Setting up a COE ( Center of Excellence ) for AI ( Artificial Intelligence ) could be a daunting task. Lack of skills and quality data sets could hold you back. But still you should not wait any longer and start with what you have, build skills by training people, and move ahead in gettering executive approval for building an artificial intelligence center of excellence
How to set up an ai center of excellenceShranik Jain
Recently while exploring the field of "Artificial Intelligence in Organization context" able to create some content around "How Setting up an AI centre of excellence" can provide a leap in the dynamic environment of AI
#mba #organization #artificalintelligence
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
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
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
Journey to Cloud - Enabling the Digital Enterprise - AccentureAmazon Web Services
Speed your customer's move to AWS by Leveraging Accenture's Unique combination of agility and Scale. The AAS journey culminates with the advanced use of Digital, Analytics, Mobility, and IOT capabilities enabled by an Integrated cloud platform across
applications, platform and infrastructure
Speaker:
Aman Neil Dokania, Managing Director and APAC Journey to Cloud Lead, Accenture
How to set up an ai center of excellenceShranik Jain
Recently while exploring the field of "Artificial Intelligence in Organization context" able to create some content around "How Setting up an AI centre of excellence" can provide a leap in the dynamic environment of AI
#mba #organization #artificalintelligence
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
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
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
Journey to Cloud - Enabling the Digital Enterprise - AccentureAmazon Web Services
Speed your customer's move to AWS by Leveraging Accenture's Unique combination of agility and Scale. The AAS journey culminates with the advanced use of Digital, Analytics, Mobility, and IOT capabilities enabled by an Integrated cloud platform across
applications, platform and infrastructure
Speaker:
Aman Neil Dokania, Managing Director and APAC Journey to Cloud Lead, Accenture
Digital Transformation Strategy & Framework | By ex-McKinseyAurelien Domont, MBA
Go to www.slidebooks.com to Download and Reuse Now a Digital Transformation Strategy & Framework in Powerpoint | Created By ex-McKinsey & Deloitte Strategy Consultants.
The Analytics COE positioning your business analytics program for successKiran Garimella
You should consider the following three aspects of your Business Analytics Program:
* The Business (not just data science, big data, and technology)
* Analytics as the DNA of the company (and not just a competency of an elite few)
* A Programmatic approach that sustainable for the life of the company (and not just a one-time project or initiative)
What role do classical statistics, Bayesian statistics, judgment under uncertainty, heuristics, biases, categorical data analysis, etc., play in such a program?
A COE (Center of Excellence) framework seeks to address these aspects and ensure the company can progress on all fronts.
Accenture is leading the next evolution of Regulatory by applying leading edge solutions across the drug and device lifecycle that unify innovative technologies with compliance-focused processes. Visit https://accntu.re/2YqL18r to learn more.
McKinsey Global Institute Report - A labor market that works: Connecting tale...McKinsey & Company
This presentation offers highlights from a new report by the McKinsey Global Institute, "A labor market that works: Connecting talent with opportunity in the digital age".
From shopping to social media, online platforms have transformed major segments of the global economy. They now are about to do the same for labor markets around the world. MGI examines the stubborn disconnect between people and jobs and the potential for online talent platforms to unlock real economic value over the next decade by creating better, faster matching between workers and available work opportunities.
Read the report in full:
http://mckinsey.com/Insights/Employment_and_growth/Connecting_talent_with_opportunity_in_the_digital_age
Business Strategy Presentation Template 2023 - By ex-Mckinsey and BCG consult...Slideworks
A comprehensive, end-to-end strategy presentation template based on proven frameworks created by ex-McKinsey and BCG consultants.
277 PowerPoint slides organized in a complete storyline with best-practice slide-layouts, titles, and graphics
4 real-life full-length examples from Fortune500 companies so you can see how a strategy is presented in other organizations
Helpful checklist used in top-tier consulting firms
Excel model to support your strategy document.
Access full powerpoint at www.slideworks.io.
Thrive with accenture product and platform engineering servicesAccenture Technology
Accenture’s Product and Platform Engineering Services team helps clients navigate through the new wave of platforms. With more than three decades of experience, our deep engineering-skilled workforce of over 8,000 professionals has grown out of the development of our own commercially-available software and platforms. With expertise across more than 40 industries and all business functions, we deliver transformational outcomes for a demanding, new digital world.
IT M&A (Mergers & Acquisitions) is an integral part of any transformation. Whether IT to carve out, rebuilding or IT integration, we support you with competence and know-how. In this business best practice slide deck you learn how to:
- industrialize M&A project management and thus professionalize & secure M&A operations (both acquisition and divestiture)
- define the M&A methodology tool-kit for the IT function & speed up the planning and delivery of IT integrations / carve-outs
- get buy-in on the IT Methodological Book from other IT Team members and use it to animate the M&A IT network
We provide you with the following best practices:
- Introduction
- IT M&A Frameworks
- IT M&A Operating Models
- IT M&A Processes
- IT M&A Post Merger Integration
The Analytics CoE: Positioning your Business Analytics Program for SuccessCartegraph
This Loras College Business Analytics Symposium breakout session presentation by Kiran Garimella, Ph.D., president and founder of XBITALIGN, explored the analytics center of excellence (CoE).
A business analytics program is more than the application of data science and Big Data technology to data. Success should be measured not only by the valuable insights the program delivers, but also by how well it is sustained and how much the ‘analytics mindset’ becomes part of the company’s DNA. The journey is not only from data to information, but also from information to knowledge, and from knowledge to intelligence. The foundation for making this happen is a well-structured Analytics Center of Excellence (CoE).
Digital Transformation Strategy & Framework | By ex-McKinseyAurelien Domont, MBA
Go to www.slidebooks.com to Download and Reuse Now a Digital Transformation Strategy & Framework in Powerpoint | Created By ex-McKinsey & Deloitte Strategy Consultants.
The Analytics COE positioning your business analytics program for successKiran Garimella
You should consider the following three aspects of your Business Analytics Program:
* The Business (not just data science, big data, and technology)
* Analytics as the DNA of the company (and not just a competency of an elite few)
* A Programmatic approach that sustainable for the life of the company (and not just a one-time project or initiative)
What role do classical statistics, Bayesian statistics, judgment under uncertainty, heuristics, biases, categorical data analysis, etc., play in such a program?
A COE (Center of Excellence) framework seeks to address these aspects and ensure the company can progress on all fronts.
Accenture is leading the next evolution of Regulatory by applying leading edge solutions across the drug and device lifecycle that unify innovative technologies with compliance-focused processes. Visit https://accntu.re/2YqL18r to learn more.
McKinsey Global Institute Report - A labor market that works: Connecting tale...McKinsey & Company
This presentation offers highlights from a new report by the McKinsey Global Institute, "A labor market that works: Connecting talent with opportunity in the digital age".
From shopping to social media, online platforms have transformed major segments of the global economy. They now are about to do the same for labor markets around the world. MGI examines the stubborn disconnect between people and jobs and the potential for online talent platforms to unlock real economic value over the next decade by creating better, faster matching between workers and available work opportunities.
Read the report in full:
http://mckinsey.com/Insights/Employment_and_growth/Connecting_talent_with_opportunity_in_the_digital_age
Business Strategy Presentation Template 2023 - By ex-Mckinsey and BCG consult...Slideworks
A comprehensive, end-to-end strategy presentation template based on proven frameworks created by ex-McKinsey and BCG consultants.
277 PowerPoint slides organized in a complete storyline with best-practice slide-layouts, titles, and graphics
4 real-life full-length examples from Fortune500 companies so you can see how a strategy is presented in other organizations
Helpful checklist used in top-tier consulting firms
Excel model to support your strategy document.
Access full powerpoint at www.slideworks.io.
Thrive with accenture product and platform engineering servicesAccenture Technology
Accenture’s Product and Platform Engineering Services team helps clients navigate through the new wave of platforms. With more than three decades of experience, our deep engineering-skilled workforce of over 8,000 professionals has grown out of the development of our own commercially-available software and platforms. With expertise across more than 40 industries and all business functions, we deliver transformational outcomes for a demanding, new digital world.
IT M&A (Mergers & Acquisitions) is an integral part of any transformation. Whether IT to carve out, rebuilding or IT integration, we support you with competence and know-how. In this business best practice slide deck you learn how to:
- industrialize M&A project management and thus professionalize & secure M&A operations (both acquisition and divestiture)
- define the M&A methodology tool-kit for the IT function & speed up the planning and delivery of IT integrations / carve-outs
- get buy-in on the IT Methodological Book from other IT Team members and use it to animate the M&A IT network
We provide you with the following best practices:
- Introduction
- IT M&A Frameworks
- IT M&A Operating Models
- IT M&A Processes
- IT M&A Post Merger Integration
The Analytics CoE: Positioning your Business Analytics Program for SuccessCartegraph
This Loras College Business Analytics Symposium breakout session presentation by Kiran Garimella, Ph.D., president and founder of XBITALIGN, explored the analytics center of excellence (CoE).
A business analytics program is more than the application of data science and Big Data technology to data. Success should be measured not only by the valuable insights the program delivers, but also by how well it is sustained and how much the ‘analytics mindset’ becomes part of the company’s DNA. The journey is not only from data to information, but also from information to knowledge, and from knowledge to intelligence. The foundation for making this happen is a well-structured Analytics Center of Excellence (CoE).
IW14 Keynote, Michelle Shuttleworth, Deloitte ConsultingSoftware AG
Presentation Title: Tech Trends 2014; Inspiring Disruption
Michelle Shuttleworth, Director, Deloitte Consulting
Innovation World 2014 conference, Oct 13-15, 2014, at the Hyatt Regency New Orleans, produced by Software AG. Three days of vision, inspiration and insight. Innovation World is THE global event for digital leaders who are driven to leverage the Software AG Suite: Alfabet, Apama, ARIS, webMethods, Software AG Live, Terracotta and Adabas-Natural.
Marcus Baker: People Analytics at Scale
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
With new upgrades made in the field, AI adoptions are slowly becoming mainstream as more and more companies are experimenting with new AI projects. While this spirit is high, there are several things that can go downhill in an AI project. Follow the steps mentioned to avoid making project mistakes.
AI for RoI - How to choose the right AI solution?Abhinav Singhal
Companies looking to adopt AI today are bombarded
with technology companies and start-ups selling advanced
machine learning based solutions built on exciting use
cases. However, before kickstarting newer pilots and
investing in these advanced solutions it is useful to step
back and reflect on the overall intent of using AI for
the organization and the traditional suite of analytical
techniques and resources available.Oneway, CIOs can assess
the suitability of an AI solution is it to break it down into
simpler elements and ask five basic questions.
AI (Artificial Intelligence) technology is now poised to
transform every industry, just as electricity did 100 years
ago. Between now and 2030, it will create an estimated
$13 trillion of GDP growth. While it has already created
tremendous value in leading technology companies
such as Google, Baidu, Microsoft and Facebook, much
of the additional waves of value creation will go beyond
the software sector.
This AI Transformation Playbook draws on insights
gleaned from leading the Google Brain team and
the Baidu AI Group, which played leading roles in
transforming both Google and Baidu into great AI
companies. It is possible for any enterprise to follow
this Playbook and become a strong AI company,
though these recommendations are tailored primarily
for larger enterprises with a market cap/valuation from
$500M to $500B.
PLAYBOOK STEPS
1. Execute pilot projects to gain momentum
2. Build an in-house AI team
3. Provide broad AI training
4. Develop an AI strategy
5. Develop internal and external communications
The rising collection and analysis of data has shifted the way companies do business. Four key ingredients to develop a data strategy, how to leverage next-generation technologies, and three essential steps for rolling out implementation are included. The Data Ecosystem will show you how to develop and implement the strategies that will meet the needs of your business.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
A Guide to Machine Learning Developer in 2024.pdfJPLoft Solutions
Today, cooperation among developers and Machine Learning Development Companies has been instrumental in accelerating innovation and scaling. The study examines how these collaborations create synergies and allow developers to draw on ML development companies' knowledge and capabilities to speed project delivery and improve efficiency.
Top 5 attributes of a future ready companyAnushree
Companies that implement a development strategy that is future-ready would be well-positioned to succeed in the new economy. However, a shortage of resources, a risk-averse culture, and a lack of leadership engagement stymie attempts to meet digital demand.
This AI business checklist is a tool that provides an easy-to-use structure for strategic discussions, goal setting and critical decisions in your leadership team. A structure that you can use as a business leader to guide your decisions towards getting full value out of AI technology in your organisation. It is meant to be a tool that you can return to to guide your progress.
How the Analytics Translator can make your organisation more AI drivenSteven Nooijen
Today, about 80% of companies considers data as an essential part of their strategy. However, although most of these companies are taking models into production, they still have trouble turning their data and insights into valuable AI solutions. With businesses heavily invested in data and AI, what is it that actually makes the difference for being successful with AI?
In this talk, I will argue that the extent to which AI is embedded in the organisation is crucial to success. Furthermore, I will show why the Analytics Translator is the designated person to drive AI adoption by the business and what his or her tasks should look like. The insights shared come from our own experience as consultants as well as interviews with top Dutch enterprises about their AI maturity.
AI Maturity Levels and the Analytics TranslatorGoDataDriven
Buzzwords like Big Data, Cloud, and AI have been out there now for a couple of years. But today, businesses have a clear focus on the application of data use cases and the challenges around that such as metadata management, governance, security, and maintainability in general. Everybody seems to have some version of a data lake and wants to consolidate it into something (more) useful, or move from an on-premise version to the cloud. There is a general need to streamline current practices while also attempting to give multiple segments of users (data scientists, analysts, marketeers, business people, and HR) access in a way that is tailored to their needs and skills. In other words: businesses today are heavily invested in data and AI, but many have a hard time knowing how to mature it to the next level.
This is exactly where a "maturity model" comes into play. The goal of a maturity model is to help businesses in understanding their current and target competencies. This helps organisations in defining a roadmap for improving their competency. A maturity model is therefore one way of structuring progression, whether the company already embraces data science as a core competency, or, if it is just getting started.
In this presentation on maturity models, we answer the following questions:
1. What exactly is a maturity model and why would you need it? We address this by sharing GoDataDriven's maturity model and describing the different phases we have identified based on our experience in the field.
2. How can you use a maturity model to advance your organisation? Having a maturity model alone is not enough, in order for it to be valuable you need to act upon it. This paper provides concrete examples on how to do act based on practical stories and experiences from our clients and ourselves.
Outcome Engineering 101: Five Guidelines to Delivering Products that Create I...Cognizant
It’s time to shift to an evolved, technology-empowered design mindset. As technology informs design, and good design arms technology to become most effective by engaging with users, the two now sit at the top of the product development pyramid to co-create success.
Similar to How to set up an artificial intelligence center of excellence in your organization (20)
From Cave Walls to Digital Screens_ Understanding Why We Still Think Like the...Yogesh Malik
As we continue our journey from cave walls to digital screens, understanding the intricate tapestry of biases, emotions, and technology is paramount.
The very essence of self-help lies in recognizing our evolutionary history, understanding its influence on modern-day decision-making, and striving for a future where technology complements, rather than complicates, our innate nature.
The Mirror of AI — Externalizing the Burden of Existential Angst _ by Yogesh ...Yogesh Malik
The externalization of our existential fears onto AI risk is a fascinating testament to the human psyche’s adaptability.
It’s a reminder that while the face of our anxieties might change with the times, the underlying concerns remain consistent. Whether we’re pondering the vastness of the universe or the potentials of an AI-driven future, the heart of the matter remains: our shared quest for understanding, meaning, and a sense of purpose in an ever-changing world.
AI’s rise is not just a tech evolution, but a canvas for humanity’s existential art.
The Paradox of Painless Art_ Rediscovering the Artist’s Sacrifice in the Age ...Yogesh Malik
Can AI truly replicate the emotional depth and unique perspectives that human artists bring to their work, or will AI-generated art always lack a certain authenticity?
Will the democratization of art through AI tools lead to a proliferation of creativity, or will it oversaturate the art world with derivative works?
How can artists protect their intellectual property and maintain creative control when using AI tools to assist in their work?
What psychological and emotional challenges might artists face as they navigate a world where AI is an integral part of the creative process?
Are there limits to the types of art that AI can create, or are there artistic domains where human creativity will always be indispensable?
The Silent Oppressor_ How Our Love for Amusement Numbs Us to Reality _ by Yog...Yogesh Malik
Drawing from Postman’s observations, the solution lies not in demonizing media, advertisement, or technology but recognizing their potential for manipulation and mindless consumption.
We must foster media literacy, enabling individuals to discern between entertainment and enlightenment
The key isn’t to avoid amusement but to not let it become the sole purpose of existence.
We need to actively cultivate our ability to engage deeply with issues, nurture independent thought, and promote meaningful dialogues.
What is the ultimate goal of artificial intelligence yogesh malikYogesh Malik
Can artificial intelligence solve world hunger and bring eternal peace? We will see that when the time comes but the inevitability of artificial intelligence becoming smarter than human has raised many questions about the long-term survival of the human race
More on ArtificialIntelligence ,Singularity ,Transhumanism ,Automation ,AGI ,AITakeover ,Algocracy ,MachineLearning ,DeepLearning
7 reasons why india is going to lose the artificial intelligence raceYogesh Malik
From sorting cucumbers to curing cancers, Artificial Intelligence algorithms will not pause at anything.For the past 30 years, technology outsourcing has generated massive employment for India, but now AI and Automation is changing the traditional IT Service Model. Indian outsourcing industry is under serious threat and is going to change sooner than we can think of.
Artificial intelligence — creator, operator and destroyer, story of brahma, v...Yogesh Malik
Whether it is man’s desire that is so great that he is creating technological God or his lack of knowledge that he aspires to cut loose of the shackles of his biological structure, but this plug and play mentality that technology has given us is spreading its reach to the extreme and soon we will be able to do cut-and-paste with genetic editing
Internet of things bringing fog, edge & mist computing by Yogesh MalikYogesh Malik
Cloud computing future is not foggy, but more and more companies are now exploring Internet of Things so there are other types like fog, edge and mist computing that are catching up.
Cloud, fog, and mist computing can be seen as application of fluid computing, now that is another term for you to remember.
Artificial intelligence — creator, operator and destroyer, story of brahma, v...Yogesh Malik
Artificial intelligence — creator, operator and destroyer, story of brahma, vishnu and; mahesh
Artificial Intelligence might be producing a useless class of human, Artificial Intelligence might beat us at everything by 2060, or like what Stephen Hawking warned us that Artificial Intelligence could end mankind.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
PHP Frameworks: I want to break free (IPC Berlin 2024)
How to set up an artificial intelligence center of excellence in your organization
1. Yogesh Malik
Exponential Thinker | Technology Evangelist | Digital Futurist @ https://FutureMonger.com/ &
https://welcome2050.com/
May 1 · 8 min read
How to Set-up an Arti cial Intelligence
Center of Excellence inYour Organization?
Harnessing the potential of arti cial intelligence as part of a digital
transformation strategy will be the key component in creating the
intelligent enterprise of tomorrow. Arti cial intelligence is already
rising to play a signi cant role in helping enterprises re-imagine their
products and services, drive revenue, realize business e ciencies, and
enrich the customer experience.
Photo by Venveo on Unsplash
2. Andrew Ng is one of the most recognizable names in the Arti cial
Intelligence community. He is Chief Scientist of Baidu. Andrew formed
the Google Brain deep learning AI system while at Mountain View.
Once he expressed: -
In the past, a lot of S&P 500 CEOs wished they had
started thinking sooner than they did about their
Internet strategy. I think ve years from now there
will be a number of S&P 500 CEOs that will wish
they’d started thinking earlier about their AI strategy.
But how do you start arti cial intelligence related initiatives in your
organization? What problems will it solve? Stick with me here, because
in this article we will be talking about those things.
. . .
Why DoYou Need an Arti cial Intelligence
COE inYour Organization?
It may seem that there are no immediate advantages of using Arti cial
Intelligence in your organization and you may hesitate and ask, “Is
there an AI solution to every business problem?”. Like the Internet
revolution a decade ago AI is not optional anymore. Any delay is
dangerous and if you want your organization to survive for another
decade, you need to start embracing AI today. We already know that
algorithms are ruling the world—from sorting cucumbers to curing
cancers arti cial intelligence based algorithms are doing everything
With this in mind let’s turn our attention to how today’s organizations
can start AI Initiative within a minuscule group that can expand
throughout the organization. But, is there a size that ts all solution?
Small and medium size companies—Can they ignore AI? If not, then
how do they start Center of Excellence for Arti cial Intelligence? No
matter, what your think of AI but recent technological development
associated with new patterns of globalization is threatening to create a
new tomorrow—forcing businesses to re-imagine their products and
3. services, realize business e ciencies and drive customer experience to
an altogether di erent level.
You need to develop a team of inspired and talented, take initiatives for
developing AI systems in your organization. This would take executive
sponsorship, capturing benchmark that can demonstrate values—all
focused on organization needs, making you propel AI initiatives from
conceptualization to implementation
. . .
Strategy for Arti cial Intelligence Center
of Excellence
You need to bring in research orientated tech expertise and establish a
platform that can catalyze the growth of the deep technology
ecosystem and present the stimulus for innovation in the elds of data
science and arti cial intelligence.
This new team should function like an extended team to the external
units to deliver excellence by scaling up existing/new product/project
activities build around arti cial intelligence technology.
From value creation to value realization, this new AI COE should
deliver minimum viable product (MVP) with original ideas by
experimenting with emerging technologies. A key step towards
preparing COE for its success in the organization is to build prototypes
with a long-term view and enhance ecosystems and partnerships to
promote purposeful arti cial intelligence
The people, process and technology must be in alignment for any
new venture to be competent and holistic. These three components are
the key steps towards a successful organizational transformation
Executive sponsorship: Senior management people play a signi cant
role in the organization and an e ective sponsorship from them is
required so that you can improve collaboration across the organization.
This will bene t you stay aligned on the strategic priorities. Once you
secure their commitment sponsors can function as a point of escalation
—but make clear you don’t engage them for day-to-day tactical issues
4. Finding the right team: This could be your biggest trouble. Getting
people on-boarded for the right skills could make or break your center
of excellence strategic plan. How do you source these people? Internal
hire or external, local to your COE region or a mobile/remote worker
from some another country’s o ce
Capture benchmark that can demonstrate value: If you can’t
measure it you can’t manage it. Your leadership want to see measured
progress and you need to have a dashboard to represent internal
performance. Make it transparent and comprehensive.
Finding the right problem: Be it your organization internal problem
or customer use case, you need to research multiple launches. You need
a design thinking before you act on building an AI platform. Talk to
sales, interview clients, know their business pain points that can be
solved by the adoption of arti cial intelligence. Problem need to be
thoroughly investigated so that you can apply the right AI tool to iron
out, and present COE value proposition even for the early pilot launch.
Leveraging the data to solve it: The importance of data availability
and data quality are of pivotal value. Arti cial Intelligence provides a
whole di erent meaning to the importance of data. The collection,
distribution, and validation of data are thus important issues in the
formulation of solutions involving AI
Build a repeatable AI solution: A repeatable solution that can be
o ered to others in the company or marketed to the customer, drives
value to the business. A template approach that can be applied to the
di erent situations is invariably favorable. Center of excellence leaders
need to hook up with numerous product owners, designers, business
analysts in building horizontal or industry speci c o ering solutions
5. . . .
Challenge No #1: BuildingTheTeam
Getting the right people with the right skills could be your single most
prominent and serious challenge. Your current technology leaders and
team members may be nding themselves in the corners. Where do you
start? How do you source AI skills?
Arti cial Intelligence, machine learning, data science and
programming, people with these skills need to work together along
with domain/industry expert because arti cial intelligence approach is
not just about technical function alone. Recruiting and retaining AI
talent could be a nightmare and this can continue for months. You need
to get HR involved in nding individuals with arti cial intelligence
skills and convince executive sponsors on this or your business take the
risk of being left behind.
You may uncover that the re-skilling of your current open-minded tech
team could be a valuable option, so encourage them to take various
6. courses on arti cial intelligence and machine learning. Learning and
research platform for your internal COE members, provisioning of new
AI tools and test beds for existing and new projects, all this takes rather
a bit serious thinking in terms or research work and
operational/organizational skills.
Go for few people who are cross-skilled, multi-skilled and harmonized
with non-technical skills like communication, creativity and versatility.
If you want to extend your AI portfolio beyond proof-of-concept and
sandbox implementation, you will require real AI experts and those
skills come at a price.
Not just new AI skills, organizational level behavioral changes are
needed, not just business knowledge and technology innovation but
human insight need to bring in the products and features for
developing highly successful long-term strategies
. . .
Challenge No #2: Making Data Actionable
Many companies do not realize that they are sitting on the pile of data
that could be a virtual goldmine if managed properly. On the other
hand, lack of quality data could cost you a huge money if you want to
get it xed. But the chances are that most of the data your organization
has might not be all bad, but it won’t be able to solve the problem that
you have. Having quality data requires building e cient data strategies
and robust data management infrastructure. Your customers, partners,
employees and brand ambassadors—data is everywhere. It is up to you
how you to make data actionable by fetching insights and intelligence
out of it.
Lack of quality data might hamper your AI project as machine learning
algorithms require a massive amount of data. So if you want to write a
brand new data strategy for your organization, do not hesitate to put
required e orts in doing so.
7. Identify various types of data available; its meaning, location,
origin & structure.
How data will be stored, analyzed, processed and protected.
How various data will be packaged for reuse? What policies to
implement for e ective data governance?
Once done, do not consider it done. Make data review and
measurement an ongoing process. Your data strategy is a roadmap and
a key component for your arti cial intelligence projects. More than
ever, your ability to manage data could be the single more critical
component to your company’s success.
•
•
•
. . .
OverToYou Now
Start with what you have. Create building best practices, get your team
trained on AI skills and collaborate with others to build minimum
viable product for a pilot problem given the right dataset. Use cloud-
based AI stack from AWS, Google or Azure if you don’t want to invest in
hardware and licenses. You need to invest time and money in hiring
AI-COE Project Lifecycle
8. skilled resources and keep your current team engaged in continuing
learning new skills.
So, tried old management advice on starting a COE won’t cut it, your
approach must be unique and strategic for AI initiatives. Building an “AI
rst” culture or “AI rst” policies could be very challenging, but without
any delay you need to start looking at your organization’s existing
products and services through the lens of arti cial intelligence. You
need to start building good data strategies and create unique data sets
so that you are ready with a failure-proof arti cial intelligence center of
excellence.
. . .
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