“Enterprise AI - Artificial Intelligence for the Enterprise."
AI is impacting many areas today. This talk discusses how AI will impact the Enterprise and what it means in the near future. The talk is based on my course I teach at the University of Oxford.
Keynote from Intellifest 2012 addressing the differences between narrow (classical) Artificial Intelligence and Artificial General Intelligence. Implications of cloud computing for AGI are also discussed.
This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Application Domains, and
Features of rule based Expert (KB) Systems
Artificial Intelligence Research Topics for PhD Manuscripts 2021 - PhdassistancePhD Assistance
Imagine a world where knowledge isn’t limited to humans!!! A world in which computers will think and collaborate with humans to create a more exciting universe. Although this future is still a long way off, Artificial Intelligence has made significant progress in recent years. In almost every area of AI, such as quantum computing, healthcare, autonomous vehicles, the internet of things, robotics, and so on, there is a lot of research going on. So much so that the number of annual Published Research Papers on Artificial Intelligence has increased by 90% since 1996.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/2Sdlfn4
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
Keynote from Intellifest 2012 addressing the differences between narrow (classical) Artificial Intelligence and Artificial General Intelligence. Implications of cloud computing for AGI are also discussed.
This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Application Domains, and
Features of rule based Expert (KB) Systems
Artificial Intelligence Research Topics for PhD Manuscripts 2021 - PhdassistancePhD Assistance
Imagine a world where knowledge isn’t limited to humans!!! A world in which computers will think and collaborate with humans to create a more exciting universe. Although this future is still a long way off, Artificial Intelligence has made significant progress in recent years. In almost every area of AI, such as quantum computing, healthcare, autonomous vehicles, the internet of things, robotics, and so on, there is a lot of research going on. So much so that the number of annual Published Research Papers on Artificial Intelligence has increased by 90% since 1996.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/2Sdlfn4
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
Deep Learning Explained: The future of Artificial Intelligence and Smart Netw...Melanie Swan
This talk provides an overview of an important emerging artificial intelligence technology, deep learning neural networks. Deep learning is a branch of computer science focused on machine learning algorithms that model and make predictions about data. A key distinction is that deep learning is not merely a software program, but a new class of information technology that is changing the concept of the modern technology project by replacing hard-coded software with a capacity to learn and execute tasks. In the future, deep learning smart networks might comprise a global computational infrastructure tackling real-time data science problems such as global health monitoring, energy storage and transmission, and financial risk assessment.
Artificial Intelligence in Project Management by Dr. Khaled A. HamdyAgile ME
Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E.
The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.
Artificial Intelligence (AI) -> understanding what it is & how you can use it...Adela VILLANUEVA
The goal of this presentation is to provide you with a basic understanding of AI and to prepare you to think about how your organization might apply it.
Artificial intelligence (Ai) is back, and the tech industry’s interest is stronger than ever. Ai will have an important impact on the design and creation of software. Application development and delivery (AD&D) professionals need to understand the potential benefits Ai will bring, not only to how they build software but also to the nature of the applications themselves. in parallel, AD&D pros should not ignore the challenges and risks that come with Ai. this report is the first of a series that will examine the impact of Ai on software development and separate myth from reality
A very first dip into the Ocean of Artificial Intelligence. The nuances of AI, its origin and meaning, terms related, technologies used, AI Effect, remarkable examples and discoveries, Explained Simply!
A quick guide to artificial intelligence working - TechaheadJatin Sapra
It is already on its way to achieving so as it has empowered the mobile app development agencies to build what was once assumed impossible. Despite this, much of this field remains undiscovered.
Artificial intelligence in practice- part-1GMR Group
Summary is made in 5 parts-
This is Part -1
Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe.
• The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment.
• Artificial intelligence and machine learning are cited as the most important modern business trends to drive success.
• It is used in areas ranging from banking and finance to social media and marketing.
• This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries.
• This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others.
• This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution.
• Each case study provides a comprehensive overview, including some technical details as well as key learning summaries:
o Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations
o Expand your knowledge of recent AI advancements in technology
o Gain insight on the future of AI and its increasing role in business and industry
o Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the trans-formative power of technology in 21st century commerce
Understanding Artificial Intelligence - Major concepts for enterprise applica...APPANION
Artificial Intelligence is a fundamental topic – for us as humans, as a society but also for businesses. For business executives and decision-makers, it is sometimes hard to keep up with rapidly evolving technologies as part of the day-to-day business. By providing this curated compilation of information about the fundamental aspects of AI, we want to captivate and inspire you to become more involved with the technology by better understanding the underlying concepts and value drivers of this technology
Deep Learning Explained: The future of Artificial Intelligence and Smart Netw...Melanie Swan
This talk provides an overview of an important emerging artificial intelligence technology, deep learning neural networks. Deep learning is a branch of computer science focused on machine learning algorithms that model and make predictions about data. A key distinction is that deep learning is not merely a software program, but a new class of information technology that is changing the concept of the modern technology project by replacing hard-coded software with a capacity to learn and execute tasks. In the future, deep learning smart networks might comprise a global computational infrastructure tackling real-time data science problems such as global health monitoring, energy storage and transmission, and financial risk assessment.
Artificial Intelligence in Project Management by Dr. Khaled A. HamdyAgile ME
Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E.
The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.
Artificial Intelligence (AI) -> understanding what it is & how you can use it...Adela VILLANUEVA
The goal of this presentation is to provide you with a basic understanding of AI and to prepare you to think about how your organization might apply it.
Artificial intelligence (Ai) is back, and the tech industry’s interest is stronger than ever. Ai will have an important impact on the design and creation of software. Application development and delivery (AD&D) professionals need to understand the potential benefits Ai will bring, not only to how they build software but also to the nature of the applications themselves. in parallel, AD&D pros should not ignore the challenges and risks that come with Ai. this report is the first of a series that will examine the impact of Ai on software development and separate myth from reality
A very first dip into the Ocean of Artificial Intelligence. The nuances of AI, its origin and meaning, terms related, technologies used, AI Effect, remarkable examples and discoveries, Explained Simply!
A quick guide to artificial intelligence working - TechaheadJatin Sapra
It is already on its way to achieving so as it has empowered the mobile app development agencies to build what was once assumed impossible. Despite this, much of this field remains undiscovered.
Artificial intelligence in practice- part-1GMR Group
Summary is made in 5 parts-
This is Part -1
Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe.
• The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment.
• Artificial intelligence and machine learning are cited as the most important modern business trends to drive success.
• It is used in areas ranging from banking and finance to social media and marketing.
• This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries.
• This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others.
• This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution.
• Each case study provides a comprehensive overview, including some technical details as well as key learning summaries:
o Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations
o Expand your knowledge of recent AI advancements in technology
o Gain insight on the future of AI and its increasing role in business and industry
o Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the trans-formative power of technology in 21st century commerce
Understanding Artificial Intelligence - Major concepts for enterprise applica...APPANION
Artificial Intelligence is a fundamental topic – for us as humans, as a society but also for businesses. For business executives and decision-makers, it is sometimes hard to keep up with rapidly evolving technologies as part of the day-to-day business. By providing this curated compilation of information about the fundamental aspects of AI, we want to captivate and inspire you to become more involved with the technology by better understanding the underlying concepts and value drivers of this technology
"Startupbootcamp and Data startups", Angel Garcia, Co-Founder and Tech Mentor...Dataconomy Media
"Startupbootcamp and Data startups", Angel Garcia, Co-Founder and Tech Mentor at Startupbootcamp IoT & Data in Barcelona
Watch more from Data Natives Tel Aviv 2016 here: http://bit.ly/2hw1MY0
Visit the conference website to learn more: http://telaviv.datanatives.io/
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Currently is Founding Partner at Startupbootcamp Internet of Things & Data and Lanta Digital Ventures. Startupbootcamp is Europe’s first, global and leading accelerator program for startups and Lanta is an early stage venture capital fund that invests in innovative start-ups. Angel is an experienced executive, entrepreneur and investor. He has broad experience over more than 15 years acting in an international environments both in Asia and US building up a startup project. Angel is shareholder at Fractus, a well-known growing European Technology start-up in the global telecom industry which is currently implementing a patent licensing program having collected close to $ 100 million in royalties to this day from top worldwide cellphone manufacturers.
"BI-Havior, Advanced Analytics as it should be", Yogev Peled, Founder and lea...Dataconomy Media
"BI-Havior, Advanced Analytics as it should be", Yogev Peled, Founder and leading innovation of QlikView-Israel
Watch more from Data Natives Tel Aviv 2016 here: http://bit.ly/2hw1MY0
Visit the conference website to learn more: http://telaviv.datanatives.io/
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Founder and leading innovation of QlikView-Israel Master of Business Administration 20 years of experience in the field of business intelligence and data analysis. Winner of world Technologist Award for the year 2010 In Qlik.
"Barclays Accelerator", Liron Rose, Managing Director at Tech Stars Tel AvivDataconomy Media
"Barclays Accelerator", Liron Rose, Managing Director at Tech Stars Tel Aviv
Watch more from Data Natives Tel Aviv 2016 here: http://bit.ly/2hw1MY0
Visit the conference website to learn more: http://telaviv.datanatives.io/
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
Embedding Artificial Intelligence in the EnterpriseDr David Probert
Influential Presentation that was presented during DECVille 1988 @ the Cannes Palais des Congress for Digital Equipment Corporation (DEC). The author introduces the concept of the "Knowledge Lens" which is used to show how Artificial Intelligence (A.I.) is now being embedded in enterprise products, software and applications. The talk also discusses the 3 Ages of Computing that span the 1960s to 21st Century. This talk was subsequently used as the basis of a Keynote Speech for the British Computer Society Conference on Expert Systems that was held in Brighton, UK during December 1988.
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesMatthew Lease
Talk given at the 8th Forum for Information Retrieval Evaluation (FIRE, http://fire.irsi.res.in/fire/2016/), December 10, 2016, and at the Qatar Computing Research Institute (QCRI), December 15, 2016.
"Building Anomaly Detection For Large Scale Analytics", Yonatan Ben Shimon, A...Dataconomy Media
"Building Anomaly Detection For Large Scale Analytics", Yonatan Ben Shimon, Anodot Architect
Watch more from Data Natives Tel Aviv 2016 here: http://bit.ly/2hw1MY0
Visit the conference website to learn more: http://telaviv.datanatives.io/
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
IoT in the combination of ML can help you automate your business and optimize the processes. Let's explore the future possibilities of combining ML with IoT.
JyotPrakash Gugnani, Student of sem 2 from department of journalism and mass communication, JIMS Vasant Kunj II talk about Areas of Artificial Intelligence. Have a Look!! For more updates: visit: jimssouthdelhi.com
When computers mimic the capabilities of the human brain, that is artificial
intelligence (AI). From the outside, AI looks like computers that have independent
thoughts. Have no fear, however. The gears of their machine “brains” may be turning,
but, for right now, they’re not really thinking—at least not the way that human beings
think.
leewayhertz.com-How to build an AI app.pdfrobertsamuel23
The power and potential of artificial intelligence cannot be overstated. It has transformed
how we interact with technology, from introducing us to robots that can perform tasks
with precision to bringing us to the brink of an era of self-driving vehicles and rockets
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for misstatement of information thru its source, content material, or author and save you the unauthenticated assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for fake information presence. The implementation setup produced most volume 99% category accuracy, even as dataset is tested for binary (real or fake) labelling with multiple epochs.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it
tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for
misstatement of information thru its source, content material, or author and save you the unauthenticated
assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network
entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for
fake information presence. The implementation setup produced most volume 99% category accuracy, even
as dataset is tested for binary (real or fake) labelling with multiple epochs.
IoT and machine learning - Computational Intelligence conferenceAjit Jaokar
Slides for IoT and Machine learning talk. Sign up at Sign up at www.futuretext.com to get forthcoming copies of papers on IoT and Machine learning, Real time algorithms for IoT and Machine learning algorithms for Smart cities
Contents:
Introduction
History
Definition
Examples
New Related Literature
Advantage
Disadvantage
Summary
Conclusion
HISTORY
The idea of AI as far back as ancient Greece. Greek myths speak of Hephaestus, a blacksmith who created mechanical servants.
Fast forward to 1935, when the earliest substantial work in this field was done by Alan Turing, a logician and compter pioneer.
-TURING MACHINE
1951: Christopher Strachey wrote the first successful AI program
- COMPUTER CHECKERS PROGRAM
1956: John McCarthy coined the term Artificial Intelligence
1963: ANALOGY, a program created by Thomas Evans, proved that computers can solve IQ test analogy problems
1967: First successful knowledge-based program in science and mathematics
1972: SHRDLU created by Terry Winograd
- Robot arm responded to commands
1987: Marvin Minsky publishes The Society of Mind, which portrays the brain as a series of cooperating agents
1997: A chess program, Deep Blue, beats the current world chess champion, Gary Kasparov
2000’s: Interactive robot smart toys are made commercially available
Define an Artificial Intelligence……. ?
EXAMPLES
1. Google Maps and Ride-Hailing Applications
2. Face Detection and Recognition
3. Text Editors or Autocorrect
4. Chatbots
5. Online-Payments
NEWS RELATED LITERATURE
ADVANTAGE
Similar to Ajit Jaokar, Data Science for IoT professor at Oxford University “Enterprise AI - Artificial Intelligence for the Enterprise." (20)
Data Natives Frankfurt v 11.0 | "Competitive advantages with knowledge graphs...Dataconomy Media
The challenges of increasing complexity of organizations, companies and projects are obvious and omnipresent. Everywhere there are connections and dependencies that are often not adequately managed or not considered at all because of a lack of technology or expertise to uncover and leverage the relationships in data and information. In his presentation, Axel Morgner talks about graph technology and knowledge graphs as indispensable building blocks for successful companies.
Data Natives Munich v 12.0 | "How to be more productive with Autonomous Data ...Dataconomy Media
Every day we are challenged with more data, more use cases and an ever increasing demand for analytics. In this talk Bjorn will explain how autonomous data management and machine learning help innovators to more productive and give examples how to deliver new data driven projects with less risk at lower costs.
Data Natives meets DataRobot | "Build and deploy an anti-money laundering mo...Dataconomy Media
Compliance departments within banks and other financial institutions are turning to machine learning for improving their Anti Money Laundering compliance activities. Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates. DataRobot will discuss how their Automated Machine Learning platform was successfully used for a real use case to reduce their false positives and to enhance their Anti-Money Laundering activities.
Data Natives Munich v 12.0 | "Political Data Science: A tale of Fake News, So...Dataconomy Media
Trump, Brexit, Cambridge Analytica... In the last few years, we have had to confront the consequences of the use and misuse of data science algorithms in manipulating public opinion through social media. The use of private data to microtarget individuals is a daily practice (and a trillion-dollar industry), which has serious side-effects when the selling product is your political ideology. How can we cope with this new scenario?
Data Natives Vienna v 7.0 | "The Ingredients of Data Innovation" - Robbert de...Dataconomy Media
When taking a deep dive into the world of data, one thing is certain: the ultimate goal is to create something new, something better, something faster. In other words, innovation should always be at the forefront of companies strategic outlook, whether their goal is to pioneer new processes, user experiences, products or services.
Data Natives Cologne v 4.0 | "The Data Lorax: Planting the Seeds of Fairness...Dataconomy Media
What does it take to build a good data product or service? Data practitioners always think about the technology, user experience and commercial viability. But rarely do they think about the implications of the systems they build. This talk will shed light on the impact of AI systems and the unintended consequences of the use of data in different products. It will also discuss our role, as data practitioners, in planting the seeds of fairness in the systems we build.
Data Natives Cologne v 4.0 | "How People Analytics Can Reveal the Hidden Aspe...Dataconomy Media
We all hear about the power of data, big data and data analysis in todays market place. But rarely feel it's touchable effects on our own business decisions and performance.
Let's dive into it and see how can people analytics increase people performance, motivation and business revenue?
Data Natives Amsterdam v 9.0 | "Ten Little Servers: A Story of no Downtime" -...Dataconomy Media
Cloud Infrastructure is a hostile environment: a power supply failure or a network outage leads to downtime and big losses. There is nothing we can trust: a single server, a server rack, even a whole datacenter can fail, and if an application is fragile by design, disruption is inevitable. We must distribute our application and diversify cloud data strategy to survive disturbances of any scale. Apache Cassandra is a cloud-native platform-agnostic database that stores data with a distributed redundancy so it easily survives any issue. What to know how Apple and Netflix handle petabytes of data, keeping it highly available? Join us and listen to a story of 10 little servers and no downtime!
Data Natives Amsterdam v 9.0 | "Point in Time Labeling at Scale" - Timothy Th...Dataconomy Media
In the data industry, having correctly labelled datasets is vital. Timothy Thatcher explains how tagging your data while considering time and location and complex hierarchical rules at scale can be handled.
Data NativesBerlin v 20.0 | "Serving A/B experimentation platform end-to-end"...Dataconomy Media
During the lifetime of an A/B test product managers and analysts in GetYourGuide require various tools and different kinds of data to plan the trial properly, control it during the run and analyze the results at the end. This talk would be about the architecture, tools and data flow for serving their needs.
Data Natives Berlin v 20.0 | "Ten Little Servers: A Story of no Downtime" - A...Dataconomy Media
Cloud Infrastructure is a hostile environment: a power supply failure or a network outage leads to downtime and big losses. There is nothing we can trust: a single server, a server rack, even a whole datacenter can fail, and if an application is fragile by design, disruption is inevitable. We must distribute our application and diversify cloud data strategy to survive disturbances of any scale. Apache Cassandra is a cloud-native platform-agnostic database that stores data with a distributed redundancy so it easily survives any issue. What to know how Apple and Netflix handle petabytes of data, keeping it highly available? Join us and listen to a story of 10 little servers and no downtime!
Big Data Frankfurt meets Thinkport | "The Cloud as a Driver of Innovation" - ...Dataconomy Media
Creativity is the mental ability to create new ideas and designs. Innovation, on the other hand, Means developing useful solutions from new ideas. Creativity can be goal-oriented, Whereas innovation is always goal-oriented. This bedeutet, dass innovation aims to achieve defined goals. The use of cloud services and technologies promises enterprise users many benefits in terms of more flexible use of IT resources and faster access to innovative solutions. That’s why we want to examine the question in this talk, of what role cloud computing plays for innovation in companies.
Thinkport meets Frankfurt | "Financial Time Series Analysis using Wavelets" -...Dataconomy Media
Presentation of Time Series Properties of Financial Instrument and Possibilities in Frequency Decomposition and Information Extraction using FT, STFT and Wavelets with Outlook in Current Research on Wavelet Neural Networks
Big Data Helsinki v 3 | "Distributed Machine and Deep Learning at Scale with ...Dataconomy Media
"With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data for ETL, and hours to train models. It's also hard to scale, with data sets increasingly being larger than the capacity of any single server. The amount of the data also makes it hard to incrementally test and retrain models in near real-time.
Learn how Apache Ignite and GridGain help to address limitations like ETL costs, scaling issues and Time-To-Market for the new models and help achieve near-real-time, continuous learning.
Yuriy Babak, the head of ML/DL framework development at GridGain and Apache Ignite committer, will explain how ML/DL work with Apache Ignite, and how to get started.
Topics include:
— Overview of distributed ML/DL including architecture, implementation, usage patterns, pros and cons
— Overview of Apache Ignite ML/DL, including built-in ML/DL algorithms, and how to implement your own
— Model inference with Apache Ignite, including how to train models with other libraries, like Apache Spark, and deploy them in Ignite
— How Apache Ignite and TensorFlow can be used together to build distributed DL model training and inference"
Big Data Helsinki v 3 | "Federated Learning and Privacy-preserving AI" - Oguz...Dataconomy Media
"Machine learning algorithms require significant amounts of training data which has been centralized on one machine or in a datacenter so far. For numerous applications, such need of collecting data can be extremely privacy-invasive. Recent advancements in AI research approach this issue by a new paradigm of training AI models, i.e., Federated Learning.
In federated learning, edge devices (phones, computers, cars etc.) collaboratively learn a shared AI model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. From personal data perspective, this paradigm enables a way of training a model on the device without directly inspecting users’ data on a server. This talk will pinpoint several examples of AI applications benefiting from federated learning and the likely future of privacy-aware systems."
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Ajit Jaokar, Data Science for IoT professor at Oxford University “Enterprise AI - Artificial Intelligence for the Enterprise."
1. Copyright : Futuretext Ltd. London0
Ajit Jaokar
Enterprise AI
Artificial Intelligence for the Enterprise
https://www.meetup.com/Big-Data-Berlin/events/236608419/
2. Copyright : Futuretext Ltd. London1
Ajit Jaokar
Oxford Uni – Data Science for IoT. Rated top
influencer for DS and IoT by kdnuggets and DS
central - World Economic Forum - Spoken at MWC(5
times), CEBIT, CTIA, Web 2.0, CNN, BBC, Oxford
Uni, Uni St Gallen, European Parliament. @feynlabs
– teaching kids Computer Science. Adivsory –
Connected Liverpool
4. Copyright : Futuretext Ltd. London3
AI vs. Deep Learning vs. Machine Learning.
The term Artificial Intelligence (AI) implies a machine that can
Reason. A more complete list or AI characteristics
Reasoning - Knowledge representation – Planning –
Communication - Perception:
http://cdn04.androidauthority.net/wp-content/uploads/2015/07/machine-
learning-ai-artificial-intelligence-e1462471461626.jpg
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Deep Learning algorithms are currently driving AI. Finally, in a
broad sense, the term Machine Learning means the application of
any algorithm that can be applied against a dataset to find a
pattern in the data. This includes algorithms like supervised,
unsupervised, segmentation, classification, or regression.
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The holy grail of AI is artificial general intelligence (aka like
Terminator!)
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What we see today is mostly narrow AI (ex like the NEST thermostat).
AI is evolving rapidly.
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Ajit Jaokar
What problem does Deep Learning Address
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Deep learning is really about automated feature engineering.
Feature engineering involves finding connections between
variables and packaging them into a new single variable
Deep Learning suits problems
where the target function is
complex and datasets are
large but with examples of
positive and negative
cases. Deep Learning
also suits problems that
involve Hierarchy and
Abstraction.
(image source:
Yoshua Bengio)
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With this background, we now discuss the twelve types of AI
problems.
1) Domain expert: Problems which involve Reasoning based on
a complex body of knowledge
This includes tasks which are based on learning a body of knowledge
like Legal, financial etc. and then formulating a process where the
machine can simulate an expert in the field
2) Domain extension: Problems which involve extending a
complex body of Knowledge
Here, the machine learns a complex body of knowledge like
information about existing medication etc. and then can suggest new
insights to the domain itself – for example new drugs to cure diseases.
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3) Complex Planner: Tasks which involve Planning
Many logistics and scheduling tasks can be done by current (non AI)
algorithms. But increasingly, as the optimization becomes complex AI
could help. AI techniques help on this case because we have large and
complex datasets where human beings cannot detect patterns but a
machine can do so easily.
4) Better communicator: Tasks which involve improving
existing communication
AI and Deep Learning benefit many communication modes such as
automatic translation, intelligent agents etc
5) New Perception: Tasks which involve Perception
AI and Deep Learning enable newer forms of Perception which enables
new services such as autonomous vehicles
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6) Enterprise AI: AI meets Re-engineering the corporation!
7) Enterprise AI adding unstructured data and Cognitive
capabilities to ERP and Data warehousing
For reasons listed above, unstructured data offers a huge opportunity
for Deep Learning and hence AI.
8) Problems which impact domains due to second order
consequences of AI
“The second-order consequences of machine learning will exceed
its immediate impact. “ ex insurance
9) Problems in the near future that could benefit from improved algorithms
A catch-all category for things which were not possible in the past, could be
possible in the near future due to better algorithms or better hardware. Ex
speech recognition and translation capabilities
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10) Evolution of Expert systems
Expert systems have been around for a long time. Much of the vision
of Expert systems could be implemented in AI/Deep Learning
algorithms in the near future. The IBM Watson strategy leads to an
Expert system vision. Of course, the same ideas can be implemented
independently of Watson today.
11) Super Long sequence pattern recognition
I got this title from a slide from Uber’s head of Deep Learning. The application of
AI techniques to sequential pattern recognition is still an early stage domain(and
does not yet get the kind of attention as CNNs for example) – but in my view, this
will be a rapidly expanding space. LSTMs fall in this category
12) Extending Sentiment Analysis using AI
The interplay between AI and Sentiment analysis is also a new area.
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How to train a Big Data algorithm?
Start with the Rules and apply them to Data OR Start with the data and
find the rules from the Data
The Top-down approach involved writing enough rules for all possible
circumstances. But this approach is obviously limited by the number of
rules and by its finite rules base.
Bottom up approach: where there are no rules :
a) No models(schema),
b) Linearity(sequence) and hierarchy is not known
c) Non deterministic – output is not known
d) Problem domain is not finite
In contrast – transactional computing is straight forward
Image source: https://www.simplilearn.com
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10 million images from YouTube videos – recognise pictures of Cats
- without telling what a cat is
Apply them to real problems” such as image recognition, search,
and natural-language understanding,
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10 million images from YouTube videos – recognise pictures of Cats
- without telling what a cat is
Apply them to real problems” such as image recognition, search,
and natural-language understanding,
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1.Natural Language Generation: Producing text from computer
data. Currently used in customer service, report generation, and
summarizing business intelligence insights.
2.Speech Recognition: Transcribe and transform human speech into
format useful for computer applications. Currently used in interactive
voice response systems and mobile applications.
3.Virtual Agents: Getting a lot of media attention Ex Amazon, Apple
etc
4.Machine Learning Platforms: Providing algorithms, APIs, development and
training toolkits, data, as well as computing power to design, train, and deploy
models into applications, processes, and other machines.
5.AI-optimized Hardware: Graphics processing units (GPU) and appliances
specifically designed and architected to efficiently run AI-oriented computational
jobs. Ex Nvidia.
6.Decision Management: Engines that insert rules and logic into AI systems and
used for initial setup/training and ongoing maintenance and tuning. Sample
vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems,
UiPath.
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7.Deep Learning Platforms: A special type of machine learning
consisting of artificial neural networks with multiple abstraction
layers.
8.Biometrics: Enable more natural interactions between humans
and machines, including but not limited to image and touch
recognition, speech, and body language.
9.Robotic Process Automation: Using scripts and other methods to
automate human action to support efficient business processes.
Currently used where it’s too expensive or inefficient for humans to
execute a task or a process. Sample vendors: Advanced Systems
Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion.
10.Text Analytics and NLP: Natural language processing (NLP) uses and
supports text analytics by facilitating the understanding of sentence structure
and meaning, sentiment, and intent through statistical and machine learning
methods. Currently used in fraud detection and security, a wide range of
automated assistants, and applications for mining unstructured data.
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A logical concept called the AI layer for the Enterprise. We could see
such a layer as an extension to the Data Warehouse or the ERP system.
This has tangible and practical benefits for the Enterprise with a clear
business model. One simple way is to think of it as an ‘Intelligent
Data warehouse’ i.e. an extension to either the Data warehouse or the
ERP system. For instance, an organization would transcribe call centre
agents’ interactions with customers create a more intelligent workflow, bot
etc using Deep learning algorithms.
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Enterprise AI layer – What it mean to the Enterprise
So, if we imagine such a conceptual AI layer for the enterprise, what does it
mean in terms of new services that can be offered? Here are some
examples
• Bots : Bots are a great example of the use of AI to automate repetitive
tasks like scheduling meetings. Bots are often the starting point of
engagement for AI especially in Retail and Financial services
• Inferring from textual/voice narrative: Security applications to
detect suspicious behaviour, Algorithms that can draw connections
between how patients describe their symptoms etc
• Detecting patterns from vast amounts of data: Using log files to
predict future failures, predicting cyberseurity attacks etc
• Creating a knowledge base from large datasets: for example an AI
program that can read all of Wikipedia or Github.
• Creating content on scale: Using Robots to replace Writers or even to
compose Pop songs
• Predicting future workflows: Using existing patterns to predict future
workflows
• Mass personalization: in advertising
• Video and image analytics: Collision Avoidance for Drones,
Autonomous vehicles, Agricultural Crop Health Analysis etc
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Ajit Jaokar
How artificial Intelligence will redefine management
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Practice 1: Leave Administration to AI
Ex - juggle shift schedules because of staff members’
illnesses, vacations, or sudden departures. AI will automate
many of these tasks. Including report writing. Recently, the data
analytics company Tableau announced a partnership with Narrative
Science, a Chicago-based provider of natural language generation
tools. The result of the collaboration is Narratives for Tableau, a free
Chrome extension that automatically creates written explanations for
Tableau graphics.
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Practice 2: Focus on Judgment Work
Essence of human judgment — the application of experience and
expertise to critical business decisions and practices. (knowledge of
organizational history and culture, as well as empathy and
ethical reflection. Also creative thinking and experimentation,
data analysis and interpretation, and strategy development
Practice 3: Treat Intelligent Machines as “Colleagues”
Practice 4: Work Like a Designer
Manager-designers bring together diverse ideas into integrated,
workable, and appealing solutions. They embed design thinking into the
practices of their teams and organizations.