Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior. Major AI researchers and textbooks define this field as "the study and design of intelligent agents", in which an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines".
AI research is highly technical and specialized, and is deeply divided into sub fields that often fail to communicate with each other. Some of the division is due to social and cultural factors: sub fields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some sub fields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.
The field was founded on the claim that a central property of humans, human intelligence—the sapience of Homo sapiens sapiens—"can be so precisely described that a machine can be made to simulate it." This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. Today AI techniques have become an essential part of the technology industry, providing the heavy lifting for many of the most challenging problems in computer science.
Gauging Business Disruption with the Disruptability Index | Accentureaccenture
Accenture research reports business disruption is a growing challenge for all industries. See how your industry could be affected by disruption with Accenture's disruptability index. Read more.
10 Key Considerations for AI/ML Model GovernanceQuantUniversity
As the financial industry continues to embrace AI and Machine Learning models, model risk management (MRM) departments are grappling with challenges on how to update model governance frameworks to adapt to the changing landscape of model management. While most MRM departments are structured and processes defined to address traditional statistical and quant models, data-driven models like Machine Learning models require modifications in the way models are defined, tested, validated, and governed.
In this webinar, we will discuss ten key aspects to factor when developing your model risk management framework when integrating Machine Learning models. We will discuss key drivers of model risk in today’s environment and how the scope of model governance is changing. We will introduce key concepts and discuss key aspects to be considered when developing a model governance framework when incorporating data science techniques and AI methodologies. Through this Decalogue, we aim to bring clarity on some of the model governance challenges when adopting data science, AI and machine learning methods in the enterprise.
The State of Global AI Adoption in 2023InData Labs
In our inaugural report, 2023 State of AI, we examine trends in AI adoption across industries, the current state of the market, and technologies that shape the field.
The goal of this report is to help company leaders and executives get a better handle on the AI landscape and the opportunities it brings for the business.
2023 State of AI report will help you to answer questions such as:
-How are organizations applying artificial intelligence in the real world in 2023?
-What industries are leading in terms of AI maturity?
-How has generative AI impacted businesses?
-How can organizations prepare for AI transformation?
Download your free copy now and adopt the key technologies to improve your business.
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior. Major AI researchers and textbooks define this field as "the study and design of intelligent agents", in which an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines".
AI research is highly technical and specialized, and is deeply divided into sub fields that often fail to communicate with each other. Some of the division is due to social and cultural factors: sub fields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some sub fields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.
The field was founded on the claim that a central property of humans, human intelligence—the sapience of Homo sapiens sapiens—"can be so precisely described that a machine can be made to simulate it." This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. Today AI techniques have become an essential part of the technology industry, providing the heavy lifting for many of the most challenging problems in computer science.
Gauging Business Disruption with the Disruptability Index | Accentureaccenture
Accenture research reports business disruption is a growing challenge for all industries. See how your industry could be affected by disruption with Accenture's disruptability index. Read more.
10 Key Considerations for AI/ML Model GovernanceQuantUniversity
As the financial industry continues to embrace AI and Machine Learning models, model risk management (MRM) departments are grappling with challenges on how to update model governance frameworks to adapt to the changing landscape of model management. While most MRM departments are structured and processes defined to address traditional statistical and quant models, data-driven models like Machine Learning models require modifications in the way models are defined, tested, validated, and governed.
In this webinar, we will discuss ten key aspects to factor when developing your model risk management framework when integrating Machine Learning models. We will discuss key drivers of model risk in today’s environment and how the scope of model governance is changing. We will introduce key concepts and discuss key aspects to be considered when developing a model governance framework when incorporating data science techniques and AI methodologies. Through this Decalogue, we aim to bring clarity on some of the model governance challenges when adopting data science, AI and machine learning methods in the enterprise.
The State of Global AI Adoption in 2023InData Labs
In our inaugural report, 2023 State of AI, we examine trends in AI adoption across industries, the current state of the market, and technologies that shape the field.
The goal of this report is to help company leaders and executives get a better handle on the AI landscape and the opportunities it brings for the business.
2023 State of AI report will help you to answer questions such as:
-How are organizations applying artificial intelligence in the real world in 2023?
-What industries are leading in terms of AI maturity?
-How has generative AI impacted businesses?
-How can organizations prepare for AI transformation?
Download your free copy now and adopt the key technologies to improve your business.
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
Introductory presentation to Explainable AI, defending its main motivations and importance. We describe briefly the main techniques available in March 2020 and share many references to allow the reader to continue his/her studies.
Artificial Intelligence and mobile robotics are transforming businesses and the economy: this deck explores possible futures for companies and workers.
Artificial intelligence: Driving future growth in Singapore- AccentureAccenture ASEAN
Businesses that successfully apply artificial intelligence (AI) could create up to US$215 billion in gross value added (GVA) in Singapore by 2035. Business services, financial services, and manufacturing look set to benefit the most out of the 11 industries studied in Singapore.
To capitalise on the opportunity, the report Artificial Intelligence: Driving Future Growth in Singapore identifies eight key strategies for successfully implementing AI that focus on adopting a human-centric approach and taking bold and responsible steps to applying the technology within businesses and organisations.
Government as a platform: 2018 GaaP Readiness Indexaccenture
Accenture research identified which factors play a crucial and mutually enforcing role for implementing GaaP in depth and at scale. Learn about the four pillars of GaaP readiness.
McKinsey Global Institute's latest report shows how soaring flows of data and information now generate more economic value than the global goods trade. Here are the key charts and graphs that tell the story. For the full report, visit http://bit.ly/digiflows.
Automate your business operations by incorporating these Artificial Intelligence Overview PowerPoint Presentation Slides. The scope of machine learning is increasing day by day as it is much more convenient and efficient. Facilitate business transformation using this machine learning PowerPoint presentation. With the advent of new and improved technology, it is important to replace human intelligence with robotic process automation. Showcase the stimulation of human intelligence and how applying artificial intelligence can help the organization to grow using this computer science PowerPoint slideshow. You can also present a detailed analysis of AI along with its components, objectives, key statistics, reasons and many other points with the help of this machine intelligence PowerPoint visual. Some of the problems are beyond the control of a human. They do require cognitive intelligence. Utilize this problem-solving PowerPoint graphic in that situation to find apt solutions to your organizational problems. Therefore, download this learning algorithm complete deck now to replace your old technology with machine consciousness, sentience, and mind. https://bit.ly/3xH1aFf
The financial industry is witnessing an emerging trend of Large Language Models (LLMs) applications to improve operational efficiency. This article, based on a round table discussion hosted by TruEra and QuantUniversity in New York in May 2023, explores the potential use cases of LLMs in financial institutions (FIs), the risks to consider, approaches to manage these risks, and the implications for people, skills, and ways of working. Frontline personnel from Data and Analytics/AI teams, Model Risk, Data Management, and other roles from fifteen financial institutions devoted over two hours to discussing the LLM opportunities within their industry, as well as strategies for mitigating associated risks.
The discussions revealed a preference for discriminative use cases over generative ones, with a focus on information retrieval and operational automation. The necessity for a human-in-the-loop was emphasized, along with a detailed discourse on risks and their mitigation.
The fifth annual MIT Sloan and Deloitte study of digital business reveals digitally mature organizations don't just innovate more, they innovate differently—leveraging ecosystems and cross-functional teams that play critical roles.
The Accenture Life Sciences practice helps companies pivot to the patient for best outcomes. See how we can help you. Visit https://accntu.re/2l5mUGu to learn more.
Artificial Intelligence in ManufacturingAmit Sheth
Video at: https://www.linkedin.com/video/live/urn:li:ugcPost:6705141260845412352/
In this talk, we will review some of the challenges related to Industry 4.0 or Factory of Future, and how can Artificial Intelligence help address them.
Examples include the use of semantic interoperability and integration to support the use of sensor collected data in decision making, the use of computer vision to identify deviations in the process and manage quality, and the use of predictive algorithms for device maintenance.
Part of neXt live series hosted by Prof. Ramy Harik: https://www.linkedin.com/pulse/next-live-fall-2020-ramy-harik/
Deloitte Software As A Service Deloitte SeminarTheo Slaats
Presentation of Theo Slaats, partner at Deloitte, on "Software as a Service" during a seminar of Deloitte and Oracle on October 8, 2008 in Amsterdam.
Accenture's Technology Vision 2021 details emerging technology trends that will help companies get back on track & build their future post COVID-19. Read more.
Responsible AI in Industry: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi
How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? How do we protect the privacy of users when building large-scale AI based systems? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains such as hiring, lending, and healthcare. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of responsible AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
Technology-led innovation is releasing trapped value, disrupting every company and industry. Responding to the present is a requirement for survival. Leading in the new is the formula for success.
These deck contains an overview of what is commercially available in terms of Artificial Intelligence for business applications. Be aware: it was created on April 2017 and this is a very fast moving industry....
AI for human communication is about recognition, parsing, understanding, and generating natural language. The concept of natural language is evolving. A key focus is the analysis, interpretation, and generation of verbal and written language. Other language focus areas include haptic, sonic, and visual language, data, and interaction.
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
Introductory presentation to Explainable AI, defending its main motivations and importance. We describe briefly the main techniques available in March 2020 and share many references to allow the reader to continue his/her studies.
Artificial Intelligence and mobile robotics are transforming businesses and the economy: this deck explores possible futures for companies and workers.
Artificial intelligence: Driving future growth in Singapore- AccentureAccenture ASEAN
Businesses that successfully apply artificial intelligence (AI) could create up to US$215 billion in gross value added (GVA) in Singapore by 2035. Business services, financial services, and manufacturing look set to benefit the most out of the 11 industries studied in Singapore.
To capitalise on the opportunity, the report Artificial Intelligence: Driving Future Growth in Singapore identifies eight key strategies for successfully implementing AI that focus on adopting a human-centric approach and taking bold and responsible steps to applying the technology within businesses and organisations.
Government as a platform: 2018 GaaP Readiness Indexaccenture
Accenture research identified which factors play a crucial and mutually enforcing role for implementing GaaP in depth and at scale. Learn about the four pillars of GaaP readiness.
McKinsey Global Institute's latest report shows how soaring flows of data and information now generate more economic value than the global goods trade. Here are the key charts and graphs that tell the story. For the full report, visit http://bit.ly/digiflows.
Automate your business operations by incorporating these Artificial Intelligence Overview PowerPoint Presentation Slides. The scope of machine learning is increasing day by day as it is much more convenient and efficient. Facilitate business transformation using this machine learning PowerPoint presentation. With the advent of new and improved technology, it is important to replace human intelligence with robotic process automation. Showcase the stimulation of human intelligence and how applying artificial intelligence can help the organization to grow using this computer science PowerPoint slideshow. You can also present a detailed analysis of AI along with its components, objectives, key statistics, reasons and many other points with the help of this machine intelligence PowerPoint visual. Some of the problems are beyond the control of a human. They do require cognitive intelligence. Utilize this problem-solving PowerPoint graphic in that situation to find apt solutions to your organizational problems. Therefore, download this learning algorithm complete deck now to replace your old technology with machine consciousness, sentience, and mind. https://bit.ly/3xH1aFf
The financial industry is witnessing an emerging trend of Large Language Models (LLMs) applications to improve operational efficiency. This article, based on a round table discussion hosted by TruEra and QuantUniversity in New York in May 2023, explores the potential use cases of LLMs in financial institutions (FIs), the risks to consider, approaches to manage these risks, and the implications for people, skills, and ways of working. Frontline personnel from Data and Analytics/AI teams, Model Risk, Data Management, and other roles from fifteen financial institutions devoted over two hours to discussing the LLM opportunities within their industry, as well as strategies for mitigating associated risks.
The discussions revealed a preference for discriminative use cases over generative ones, with a focus on information retrieval and operational automation. The necessity for a human-in-the-loop was emphasized, along with a detailed discourse on risks and their mitigation.
The fifth annual MIT Sloan and Deloitte study of digital business reveals digitally mature organizations don't just innovate more, they innovate differently—leveraging ecosystems and cross-functional teams that play critical roles.
The Accenture Life Sciences practice helps companies pivot to the patient for best outcomes. See how we can help you. Visit https://accntu.re/2l5mUGu to learn more.
Artificial Intelligence in ManufacturingAmit Sheth
Video at: https://www.linkedin.com/video/live/urn:li:ugcPost:6705141260845412352/
In this talk, we will review some of the challenges related to Industry 4.0 or Factory of Future, and how can Artificial Intelligence help address them.
Examples include the use of semantic interoperability and integration to support the use of sensor collected data in decision making, the use of computer vision to identify deviations in the process and manage quality, and the use of predictive algorithms for device maintenance.
Part of neXt live series hosted by Prof. Ramy Harik: https://www.linkedin.com/pulse/next-live-fall-2020-ramy-harik/
Deloitte Software As A Service Deloitte SeminarTheo Slaats
Presentation of Theo Slaats, partner at Deloitte, on "Software as a Service" during a seminar of Deloitte and Oracle on October 8, 2008 in Amsterdam.
Accenture's Technology Vision 2021 details emerging technology trends that will help companies get back on track & build their future post COVID-19. Read more.
Responsible AI in Industry: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi
How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? How do we protect the privacy of users when building large-scale AI based systems? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains such as hiring, lending, and healthcare. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of responsible AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
Technology-led innovation is releasing trapped value, disrupting every company and industry. Responding to the present is a requirement for survival. Leading in the new is the formula for success.
These deck contains an overview of what is commercially available in terms of Artificial Intelligence for business applications. Be aware: it was created on April 2017 and this is a very fast moving industry....
AI for human communication is about recognition, parsing, understanding, and generating natural language. The concept of natural language is evolving. A key focus is the analysis, interpretation, and generation of verbal and written language. Other language focus areas include haptic, sonic, and visual language, data, and interaction.
Enterprise AI transforms business, impacts performance, and increases efficiencies through insight generation, customer engagement, business acceleration, and enterprise transformation.
AI is multiple technologies combined to sense, think, and act as well as to learn from experience and adapt over time. Sense refers to pattern recognition, machine perception, biometrics, speech recognition, computer vision, and affective computing. Think refers to natural language processing, advanced analytics, knowledge representation and reasoning, machine learning, deep learning, conversational interfaces, and cognitive computing. Act refers to search, question answering, recommender systems, expert systems, planning and scheduling, robotic process automation, chatbots, virtual assistants, robots, autonomic computing, and autonomous systems.
Semantic Technology Solutions For Recovery Gov And Data Gov With Transparenc...Mills Davis
The Obama administration has set the goal of achieving and unprecedented level of openness, participation, transparency, and collaboration in government. This applies especially to the accessibility of government information and the tracking of stimulus expenditures. This presentation discusses ways that cloud computing, web 2.0, and web 3.0 semantic technologies can be used to deliver citizen-friendly solutions for recovery.gov and data.gov that fulfill the goals of the new administration.
Major conceptual advances that power economic growth seem to occur about 2-3 times a century. Previous waves, e.g., the steam engine and its descendants, mechanized muscle power. Connected intelligence (aka Industry 4.0) is the latest and most transformative because it augments and automates mental power—the ability to use our brains to understand and shape our environments — and is accelerating exponentially. Connected intelligence arises at the intersection of big data, cloud, mobility, social computing, the internet of things, and artificial intelligence.
Impact of semantic technologies on scholarly publishingMills Davis
Semantic technologies will impact future business models for scholarly publishing.
First stage was the transition from publishing based on analog artifacts, to processes built for digital documents where computers are used as electronic pencils and XML based indices.
Second stage is semantic metadata where the computer is used to describe the published content in multiple ways -- think of it as a cambrian explosion of post-it notes -- and also the description and linking together of previously disparate sources. Data and content archives move beyond XML to description logic based semantic web standards which facilitate connect across media formats, documents, domains, and across archives leading to the need for community curation. Business models are still uncertain, being based on access and delivery of content for which alternatives are economically attractive.
Third stage is publishing based on (executable) knowledge-as-a-service. More than documents, more than passive semantic description, knowledge that is expressed through content, methods, data, and processes becomes modeled, managed, and enmeshed with research processes and processes which use the results of research. In this era, publishers with dominant positions in theory will find viable business models that trump competitors.
Enterprise AI transforms business, impacts performance, and increases efficiencies in multiple ways: (1) Insight generation—using big data and cognitive analytics to extract previously unknown understanding from structured and unstructured data. (2) Customer engagement—using AI, information, analytics, and communications technology to involve someone’s interest, attention, interaction, and participation towards some end. (3) Business acceleration—augmenting staff and automating knowledge generation to drive cost savings, competitive advantage, and new business lines through smarter deployment of resources; and (4) Enterprise transformation—change associated with the application of digital technologies and artificial intelligence to all aspects of the business, its ecosystem, and human society.
AI for human communication is about recognition, parsing, understanding, and generating natural language. The concept of natural language is evolving. A key focus is the analysis, interpretation, and generation of verbal and written language. Other language focus areas include haptic, sonic, and visual language, data, and interaction.
Insights Success presents its new edition - Top 10 Pioneering CEOs to Follow in 2021. Featuring at its cover is Chad Holmes – CEO of Kivu Consulting. Chad states that as the CEO, it is critical to developing a people-first culture by developing high-performing teams.
2014 Tech M&A Monthly - Deals Closing GloballyCorum Group
Bolstered by strong public markets, record cash and continuing disruptive technological change, the high volume of Tech M&A deals has carried over into the first quarter of 2014, making for a record spring. Tune in to our Quarterly Report April 10 for field reports from the dealmakers in the M&A trenches on deals closing globally.
Plus, details on the key deals, trends and valuations in the Horizontal, Vertical, Consumer, Internet, Infrastructure and IT Services markets for Q1:2014.
Explore the transformative power of generative AI in our latest E42 Blog post, diving deep into its capabilities for enterprise-level process automation. From explaining the core principles of generative AI, to uncovering insights into the crucial role played by on-premises Large Language Models (LLMs) in facilitating secure and compliant digital transformations across industry verticals—the article also provides a glimpse into the future of AI, where multimodal enhancements and breakthroughs in bias mitigation promise to reshape the landscape of process automation.
The team fit vision for skill and expertise management october 2018Steven Forth
TeamFit is a leader in the application of AI, machine learning, semantic inference and natural language processing to skill and expertise management. We use these to automate skill profiles, uncover potential and predict performance.
Our mission is to uncover human potential and help people find the projects where they can have the most impact.
Cracking the Code to Product Team Success: Data, Empathy, and Extraordinary C...Aggregage
In this webinar, Donna Shaw and Eric Frierson will teach you how to build and cultivate a strong product team. From stakeholder organization to team communication, this webinar will cover a variety of topics to help you become the best Team Manager possible!
In recent years, the fields of Artificial Intelligence (AI) and Machine Learning (ML) have experienced explosive growth, revolutionising industries and shaping the future of technology. With this rapid advancement comes a plethora of exciting career opportunities for individuals skilled in AI and ML.
10 Best Leaders of the AI Age, shaping a New Technological Era - 2024.pdfCIO Look Magazine
Nisha Dulhani stands as a driving force in the dynamic landscape of telecommunications, where data is not just a commodity but the key to unlocking innovation. As the VP of Big Data and Advanced Analytics at Vodafone Idea Limited, Nisha leads the charge in harnessing the power of data for strategic insights. Specializing in Big Data, Python, R, SAS, SPSS, STATA, Tableau, Power BI, and SQL, she manages a variety of technologies to transform raw data into actionable intelligence.
Learning Objective: Explore strategies to leverage our unique capabilities to remain relevant and competitive in a digital environment
Description: Exponential changes in technology, new and agile ways of working, and flexible business models all present opportunities for Women of Color in STEM to rise to new heights. This workshop will explore strategies for leveraging our authentic and unique identities to navigate and flourish in an age of dramatic transformation.
Presenting the best-of-the-best Connors Group talent - some of the most skilled, accomplished, and recommended Information Technology candidates making a splash across the Retail industry. Have a hiring need? Someone specific catch your eye? Want to learn more? Email gina@theconnorsgroup.com.
Next generation semantic technologies help individuals and businesses develop semantic superpowers. That is how we break the chains of legacy systems, free resources from maintenance, and innovate the new capabilities we need to thrive in the next Internet. Learn what semantic superpowers can do for you.
Knowledge science, concept computing and intelligent citiesMills Davis
This keynote was presented at two Kent State University (KSU) Knowledge Science Center (KSC) symposia held in Canton, Ohio and Washington, DC. As the title suggests, this presentation focuses on three aspects of the Knowledge Science Center mission: knowledge science, concept computing, and intelligent cities. The mission of knowledge science is transformation. Concept computing is the next paradigm. And, intelligent cities are a destination worthy of the journey.
Concept computing is the next paradigm for Internet and enterprise software. Concept computing is a:
-- Paradigm shift from information-centric to knowledge-driven patterns of computing.
-- Spectrum of knowledge representation, from search to knowing.
-- Synthesis of AI, semantic, model-driven, mobile, and User interface technologies.
-- Solution Architecture where every aspect of computing is semantic and directly model-driven.
-- Development methodology where Every stage of the solution lifecycle becomes semantic, model-driven & super-productive.
-- New domain where value multiplies.
Concept Computing takes semantic web technology to the next level, where everything is semantic and model-driven -- data, decisions, processes, user experience and infrastructure. Be Informed is a poster child for concept computing that is mainstream, enterprise class, and ready for prime time.
Web3 And The Next Internet - New Directions And Opportunities For STM PublishingMills Davis
The new ecosystem for scientific, technical, and medical (STM) publishing is digital, trans-semiotic, data and knowledge intensive, social, connected, collaborative, community-driven, mobile, multi-channel, immersive, and massively networked and computational.
In this era of open, co-evolving, networked techno-socio-economic processes, commercial publishing models based on exclusive literature collections are simply not enough.
By understanding changes coming with Web 3.0 and the next internet, STM publishers can identify new roles and profitable business opportunities.
What is the role of cloud computing, web 2.0, and web 3.0 semantic technologi...Mills Davis
The US has a new administration that values transparency, citizen participation, collaboration, information sharing, and internet technology. This presentation maps the role of information and communication technologies (specifically, cloud computing, Web 2.0, and Web 3.0 semantic technologies) in the evolution of government information systems from e-gov (silos with web front ends) to connected governance (e.g. distributed social computing environments for collaborative work, information sharing, knowledge management, and participatory decision-making.)
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.
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
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!
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
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.
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
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
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.
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.
3. This content included for educational purposes.
Direct competitors for Publicis.Sapient include digital agencies, consultants, IT services, which are providing AI
and cognitive platforms as a basis for custom solutions, products/services, and XaaS offerings to markets
addressed by Publicis.Sapient
AI encompasses multiple technologies that can be combined to
sense, think, and act as well as to learn from experience and
adapt over time.
SENSE
Computer vision, audio and
affective processing aim to
actively perceive the world
around them by acquiring and
processing images, sounds,
speech, biometrics, and other
sensory inputs. One example is
identity analytics for facial
recognition. Lie detection is
another.
THINK
Natural language processing
and inference engines enable
AI systems to analyze,
interpret, and understand
information. One example is
speech analytics and language
translation of search engine
results. Another is
interpretation of user intent by
virtual assistants..
ACT
AI systems take action in digital
or physical worlds using
machine learning, expert
systems and inference engines.
Recommendation systems are
one example. Another is auto-
pilot and assisted-braking
capabilities in cars. Cognitive
robotics is another.
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• Cybersecurity is the body of technologies, processes and practices designed
to protect networks, computers, programs and data from attack, damage
or unauthorized access.
• User security is shifting from reliance on usernames, passwords and
security questions to incorporate biometric factors including voice
recognition, facial recognition, iris recognition, fingerprints and other
biometric data.
• Biometric security incorporates AI techniques for pattern recognition and
anomaly detection.
• Facial recognition technology is already a big business; it’s being used to
measure the effectiveness of store displays, spot cheaters in casinos, and
tailor digital ads to those passing by.
• Cognitive security analytics provide capabilities for predicting and assessing
threats, recommending best practices for system configuration, automating
defenses, and orchestrating resilient response.
AI machine perception
for user security is
incorporating biometric
factors.
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Does My ai really
understand what
he feels and
what he is
saying to
me?
Affective computing
• Detecting emotions from videos, audio, text,
facial expressions and gestures is a growth
market and important part of future cognitive
systems.
• Audio and video analytics for interpreting
sentiment, emotion and veracity
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Selected vendors* by category of machine perception analytics
AI Platforms
with APIs for
Image & Text
• Apple (Emotient)
• Facebook
• Google
• IBM
• Microsoft
Facial
Analytics
• Affectiva
• Clarifai
• CrowdEmotion
• Eyeris/EmoVu
• Faciometrics
• Imotions
• Kairos
• Noldus
• nViso
• RealEyes
• Sightcorp/
Sonic
Analytics
• BeyondVerbal
• EMO Speech
• Nemesysco
• NICE
• Verint
• Vokaturi
Gesture
Analytics
• GRT—Gesture
Recognition
Toolkit
Text
Analytics
• Clarabridge
• Crimson
Hexagon
• IBM Alchemy API
• Indico
• Receptiviti
Document
Image Analytics
• Cvision
• Parascript
• Signotec
• Topaz Systems
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* Not included in this research deck.
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Machine learning:
• Supervised— Correct classes of the
training data are known.
• Unsupervised— Correct classes of the
training data are not known
• Reinforcement— Machine or software
agent learns behavior based on feedback
from the environment. This behavior can
be learned once and for all or continue to
adapt as time goes by.
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Text analytics
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Text mining is the discovery by computer of new, previously
unknown information, by automatically extracting it from
different written resources. A key element is the linking
together of the extracted information together to form new
facts or new hypotheses to be explored further by more
conventional means of experimentation.
Text analytics is the investigation of concepts, connections,
patterns, correlations, and trends discovered in written
sources. Text analytics examine linguistic structure and apply
statistical, semantic, and machine-learning techniques to
discern entities (names, dates, places, terms) and their
attributes as well as relationships, concepts, and even
sentiments. They extract these 'features' to databases or
semantic stores for further analysis, automate classification
and processing of source documents, and exploit visualization
for exploratory analysis.
IM messages, email, call center logs, customer service survey
results, claims forms, corporate documents, blogs, message
boards, and websites are providing companies with enormous
quantities of unstructured data — data that is information-rich
but typically difficult to get at in a usable way.
Text analytics goes beyond search to turn documents and
messages into data. It extends Business Intelligence (BI) and
data mining and brings analytical power to content
management. Together, these complementary technologies
have the potential to turn knowledge management into
knowledge analytics.
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Symbolic methods
• Declarative languages (Logic)
• Imperative languages
C, C++, Java, etc.
• Hybrid languages (Prolog)
• Rules — theorem provers,
expert systems
• Frames — case-based
reasoning, model-based
reasoning
• Semantic networks, ontologies
• Facts, propositions
Symbolic methods can find
information by inference, can
explain answer
Non-Symbolic methods
• Neural networks — knowledge
encoded in the weights of the
neural network, for
embeddings, thought vectors
• Genetic algorithms
• graphical models — baysean
reasoning
• Support vectors
Neural KR is mainly about
perception, issue is lack of
common sense (there is a lot of
inference involved in everyday
human reasoning
Knowledge Representation
and Reasoning
Knowledge representation
and reasoning is:
• What any agent—human,
animal, electronic,
mechanical—needs to
know to behave
intelligently
• What computational
mechanisms allow this
knowledge to be
manipulated?
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Knowledge encoding
Natural language Documents, speech, stories
Visual language Tables, graphics, charts, maps,
illustrations, images
Formal language Models, schema, logic,
mathematics, professional and
scientific notations
Behavior language Software code, declarative
specifications, functions,
algorithms
Sensory language User experience, human-computer
interface, haptic, gestic.
Humans encode thoughts, represent knowledge, and share meanings using
paberns and language.
PaNerns are knowledge units. A pabern is a compact and rich in seman_cs
representa_on of raw data. Seman_c richness is the knowledge a pabern
reveals that is hidden in the huge quan_ty of data it represents. Compactness
is the correla_ons among data and the synthe_c, high level descrip_on of data
characteris_cs. For example, an image.
Language is a system of signs, symbols, gestures, and rules used in
communica_ng. Meaning is something that is conveyed or signified.
Humans have plenty of experience encoding thoughts and meanings using
language in one form or another… Our proficiency varies. We tend to be
beber at some kinds of language, and not so good at others.
Project teams osen combine different skills and exper_se, e.g. to make a
movie; design and construct a building; or coordinate response to an
emergency.
The table to the right gives examples of five forms of human language:
natural, visual, formal, behavioral, and sensory language.
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Ontology
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An ontology is a formal explicit specification of a shared conceptualization.
An ontology defines the terms and axioms used to describe, represent, and
reason about an area of knowledge (subject matter). It is the model (set of
concepts) for the meaning of those terms. It defines the vocabulary and the
meaning of that vocabulary as well as the assertions, rules, and constraints
used in reasoning about this subject matter. An ontology is used by people,
databases, and applications that need to share domain information.
A domain is a specific subject area or area of knowledge, like medicine, tool
manufacturing, real estate, automobile repair, financial management, etc.
Ontologies include computer-usable definitions of basic concepts in the domain
and the relationships among them. They encode domain knowledge (modular).
Knowledge that spans domains (composable). They make knowledge available
(reusable).
Ontologies are usually expressed in a logic-based language that enables
detailed, sound, meaningful distinctions to be made among the classes,
properties, & relations as well as inferencing across the knowledge model.
Source: Leo Obrst
Source: Tom Gruber
Source: Andreas Schmidt
The diagram above shows that shared ideas and knowledge can
be expressed with different degrees of formality.
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▪ Statistics is the study of the collection, analysis, interpretation,
presentation, and organization of data.
▪ Two main statistical methodologies are used in data analysis: descriptive
statistics, which summarizes data from a sample using indexes such as
the mean or standard deviation, and inferential statistics, which draws
conclusions from data that are subject to random variation (e.g.,
observational errors, sampling variation).
▪ Descriptive statistics are most often concerned with two sets of properties
of a distribution (sample or population): central tendency (or location)
seeks to characterize the distribution's central or typical value, while
dispersion (or variability) characterizes the extent to which members of the
distribution depart from its center and each other.
▪ Inferences on mathematical statistics are made under the framework of
probability theory, which deals with the analysis of random phenomena.
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Statistical inference
“He told me I was average.
I told him he was mean.”
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This diagram maps
cognitive technologies by
how autonomously they
work, and the tasks they
perform.
It shows the current state
of smart machines—and
anticipates how future
technologies might
unfold.
SPRING 2016 MIT SLOAN MANAGEMENT REVIEW
WHAT TODAY’S COGNITIVE TECHNOLOGIES CAN — AND CAN’T — DO
Mapping cognitive technologies by how autonomously they work and the tasks they perform shows the current
state of smart machines — and anticipates how future technologies might unfold.
LEVELS OF INTELLIGENCE
TASK
TYPE
SUPPORT FOR
HUMANS
REPETITIVE TASK
AUTOMATION
CONTEXT
AWARENESS
AND LEARNING SELF-AWARENESS
T
G
C
Analyze
Numbers
Business intelligence,
data visualization,
hypothesis-driven
analytics
Operational analytics,
scoring, model
management
Machine learning,
neural networks
Not yet
Analyze
Words
and
Images
Character and
speech recognition
Image recognition,
machine vision
IBM Watson, natural
language processing
Not yet
Perform
Digital
Tasks
Business process
management
Rules engines, robotic
process automation
Not yet Not yet
Perform
Physical
Tasks
Remote operation
of equipment
Industrial robotics,
collaborative robotics
Autonomous robots,
vehicles
Not yet
Source: MIT Sloan Management Review, Spring 2016
What today’s cognitive technologies can and cannot do
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Recommender system
• Recommend — to put forward (someone or
something) with approval as being suitable for a
par_cular purpose or role.
• RecommendaHon engines automate the process of
making real-_me recommenda_ons to customers.
• A simple example: an online customer who is browsing
a store for one item (e.g. a power drill), places the
item in their shopping cart, and is then recommended
to buy a complementary item (e.g., a set of drill bits).
This example is trivial. Machine learning can go
further, osen uncovering unexpected buying paberns,
based on unforeseen rela_onships between different
customers and between different products.
• Recommender systems take into account where on
the site the customer had visited, their history of
purchases at the site and even their social network
history. It may be that the customer browsed for
mortar on the last visit to the site. Perhaps the user
also asked friends about selec_ng bathroom _les on
Facebook. In this case it might make sense to
recommend a mortar mixing abachment – since it is
clear the customer is doing a _ling project. For a
machine learning algorithm, iden_fying non-explicit
rela_onships like this is typical.
• A machine learning recommender system improves
with _me. It learns from successful, and unsuccessful
recommenda_ons. The same underlying technology
can be used to provide customers with many other
kinds of personalized experiences, based on data of
many kinds.
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Source: HfS - 2016
Evolving landscape of
service agents and
intelligent automation:
• From desktop automation
to RPA, to chatbot, to
assistant, to virtual agent.
• From enhancement of
data, to augmentation of
human agents, to
substitution of digital
labor for the human agent.
Example
vendors:
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Robotic Desktop
Automation (RDA)
• Personal robots for
every employee
• Call center, retail, branches,
back office
• 20-50% improvement across
large workforce groups
• RDA also provides dashboards
and UI enhancements
Robotic Process
Automation (RPA)
• Unattended robots replicating
100% of work
• Back office, operations,
repetitive
• 100% improvement across
smaller sub-groups
• Runs on a virtual server farm
(or under your desk)
Comparing robotic
desktop automation
(RDA) and robotic
process automation
(RPA)
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• Robotic process automation gives humans the potential of attaining new
levels of process efficiency, such as improved operational cost, speed,
accuracy and throughput volume, and leaving behind the repetitive and time
consuming low added-value tasks.
• Top drivers for implementing robotic automation beyond cost savings include:
- High quality by a reduction of error rates
- Time savings via better management of repeatable tasks
- Scalability by improving standardization of process workflow
- Integration by reducing the reliance on multiple systems/screens to
complete a process
- Reducing friction (increasing straight-through processing)
• For example, back-office tasks do not require direct interaction with
customers and can be performed more efficiently and effectively off-site or by
robots. It is feasible to re-engineer hundreds of business processes with
software robots that are configured to capture and interpret information
from systems, recognize patterns, and run business processes across multiple
applications to execute activities including data entry and validation,
automated formatting, multi-format message creation, text mining, workflow
acceleration, reconciliations and currency exchange rate processing among
others.
Robotic process
automation (RPA)
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Four aspects of self-management as they are now
and as they become with autonomic computing
Concept Current computing Autonomic computing
Self-configuration Corporate data centers have multiple
vendors and platforms. Installing,
configuring, and integrating systems is
time consuming and error prone.
Automated configuration of components
and systems follows high-level policies.
Rest of system adjusts automatically and
seamlessly.
Self-optimization Systems have hundreds of manually set,
nonlinear tuning parameters, and their
number increases with each release.
Components and systems continually seek
opportunities to improve their own
performance and efficiency.
Self-healing Problem determination in large, complex
systems can take a team of programmers
weeks.
System automatically detects, diagnoses,
and repairs localized software and
hardware problems.
Self-protection Detection of and recovery from attacks and
cascading failures is manual.
System automatically defends against
malicious attacks or cascading failures. It
uses predictive analytics and early
warning to anticipate and prevent
systemwide failures.
Autonomic computing
Autonomic computing
refers to the self-managing
characteristics of AI-based
distributed computing
resources, adapting to
unpredictable changes
while hiding intrinsic
complexity to operators
and users.
113. This content included for educational purposes.
AI encompasses multiple technologies that can be combined to sense, think,
and act as well as to learn from experience and adapt over time. Sense refers to
pattern recognition, machine perception, speech recognition, computer vision
and affective computing. Think refers to natural language processing, knowledge
representation and reasoning, machine learning and deep learning, and
cognitive computing. Act refers to search engines and question answering, rules
engines, expert systems, recommender systems, automated planning and
scheduling, autonomic computing, and autonomous systems.
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