This document provides an introduction to artificial intelligence and machine learning, including their history, common examples, and applications in finance (FinTech). It discusses key concepts like the difference between artificial intelligence, machine learning, and their various subfields. The document also outlines machine learning techniques and processes, and provides examples of real-world applications. Global initiatives applying these technologies in finance are highlighted.
Organizations today have lots and lots of data. Typically when it comes to data analysis we have to know what our measures of success are before we design our BI. These are typically manifested by competency, or domain driven KPI's but what if those metrics don't actually measure success at all? In this talk we will be discussing how to leverage azure machine learning to answer questions in your organization about success and how to find the KPI's that really matter and drive results.
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
Artificial intelligence (AI) and machine learning (ML) are undergoing revolutionary changes that will affect wide swaths of our society. And the applications of this technology are increasingly diverse. Join us as we narrow in on how researchers in AL and ML are using AWS to identify and prevent financial market manipulation in a high-volume, high-velocity stock market. We also explore how to use natural language processing to aid emergency response organizations in real time during deadly disasters, such as during hurricanes and catastrophic wildfires.
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
Organizations today have lots and lots of data. Typically when it comes to data analysis we have to know what our measures of success are before we design our BI. These are typically manifested by competency, or domain driven KPI's but what if those metrics don't actually measure success at all? In this talk we will be discussing how to leverage azure machine learning to answer questions in your organization about success and how to find the KPI's that really matter and drive results.
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
Artificial intelligence (AI) and machine learning (ML) are undergoing revolutionary changes that will affect wide swaths of our society. And the applications of this technology are increasingly diverse. Join us as we narrow in on how researchers in AL and ML are using AWS to identify and prevent financial market manipulation in a high-volume, high-velocity stock market. We also explore how to use natural language processing to aid emergency response organizations in real time during deadly disasters, such as during hurricanes and catastrophic wildfires.
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
Leaders across the world are looking out for different strategies thru which they can leverage AI.
Realizing this we have successfully organized an event on "AI 4 Institution Leaders" at Nasik focused on the need for AI for educational institutions for the first time in India.
AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
The workshop - 'AI transforming Business' is conducted on 20-21st Feb 2019 at Chennai hosted by CII.in (Confederation of Indian Industry) for top Indian executives.
This is a 2-day full-time workshop focused on coaching delegates on Artificial Intelligence(AI), Transforming business with AI, AI Data Strategy and best practices from organizations leading AI adoption across the world.
Delegates attended include Ex-CEO and Vice Chair of Cognizant Mr. Lakshmi Narayanan, CEO and MD of Ameex, AVP of Infosys, MD and CEO Rane Group, Sr. General Manager of Blue Star, Joint General Manager of L&T and 30 more delegates from top management from manufacturing, agriculture banking, and healthcare.
Speaker: Ashok Kumar - AI Evangelist, Entrepreneur, Executives Coach, Ph.D. Scholar, MBA
Artificial Intelligence (AI) & Machine Learning: Are You Ready?SilverTech
Long dismissed as the realm of sci-fi, artificial intelligence (AI) and machine learning have finally arrived and their potential to disrupt every industry is quickly becoming apparent. Though they work in tandem, there is a distinction to be made between the two; AI is the ability of machines to mimic human intelligence, while machine learning is the ability of computers to learn from gathered data. Despite Elon Musk’s cautions, over the next two years AI will be pervasive in everything from household appliances to digital assistants, and yes, even your website and content!
The State of Artificial Intelligence and What It Means for the PhilippinesThinking Machines
What consumer-ready applications of artificial intelligence are out there? What are the implications of semi-autonomous agents on Manila's BPO industry? Thinking Machines CEO and data scientist Stephanie Sy delivered this presentation on the current data science and AI landscape at the "Humans + Machines: Using Artificial Intelligence to Power Your People" conference held on February 19, 2016 at BGC, Taguig, Philippines.
Artificial Intelligence & Software Testing: Hype or Hysteria?Johan Steyn
The slides from my talk at SIGiST Johannesburg on 20 June 2018. The video will be available in a few days - sign up at www.thebusinessoftesting.com
The URL of the video on the last slide: https://youtu.be/Y9FOyoS3Fag
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
Applying Machine Learning and Artificial Intelligence to BusinessRussell Miles
Machine Learning is coming out of the halls of Academia and straight into the arms of those businesses looking for a competitive edge.
This session by the experts of GoDataScience.io on machine learning is designed to give a high level overview of the field of machine learning for business consumers covering:
- What Machine Learning is
- Where it came from
- Why we need it
- Why now
- How to make it real with the various toolkits and processes.
Machine Learning for Non-Technical People - Turing Fest 2019Britney Muller
Machine Learning/AI is becoming more and more accessible and will free you up to work on higher level thinking.
ANYONE can come up with the next big ML/AI application.
What will you solve?
Intelligence Augmentation - The Next-Gen AIMelanie Cook
Robotics and AI have integrated human and mechanical capabilities at work, with jobs lost and skills condensed to a keystroke. But human intelligence is far from obsolete.
With crowd-computing we have knowledge exchanges like Wiki, and real-time curated news. Semantic technology helps leaders to understand what is happening in the work place. But neurology shows that these leaders cannot make choices, and therefore take action, without emotion.
Augmented Intelligence takes human intuition and imagination, and combines it with AI’s ability to automate and scale, making the Intelligent Workplace hard to beat.
Artificial Intelligence Introduction & Business usecasesVikas Jain
Vikas Jain is a leading keynote speaker on artificial intelligence.
Develop AI Solution mindset to help business leaders & professionals from IT/non-IT Industry can use it to solve complex problems and grow their business.
Novi Sad AI is the first AI community in Serbia with goal of democratizing knowledge of AI. On our first event we talked about Belief networks, Deep learning and many more.
Business growth principles in the new economy Ashish Bedekar
A presentation I made @ Palava #Smartcity #startup accelerator on 19th April 2018. http://bit.ly/2qz5Xr2
#1 About me:
You may like to check out https://ashishbedekar.fyi.to/ecosystems which gives an overview of my profile, including LinkedIn recommendations
#2: Connect with me http://www.linkedin.com/in/ashishrbedekar | Twitter: @ashishrbedekar
#3 I believe in giving back to the community e.g
- Pro-bono startup advisor @Zone startup- an Indo-Canadian start-up Accelerator http://bit.ly/2o9XNqa
- Pro-bono startup advisor@ Supercharger Fintech accelerator ( KL, HK) http://bit.ly/2ErcM6S
- Pro-bono startup advisor@ NIT Trichy- International biz competition- http://bit.ly/2AQxqYu
-Mentor of change- Govt. of India- Atal innovation mission http://bit.ly/2HMdWrV
-Member of IET- IoT India (The IET is one of the world's largest multi-discipline professional societies of engineers with more than 160,000 members in 127 countries) http://bit.ly/2o9Pue9
-Mentor for startup boot camp-E- Cell- IIT Madras- one of India’s leading engineering college- http://bit.ly/2G6nRMg
The most significant (not purely scientific) results in AI in the last year (2018-2019).
Disclaimer: may be very subjective :)
Slides to the set of lectures given in Feb-Apr 2019.
This one was conducted in Atlas Biomed Group, 2019-04-26
Leaders across the world are looking out for different strategies thru which they can leverage AI.
Realizing this we have successfully organized an event on "AI 4 Institution Leaders" at Nasik focused on the need for AI for educational institutions for the first time in India.
AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
The workshop - 'AI transforming Business' is conducted on 20-21st Feb 2019 at Chennai hosted by CII.in (Confederation of Indian Industry) for top Indian executives.
This is a 2-day full-time workshop focused on coaching delegates on Artificial Intelligence(AI), Transforming business with AI, AI Data Strategy and best practices from organizations leading AI adoption across the world.
Delegates attended include Ex-CEO and Vice Chair of Cognizant Mr. Lakshmi Narayanan, CEO and MD of Ameex, AVP of Infosys, MD and CEO Rane Group, Sr. General Manager of Blue Star, Joint General Manager of L&T and 30 more delegates from top management from manufacturing, agriculture banking, and healthcare.
Speaker: Ashok Kumar - AI Evangelist, Entrepreneur, Executives Coach, Ph.D. Scholar, MBA
Artificial Intelligence (AI) & Machine Learning: Are You Ready?SilverTech
Long dismissed as the realm of sci-fi, artificial intelligence (AI) and machine learning have finally arrived and their potential to disrupt every industry is quickly becoming apparent. Though they work in tandem, there is a distinction to be made between the two; AI is the ability of machines to mimic human intelligence, while machine learning is the ability of computers to learn from gathered data. Despite Elon Musk’s cautions, over the next two years AI will be pervasive in everything from household appliances to digital assistants, and yes, even your website and content!
The State of Artificial Intelligence and What It Means for the PhilippinesThinking Machines
What consumer-ready applications of artificial intelligence are out there? What are the implications of semi-autonomous agents on Manila's BPO industry? Thinking Machines CEO and data scientist Stephanie Sy delivered this presentation on the current data science and AI landscape at the "Humans + Machines: Using Artificial Intelligence to Power Your People" conference held on February 19, 2016 at BGC, Taguig, Philippines.
Artificial Intelligence & Software Testing: Hype or Hysteria?Johan Steyn
The slides from my talk at SIGiST Johannesburg on 20 June 2018. The video will be available in a few days - sign up at www.thebusinessoftesting.com
The URL of the video on the last slide: https://youtu.be/Y9FOyoS3Fag
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
Applying Machine Learning and Artificial Intelligence to BusinessRussell Miles
Machine Learning is coming out of the halls of Academia and straight into the arms of those businesses looking for a competitive edge.
This session by the experts of GoDataScience.io on machine learning is designed to give a high level overview of the field of machine learning for business consumers covering:
- What Machine Learning is
- Where it came from
- Why we need it
- Why now
- How to make it real with the various toolkits and processes.
Machine Learning for Non-Technical People - Turing Fest 2019Britney Muller
Machine Learning/AI is becoming more and more accessible and will free you up to work on higher level thinking.
ANYONE can come up with the next big ML/AI application.
What will you solve?
Intelligence Augmentation - The Next-Gen AIMelanie Cook
Robotics and AI have integrated human and mechanical capabilities at work, with jobs lost and skills condensed to a keystroke. But human intelligence is far from obsolete.
With crowd-computing we have knowledge exchanges like Wiki, and real-time curated news. Semantic technology helps leaders to understand what is happening in the work place. But neurology shows that these leaders cannot make choices, and therefore take action, without emotion.
Augmented Intelligence takes human intuition and imagination, and combines it with AI’s ability to automate and scale, making the Intelligent Workplace hard to beat.
Artificial Intelligence Introduction & Business usecasesVikas Jain
Vikas Jain is a leading keynote speaker on artificial intelligence.
Develop AI Solution mindset to help business leaders & professionals from IT/non-IT Industry can use it to solve complex problems and grow their business.
Novi Sad AI is the first AI community in Serbia with goal of democratizing knowledge of AI. On our first event we talked about Belief networks, Deep learning and many more.
Business growth principles in the new economy Ashish Bedekar
A presentation I made @ Palava #Smartcity #startup accelerator on 19th April 2018. http://bit.ly/2qz5Xr2
#1 About me:
You may like to check out https://ashishbedekar.fyi.to/ecosystems which gives an overview of my profile, including LinkedIn recommendations
#2: Connect with me http://www.linkedin.com/in/ashishrbedekar | Twitter: @ashishrbedekar
#3 I believe in giving back to the community e.g
- Pro-bono startup advisor @Zone startup- an Indo-Canadian start-up Accelerator http://bit.ly/2o9XNqa
- Pro-bono startup advisor@ Supercharger Fintech accelerator ( KL, HK) http://bit.ly/2ErcM6S
- Pro-bono startup advisor@ NIT Trichy- International biz competition- http://bit.ly/2AQxqYu
-Mentor of change- Govt. of India- Atal innovation mission http://bit.ly/2HMdWrV
-Member of IET- IoT India (The IET is one of the world's largest multi-discipline professional societies of engineers with more than 160,000 members in 127 countries) http://bit.ly/2o9Pue9
-Mentor for startup boot camp-E- Cell- IIT Madras- one of India’s leading engineering college- http://bit.ly/2G6nRMg
The most significant (not purely scientific) results in AI in the last year (2018-2019).
Disclaimer: may be very subjective :)
Slides to the set of lectures given in Feb-Apr 2019.
This one was conducted in Atlas Biomed Group, 2019-04-26
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.
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
Smart machines include robots, self-driving cars and other cognitive computing systems that are able to make decisions and solve problems without human intervention.
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
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
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
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
2. Agenda
• Introduction to Artificial Intelligence
• Introduction to Machine Learning
• Impact on FinTech
• Global Initiatives
• Case Studies
• CreditVidya
• Winjit
• Applications @ SA Taxi
3. The Mother of All Demos
9th December 1968
A computer demonstration done by Douglas Engelbart at Fall Joint Computer
Conference, SF
While all scientists concentrated in making computers smart, Engelbart was
interested in how computers could make humans smarter
In his terms, it was “Augmented Intelligence”
Ref: https://en.wikipedia.org/wiki/The_Mother_of_All_Demos
4. Automation
By definition:
Automation is the use of machines and technology to make processes run on their
own without manpower
Every action in today’s human life is a consequence of automation over the years
In history, each age progressed into the next age on the basis of automation
Data Age and “DATA IS THE NEW OIL”
5. Automation and AI
Ref: https://www.jibe.com/wp-content/uploads/2015/06/automation-and-recruiting-chart.png
6. Artificial Intelligence
Development of computer systems able to do tasks which normally require human
intelligence
Term AI is applied when machine mimics “Cognitive” functions
Field of AI Research was born @ Dartmouth College in 1956
AI Caliber
Artificial Narrow Intelligence (ANI)
Artificial General Intelligence (AGI)
Artificial Super Intelligence (ASI)
7. Common Examples of AI
Cars – Anti-lock brakes, tuning parameters of fuel injection system
Mobile Phones – navigating maps, talking to Siri, Music recommendations
Emails – Spam filters
Amazon, Netflix – Recommended for you
Google Search and Voice
Apple Siri
Googles Self Driven Cars
Mr. Delivery
9. Machine Learning
Computer systems learning from data and experience
“A field of study that gives computers the ability to learn without being explicitly
programmed” – Arthur Samuel
If a computer program can improve how it performs a certain task based on its past
experience, then we can say the program is learning
Close resemblance with Data Mining
17. Impact on FinTech
Customer Segmentation
Scoring
Applicant – Credit Scoring
Application
Behavioural – Predict likely customer default
Collection – Predict likely debts recover
Robo Advisors
Fraud Detections
Identity Theft Prevention
AI Assistants and Bots
Blockchain and Bitcoins
Debt Collections
Market Research and Sentiment Analysis
Augmented Decision Making
18. Global Initiatives
Royal Bank of Scotland brings Robo Advisors (Link)
Ernest and Young provided digital wallets to all employees, accepts BitCoin as
payment for its consulting service, and installed a BitCoin ATM (Link)
KPMG using McLaren Applied Technologies (its AI) to speed up audits (Link)
Xero (SA) bring AI to its Online Accounting System (Link)
Clevva’s (Western Cape) product helps in companies call centre operations (Link)
MyBucks provides financial services to sub-Saharan Africa using AI (Link)
Apple Machintosh : Released on January 24, 1984
He showcases
Windows
HyerText
Graphics
Efficient Navigation and Command Input
Video Conferencing
Computer Mouse
Word Processing
Dynamic File Linking
Revision Control
Collaborative Work
We are looking at the Data Age from the Current Information Technology Age where Data is the new Oil
Reliance Industries put internet in the hands of 1 billion Indians with the only motive to capture data. With USD 0 in revenue, it is valued at USD 4.5 Billion
@SA Taxi we automated processes and reaping the results of these.
ANI – Very specific stream of intelligence. E.g. Siri – Voice to Text Conversion
AGI – Human like intelligence. Could reason and learn. E.g. Jarvis
ASI – Super Human Intelligence. Can control what should be done and can make things happen. E.g. Ultron / Vision from Avengers
To understand the concept of Machine Learning better, let us consider some of its applications:
Hand written character recognition
Political campaigns: Machine learning is used to determine which voters a campaign should target and what optimal actions can bring about their desired goals.
Predictive Policing - Police departments in the US have started to direct their focus to crime prevention. The idea is to use ML techniques to identify areas where crimes are likely to occur and allocate resources more effectively and enable proactive and preventative policing strategies.
Surveillance: Systems that put people on the no-fly list use Machine learning to classify people into suspicious or not.
Facial recognition: Companies like Facebook use learning algorithms to recognize a person by photo after they have been tagged a few times.
Self-driving vehicles: An autonomous vehicle is effectively equipped with dozens of pairs of eyes (sensors), all connected to a brain (a learner) – which is wholly focused on safe and efficient driving.
Recommendation systems – Co.’s like amazon learn and predict user’s potential preferences
Ads – ML is being applied to different aspects of the advertising industry- E.g. Intent prediction of an individual, response prediction to an ad
Personal assistant – Learning algorithms are used to learn from actual language used by customers, gathered from call logs, chat histories, search terms used, twitter feeds etc.
Now how do we do Machine Learning?
This diagram depicts high level ML process.
First we have raw data, this data comes from different sources and formats
In the pre-processing stage - we clean, format and sample the data. As Figure shows, it’s an iterative process, with several different data pre-processing modules applied to the raw data. In fact, choosing the best raw data to start with, then creating prepared data from that raw data frequently takes up the majority of the total time spent on a machine learning project.
Once our data is prepared, we need to apply one or more algorithms to our data. The goal is to determine what combination of machine learning algorithm and prepared data generates the most useful results.
The resulting product of our algorithm is called a ‘model’. These models are then used to provide a solution to our problem. For e.g. It could be used to answer questions like “Is this transaction fraudulent?”
Machine learning tasks are typically classified into three broad categories,
Supervised learning – We present the algorithm with example inputs and their desired outputs, and the goal of this is learn the mapping between inputs and outputs.
Unsupervised learning – The learning algorithm is not given any example outputs and the goal is to find structure or patterns in our input data.
Reinforcement learning – RL is concerned with agents choosing actions that maximize the expected reward over a given time. This is best achieved when the agent has a good policy in hand. Learning the best policy - remains to be the goal in reinforcement learning
Lets take a closer look at these techniques.
The supervised learning technique can be further divided into two subcategories - Classification and Regression.
In classification problem, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. E.g. Identity fraud detection – classes ‘fraud’ or ‘not fraud’
Regression – the outputs are continuous rather than discrete. E.g. Forecasting population growth
Unsupervised learning techniques divided into two subcategories as Clustering and Dimensionality Reduction.
Clustering problem basically cluster samples according to similarities of these samples regardless of class information. E.g. Recommendation Systems are used to recommend something for the users. It can be a movie, music or something which is sold in the market place.
Dimension Reduction simplifies inputs by mapping them into a lower-dimensional space. E.g. Reducing the high dimensional or big data for visualization purposes
Self driving cars use Reinforcement learning to make decisions continuously – which route to take? what speed to drive on? are some of the questions which are decided after interacting with the environment
1.
2. Robo Advisors are programs guiding casual trader to mange there portfolios
3. Application are identifying behavioural and transactional patterns to identify and prevent frauds
4. Bots working with customer service agents
5. Blockchain : Distributed blocks of transaction encrypted and verified. Its basically a distributed ledge r
6. Debt Collections: Identify patterns in payment and impact of external factors (e.g. weather, news, policies,) on customers payment.
5. Xero’s Cloud accounting software is connected to bank feeds, and receipt scanning tools, can access a real-time, live ‘ledger’ which they can use to inform advice and recommendations. Focus on High Integrity Accounting. Coding would be automated and data entry would be redundant.
6. Clevva’s software claims to “clone” the organisation’s key experts, giving everyone access to their advice through software.
7. MyBucks – Credit assessment, credit decisioning and scoring technologies combined with alternate data points for easy fast and automated disbursement of loans