This presentation is a friendly introduction to Artificial Intelligence, Data Science and Machine Learning. It touches on the beginnings of AI, the steps involved in Data Science, the roles involving operations on data, and the buzz around "Technology Singularity".
It ends by looking at tools and system requirements for people who might want to start a career in AI.
Have fun exploring Artificial Intelligence!
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
[Video available at https://sites.google.com/view/ResponsibleAITutorial]
Artificial Intelligence is increasingly being used in decisions and processes that are critical for individuals, businesses, and society, especially in areas such as hiring, lending, criminal justice, healthcare, and education. Recent ethical challenges and undesirable outcomes associated with AI systems have highlighted the need for regulations, best practices, and practical tools to help data scientists and ML developers build AI systems that are secure, privacy-preserving, transparent, explainable, fair, and accountable – to avoid unintended and potentially harmful consequences and compliance challenges.
In this tutorial, we will present an overview of responsible AI, highlighting model explainability, fairness, and privacy in AI, key regulations/laws, and techniques/tools for providing understanding around AI/ML systems. Then, we will focus on the application of explainability, fairness assessment/unfairness mitigation, and privacy techniques in industry, wherein we present practical challenges/guidelines for using such techniques effectively and lessons learned from deploying models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning many industries and application domains. Finally, based on our experiences in industry, we will identify open problems and research directions for the AI community.
An introduction to the ethics of AI in educationJisc
Presentation slides from Jisc's "an introduction to the ethics of AI in education" event held on 7 December 2021.
This presentation aims:
- To introduce the ethical issues associated with using AI in education
- To explain how ethical issues can be avoided, managed, mitigated and/or overcome
- To introduce you to the Ethical Framework for AI in Education and the Pathway to Ethical AI
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
The field of Artificial Intelligence (AI) has progressed rapidly in the past few years. AI systems are having a growing impact on society and concerns have been raised whether AI system can be trusted. A way to address these concerns is to employ ethically aligned design principles to the development of AI software. Yet these principles are still far away from practical application. This talk provides state-of-the-art empirical insight into what should researchers and professionals do today when the client wants ethics to be added to their system.
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
[Video available at https://sites.google.com/view/ResponsibleAITutorial]
Artificial Intelligence is increasingly being used in decisions and processes that are critical for individuals, businesses, and society, especially in areas such as hiring, lending, criminal justice, healthcare, and education. Recent ethical challenges and undesirable outcomes associated with AI systems have highlighted the need for regulations, best practices, and practical tools to help data scientists and ML developers build AI systems that are secure, privacy-preserving, transparent, explainable, fair, and accountable – to avoid unintended and potentially harmful consequences and compliance challenges.
In this tutorial, we will present an overview of responsible AI, highlighting model explainability, fairness, and privacy in AI, key regulations/laws, and techniques/tools for providing understanding around AI/ML systems. Then, we will focus on the application of explainability, fairness assessment/unfairness mitigation, and privacy techniques in industry, wherein we present practical challenges/guidelines for using such techniques effectively and lessons learned from deploying models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning many industries and application domains. Finally, based on our experiences in industry, we will identify open problems and research directions for the AI community.
An introduction to the ethics of AI in educationJisc
Presentation slides from Jisc's "an introduction to the ethics of AI in education" event held on 7 December 2021.
This presentation aims:
- To introduce the ethical issues associated with using AI in education
- To explain how ethical issues can be avoided, managed, mitigated and/or overcome
- To introduce you to the Ethical Framework for AI in Education and the Pathway to Ethical AI
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
The field of Artificial Intelligence (AI) has progressed rapidly in the past few years. AI systems are having a growing impact on society and concerns have been raised whether AI system can be trusted. A way to address these concerns is to employ ethically aligned design principles to the development of AI software. Yet these principles are still far away from practical application. This talk provides state-of-the-art empirical insight into what should researchers and professionals do today when the client wants ethics to be added to their system.
Introduction to the ethics of machine learningDaniel Wilson
A brief introduction to the domain that is variously described as the ethics of machine learning, data science ethics, AI ethics and the ethics of big data. (Delivered as a guest lecture for COMPSCI 361 at the University of Auckland on May 29, 2019)
Artificial Intelligence (A.I.) || Introduction of A.I. || HELPFUL FOR STUDENT...Shivangi Singh
Powerpoint Presentation on Artificial Intelligence which is helpful for students and anyone who want to gain information on A.I. . Helpful in college / school / university presentation on Artificial Student. Officials Personnel also use this for their use.
This Power Point Presentation is completely made by me.
If anyone want this ppt please email at : devashreeapplications@gmail.com
Or you can DM me on my Instagram Handle==> ID:: @theshivangirajpoot(SHERNI)
Thankyou for your interest:):)
This slide deck is a compilation of slides from various sources that stitches together a gentle introduction to Artificial Intelligence, Machine Learning and Deep Learning.
Artificial Intelligence (AI) is one of the hottest topics in the tech and startup world at the moment. The field of AI and its associated technologies present a range of opportunities – as well as challenges – for corporates. Learn more about what Artificial Intelligence means for your organization.
Introduction to artifcial intelligence
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"
Nick Schmidt of BLDS, LLC to the Maryland AI meetup, June 4, 2019 (https://www.meetup.com/Maryland-AI). Nick discusses ideas of fairness and how they apply to machine learning. He explores recent academic work on identifying and mitigating bias, and how his work in lending and employment can be applied to other industries. Nick explains how to measure whether an algorithm is fair and also demonstrate the techniques that model builders can use to ameliorate bias when it is found.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Why is artificial intelligence in business analytics so critical for business...Countants
Be it in the form of deep learning technologies, autonomous vehicles, or smart robots, artificial intelligence (or AI) is making its presence felt everywhere in the connected world. With AI-enabled technologies having a prominent place in the Gartner Hype Cycle for Emerging Technologies, this technology is enhancing the capabilities of business analytics and business intelligence.
How do we protect privacy of users when building large-scale AI based systems? How do we develop machine learned models and systems taking fairness, accountability, and transparency into account? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical, legal, and technical challenges encountered by researchers and practitioners alike. In this talk, we will first motivate the need for adopting a "fairness and privacy by design" approach when developing AI/ML models and systems for different consumer and enterprise applications. We will then focus on the application of fairness-aware machine learning and privacy-preserving data mining techniques in practice, by presenting case studies spanning different LinkedIn applications (such as fairness-aware talent search ranking, privacy-preserving analytics, and LinkedIn Salary privacy & security design), and conclude with the key takeaways and open challenges.
A Brief History Of Artificial Intelligence | Developing Text To Speech Recogn...Edureka!
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This edureka tutorial on "History of Artificial Intelligence" will provide you with detailed information about the evolution of Artificial Intelligence. It will also show the various use cases of Artificial Intelligence in everyday life with an example.
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Ethical Considerations in the Design of Artificial IntelligenceJohn C. Havens
A presentation for IEEE's Ethics Symposium happening in Vancouver, May 2016. Featuring presentations from John C. Havens, Mike Van der Loos, John P. Sullins, and Alan Mackworth.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
"AI is “our greatest existential threat…”
“I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish.”
“I think there is potentially a dangerous outcome there.” (referring to Google’s Deep Mind which he invested in to keep an eye on things)."
Elon Musk
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
Discover the gateway to limitless possibilities at CBITSS. As a premier institution in technology education and consultancy, we specialize in nurturing the next generation of tech leaders. With a focus on practical skills and industry relevance, our training programs equip you with the expertise needed to excel in today's digital world. Whether you're a student aspiring to enter the tech industry or a professional seeking to upskill, CBITSS provides the perfect platform to ignite your career aspirations. Join us and embark on a transformative journey towards a brighter, tech-driven future.
Introduction to the ethics of machine learningDaniel Wilson
A brief introduction to the domain that is variously described as the ethics of machine learning, data science ethics, AI ethics and the ethics of big data. (Delivered as a guest lecture for COMPSCI 361 at the University of Auckland on May 29, 2019)
Artificial Intelligence (A.I.) || Introduction of A.I. || HELPFUL FOR STUDENT...Shivangi Singh
Powerpoint Presentation on Artificial Intelligence which is helpful for students and anyone who want to gain information on A.I. . Helpful in college / school / university presentation on Artificial Student. Officials Personnel also use this for their use.
This Power Point Presentation is completely made by me.
If anyone want this ppt please email at : devashreeapplications@gmail.com
Or you can DM me on my Instagram Handle==> ID:: @theshivangirajpoot(SHERNI)
Thankyou for your interest:):)
This slide deck is a compilation of slides from various sources that stitches together a gentle introduction to Artificial Intelligence, Machine Learning and Deep Learning.
Artificial Intelligence (AI) is one of the hottest topics in the tech and startup world at the moment. The field of AI and its associated technologies present a range of opportunities – as well as challenges – for corporates. Learn more about what Artificial Intelligence means for your organization.
Introduction to artifcial intelligence
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"
Nick Schmidt of BLDS, LLC to the Maryland AI meetup, June 4, 2019 (https://www.meetup.com/Maryland-AI). Nick discusses ideas of fairness and how they apply to machine learning. He explores recent academic work on identifying and mitigating bias, and how his work in lending and employment can be applied to other industries. Nick explains how to measure whether an algorithm is fair and also demonstrate the techniques that model builders can use to ameliorate bias when it is found.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Why is artificial intelligence in business analytics so critical for business...Countants
Be it in the form of deep learning technologies, autonomous vehicles, or smart robots, artificial intelligence (or AI) is making its presence felt everywhere in the connected world. With AI-enabled technologies having a prominent place in the Gartner Hype Cycle for Emerging Technologies, this technology is enhancing the capabilities of business analytics and business intelligence.
How do we protect privacy of users when building large-scale AI based systems? How do we develop machine learned models and systems taking fairness, accountability, and transparency into account? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical, legal, and technical challenges encountered by researchers and practitioners alike. In this talk, we will first motivate the need for adopting a "fairness and privacy by design" approach when developing AI/ML models and systems for different consumer and enterprise applications. We will then focus on the application of fairness-aware machine learning and privacy-preserving data mining techniques in practice, by presenting case studies spanning different LinkedIn applications (such as fairness-aware talent search ranking, privacy-preserving analytics, and LinkedIn Salary privacy & security design), and conclude with the key takeaways and open challenges.
A Brief History Of Artificial Intelligence | Developing Text To Speech Recogn...Edureka!
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This edureka tutorial on "History of Artificial Intelligence" will provide you with detailed information about the evolution of Artificial Intelligence. It will also show the various use cases of Artificial Intelligence in everyday life with an example.
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Ethical Considerations in the Design of Artificial IntelligenceJohn C. Havens
A presentation for IEEE's Ethics Symposium happening in Vancouver, May 2016. Featuring presentations from John C. Havens, Mike Van der Loos, John P. Sullins, and Alan Mackworth.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
"AI is “our greatest existential threat…”
“I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish.”
“I think there is potentially a dangerous outcome there.” (referring to Google’s Deep Mind which he invested in to keep an eye on things)."
Elon Musk
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
Discover the gateway to limitless possibilities at CBITSS. As a premier institution in technology education and consultancy, we specialize in nurturing the next generation of tech leaders. With a focus on practical skills and industry relevance, our training programs equip you with the expertise needed to excel in today's digital world. Whether you're a student aspiring to enter the tech industry or a professional seeking to upskill, CBITSS provides the perfect platform to ignite your career aspirations. Join us and embark on a transformative journey towards a brighter, tech-driven future.
[DSC Europe 22] On the Aspects of Artificial Intelligence and Robotic Autonom...DataScienceConferenc1
Autonomy in targeting is a function that could be applied to any intelligent system, in particular the rapidly expanding array of robotic systems, in the air, on land and at sea – including swarms of small robots. This is an area of significant investment and emphasis for many armed forces, and the question is not so much whether we will see more intelligent robots, but whether and by what means they will remain under human control. Today’s remote-controlled weapons could become tomorrow’s autonomous weapons with just a software upgrade. The central element of any future autonomous weapon system will be the software. Military powers are investing in AI for a wide range of applications10 and significant efforts are already underway to harness developments in image, facial and behavior recognition using AI and machine learning techniques for intelligence gathering and “automatic target recognition” to identify people, objects or patterns. Although not all autonomous weapon systems incorporate AI and machine learning, this software could form the basis of future autonomous weapon systems.
Gary Hope - Machine Learning: It's Not as Hard as you ThinkSaratoga
Gary Hope is currently the Data Platform Technical Specialist within Microsoft South Africa having previously worked for several large organisations including American Express and Siemens Business Solutions.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of
human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people
and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.
AI for SDGs and International Development - Basics of AIAtsushi Koshio
This siled was prepared for the training seminar on Artificial Intelligence for International Organizations. Introducing AI technologies into International Development fields for achieving SDGs would be great opportunities to accelerate development. . This material is just explaining basic of AI and some examples of AI application in this field.
[Srijan Wednesday Webinars] Artificial Intelligence & the Future of BusinessSrijan Technologies
“AI is the new electricity” – Andrew Ng, former Chief Data Scientist, Baidu
Artificial Intelligence is the new frontier for human evolution. It will upend industries, cause fundamental shifts in processes and jobs, and create unprecedented innovation.The question one wishes to answer is: how and why it impacts industry, and how can it be leveraged by businesses.
This session will introduce AI and machine learning: the process of creating AI, and go on to discuss the key applications of these emerging technologies. We will also dive into a preliminary review of ML algorithms and how they work.
Key Takeaways:
- Define AI and ML, and the philosophy behind these new technologies
- The impact of AI on jobs, communities, business, and industry
- The use cases of AI in different industries like hi-tech, manufacturing, healthcare, publishing and media, education, transportation etc.
-Introduction to machine learning algorithms like classification, regression, neural networks etc.
Check our webinars series and sign up for future webinar notifications at: www.srijan.net/webinar/past-webinars
A quick guide to artificial intelligence working - TechaheadJatin Sapra
It is already on its way to achieving so as it has empowered the mobile app development agencies to build what was once assumed impossible. Despite this, much of this field remains undiscovered.
Vertex has invested in companies across geographies addressing different industry applications leveraging AI to transform their service offerings. Read more on the trends and waves of AI developments observed.
Slide presentasi ini dibawakan oleh Imron Zuhri dalam acara Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
Similar to An Elementary Introduction to Artificial Intelligence, Data Science and Machine Learning (20)
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
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3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
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Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
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An Elementary Introduction to Artificial Intelligence, Data Science and Machine Learning
1. An Elementary Introduction to Data
Science, Machine Learning & Artificial
Intelligence
Author: Agbo Dozie
2. 00 Turing’s 1959 Question
01
Introduction to Artificial
Intelligence
02 The Machines and The Algorithms
03
Mathematics in Artificial
Intelligence
04 Real-world use cases of Artificial
Intelligence
05
Why and How to start a Data
Science & AI career
06 AI Singularity
07
System Requirements for ML, DS,
and DL
08 Tools for Artificial Intelligence
| Content
7. | Introduction to AI
What is AI?
The English Oxford Living
Dictionary gives this
definition:
“The theory and development
of computer systems able to
perform tasks normally
requiring human intelligence,
such as visual perception,
speech recognition, decision-
making, and translation
between languages.”
Artificial
Intelligence
A program that
can sense,
reason, act, and
adapt
Machine
Learning
Deep
Learning
Data Science
Merriam-Webster
Dictionary says
“Artificial
Intelligence is a
branch of
computer science
that deals with
simulating human
intelligence”.
8. | AI complements the skills that humans are naturally good at
Human
Common Sense • Morals • Imagination • Compassion • Abstraction
• Dreaming • Generalisation
AI
Locating Knowledge • Pattern Identification • Natural Language
• Machine Learning • Eliminating Bias • Endless Capacity
9. | The drivers behind AI’s projected growth
Spectacular improvements in AI
performances
Thanks to new technologies and the increase in data
generation significant improvements in AI research are made
1
Growing awareness of AI importance
and social implications
The emergence of daily life applications of AI have risen
awareness of its importance for the future and its social
implications
3
Increasing adoption of AI in businesses
and daily life
The performance improvements brought especially by deep
learning has enabled the development of more sophisticated
applications fit for business and daily life use
2
Projected global AI market revenue
$36.8b
$6.0b
$0.6b
+57%
per year
2020F 2025E2016
Source: Tractica
10. | AI: Regulatory Considerations
Government Office for Science
Artificial intelligence:
opportunities and implications for
the future of decision making
Financial Stability Board :
Artificial intelligence and machine
learning in financial services
The Institute of Internal Auditors
Artificial Intelligence –
Considerations for the Profession of
Internal Auditing
Centre for Data Ethics and
Innovation:
To provide independent and expert
advice to the UK government
Department for Digital, Culture,
Media & Sport
Centre for Data Ethics and
Innovation}
12. 294Billion
Emails get sent worldwide
everyday, in 2019.
By 2025, it’s
estimated that 463
exabytes of data
will be created each
day globally – that’s
the equivalent of
212,765,957 DVDs
per day!
500million
Tweets are sent out each day. That
means about 6000 tweet every
second. The most popular emoji in
these tweet is the tears of joy 😂
4.2 million
Times a year, we blink our eyes.
Kisses are made
everyday70
Million
$13 Billion
global AI market in
2017, it is expected
to grow at an annual
growth rate of 50.1%
65 Billion
WhatsApp messages gets sent
everyday. As at October 2019,
the 2nd most common emoji on
the platform was the “Red
❤️. There’s a lot of love in the
air, or should I say on WhatsApp.
Source: WEForum
| Data Science works because Data is being created at a Phenomenal Rate...
13.
14. | 5 W’s of Data Science
DATA DATA SCIENCE
WHEN
it is applied
At the beginning of you analysis After the data has been
gathered & organized
After BI reports have been created and discussed
WHY
you need it
Data-driven decisions require well-organized and relevant
row data stored in a digital format
Use data to create reports
and dashboards to gain
business insights
Access potential future
scenarios by using
advanced statistical
methods
Utilize artificial intelligence
to predict behavior in
unprecedented ways
WHAT
techniques are involved
Data Collection
Preprocessing
• Class labeling
(categorical vs
numerical)
• Data cleansing
• Dealing with missing
values
Data Collection
Preprocessing
• Class labeling (number,
text, images, videos,
audio)
• Data cleansing
• Dealing with missing
values
Analyze the data
Extract info and present it
in the form of
• Metrics
• KPIs
• Reports
• dashboards
Regression
Clustering
Factor Analysis
Time Series
Supervised Learning
Unsupervised Learning
Reinforcement Learning
WHERE
it be applied
Basic Customer Data
Historical Stock Price Data
Social Media
Financial Trading Data
Price Optimization
Inventory Management
User Experience (UX)
Sales Forecasting
Fraud Detection
Client Retention
WHO
is performs tasks
Data Architect
Data Engineer
Database Administrator
Big Data Architect
Big Data Engineer
BI Analyst
BI Consultant
BI Developer
Data Scientist
Data Analyst
Data Scientist
Machine Learning Engineer
Machine
Learning
Traditional Big
Business
Intelligence
Traditional
Methods
NOWPAST FUTURE
15. Problem
Understanding
Data Mining
Data Cleaning
Feature Engineering
Predictive Analytics
Visualization
| The Data Science Lifecycle
▪ The solution to the problem is likely
to have enough positive impact to
justify the effort.
▪ Enough data is available in a
usable format.
▪ Stakeholders are interested in
applying data science to solve the
problem.
The problem should be clear, concise, and measurable.
Basic characteristics of a well-defined data problem:
16. Problem
Understanding
Data Mining
Data Cleaning
Feature Engineering
Predictive Analytics
Visualization
| The Data Science Lifecycle
In simple words, data mining is defined as a process used to
extract usable data from a larger set of any raw data.
17. Problem
Understanding
Data Mining
Data Cleaning
Feature Engineering
Predictive Analytics
Visualization
| The Data Science Lifecycle
Data cleansing or data cleaning is the process of detecting and
correcting (or removing) corrupt or inaccurate records from a record set,
table, or database and
refers to identifying
incomplete, incorrect,
inaccurate or irrelevant
parts of the data and
then replacing,
modifying, or deleting
the dirty or coarse data.
18. Problem
Understanding
Data Mining
Data Cleaning
Feature Engineering
Predictive Analytics
Visualization
| The Data Science Lifecycle
Feature engineering is the process of using domain knowledge to extract
features from raw data via data mining techniques.
Too many cooks spoil the broth.—Old Proverb
19. Problem
Understanding
Data Mining
Data Cleaning
Feature Engineering
Predictive Analytics
Visualization
| The Data Science Lifecycle
Predictive analytics encompasses a variety of statistical techniques from
data mining, predictive modelling, and machine learning, that analyze
current and historical facts to make predictions about future or
otherwise unknown events.—Wikipedia
20. Problem
Understanding
Data Mining
Data Cleaning
Feature Engineering
Predictive Analytics
Visualization
| The Data Science Lifecycle
Data visualization is the graphical representation of information
and data. By using visual elements like charts, graphs, and maps, data
visualization tools provide an accessible way to see and understand
trends, outliers, and patterns in data.—Tableau
22. | Machine Learning & Branches
Machine learning is an application of artificial intelligence (AI) that provides systems the
ability to automatically learn and improve from experience without being explicitly
programmed. (Source: Expertsystem)
Branches of Machine Learning
Supervised Learning Unsupervised Learning Reinforcement Learning
Supervised learning is the
machine learning task of learning
a function that maps an input to an
output based on example input-
output pairs.
It infers a function from labeled
training data consisting of a set of
training examples.
Source: Wikipedia
Unsupervised learning is a type of
machine learning algorithm used
to draw inferences from datasets
consisting of input data without
labeled responses. The most
common unsupervised learning
method is cluster analysis, which
is used for exploratory data
analysis to find hidden patterns or
grouping in data.
Source: Mathworks
Reinforcement learning is the training
of machine learning models to make a
sequence of decisions. The agent
learns to achieve a goal in an uncertain
environment.
An agent gets trained based on a
reward-punishment system for right
and wrong choices respectively. Hence
the right choices are reinforced.
Source: Deepsense.ai
Machine learning involves the use of algorithm to detect patterns in large sets of data.
24. | Deep Learning (On a very High Level)
Deep learning is a machine learning technique that teaches computers to do what comes
naturally to humans: learn by example. In Deep Learning, artificial neural networks learn
patterns by propagating forward and backward through the network, updating assumed
weights and biases. It is the key to voice control in consumer devices like phones, tablets,
TVs, and hands-free speakers. (Source: Mathworks)
How Deep Learning Works
29. | The Place of Math in AI or AI in mAthematIcs…
“A person working
in the field of AI
who doesn’t know
math is like a
politician who
doesn’t know how
to persuade. Both
have an
inescapable area
to work upon!”
—Abhishek Parbhakar
▪ Linear Algebra and Calculus (Multivariate)
▪ Probability (Baye’s Theorem, Probability Distributions,
Conjugate Priors, Random Variable, etc)
▪ Statistics
▪ Markov Chains - definition, transition matrix, stationarity
▪ Information theory - entropy, cross-entropy, KL
divergence, mutual information
▪ And imo, we should just learn more Math; you never know
when you would need it.
31. | Use Cases of Artificial Intelligence
▪ Tesla
▪ Netflix and YouTube
▪ Siri, Alexa, and Amazon Echo
▪ IBM Watson
▪ Retina AI
32. Why and How to
Start a Data
Science & AI
Career
5
33. | Why Start a Data Science / AI Career
▪ It is dubbed the “sexiest job of the 21st century” by the Harvard
Business Review
▪ The average data scientist salary is $113,436, according to Glassdoor.
Okay, that is in the United States, but the pay is also fairly decent in
other parts of the world if you know your onions
▪ With the astronomical rise in data generation, the job of a data scientist
would only go higher. If you would not mind crunching data to solve
problems, why restrain yourself from becoming a data scientist?
34. | How to Start a Data Science / AI Career
▪ Choose the role that interests you
▪ Take up a course and complete it
▪ Choose a language and stick to it
▪ Join a peer group
▪ Focus on applications and not just theories
▪ Follow the right resources
▪ Work on communication skills
35. How Long
Would it Take
Before the
Machines Take
Over? An AI
Apocalypse…
or more
accurately, the
infamous
“Technology
Singularity”?
666
36. | AI Armageddon or Not AI Armageddon…?
SO the Question is: Do
you Think AI will get
so powerful and
colonize the planet?
“The pace of progress in artificial
intelligence (I’m not referring to
narrow AI) is incredibly fast. Unless
you have direct exposure to groups
like DeepMind, you have no idea
how fast—it is growing at a pace
close to exponential. The risk of
something seriously dangerous
happening is in the five-year
timeframe. 10 years at most.” —Elon
Musk wrote in a comment on
Edge.org
“The Development of full Artificial
Intelligence could spell the end of the
human race. It would take off on its own,
and re-design itself at an ever increasing
rate. Humans, who are limited by slow
biological evolution, couldn’t compete, and
would be superseded.” —Stephen Hawking
told BBC
37. | Not in a 100 Years!
“The big AI dreams of making
machines that could someday evolve
to do intelligent things like humans
could - I was turned off by that. I
didn't really think that was feasible
when I first joined Stanford.”—
Andrew Ng
My Argument is based on:
▪ Moore’s law would not support rapid demand for AI processing: this
resembles the Computational Complexity argument.
▪ In physics and philosophy, we are still battling to understand
consciousness; to understand existential and emotional intelligence. Those
qualities would be needed by an AI that wishes to take over the world. Like,
can AI appreciate good music yet? No.
▪ Not very traditional, my instincts. It worked for Ramanujan, after all ☺.
I do not think we should fear any super-intelligent AI colonization in at least xx years
39. | Other Reasons Against a Singularity
There are fundamental limits in the Universe; no signal for instance propagates faster than the speed of
light. Dunbar’s number is the observed correlation between brain size for primates and average social
group size. This puts a limit of between 100 and 250 stable relationships on human social groups. There is
no proofs that AI can maintain a stable relationship.
And how do we forget Vernor Vinge?
41. ▪ GPU: RTX 2070 or RTX 2080 Ti. GTX 1070, GTX 1080, GTX 1070 Ti, and GTX 1080.
▪ CPU: 1-2 cores per GPU depending how you preprocess data. > 2GHz; CPU should support the number of
GPUs that you want to run. PCIe lanes do not matter.
▪ RAM:– Clock rates do not matter — buy the cheapest RAM.– Buy at least as much CPU RAM to match the RAM
of your largest GPU.– More RAM can be useful if you frequently work with large datasets.
▪ Hard drive/SSD:– Hard drive for data (>= 3TB)– Use SSD for comfort and preprocessing small datasets.
▪ PSU:– Add up watts of GPUs + CPU. Then multiply the total by 110% for required Wattage.– Get a high
efficiency rating if you use a multiple GPUs.– Make sure the PSU has enough PCIe connectors (6+8pins)
▪ Cooling:– CPU: get standard CPU cooler or all-in-one (AIO) water cooling solution– GPU:– Use air cooling–
Get GPUs with “blower-style” fans if you buy multiple GPUs– Set coolbits flag in your Xorg config to control
fan speeds
▪ Motherboard:– Get as many PCIe slots as you need for your (future) GPUs (one GPU takes two slots; max 4
GPUs per system)
▪ Monitors:– An additional monitor might make you more productive than an additional GPU.
| Focus on Requirements for Deep Learning
43. ▪ Language: Python, Julia, R, etc.
▪ Platform: Jupyter Notebook, Anaconda, Google Colaboratory, and Text Editors from Atom to VS Code
and PyCharm etc
▪ Excel, Tableau, Power BI for visualization
General Data Science & Machine Learning Tools
Frameworks for Deep Learning
▪ Tensorflow
▪ PyTorch
▪ Keras
▪ MXNet
| Tools for Data Science + Deep Learning Framework
▪ CNTK (Microsoft Cognitive Toolkit)
▪ Caffe and Caffe2
▪ DeepLearning4J
▪ Chainer
44. ?
Thanks for Listening!!
Any Questions?
Along with this presentation is a 5-month guide to bootstrap a career in Data Science; someone graciously
compiled the document, the Universe bless their souls. Contact me at agbodozie660@gmail.com.