How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
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Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Machine Learning for Statisticians - IntroductionDr Ganesh Iyer
Introduction to Machine Learning for Statisticians. From the webinar given for Sacred Hearts College, Tevara, Ernakulam, India on 8/8/2020. It briefly introduces ML concepts and what does it mean for statisticians.
Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019Dhiana Deva
Introducing Machine Learning is like opening the Pandora's Box - it unveils important issues in your data, metrics, and product. In order to deal with such complexity, pragmatic practices are required to obtain reliable results. In this talk, we will go through learnings gained from introducing Machine Learning in different contexts, from academia, start-ups, consulting to tech giants - covering practices for experimentation, infrastructure, planning, performance evaluation and product vision in the context of machine learning products.
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Presenting this set of slides with name - Artificial Intelligence Overview Powerpoint Presentation Slides. This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with thirtyseven slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Artificial Intelligence Overview Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
What your employees need to learn to work with data in the 21 st century Human Capital Media
The data revolution is well underway. Regardless of the industry or department you manage, working with data will soon be an essential part of all of our jobs, if it isn’t already. This could take the form of basic data analytics, data science, machine learning or artificial intelligence. This can be overwhelming: what do all these terms mean and how can they be leveraged to impact your employees’ work, whether that be in finance, healthcare, tech or the public sector, among many others? This webinar will give you a primer for understanding how data can impact your employees’ work, what they need to know and how to go about educating them on it.
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.
It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.
After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.
What you'll learn:
A deeper understanding of the end-to-end machine learning workflow.
The tools needed to effectively create, design, and manage machine learning projects.
The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle.
Demo: Skyl Platform for End-End machine learning workflow.
This is the slide deck for this webinar:
https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects
IBM Watson Question-Answering System and Cognitive ComputingRakuten Group, Inc.
IBM's vision of cognitive computing has been steadily embraced across the industries since IBM's Watson question-answering system made a sensational debut at the US Jeopardy! television quiz show in 2011. As a core member of the Watson project, I would like to share the excitement of the project and the last five and a half year of its progress into the cognitive business. In this talk, I will also give a technical overview of Watson, major use cases, and perspectives on the future of cognitive computing.
https://tech.rakuten.co.jp/
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
Data Science vs Machine Learning – What’s The Difference? | Data Science Cour...Edureka!
**Python Data Science Training: https://www.edureka.co/python **
In this video on Data Science vs Machine Learning, we’ll be discussing the importance of Data Science and Machine Learning and we’ll compare them based on a few key parameters. The following topics are covered in this session:
What Is Data Science?
What Is Machine Learning?
Fields Of Data Science
Use Case
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Instagram: https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck is a comprehensive virtual solution for technology experts. With the help of this PowerPoint theme, you can elucidate the differences between machine intelligence, machine learning, and deep learning. Employ our PPT presentation to cover merits, demerits, learning techniques, and types of supervised machine learning. You can also elucidate the benefits, limitations, and types of unsupervised machine learning. Similarly, cover important aspects related to reinforcement learning. Our AI PowerPoint slideshow also helps you in elaborating back propagation of neural networks. Walk your audience through the expert system in artificial intelligence. Cover examples, features, components, application, benefits, limitations, and other aspects of the expert system. Consolidate the deep learning process, recurrent neural networks, and convolutional neural networks through this PPT template deck. Give a crisp introduction to artificial intelligence. Introduce types, algorithms, trends, and use cases of artificial intelligence. Hit the download icon and begin instant personalization. Our Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3nfgjaT
Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.
How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
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
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Machine Learning for Statisticians - IntroductionDr Ganesh Iyer
Introduction to Machine Learning for Statisticians. From the webinar given for Sacred Hearts College, Tevara, Ernakulam, India on 8/8/2020. It briefly introduces ML concepts and what does it mean for statisticians.
Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019Dhiana Deva
Introducing Machine Learning is like opening the Pandora's Box - it unveils important issues in your data, metrics, and product. In order to deal with such complexity, pragmatic practices are required to obtain reliable results. In this talk, we will go through learnings gained from introducing Machine Learning in different contexts, from academia, start-ups, consulting to tech giants - covering practices for experimentation, infrastructure, planning, performance evaluation and product vision in the context of machine learning products.
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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
Castbox: https://castbox.fm/networks/505?country=in
Presenting this set of slides with name - Artificial Intelligence Overview Powerpoint Presentation Slides. This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with thirtyseven slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Artificial Intelligence Overview Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
What your employees need to learn to work with data in the 21 st century Human Capital Media
The data revolution is well underway. Regardless of the industry or department you manage, working with data will soon be an essential part of all of our jobs, if it isn’t already. This could take the form of basic data analytics, data science, machine learning or artificial intelligence. This can be overwhelming: what do all these terms mean and how can they be leveraged to impact your employees’ work, whether that be in finance, healthcare, tech or the public sector, among many others? This webinar will give you a primer for understanding how data can impact your employees’ work, what they need to know and how to go about educating them on it.
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.
It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.
After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.
What you'll learn:
A deeper understanding of the end-to-end machine learning workflow.
The tools needed to effectively create, design, and manage machine learning projects.
The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle.
Demo: Skyl Platform for End-End machine learning workflow.
This is the slide deck for this webinar:
https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects
IBM Watson Question-Answering System and Cognitive ComputingRakuten Group, Inc.
IBM's vision of cognitive computing has been steadily embraced across the industries since IBM's Watson question-answering system made a sensational debut at the US Jeopardy! television quiz show in 2011. As a core member of the Watson project, I would like to share the excitement of the project and the last five and a half year of its progress into the cognitive business. In this talk, I will also give a technical overview of Watson, major use cases, and perspectives on the future of cognitive computing.
https://tech.rakuten.co.jp/
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
Data Science vs Machine Learning – What’s The Difference? | Data Science Cour...Edureka!
**Python Data Science Training: https://www.edureka.co/python **
In this video on Data Science vs Machine Learning, we’ll be discussing the importance of Data Science and Machine Learning and we’ll compare them based on a few key parameters. The following topics are covered in this session:
What Is Data Science?
What Is Machine Learning?
Fields Of Data Science
Use Case
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Instagram: https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck is a comprehensive virtual solution for technology experts. With the help of this PowerPoint theme, you can elucidate the differences between machine intelligence, machine learning, and deep learning. Employ our PPT presentation to cover merits, demerits, learning techniques, and types of supervised machine learning. You can also elucidate the benefits, limitations, and types of unsupervised machine learning. Similarly, cover important aspects related to reinforcement learning. Our AI PowerPoint slideshow also helps you in elaborating back propagation of neural networks. Walk your audience through the expert system in artificial intelligence. Cover examples, features, components, application, benefits, limitations, and other aspects of the expert system. Consolidate the deep learning process, recurrent neural networks, and convolutional neural networks through this PPT template deck. Give a crisp introduction to artificial intelligence. Introduce types, algorithms, trends, and use cases of artificial intelligence. Hit the download icon and begin instant personalization. Our Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3nfgjaT
Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.
Future of data science as a professionJose Quesada
How can you thrive in a future where machine learning has been popular for a few years already?
In this talk, I will give you actionable advice from my experience training serious data scientists at our retreat center in Berlin. You are going to face these pointy, hard questions:
- What is the promise of machine learning? Has it happened yet?
- Is it easy to take advance of machine learning, now that most algorithms are nicely packaged in APIs and libraries?
- How much time should I spend getting good at machine learning? Am I good enough now?
- Are data scientists going to be replaced by algorithms? Are we all?
- Is it easy to hire talent in machine learning after the explosion of MOOCs?
Discussion - Weeks 1–2COLLAPSETop of FormShared Practice—Rol.docxcuddietheresa
Discussion - Weeks 1–2
COLLAPSE
Top of Form
Shared Practice—Role of Business Information Systems
Note: This Discussion has slightly different due dates than what is typical for this program. Be mindful of this as you post and respond in the Discussion. Your post is due on Day 7 and your Response is due on Day 3 of Week 2.
As a manager, it is critical for you to understand the types of business information systems available to support business operations, management, and strategy. As of 2013, these include, but are certainly not limited to the following:
· Supply Chain Management (SCM)
· Accounting Information System
· Customer Relationship Management (CRM)
· Decision Support Systems (DSS)
· Enterprise Resource Planning (ERP)
· Human Resource Management
These types of systems support critical business functions and operations that every organization must manage. The effective manager understands the purpose of these types of systems and how they can be best used to manage the organization's data and information.
In this Discussion, you will share your knowledge and findings related to business information systems and the role they play in your organization. You will also consider your colleagues' experiences to explore additional ways business information systems might be applied in your colleagues' organizations, or an organization with which you are familiar.
By Day 7
· Describe two or three of the more important technologies or business information systems used in your organization, or in one with which you are familiar.
· Discuss two examples of how these business information systems are affecting the organization you selected. Be sure to discuss how individual behaviors and organizational or individual processes are changing and what you can learn from the issues encountered.
· Summarize what you have learned about the importance of business information systems and why managers need to understand how systems can be used to the organization's advantage.
You should find and use at least one additional current article from a credible resource, either from the Walden Library or the Internet. Please be specific, and remember to use citations and references as necessary.
General Guidance: Your initial Discussion post, due by Day 7, will typically be 3–4 paragraphs in length as a general expectation/estimate. Refer to the rubric for the Week 1 Discussion for grading elements and criteria. Your Instructor will use the rubric to assess your work.
Week 2
By Day 3
In your Week 1 Discussion you described how business information systems have been applied in an organization with which you are familiar. Read through your colleagues' posts and by Day 3 (Week 2), respond to two of your colleagues in one or more of the following ways:
· Examine how the business information systems described by your colleague could be or are being used by your organization. Offer additional ways either organization might take advantage of these systems.
· Examine how the b ...
Artificial Intelligence beyond the hype: Local (Belgian) Machine Learning suc...Patrick Van Renterghem
Presentation on "AI beyond the hype: Local (Belgian) Machine Learning success stories" by Peter Depypere (element61), at the BI & Data Analytics Summit on June 13th, 2019 in Diegem (Belgium)
WHY DO SO MANY ANALYTICS PROJECTS STILL FAIL?Haluk Demirkan
“KEY CONSIDERATIONS FOR DEEP ANALYTICS ON BIG DATA FOR DEEP LEARNING”
What is Big Data? Big Data, which means many things to many people, is not a new technological fad. In addition to providing innovative solutions and operational insights to enduring challenges and opportunuties, big data with deep analytics instigate new ways to transform processes, organizations, entire industries, and even society all together. Pushing the boundaries of deep data analytics uncovers new.
Big Data is not just “big.” The exponentially growing volume of the data is only one of many characteristics that are often associated with Big Data, such as variety, velocity, veracity and others (6Vs).
By now, we should already have knowledge and experience to have successful data and analytics enabled decision support systems. So why do these projects still fail, and why are executives and users are still so unhappy? While there are many reasons for this high failure rate, the biggest is that companies still treat these projects as just another IT project. Big data analytics is neither a product nor a computer system. It is, rather, a constantly evolving strategy, vision and architecture that continuously seek to align an organization’s operations and direction with its strategic business goals with strategic, tactical and operational decisions.
Where have all the data entry candidates gone?Infrrd
If you are struggling to hire data entry roles to help extract data from documents, please take comfort in the fact that you are not alone. Businesses and institutions of all sizes, even the IRS, are challenged by an acute labor shortage.
Complete Article: https://hubs.ly/Q01b-7Cg0
Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
Cognitive Analytics
Machine Learning
Deep Learning
Reasoning
And next generation artificial intelligence (AI)
And get involved in the discussion with your own questions to present to the panel.
Looks at the different AI approaches and provides some practical categorisation and case studies. Then talks about the data fabric you need to put in place to improve model accuracy and deployment. Covers: supervised, unsupervised, machine learning, deep learning, RPA, etc. Finishes with how to create successful AI projects.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
3. • AI is a form of advanced computer science, that learns from data in order to expand
its generalization abilities on narrow tasks, as opposed to regular software
hardcoded instructions
• AI can be subdivided into supervised learning - the bulk of modern applications,
unsupervised learning - grouping for visualization and exploration purpose mainly,
and reinforcement learning - difficult to implement but powerful in some optimization
with actions cases
• The list of tasks AI can solve can broadly be divided into : classification, prediction,
clustering, outlier detection, recommandation, data generation
• The different subdomain of applications can be determined by the data input/output
types : vision, NLP (text&speech), structured classic, robotics
What have we seen last time?
3
5. if color == "green":
return "apple"
elif color == "orange":
return "orange"
else return "banana"
“apple”
“orange”
“banana”
“apple”
5
Can you write a computer program that does that ?
12. • AI is a form of advanced computer science, that learns from data in order to expand
its generalisation abilities on narrow tasks, as opposed to regular software
hardcoded instructions
• AI can be subdivided into supervised learning - the bulk of modern applications,
unsupervised learning - grouping for visualisation and exploration purpose mainly,
and reinforcement learning - difficult to implement but powerful in some optimisation
with actions cases
• The list of tasks AI can solve can broadly be divided into : classification, prediction,
clustering, outlier detection, recommandation, data generation
• The different subdomain of applications can be determined by the data input/output
types : vision, NLP (text&speech), structured classic, robotics
What have we seen last time?
12
13. •Understand the limits of AI and the main biases when it comes to
create intelligent machines in real life
•Lifecycle of an AI application, and how it differs from regular
workflows
•How to detect opportunities / use cases, and evaluate their impact on
the revenue of the company. Cost per task, revenue per task
•Team management, project management (create and deploy) and data
management
Our plan for today - the real world
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17. Key steps of a machine learning project
Echo / Alexa
1. Collect data
2. Train model
Iterate many times until good enough
3. Deploy model
Get data back
Maintain / update the model
01
03
02
06
04
05
MaintenanceIdentify
DeployData
EvaluateModel
Source : deeplearning.ai
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18. Key steps of a machine learning project
Self-driving car
1. Collect data image position of other cars
2. Train model
3. Deploy model
Get data back
Maintain model
01
03
02
06
04
05
MaintenanceIdentify
DeployData
EvaluateModel
Source : deeplearning.ai
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21. Anything you can do with 1 second of
thought, can probably be automated today
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22. “The toy arrived two days late, so I wasn’t able to give it to
my nephew for his birthday.
Can I return it ?”
“Refund request”
Refund/Shipping/OrderInput text
“Oh sorry to hear that!
I hope you nephew had a good birthday.
Yes, we can help with ...
Complex personalised
empathetic response
Input text
“Yes you can. The refund procedure is ...”
Simple responseInput text
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23. Diagnose pneumonia on ~ 10.000
images
Diagnose pneumonia from 10
images of a medical textbook
Ask to perform on new type of
data
23
24. Take a (deep) look at your work
Break down your workflow and your business unit
24
26. Baby food ingredient: safe or spoiled?
Patient: ideal medication dosage?
Email: spam or ham?
Recorded phone call to call center: issue topic?
Bottle of wine: will I like it or not?
Steering wheel: left or right?
Photo: which animal?
Game piece: which location on the board?
Start of a sentence: end of that sentence?
Stock: tomorrow’s price?
Transaction: legitimate or fraudulent?
Data center cooling system: warmer or cooler?
Machine: when will it need maintenance?
Inventory: when to restock?
Scene description: pixels in a visual rendering?
Today’s temperature: tomorrow’s temperature?
Auction: how much to bid?
Movie: will you like it or not?
Live lecture: text captions?
Poem: what does it sound like out loud?
Image of an invoice: total amount in dollars?
Service request: waiting time?
Expense report: budget category?
Sound recording: correct text captions?
Song lyrics: language?
Sentence in English: same meaning in Chinese?
Form incorrectly filled out: correct fields?
Clothing item: skirt or blouse or …?
Video: which actors?
Video game: joystick motion?
Toilet user: did they wash their hands?
Idea 1
Ask simple guesswork labelling question
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28. Idea 2
find the ROI of (cheap) prediction
Level 1: as an optimisation tool
Level 2: as an improvement / help / recommandation
Level 3: as a new feature / product
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30. Discover Opportunities-
Brainstorming framework
1. Think about automating tasks rather
than jobs!
2. What are the main drivers of
business value?
3. What are the main pain points in
your business ?
4. How much data is needed ? Is my
data clean ? Are we mature in terms
of data ?
30
31. What AI can do
Valuable cases
for your business
AI experts Domain experts
Cross-functional team 31
35. Real life case studies
Fromcorebusinesstolow-hangingfruits
35
35
36. Recommandations
“35 percent of what consumers purchase on Amazon come from product recommendations”
https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers
36
36
37. Amazon Go
“Amazon.com Inc. may open up to 3,000 Amazon Go outlets by 2021”
https://www.bloomberg.com/news/articles/2018-09-19/amazon-is-said-to-plan-up-to-3-000-cashierless-stores-by-2021
37
37
44. Optimizing vs satisficing metric
Possible metrics
Cost = accuracy – 0.5 x running time
Or
Maximize accuracy
Subject to running time <= 100 ms
Use case 1 : Cat classifier
Use case 2 :
Detect trigger words for Amazon Alexa Device
Possible metrics
Accuracy
Or
Maximize accuracy
Subject to <= 1 FP for every 24 hours
44
56. Where will you get it?
Then prioritise by availability, accessibility & cost
- existing data sources
- data enrichment (feature engineering)
- data augmentation
- data generation
- manual data labeling
- create new data sources (e.g. sensors)
- Public data, scraping, etc
56
58. If the benefit of the performance
increase outweighs the cost of
acquiring more data, get more
data!
Diminishing returns mean that
more data won’t help
What amount of data ?
58
75. Design with Fairness in mind
! Consider the problem
! Ask experts
! Train the models to account for bias
! Interpret outcomes
! Publish with context
75
76. ⊙ Big data is always better, but not
necessary
⊙ Clean data better than a lot of messy
data
⊙ Small data is almost always enough to
make progress (activate feedback loop)
⊙ If no data, don’t give up, if can be
generated or augmented!
⊙ Design ML model with fairness in mind
Go talk to a ML Engineer to figure it out
Data
Consideratio
ns
76
79. Main steps for model training
1. Select your model family (and your performance metric)
2. Split you dataset into Train/Dev/Test set
3. Train model on training set
4. Take care of overfitting vs underfitting
5. Tuning hyper parameters
6. Select best model
79
87. 6) Selecting best model
For each algorithm (i.e. regularized regression, random forest, etc.):
For each set of hyperparameter values to try:
Perform cross-validation using the training set.
Calculate cross-validated score.
87
88. Checkpoint quizz
! Pick one: better data or fancier algorithms ?
! When should you split your dataset into training and test sets, and why?
! What's the key difference between model parameters and hyperparameters?
! Explain how cross-validation helps you "tune" your models?
88
94. Explainability of the model
● Depending on the machine learning model used, the results could be :
● Very simple to interpret: Like decision trees
● Very difficult to interpret: Like deep-learning neural networks
94
96. Explainability of the model
On a deep-learning neural network, interpretability of weights is difficult.
96
97. Explainability of the model
We could still use more sophisticated technique to partially understand
their predictions. This is an example on logo detection algorithms
Image Grad-cam Image Grad-cam
97
98. Performance of the model
TP
TNFP
FN
YES NO
YES
NO
Predicted
Actual
Confusion Matrix
98
99. Performance of the model
1.
4.3.
2.
YES NO
YES
NO
Predicted
Actual
How confusion matrix can help understand the model performance
Imagine you have a medical problem, do you go see your doctor?
1. If you should and you did, the fee is 25€
2. If you should and you didn’t, it gets worse and you will see a specialist, the fee is
70 €
3. If you shouldn't and you did, you still pay 25€
4. If you shouldn't and you didn’t, you do not pay anything
OK
OK
Loose 25 €
Loose 45 €
99
100. Performance of the model
200
10020
40
YES NO
YES
NO
Predicted
Actual
How confusion matrix can help understand the model performance
Which ML model is better, according to confusion matrices ?
Loose 25 €
Loose 45 €
210
8535
30
YES NO
YES
NO
Predicted
Actual
Loose 25 €
Loose 45 €
Loose 20 * 25 € + 40 * 45 € = 2 300 € Loose 35 * 25 € + 30 * 45 € = 2 225 € 100
101. Accuracy metric
Let us speak in terms of seeing your doctor:
● Accuracy: Over all the choices (see or not your doctor)
you make, how many of them were correct?
!""#$%"& =
() + (+
() + ,+ + ,) + (+
TP
TNFP
FN
YES NO
YES
NO
Predicted
Actual
101
102. Precision & Recall metrics
Let us speak in terms of seeing your doctor:
● Recall: Over all the times you should go see your doctor, how
many times you really went?
!"#$%% =
'(
'( + *+
● Precision: Over all the times you did go see your doctor, how
many of times you really needed to see him?
(,"#-.-/0 =
'(
'( + *(
TP
TNFP
FN
YES NO
YES
NO
Predicted
Actual
102
103. Accuracy VS Precision & Recall
● The accuracy is not used when the problem is not balanced.
● If 99% of your data are just one class
● An accuracy of 99% is just a majority vote
● Precision and recall are more useful in this case since you can focus on each class
individually
103
104. Which ML method is preferred ?
Use Case : Customer Churn
Target action A : phone call to potential churning customer
Target action B : send generous discount to potential churners
Which method is preferred for each target ?
104
113. ・Identifying fraudulent claims so that they can select claims for
deeper manual investigation; they have a business goal of
reducing fraud by 5% this year.
・Predicting weather patterns so that they can advise
customers to protect their vehicles by bringing them inside when
there’s a high chance of storms — thereby reducing vehicle
damage claims by 2%.
・Upselling other insurance products to the customer based on
the products they already have. The goal is to increase the
conversion rate for online upselling by 3%.
Still think vertical – 3 use cases
113