Intelligent Systems - Tommy Richardson, CTO, Teradata Marketing Applications
•Download as PPTX, PDF•
1 like•1,006 views
The document discusses the evolution of technology from the 1960s to present day, focusing on changes in data processing and the rise of machine learning and artificial intelligence. It notes that while early systems involved manual data entry and basic processing, modern systems use machine learning techniques to process large amounts of data and make automated decisions. Specific applications discussed include logistics, education, HR/payroll, and healthcare. The document concludes by providing recommendations for introducing more intelligent technologies, such as leveraging open source tools and mobile interfaces.
Report
Share
Report
Share
Recommended
SmartData Webinar: Commercial Cognitive Computing -- How to choose and build ...
This document provides an overview of cognitive computing and how to build a first cognitive computing application. It discusses fundamentals like natural cognitive processes, approaches to machine learning, and perception. It also outlines the cognitive computing technology ecosystem, including machine learning platforms, input/output technologies, infrastructure providers, and analytics/visualization tools. Finally, it offers advice on first steps like identifying a domain and data sources, choosing a machine learning model, and building a virtuous learning cycle.
AI in the Real World: Challenges, and Risks and how to handle them?
This document discusses challenges, risks, and how to handle them with AI in the real world. It covers:
- AI can perform tasks like driving a car faster and cheaper than humans, but can't fully explain how.
- Deploying and managing AI models at scale is complex, as is integrating models with user experiences. Bias and lack of transparency are also risks.
- When applying AI, such as in high-risk domains like medicine, it is important to audit models, gradually introduce them with trials, monitor outcomes, and find ways to identify and address errors or unfair impacts. With care and oversight, AI can be developed to help more people than it harms.
Semantic Computing Executive Briefing
The document discusses semantic computing and its benefits. It provides an agenda for introducing semantic software, IoT/big data, and semantic computing concepts. Semantic computing transforms unstructured data into structured triples that can be queried using ontologies to add context and meaning. It discusses how semantic computing supports applications in various domains like finance, government, and healthcare by integrating diverse data sources and enabling expanded analytics. The US Navy case study shows how semantic computing helped the Navy reduce energy costs.
Introduction to Semantic Computing
Semantic Software is an Australian cognitive computing company that has developed a comprehensive semantic computing platform to help analyze large amounts of data. Their platform uses various AI techniques like deep learning, machine learning, natural language processing, and semantic computing with reasoning and inferencing to help computers analyze data without needing as much human input. They believe semantic computing is key to truly cognitive applications and that their platform is one of the most advanced for these purposes.
Ateneo
Automation of knowledge work involves using computers to perform tasks previously done by knowledge workers such as data analysis, problem solving, and decision making. As computers get better at mimicking human reasoning, they are taking over more high-paying jobs. Trends include increased automation of workflows, accessibility of information, remote and distributed work, and exponential increases in computer processing power. While automation shortens time to acquire knowledge and provides accurate information, it also risks human manipulation and could displace human jobs. It may help industries like medicine, manufacturing, law, education and finance by enabling faster research, consistent learning, and identification of existing information. However, there are also concerns about its impact on the economy and jobs.
RESEARCH AND DEVELOPMENT OF INFORMATION TECHNOLOGY
This document summarizes a research paper on the information technology (IT) infrastructure in Badin, Pakistan. The research was conducted through a literature review of 4 papers and collecting data via 60 closed-ended questionnaires. The results showed that the IT infrastructure in Badin is weak and not developing well due to issues like frequent power outages, poor internet speeds, lack of IT training institutes, and unreliable cellular networks. Disasters were also found to negatively impact the local IT network. The conclusion is that Badin requires improvements to its IT infrastructure.
Integrating AI - Business Applications
This document provides guidance on integrating AI into organizations. It recommends aligning AI projects with business drivers to create value, focusing on scaling human capabilities with assisted and augmented intelligence, and taking a portfolio approach to AI innovation around customer needs. It also stresses the importance of developing an effective operating model with the right outcomes, team, tools, and iterative process, as well as understanding the range of AI and machine learning efforts.
Robotics and Artificial Intelligence - The good the bad and the data - John T...
John Thompson from Client Solutions Ireland presents a brief history of Artificial Intelligence, why it's such a hot topic, the benefits it can bring and the challenges it presents.
Recommended
SmartData Webinar: Commercial Cognitive Computing -- How to choose and build ...
This document provides an overview of cognitive computing and how to build a first cognitive computing application. It discusses fundamentals like natural cognitive processes, approaches to machine learning, and perception. It also outlines the cognitive computing technology ecosystem, including machine learning platforms, input/output technologies, infrastructure providers, and analytics/visualization tools. Finally, it offers advice on first steps like identifying a domain and data sources, choosing a machine learning model, and building a virtuous learning cycle.
AI in the Real World: Challenges, and Risks and how to handle them?
This document discusses challenges, risks, and how to handle them with AI in the real world. It covers:
- AI can perform tasks like driving a car faster and cheaper than humans, but can't fully explain how.
- Deploying and managing AI models at scale is complex, as is integrating models with user experiences. Bias and lack of transparency are also risks.
- When applying AI, such as in high-risk domains like medicine, it is important to audit models, gradually introduce them with trials, monitor outcomes, and find ways to identify and address errors or unfair impacts. With care and oversight, AI can be developed to help more people than it harms.
Semantic Computing Executive Briefing
The document discusses semantic computing and its benefits. It provides an agenda for introducing semantic software, IoT/big data, and semantic computing concepts. Semantic computing transforms unstructured data into structured triples that can be queried using ontologies to add context and meaning. It discusses how semantic computing supports applications in various domains like finance, government, and healthcare by integrating diverse data sources and enabling expanded analytics. The US Navy case study shows how semantic computing helped the Navy reduce energy costs.
Introduction to Semantic Computing
Semantic Software is an Australian cognitive computing company that has developed a comprehensive semantic computing platform to help analyze large amounts of data. Their platform uses various AI techniques like deep learning, machine learning, natural language processing, and semantic computing with reasoning and inferencing to help computers analyze data without needing as much human input. They believe semantic computing is key to truly cognitive applications and that their platform is one of the most advanced for these purposes.
Ateneo
Automation of knowledge work involves using computers to perform tasks previously done by knowledge workers such as data analysis, problem solving, and decision making. As computers get better at mimicking human reasoning, they are taking over more high-paying jobs. Trends include increased automation of workflows, accessibility of information, remote and distributed work, and exponential increases in computer processing power. While automation shortens time to acquire knowledge and provides accurate information, it also risks human manipulation and could displace human jobs. It may help industries like medicine, manufacturing, law, education and finance by enabling faster research, consistent learning, and identification of existing information. However, there are also concerns about its impact on the economy and jobs.
RESEARCH AND DEVELOPMENT OF INFORMATION TECHNOLOGY
This document summarizes a research paper on the information technology (IT) infrastructure in Badin, Pakistan. The research was conducted through a literature review of 4 papers and collecting data via 60 closed-ended questionnaires. The results showed that the IT infrastructure in Badin is weak and not developing well due to issues like frequent power outages, poor internet speeds, lack of IT training institutes, and unreliable cellular networks. Disasters were also found to negatively impact the local IT network. The conclusion is that Badin requires improvements to its IT infrastructure.
Integrating AI - Business Applications
This document provides guidance on integrating AI into organizations. It recommends aligning AI projects with business drivers to create value, focusing on scaling human capabilities with assisted and augmented intelligence, and taking a portfolio approach to AI innovation around customer needs. It also stresses the importance of developing an effective operating model with the right outcomes, team, tools, and iterative process, as well as understanding the range of AI and machine learning efforts.
Robotics and Artificial Intelligence - The good the bad and the data - John T...
John Thompson from Client Solutions Ireland presents a brief history of Artificial Intelligence, why it's such a hot topic, the benefits it can bring and the challenges it presents.
Cognitive Computing.PDF
This document discusses cognitive computing capabilities and their potential to change how people live and work. It outlines three areas of cognitive capability: engagement, discovery, and decision. Engagement capabilities allow systems to interact naturally with humans through dialogue. Discovery capabilities help systems find new patterns and insights in data. Decision capabilities allow systems to make evidence-based decisions that evolve over time. The document also notes six forces that will influence adoption rates and five dimensions that will impact future cognitive capabilities. It provides an example of how USAA uses cognitive computing to help military members transition to civilian life by answering their questions.
Cognitive computing
This document discusses cognitive computing systems and their key components and processes. It defines cognitive computing as simulating human thought processes using computer models. A cognitive system consists of contextual insights from models, hypothesis generation, and continuous self-learning. Key features include learning from data without reprogramming, generating and evaluating hypotheses based on current knowledge, and discovering patterns in data with or without guidance. The document outlines the high-level process flow of a cognitive system including ingestion, categorization, matching, exploration and dialog loops. It describes the elements of a cognitive system such as iterative hypothesis generation and evaluation, data access and management services, corpora/ontologies, analytics services, and presentation services. Automated hypothesis generation from text data is discussed
Artificial intelligence: Simulation of Intelligence
1. The document discusses the history and development of artificial intelligence and machine learning, from early concepts in probability and statistics in the 18th century to modern algorithms and applications.
2. It outlines important early milestones like the McCulloch-Pitts neural network model from 1943 and the Turing Test in 1950. Major algorithms like perceptron and modern frameworks like TensorFlow are also mentioned.
3. The text advocates for applying machine learning to solve real-world business problems by understanding the problem domain, acquiring relevant data, selecting an appropriate algorithm, and iterating through the problem solving process.
Machine learning and language v2
This is the second edition of Machine Learning and Language. If it seems to be almost identical to the initial version, which focused on a different area of science, that's the point...
AI/ML-Innovation-2019
The document discusses the 5 steps of an AI/ML innovation project:
1. Empathize and analyze to understand user needs and objectives.
2. Define and synthesize insights about key decisions and potential variables and metrics.
3. Ideate potential solutions by choosing a suitable machine learning model.
4. Prototype and tune by creating mockups to study user interactions and identify insights.
5. Test and validate prototypes using user testing and evaluating the machine learning model on test data.
What AI is and examples of how it is used in legal
This presentation was given at Legal Geek on 10th Dec 2015. It is a scenesetting peice that looks to de-mystify artificial intelligence by looking beyond the hype.
A quick guide to artificial intelligence working - Techahead
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.
Developing cognitive applications v1
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
Cse ppt
The document discusses the field of computer science and engineering. It provides an overview of the origins and focus of the field, the department's mission to impart fundamental concepts and practical skills. It outlines the program's objectives to provide a strong foundation in math, science and engineering, and to prepare students for careers in industry. Finally, it discusses the wide career opportunities available to computer science graduates in roles such as software development, networking, and databases in top companies in India and internationally.
AI Panel: AI in Practice- the Good, the Bad, and the Ugly
The document discusses AI in practice from three perspectives - the good, the bad, and the ugly. It outlines an agenda for a panel discussion on AI that will explore where AI has been successfully applied ("the good"), where it has broken or failed in practice ("the bad"), and how to avoid bias in developing and applying AI systems ("the ugly"). The panelists are experts from companies like Reddit, Slack, Github, and Lucidworks who will share their experiences and take questions from the audience.
CS 561a: Introduction to Artificial Intelligence
This document provides an overview and syllabus for a CS 561 Artificial Intelligence course. It introduces key topics that will be covered over the semester including intelligent agents, search, problem solving, logic, knowledge representation, reasoning, and learning. It outlines the course structure, assignments, exams and grading. Administrative details like the instructors, TAs, office hours and course website are also provided.
Machine Learning and Languge
This brief presentation provides a high level perspective of the benefits of machine learning, its impact on unstructured and structured data, and how the aspects of language are important
Cognitive computing big_data_statistical_analytics
Cognitive computing, big data, and statistical analytics represent new frontiers for innovation that will transform organizations. These emerging technologies rely on analyzing vast amounts of structured and unstructured data using powerful computers and sophisticated algorithms to generate novel insights. Realizing their full potential will require integrating data and analytics from hundreds or thousands of diverse sources to reduce uncertainty and construct high-value context. This represents a strategic challenge for organizations to create an integrated view of information from all available data channels.
Bringing AI to Business Intelligence
The document discusses bringing artificial intelligence (AI) to business intelligence (BI). It provides an overview of the current BI environment and how it is lacking in its ability to answer "why" questions and provide prescriptive recommendations. The document then defines different types of AI, from weak AI to artificial general intelligence. It also outlines various AI technologies, especially machine learning techniques like supervised and unsupervised learning. The overall document serves to introduce the topic of integrating AI capabilities into BI tools and analytics.
Cognitive Computing - A Primer
AI & Cognitive Computing are some of the most popular business an technical words out there. It is critical to get the basic understanding of Cognitive Computing, which helps us appreciate the technical possibilities and business benefits of the technology.
Artificial Intelligence & Business Intelligence in Management by Ojas
What is AI?What is BI? How its helpful in Human resource & Management & Impact & Models of Business Intelligence
[Seminar] 200731 Hyeonwook Lee
1) The document defines AI literacy as a set of competencies that enables individuals to critically evaluate AI technologies, communicate effectively with AI, and use AI as a tool.
2) It proposes 15 competencies across 5 themes - what AI is, what it can do, how it works, how it should be used, and how people perceive it.
3) The competencies focus on understanding intelligence, different types of AI, their strengths/weaknesses, how machine learning and data work, ethics, and interpreting AI systems.
Big Data & Artificial Intelligence
Big data and artificial intelligence have developed through an iterative process where increased data leads to improved infrastructure which then enables the collection of even more data. This virtuous cycle began with the rise of the internet and web data in the 1990s. Modern frameworks like Hadoop and algorithms like MapReduce established the infrastructure needed to analyze large, distributed datasets and fuel machine learning applications. Deep learning techniques are now widely used for tasks involving images, text, video and other complex data types, with many companies seeking to gain advantages by leveraging proprietary datasets.
Introduction to DS, ML and IBM Tools
This document provides an introduction to data science, machine learning, and IBM tools. It defines key concepts like artificial intelligence, machine learning, deep learning, and data science. It outlines the typical data science workflow including data collection, cleaning, exploration, modeling, and deployment. It also discusses different types of data analysis and model types. Finally, it provides an overview of popular data science tools and frameworks and lists some IBM specific analytics tools.
Smart Data 2017 #AI & #FutureofWork
1) The document discusses how AI is impacting various industries and the future of work.
2) Machines are able to process vast amounts of information and handle complex processes better than humans.
3) Many jobs will be automated but AI will drive humans to perform higher-level cognitive work. Augmented intelligence where humans and machines work together will increase.
DSA-2012-Lect00
The document is a presentation by Dr. Haitham A. El-Ghareeb about a data structures and algorithms course at Mansoura University. It introduces Dr. El-Ghareeb and provides an overview of course topics, importance, details on topics covered, and grading structure. The course covers algorithm and data structure design, abstract data types, common data structures, sorting and searching algorithms, and algorithm complexity analysis. Student performance is evaluated based on a midterm exam, labs, attendance, reports, and an oral exam.
Lect07
The document discusses algorithm analysis and complexity analysis. It introduces the concept of analyzing an algorithm's runtime by examining the number of key operations like comparisons and assignments, rather than just measuring execution time. This is known as complexity analysis. The document uses an example of summing the rows and values of a matrix to illustrate how complexity analysis can identify the most efficient of multiple algorithms for the same problem. It determines that two example algorithms for summing a matrix have the same asymptotic runtime of O(n^2). The document then introduces Big-O notation for describing an algorithm's asymptotic worst-case runtime.
More Related Content
What's hot
Cognitive Computing.PDF
This document discusses cognitive computing capabilities and their potential to change how people live and work. It outlines three areas of cognitive capability: engagement, discovery, and decision. Engagement capabilities allow systems to interact naturally with humans through dialogue. Discovery capabilities help systems find new patterns and insights in data. Decision capabilities allow systems to make evidence-based decisions that evolve over time. The document also notes six forces that will influence adoption rates and five dimensions that will impact future cognitive capabilities. It provides an example of how USAA uses cognitive computing to help military members transition to civilian life by answering their questions.
Cognitive computing
This document discusses cognitive computing systems and their key components and processes. It defines cognitive computing as simulating human thought processes using computer models. A cognitive system consists of contextual insights from models, hypothesis generation, and continuous self-learning. Key features include learning from data without reprogramming, generating and evaluating hypotheses based on current knowledge, and discovering patterns in data with or without guidance. The document outlines the high-level process flow of a cognitive system including ingestion, categorization, matching, exploration and dialog loops. It describes the elements of a cognitive system such as iterative hypothesis generation and evaluation, data access and management services, corpora/ontologies, analytics services, and presentation services. Automated hypothesis generation from text data is discussed
Artificial intelligence: Simulation of Intelligence
1. The document discusses the history and development of artificial intelligence and machine learning, from early concepts in probability and statistics in the 18th century to modern algorithms and applications.
2. It outlines important early milestones like the McCulloch-Pitts neural network model from 1943 and the Turing Test in 1950. Major algorithms like perceptron and modern frameworks like TensorFlow are also mentioned.
3. The text advocates for applying machine learning to solve real-world business problems by understanding the problem domain, acquiring relevant data, selecting an appropriate algorithm, and iterating through the problem solving process.
Machine learning and language v2
This is the second edition of Machine Learning and Language. If it seems to be almost identical to the initial version, which focused on a different area of science, that's the point...
AI/ML-Innovation-2019
The document discusses the 5 steps of an AI/ML innovation project:
1. Empathize and analyze to understand user needs and objectives.
2. Define and synthesize insights about key decisions and potential variables and metrics.
3. Ideate potential solutions by choosing a suitable machine learning model.
4. Prototype and tune by creating mockups to study user interactions and identify insights.
5. Test and validate prototypes using user testing and evaluating the machine learning model on test data.
What AI is and examples of how it is used in legal
This presentation was given at Legal Geek on 10th Dec 2015. It is a scenesetting peice that looks to de-mystify artificial intelligence by looking beyond the hype.
A quick guide to artificial intelligence working - Techahead
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.
Developing cognitive applications v1
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
Cse ppt
The document discusses the field of computer science and engineering. It provides an overview of the origins and focus of the field, the department's mission to impart fundamental concepts and practical skills. It outlines the program's objectives to provide a strong foundation in math, science and engineering, and to prepare students for careers in industry. Finally, it discusses the wide career opportunities available to computer science graduates in roles such as software development, networking, and databases in top companies in India and internationally.
AI Panel: AI in Practice- the Good, the Bad, and the Ugly
The document discusses AI in practice from three perspectives - the good, the bad, and the ugly. It outlines an agenda for a panel discussion on AI that will explore where AI has been successfully applied ("the good"), where it has broken or failed in practice ("the bad"), and how to avoid bias in developing and applying AI systems ("the ugly"). The panelists are experts from companies like Reddit, Slack, Github, and Lucidworks who will share their experiences and take questions from the audience.
CS 561a: Introduction to Artificial Intelligence
This document provides an overview and syllabus for a CS 561 Artificial Intelligence course. It introduces key topics that will be covered over the semester including intelligent agents, search, problem solving, logic, knowledge representation, reasoning, and learning. It outlines the course structure, assignments, exams and grading. Administrative details like the instructors, TAs, office hours and course website are also provided.
Machine Learning and Languge
This brief presentation provides a high level perspective of the benefits of machine learning, its impact on unstructured and structured data, and how the aspects of language are important
Cognitive computing big_data_statistical_analytics
Cognitive computing, big data, and statistical analytics represent new frontiers for innovation that will transform organizations. These emerging technologies rely on analyzing vast amounts of structured and unstructured data using powerful computers and sophisticated algorithms to generate novel insights. Realizing their full potential will require integrating data and analytics from hundreds or thousands of diverse sources to reduce uncertainty and construct high-value context. This represents a strategic challenge for organizations to create an integrated view of information from all available data channels.
Bringing AI to Business Intelligence
The document discusses bringing artificial intelligence (AI) to business intelligence (BI). It provides an overview of the current BI environment and how it is lacking in its ability to answer "why" questions and provide prescriptive recommendations. The document then defines different types of AI, from weak AI to artificial general intelligence. It also outlines various AI technologies, especially machine learning techniques like supervised and unsupervised learning. The overall document serves to introduce the topic of integrating AI capabilities into BI tools and analytics.
Cognitive Computing - A Primer
AI & Cognitive Computing are some of the most popular business an technical words out there. It is critical to get the basic understanding of Cognitive Computing, which helps us appreciate the technical possibilities and business benefits of the technology.
Artificial Intelligence & Business Intelligence in Management by Ojas
What is AI?What is BI? How its helpful in Human resource & Management & Impact & Models of Business Intelligence
[Seminar] 200731 Hyeonwook Lee
1) The document defines AI literacy as a set of competencies that enables individuals to critically evaluate AI technologies, communicate effectively with AI, and use AI as a tool.
2) It proposes 15 competencies across 5 themes - what AI is, what it can do, how it works, how it should be used, and how people perceive it.
3) The competencies focus on understanding intelligence, different types of AI, their strengths/weaknesses, how machine learning and data work, ethics, and interpreting AI systems.
Big Data & Artificial Intelligence
Big data and artificial intelligence have developed through an iterative process where increased data leads to improved infrastructure which then enables the collection of even more data. This virtuous cycle began with the rise of the internet and web data in the 1990s. Modern frameworks like Hadoop and algorithms like MapReduce established the infrastructure needed to analyze large, distributed datasets and fuel machine learning applications. Deep learning techniques are now widely used for tasks involving images, text, video and other complex data types, with many companies seeking to gain advantages by leveraging proprietary datasets.
Introduction to DS, ML and IBM Tools
This document provides an introduction to data science, machine learning, and IBM tools. It defines key concepts like artificial intelligence, machine learning, deep learning, and data science. It outlines the typical data science workflow including data collection, cleaning, exploration, modeling, and deployment. It also discusses different types of data analysis and model types. Finally, it provides an overview of popular data science tools and frameworks and lists some IBM specific analytics tools.
Smart Data 2017 #AI & #FutureofWork
1) The document discusses how AI is impacting various industries and the future of work.
2) Machines are able to process vast amounts of information and handle complex processes better than humans.
3) Many jobs will be automated but AI will drive humans to perform higher-level cognitive work. Augmented intelligence where humans and machines work together will increase.
What's hot (20)
Artificial intelligence: Simulation of Intelligence
Artificial intelligence: Simulation of Intelligence
What AI is and examples of how it is used in legal
What AI is and examples of how it is used in legal
A quick guide to artificial intelligence working - Techahead
A quick guide to artificial intelligence working - Techahead
AI Panel: AI in Practice- the Good, the Bad, and the Ugly
AI Panel: AI in Practice- the Good, the Bad, and the Ugly
Cognitive computing big_data_statistical_analytics
Cognitive computing big_data_statistical_analytics
Artificial Intelligence & Business Intelligence in Management by Ojas
Artificial Intelligence & Business Intelligence in Management by Ojas
Viewers also liked
DSA-2012-Lect00
The document is a presentation by Dr. Haitham A. El-Ghareeb about a data structures and algorithms course at Mansoura University. It introduces Dr. El-Ghareeb and provides an overview of course topics, importance, details on topics covered, and grading structure. The course covers algorithm and data structure design, abstract data types, common data structures, sorting and searching algorithms, and algorithm complexity analysis. Student performance is evaluated based on a midterm exam, labs, attendance, reports, and an oral exam.
Lect07
The document discusses algorithm analysis and complexity analysis. It introduces the concept of analyzing an algorithm's runtime by examining the number of key operations like comparisons and assignments, rather than just measuring execution time. This is known as complexity analysis. The document uses an example of summing the rows and values of a matrix to illustrate how complexity analysis can identify the most efficient of multiple algorithms for the same problem. It determines that two example algorithms for summing a matrix have the same asymptotic runtime of O(n^2). The document then introduces Big-O notation for describing an algorithm's asymptotic worst-case runtime.
Python Tutorial Part 2
This document provides an overview of key Python concepts:
1. Modules allow organizing Python code into files and namespaces. The file name is the module name with a .py extension.
2. Python code is compiled into bytecode cache files (.pyc) for improved performance. These files are platform independent.
3. Advanced optimizations can be applied to bytecode with command line flags, but may affect program functionality in rare cases.
4. Standard modules provide useful functions like dir() to inspect modules and packages for organizing code. Input/output, strings, files and exceptions are also covered.
Python Tutorial Part 1
This document provides an agenda and overview for a Python tutorial presented over multiple sessions. The first session introduces Python and demonstrates how to use the Python interpreter. The second session covers basic Python data structures like lists, modules, input/output, and exceptions. An optional third session discusses unit testing. The document explains that Python is an easy to learn yet powerful programming language that supports object-oriented programming and high-level data structures in an interpreted, dynamic environment.
Internet of Things (IoT) - We Are at the Tip of An Iceberg
You are likely benefitting from The Internet of Things (IoT) today, whether or not you’re familiar with the term. If your phone automatically connects to your car radio, or if you have a smartwatch counting your steps, congratulations! You have adopted one small piece of a very large IoT pie, even if you haven't adopted the name yet.
IoT may sound like a business buzzword, but in reality, it’s a real technological revolution that will impact everything we do. It's the next IT Tsunami of new possibility that is destined to change the face of technology, as we know it. IoT is the interconnectivity between things using wireless communication technology (each with their own unique identifiers) to connect objects, locations, animals, or people to the Internet, thus allowing for the direct transmission of and seamless sharing of data.
IoT represents a massive wave of technical innovation. Highly valuable companies will be built and new ecosystems will emerge from bridging the offline world with the online into one gigantic new network. Our limited understanding of the possibilities hinders our ability to see future applications for any new technology. Mainstream adoption of desktop computers and the Internet didn’t take hold until they became affordable and usable. When that occurred, fantastic and creative new innovation ensued. We are on the cusp of that tipping point with the Internet of Things.
IoT matters because it will create new industries, new companies, new jobs, and new economic growth. It will transform existing segments of our economy: retail, farming, industrial, logistics, cities, and the environment. It will turn your smartphone into the command center for the both digital and physical objects in your life. You will live and work smarter, not harder – and what we are seeing now is only the tip of the iceberg.
Internet of Things and its applications
A general overview about Internet of Things and its possible applications.
http://www.pasqualepuzio.it
Internet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-g
The document provides an overview of the Internet of Things (IoT) in 3 sentences:
The Internet of Things (IoT) connects physical objects through sensors, software and network connectivity which allows these "things" to collect and exchange data between other devices. The document outlines what IoT is, how it works, current applications and challenges, and the future potential of a world where many everyday objects are connected to the internet and able to send and receive data. The increasing interconnectivity of physical objects through technologies like RFID, sensors and networking promises both benefits and risks relating to privacy, security, and how IoT may influence human behavior.
Internet of Things
The document describes various smart and connected devices for homes and consumers. It provides examples of Internet of Things devices such as a smart fork that monitors eating habits, a smart cup that tracks liquid consumption, and a smart toothbrush that engages users in their oral hygiene routine. It also lists devices for other activities like gardening, sports training, home security, pet care, and more that connect to smartphones and the Internet to provide remote access and data collection. The devices demonstrate how almost any everyday object can be made smart and integrated into the growing Internet of Things ecosystem.
Viewers also liked (8)
Internet of Things (IoT) - We Are at the Tip of An Iceberg
Internet of Things (IoT) - We Are at the Tip of An Iceberg
Internet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-g
Internet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-g
Similar to Intelligent Systems - Tommy Richardson, CTO, Teradata Marketing Applications
Datascience
This document discusses data science and the growing field of big data. It notes that data science uses scientific methods and processes to extract knowledge and insights from structured and unstructured data. It provides some key facts about the massive amount of data being generated every day from various sources like social media, internet transactions, sensors and devices. The document also discusses the differences between data science and computer science, with data science focusing more on analyzing large datasets to answer questions and find insights, while computer science focuses more on software development and engineering.
Data science
Data science is a multi-disciplinary field that uses scientific methods and processes to extract knowledge and insights from large amounts of structured and unstructured data. As the amount of data in the world grows exponentially, the need for data scientists to analyze this big data and discover useful patterns will also grow dramatically. By 2025, it is estimated that there will be over 200 zettabytes of cloud data storage worldwide and data science jobs are projected to be the highest paid and most in-demand jobs of the future.
Computing and the future of everything
The document discusses how computing is impacting all areas of society and the future. It makes three key points:
1) Computing is integrated into nearly all industries and fields of study. All subjects can benefit from including computing in their curriculum and all industries need IT workers.
2) Careers in computing are in high demand and pay well. Starting salaries for many computing jobs like data security analysts and mobile developers are comparable or higher than careers like engineering and law.
3) Computing will continue transforming society and industries in the future. Many technologies are emerging like virtual reality, drones, 3D printing and self-driving cars that will impact jobs, education and more. The future will be shaped by computing innovations and
Presentation v3
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
Data, AI and Tokens: A Glimpse of What is to Come
A conversation grounded in slides, which was with a group of Swedish CIOs in March 2018. We talked about the implications of data collection for those who weren't even directly involved in data collection; for organisations, talent hunters, and ultimately ecosystems.
Addressing learning gaps and career oppurtunities after B.Sc computer science
Addressees the learning gaps i.e why after graduation in B.Sc computer science student is still unemployed. PPts discusses various examples for learning methods. The second part of the presentation discusses the various opportunities after B.Sc in Computer science
EDW 2015 cognitive computing panel session
Understanding Cognitive Computing panel http://goo.gl/YCXn51 at Enterprise Data World http://edw2015.dataversity.net/ in Wash DC on April 1, 2015
Scope of Information Technology
I was invited as speaker to share my views on Scope of IT. These slides contains some points I shared with audience (mostly students).
Data Science - NXT Level_Dr.Arun.pdf
This document provides an overview of artificial intelligence (AI), machine learning (ML), and data science. It discusses how these fields are booming technologies that are taking engineering to the next level. The document outlines some real-world applications of AI, ML, and data science, as well as important skills needed in 2022 for these fields, such as programming languages, algorithms, data analytics, and machine learning models. It also lists some free and open-source tools that students can use to learn and work with data science.
What Managers Need to Know about Data Science
What technical, statistical and communication skills a manager should know to manage a data science project
DataScience_introduction.pdf
Data science is a multidisciplinary field that uses statistics, programming, and machine learning to extract knowledge and insights from large amounts of data. It has various applications like email spam detection, medical diagnosis, predicting stock prices, and self-driving cars. The document discusses how the size of data is rapidly increasing and will continue to do so, with an estimated 463 exabytes of new data generated daily by 2025. It also outlines common tasks performed by data scientists like understanding business problems, analyzing and visualizing data, making recommendations, and predicting future values. Theoretical and practical aspects of data science are also covered, along with examples of how it relates to other fields.
How-to-Build-a-Career-in-AI.pdf
This document provides guidance on building a career in AI through three key steps: learning foundational skills, working on projects, and finding a job. It discusses each step in detail with chapters focused on learning technical skills, scoping AI projects, and using projects to complement career goals. The overall message is that an AI career requires lifelong learning, gaining experience through meaningful projects, and navigating an evolving job market. Building a supportive community is also important for support throughout the career journey.
Build a Career in AI
*Uses of AI and data science can be found in almost any situation that produces data
* More uses for custom AI applications and data-derived
insights than for traditional software engineering
* Literacy in AI-oriented coding will be more valuable than traditional coding
Management Information Systems Course Project
The document provides information about 7 entry level jobs in the information technology industry: IT consultant, mobile application developer, cloud architect, software developer, network and computer systems administrator, web developer, and computer forensic investigator. For each job, it lists the typical salary, job description, required soft skills like communication and problem solving, and technical skills such as programming languages and IT knowledge. The document concludes with the student reflecting on what they learned about the breadth of opportunities in the growing IT field and the importance of staying up to date with changing technologies.
Machine Learning: Need of Machine Learning, Its Challenges and its Applications
BCA Department of JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR. The curriculum is well updated and the subjects included all the latest technologies which are in demand.
JIMS BCA course teaches Python to II semester students and Artificial Intelligence Using Python to Sixth Semester students.
Here is a small article on the Future of Machine Learning, hope you will find it useful.
Machine Learning is a field of Computer science in which computer systems are able to learn from past experiences, examples, environments. With help of various Machine Learning Algorithms, Computers are provided with the ability to sense the data and produce some relevant results.
Machine learning Algorithms provide the technique of predicting the future outcomes or classifying information from the given input to the Machines so that the appropriate decisions can be taken.
Algorithm Marketplace and the new "Algorithm Economy"
Diego Oppenheimer discusses the rise of algorithm marketplaces and the new "algorithm economy". Key points include:
- Advances in machine learning, computer vision, speech recognition and natural language processing are enabling algorithms to interpret unstructured data at scale.
- Algorithm marketplaces allow algorithms to be hosted, discovered, monetized and composed modularly to address a wide range of use cases across many industries.
- The algorithm economy will lower barriers to applying machine intelligence and foster innovation as algorithms become reusable assets that creators and users can both benefit from.
The Digital Technologies Curriculum
The document provides an overview of the Digital Technologies curriculum in Australia to demystify it for teachers. It discusses how a digital economic future is inevitable and schools need to prepare students with skills like being entrepreneurial, adaptive to change, and digitally discerning. The curriculum focuses on developing skills in areas like computational thinking, design thinking, data representation, and digital systems. It differentiates Digital Technologies, which teaches specific computer science concepts, from general ICT capability. It provides examples of what ICT capability and computational thinking look like at different year levels. The goal is to provide practical opportunities for students to develop innovative solutions through design thinking and information systems knowledge.
How to crack Big Data and Data Science roles
How to crack Big Data and Data Science roles is the flagship event of UpX Academy. This slide was used for the event on 10th Sept that was attended by hundreds of participants globally.
Cse
The document provides 10 reasons for opting for a career in computer science. It discusses how computer science is involved in many aspects of modern life and enables one to solve complex problems and make a positive impact. Computer science careers offer high pay and job satisfaction with many open positions available. Expertise in computer science is valuable for other careers as well by providing problem-solving skills. Computer science allows for both individual creativity and team collaboration. It is seen as an essential part of a well-rounded education, and future opportunities in the field are vast due to its ability to drive innovation.
Why Computer Science.pptx
........................................................................................................................................................................................................................................................................... .................................................................................................................................................................................................................................................................................... .oujjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj.jjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj........................................................................................................................................................................................................................................................................... .................................................................................................................................................................................................................................................................................... .oujjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj.jjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj........................................................................................................................................................................................................................................................................... .................................................................................................................................................................................................................................................................................... .oujjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj.jjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj........................................................................................................................................................................................................................................................................... .................................................................................................................................................................................................................................................................................... .oujjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj.jjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj
Similar to Intelligent Systems - Tommy Richardson, CTO, Teradata Marketing Applications (20)
Addressing learning gaps and career oppurtunities after B.Sc computer science
Addressing learning gaps and career oppurtunities after B.Sc computer science
Machine Learning: Need of Machine Learning, Its Challenges and its Applications
Machine Learning: Need of Machine Learning, Its Challenges and its Applications
Algorithm Marketplace and the new "Algorithm Economy"
Algorithm Marketplace and the new "Algorithm Economy"
Recently uploaded
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
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.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GridMate - End to end testing is a critical piece to ensure quality and avoid...
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
DevOps and Testing slides at DASA Connect
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.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Recently uploaded (20)
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Intelligent Systems - Tommy Richardson, CTO, Teradata Marketing Applications
- 1. 1 IT Career Started in 1991 @techdadda
- 2. 2 1960-Mid 2010 Transactional World • Lots of technology changes from Mainframe, Client Server, Web • Screen input changing from Green to Thick to Thin to Small and Thick to ? • Access to systems changed from a few users to millions • Maybe some improvement on the COGS for IT • From mainly batch processing to real-time processing of data • People still keying or loading transactions in and making decisions based on screens and reports What has REALLY changed with how applications work? @techdadda
- 3. 3 We are in the Age of Machines @techdadda
- 4. 4 What is the Definition of Machine Intelligence? 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. Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.[2] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[3]:2 rather than following strictly static program instructions. @techdadda
- 6. 6 In Hindsight - Military Logistics • How many bombs should I ship where? • Should I buy more meatloaf MRE? • What’s the quickest and cheapest way to deliver those bullet proof vests? @techdadda
- 7. 7 In Hindsight – K12 Education • Student to School assignment • Most Productive class • Self guided learning @techdadda
- 8. 8 In Hindsight - HR and Payroll • How much should I pay him? • Find more people like this • At risk employees @techdadda
- 9. 9 In Hindsight - Healthcare • No more keying in fields Doc • Finding the disease in the data • You can’t hide from your doc @techdadda
- 10. 10 Top Things to do to Introduce Intelligence • Look for repetitive simple tasks people are doing that a machine can do today. • Start simple with regression analysis (Look alike) then move to more sophisticated items such as Bayesian, Game Theory, or Reinforcement learning. • Look for opportunities where you couldn’t solve problems in the past or depended on manual keying of data. • Micro Services and APIs so its easy introduce things a little at a time. • Try and store all the data even if you aren’t sure you’ll process it. • Mobile is changing the old keyboard and mouse stigma. Think about this in desktop UI as well. • Don’t feel like you have to build it yourself leverage Open Source or COTs – Nuance, Spark, Watson, Mahout, h20.ai, R, AWS ML, etc… @techdadda
- 11. 11 Is Teradata Marketing Doing These Things? • There is opportunity in Marketing Planning • Traditional Campaign management • Consumer engagement tools such as email, social, and mobile. • Advertising buying, Marketing Mix, Attribution You will have to join us to learn more… Tweet me for more information @techdadda