crossing the chasm with AI and patient chronic disease management
AI is the new mantra in healthcare and is being touted by many as one of the impending saviours of personalising the patient experience, as it is a key component of the much-vaunted Transformation of Self-Care. By others it’s seen as flawed and impractical to adopt, as the quality of data and process collation is driven by the behaviour of the patient and therapy ecosystem they currently live in. This talk looks at lessons learnt from when AI has attempted to digitally transform patient and caregiver behaviour as part of chronic disease self-care. The focus will be on digital medical devices for asthma, allergies and regular medication based on the implementation and pilot testing of real-world AI solutions, which have been key to learning what design-thinking works and does not work when designing AI into a patients life. Attendees will hear what design-thinking approaches were taken, the benefits-and drawbacks of how this impacted factors that make adjustment to chronic medical illness psychologically demanding, and how cross-collaboration worked with business product owners, data scientists, engineers, patients and caregivers. The talk concludes with a summary of challenges, opportunities and future capabilities needed by designers when creating self-care solutions for patients and caregivers, where AI is trusted enough to act on insights.
ProductTank HK #31 - Maximizing Product Ops Efficiency with Generative AIAmanda Lam
ProductTank Hong Kong #31 - Maximizing Product Ops Efficiency with Generative AI
by Mushroom Luk and Amanda Lam
In his presentation, Mushroom will share his secrets for using generative AI tools like ChatGPT and GitHub Copilot to generate code and streamline workflows. He'll also take you on a journey through the exciting new field of text-to-image AI generation, showing you how to turn text-based descriptions into stunning visual content.
Get ready to be spellbound by the power of generative AI technology, as Mushroom demonstrates how to use these tools to create real value for your organization. Don't miss this chance to learn from the data wizard himself!
In addition, Amanda will also share her experiences in using more generative AI tools such as Bing Chat, Google Bard, Dall-E, Bing Image Generator and MidJourney etc., to tackle real life product ops problems such as user story preparation, budget and timeline estimation, slide preparations etc. This session will be based on live demo and will be interactive with the audience.
About the speakers
Introducing Mushroom Luk, the data analyst who knows as much about programming as a fish knows about tightrope walking! He's found the ultimate cheat code in life: an AI sidekick that does all the heavy lifting.
Amanda Lam is a tech enthusiast and community activist. She organizes ProductTank Hong Kong, Women Techmakers Hong Kong and HKPUG events, and is a consistent host of the weekly Cantonese tech podcast, HKPUG Podcast.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
How AI is going to change the world _M.Mujeeb Riaz.pdfMujeeb Riaz
How AI is going to change the world?
"AI: The Future of Our World“
"AI and its Transformative Impact on the World: Understanding the Potential of Chatbots and Conversational AI"
What is Artificial Intelligence and how it works?
What are Chatbots?
What Is ChatGPT?
Difference between chatGPT 3 and chatGPT 4?
Is Jasper artificial intelligence?
What is Character AI and how it works?
How chatGPT is going to change the world?
Why we are calling ChatGPT the future?
As NFT projects continue to pop up and censorship woes become a reality, decentralized storage has become a beacon of hope for many. Let’s check out how much the decentralized storage sector has grown!
Details regarding the working of chatgpt and basic use cases can be found in this presentation. The presentation also contains details regarding other Open AI products and their useability. You can also find ways in which chatgpt can be implemented in existing App and websites.
The numbers tell the story: 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics?
To answer that question, Accenture conducted a landmark global study involving 1,500 C-suite executives from organizations across 16 industries. The aim: Help companies progress on their AI journey, from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.
Read the full report:
http://www.accenture.com/AI-Built-to-Scale-Slideshare
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
Artificial Intelligence (AI) is one of the hottest topics in the tech and startup world at the moment. The field of AI and its associated technologies present a range of opportunities – as well as challenges – for corporates. Learn more about what Artificial Intelligence means for your organization.
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
IBM Watson overview presented by Mike Pointer, Watson Sr. Solution Architect, at Penn State's Nittany Watson Challenge Immersion event on January 19-20, 2017.
I will talk about Generative AI and its applications to 2D art production in the gaming industry. We will explore the Stable Diffusion neural net and concepts such as Prompt Engineering, Image-to-Image, ControlNet, and Dreambooth and how they can enhance game development. Moreover, we will compare the pros and cons of Stable Diffusion with Midjourney. As a result, you will better understand the potential benefits of incorporating generative AI into your game development workflow.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
Beyond the Hype: Crossing the chasm with AI and patient chronic disease management
AI is the new mantra in healthcare and is being touted by many as one of the impending saviours of personalising the patient experience, as it is a key component of the much-vaunted Transformation of Self-Care. By others it’s seen as flawed and impractical to adopt, as the quality of data and process collation is driven by the behaviour of the patient and therapy ecosystem they currently live in. This talk looks at lessons learnt from when AI has attempted to digitally transform patient and caregiver behaviour as part of chronic disease self-care. The focus will be on digital medical devices for asthma, allergies and regular medication based on the implementation and pilot testing of real-world AI solutions, which have been key to learning what design-thinking works and does not work when designing AI into a patients life. Attendees will hear what design-thinking approaches were taken, the benefits-and drawbacks of how this impacted factors that make adjustment to chronic medical illness psychologically demanding, and how cross-collaboration worked with business product owners, data scientists, engineers, patients and caregivers. The talk concludes with a summary of challenges, opportunities and future capabilities needed by designers when creating self-care solutions for patients and caregivers, where AI is trusted enough to act on insights.
ProductTank HK #31 - Maximizing Product Ops Efficiency with Generative AIAmanda Lam
ProductTank Hong Kong #31 - Maximizing Product Ops Efficiency with Generative AI
by Mushroom Luk and Amanda Lam
In his presentation, Mushroom will share his secrets for using generative AI tools like ChatGPT and GitHub Copilot to generate code and streamline workflows. He'll also take you on a journey through the exciting new field of text-to-image AI generation, showing you how to turn text-based descriptions into stunning visual content.
Get ready to be spellbound by the power of generative AI technology, as Mushroom demonstrates how to use these tools to create real value for your organization. Don't miss this chance to learn from the data wizard himself!
In addition, Amanda will also share her experiences in using more generative AI tools such as Bing Chat, Google Bard, Dall-E, Bing Image Generator and MidJourney etc., to tackle real life product ops problems such as user story preparation, budget and timeline estimation, slide preparations etc. This session will be based on live demo and will be interactive with the audience.
About the speakers
Introducing Mushroom Luk, the data analyst who knows as much about programming as a fish knows about tightrope walking! He's found the ultimate cheat code in life: an AI sidekick that does all the heavy lifting.
Amanda Lam is a tech enthusiast and community activist. She organizes ProductTank Hong Kong, Women Techmakers Hong Kong and HKPUG events, and is a consistent host of the weekly Cantonese tech podcast, HKPUG Podcast.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
How AI is going to change the world _M.Mujeeb Riaz.pdfMujeeb Riaz
How AI is going to change the world?
"AI: The Future of Our World“
"AI and its Transformative Impact on the World: Understanding the Potential of Chatbots and Conversational AI"
What is Artificial Intelligence and how it works?
What are Chatbots?
What Is ChatGPT?
Difference between chatGPT 3 and chatGPT 4?
Is Jasper artificial intelligence?
What is Character AI and how it works?
How chatGPT is going to change the world?
Why we are calling ChatGPT the future?
As NFT projects continue to pop up and censorship woes become a reality, decentralized storage has become a beacon of hope for many. Let’s check out how much the decentralized storage sector has grown!
Details regarding the working of chatgpt and basic use cases can be found in this presentation. The presentation also contains details regarding other Open AI products and their useability. You can also find ways in which chatgpt can be implemented in existing App and websites.
The numbers tell the story: 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics?
To answer that question, Accenture conducted a landmark global study involving 1,500 C-suite executives from organizations across 16 industries. The aim: Help companies progress on their AI journey, from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.
Read the full report:
http://www.accenture.com/AI-Built-to-Scale-Slideshare
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
Artificial Intelligence (AI) is one of the hottest topics in the tech and startup world at the moment. The field of AI and its associated technologies present a range of opportunities – as well as challenges – for corporates. Learn more about what Artificial Intelligence means for your organization.
Is Artificial Intelligence Dangerous? 6 AI Risks Everyone Should Know AboutBernard Marr
Discussions about artificial intelligence often focus on its positive impacts for society while disregarding the more difficult and less-popular idea that AI could also potentially be dangerous. Just like any powerful tool, AI can be used for good and bad. Here are a few AI risks everyone should know about.
[DSC Europe 23] Shahab Anbarjafari - Generative AI: Impact of Responsible AIDataScienceConferenc1
Today, we embark on a journey into the realm of Generative AI (Gen AI), a force of innovation and possibility. We'll not only unveil the vast opportunities it offers but also confront the ethical challenges it poses. In the spirit of responsible innovation, we'll then dive deep into Responsible AI, illuminating the path to its implementation in this era of Gen AI. Join us for a profound exploration of this technological frontier, where our commitment to responsibility and foresight shapes the future.
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!
How to lead in the age of Superintelligence.pptxPrerna Kaul
In an era where AI will work in tandem and at times supercede humans, this lightning talk will share a perspective on how product leaders can empower their employees, create psychological safety, and build products ethically.
Managing Future Impacts of Artificial Narrow, General, and Super Intelligence...Jerome Glenn
Reviews Millennium Project's Work/Technology 2050: Scenarios and Actions plus preparations for an international assessment for global governance of the transition from artificial narrow intelligence to artificial general intelligence
Impact of Human Centric AI on Our World of Audit - 20.12.2019.pdfVinod Kashyap
Artificial Intelligence (AI) has undergone an unprecedented technological revolution during the last couple of years. It has brought changes in the industry structure and its processes. Augmented AI can help organizations & individuals to achieve things which they couldn’t do otherwise by assisting human judgements. However, use of AI in the business of auditing has ethical implications. The introduction of bias into decision-making is one of the most harmful hazards of AI.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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!
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
23. Cognizant/Microsoft | 360° Partnership Overview
23
Groundbreaking
5-year initiative
CEO driven focus
on digital
transformation
Built on a deep
15-year
relationship
Global Go-To-
Partner for
Cognitive Services
Leveraging Synergies
Industry Capability
Technology/Architecture
Knowledge and IP
Innovative
Breakthroughs
Deeper Customer
Relevance
Impart
Reliability
Build
Preferences
Higher Azure
Consumption
“Our industry does not respect tradition,
only innovation ”
Satya Nadella
Microsoft CEO
Winning Together
Sell, Build, and Enable
Together
“The only sustainable advantage you can
have over others is agility ”
Francisco D’Souza
Cognizant CEO
24. We’re Embarking on a Collaborative, Cognitive Journey
24
Cognitive Services Stack
Sense
Foundation
Layer
Infer
Engage
Interpret
CognitiveLayer
Visuali
zation
1
6
5
2
3
Natural Language Processing
Entity Extraction | Fact Extraction | Image &
Sound Recognition
Hybrid Compute Layer
AWS, Azure
Distributed Server | Distributed Storage | Cloud
Big Data and analytics
Fast data processing | Data lakes | ML
Machine learning
Unsupervised Learning | Search & optimization
Intelligent Process Automation
Entity Resolution | Categorization| Similarity
Algorithm
Analytical Story Telling
Visualization Layer | CEP | Decision engine |
Business rules
4
Phonetic identification
Speaker identification
Speech-to-text
transcription
Language detection
Emotion detection
Topic and entity
identification with NLP
Sentiment
identification
Text Analytics
Language translation
Image Recognition
Facial Recognition
Action detection
Interactive
Automation
Autonomous
Automation
Industry Point
Solutions
Personalized
Interaction
Intelligent Process
Automation
Cognizant’s Cognitive PlatformMicrosoft Services Stack
Chat Bot Builder, an automated
framework to enable faster and efficient
Chatbot building with LUIS API
Implementation of Virtual
Assistants – Chatbots
Azure
Data Lake, HDInsight,
Stream Analytics
Power BI
Computer Vision
Text Analytics
Statistical Functions
Regression
Recommendation
Clustering
Classification
Anomaly Detection
Premium HDI with R server on Spark
SQL Server 2016 R Services (in-database