Discussing the possibilities of large language models for the automatic generation of academic content by the students (e.g. master thesis), and the related need for changes in the way in which to educate and evaluate students.
And then there were ... Large Language ModelsLeon Dohmen
It is not often even in the ICT world that one witnesses a revolution. The rise of the Personal Computer, the rise of mobile telephony and, of course, the rise of the Internet are some of those revolutions. So what is ChatGPT really? Is ChatGPT also such a revolution? And like any revolution, does ChatGPT have its winners and losers? And who are they? How do we ensure that ChatGPT contributes to a positive impulse for "Smart Humanity?".
During a key note om April 3 and 13 2023 Piek Vossen explained the impact of Large Language Models like ChatGPT.
Prof. PhD. Piek Th.J.M. Vossen, is Full professor of Computational Lexicology at the Faculty of Humanities, Department of Language, Literature and Communication (LCC) at VU Amsterdam:
What is ChatGPT? What technology and thought processes underlie it? What are its consequences? What choices are being made? In the presentation, Piek will elaborate on the basic principles behind Large Language Models and how they are used as a basis for Deep Learning in which they are fine-tuned for specific tasks. He will also discuss a specific variant GPT that underlies ChatGPT. It covers what ChatGPT can and cannot do, what it is good for and what the risks are.
And then there were ... Large Language ModelsLeon Dohmen
It is not often even in the ICT world that one witnesses a revolution. The rise of the Personal Computer, the rise of mobile telephony and, of course, the rise of the Internet are some of those revolutions. So what is ChatGPT really? Is ChatGPT also such a revolution? And like any revolution, does ChatGPT have its winners and losers? And who are they? How do we ensure that ChatGPT contributes to a positive impulse for "Smart Humanity?".
During a key note om April 3 and 13 2023 Piek Vossen explained the impact of Large Language Models like ChatGPT.
Prof. PhD. Piek Th.J.M. Vossen, is Full professor of Computational Lexicology at the Faculty of Humanities, Department of Language, Literature and Communication (LCC) at VU Amsterdam:
What is ChatGPT? What technology and thought processes underlie it? What are its consequences? What choices are being made? In the presentation, Piek will elaborate on the basic principles behind Large Language Models and how they are used as a basis for Deep Learning in which they are fine-tuned for specific tasks. He will also discuss a specific variant GPT that underlies ChatGPT. It covers what ChatGPT can and cannot do, what it is good for and what the risks are.
LangChain Intro, Keymate.AI Search Plugin for ChatGPT, How to use langchain library? How to implement similar functionality in programming language of your choice? Example LangChain applications.
The presentation revolves around the concept of "langChain", This innovative framework is designed to "chain" together different components to create more advanced use cases around Large Language Models (LLMs). The idea is to leverage the power of LLMs to tackle complex problems and generate solutions that are more than the sum of their parts.
One of the key features of the presentation is the application of the "Keymate.AI Search" plugin in conjunction with the Reasoning and Acting Chain of Thought (ReAct) framework. The presenter encourages the audience to utilize these tools to generate reasoning traces and actions. The ReAct framework, learned from an initial search, is then applied to these traces and actions, demonstrating the potential of LLMs to learn and apply complex frameworks.
The presentation also delves into the impact of climate change on biodiversity. The presenter prompts the audience to look up the latest research on this topic and summarize the key findings. This exercise not only highlights the importance of climate change but also demonstrates the capabilities of LLMs in researching and summarizing complex topics.
The presentation concludes with several key takeaways. The presenter emphasizes that specialized custom solutions work best and suggests a bottom-up approach to expert systems. However, they caution that over-abstraction can lead to leakages, causing time and money limits to hit early and tasks to fail or require many iterations. The presenter also notes that while prompt engineering is important, it's not necessary to over-optimize if the LLM is clever. The presentation ends on a hopeful note, expressing a need for more clever LLMs and acknowledging that good applications are rare but achievable.
Overall, the presentation provides a comprehensive overview of the LanGCHAIN framework, its applications, and the potential of LLMs in solving complex problems. It serves as a call to action for the audience to explore these tools and frameworks.
Build an LLM-powered application using LangChain.pdfAnastasiaSteele10
LangChain is an advanced framework that allows developers to create language model-powered applications. It provides a set of tools, components, and interfaces that make building LLM-based applications easier. With LangChain, managing interactions with language models, chaining together various components, and integrating resources like APIs and databases is a breeze. The platform includes a set of APIs that can be integrated into applications, allowing developers to add language processing capabilities without having to start from scratch.
How to fine-tune and develop your own large language model.pptxKnoldus Inc.
In this session, we will what are large language models, how we can fin-tune a pre-trained LLM with our data, including data preparation, model training, model evaluation.
Using Large Language Models in 10 Lines of CodeGautier Marti
Modern NLP models can be daunting: No more bag-of-words but complex neural network architectures, with billions of parameters. Engineers, financial analysts, entrepreneurs, and mere tinkerers, fear not! You can get started with as little as 10 lines of code.
Presentation prepared for the Abu Dhabi Machine Learning Meetup Season 3 Episode 3 hosted at ADGM in Abu Dhabi.
Automate your Job and Business with ChatGPT #3 - Fundamentals of LLM/GPTAnant Corporation
Episode 3: The LLM / GPT / AI Prompt / Data Engineer Roadmap
In this episode, we'll discuss the history, fundamentals, and the different flavors of LLMs available, beyond GPT/ChatGPT. This is a dry run of a session that will be on a LLM Bootcamp ( Fill out the survey on the link if you are interested in an in-person vs. virtual session)
Intro / Fundamentals of LLM
LLM Foundations
History of LLMs
Tuning, Training, or "In Context Learning" with LLMs
What is "Prompt Engineering"
Case for Augmenting LLMs
Series: Using AI / ChatGPT at Work - GPT Automation
Are you a small business owner or web developer interested in leveraging the power of GPT (Generative Pretrained Transformer) technology to enhance your business processes? If so, Join us for a series of events focused on using GPT in business. Whether you're a small business owner or a web developer, you'll learn how to leverage GPT to improve your workflow and provide better services to your customers.
GPT Automation: What it is and How it Works
How Time-Saving GPT Automation Can Improve Your Business
Cost-Effective GPT Automation: How it Can Save Your Business Money
Using GPT Automation for Customer Service: Benefits and Best Practices
The Power of GPT Automation for Content Creation
Data Analysis Made Easy with GPT Automation
Top GPT-3 Automation Tools for Businesses
The Ethical Considerations of GPT Automation
Overcoming Bias in GPT Automation: Best Practices
The Future of GPT Automation: Trends and Predictions
Since we focus on "no code" here, we'll explore the tools that are already out there such as ChatGPT plugins for Chrome, OpenAI GPT API, low-code/no-code platforms like Make/Integromat and Zapier, existing apps like Jasper/Rytr, and ecosystem tools like Everyprompt. We'll also discuss the resources available for those interested in learning more about GPT, including other people’s prompts.
Benchmark comparison of Large Language ModelsMatej Varga
Benchmark comparison of Large Language Models. Check out which one performed the best, according to a study by
Ye, Seonghyeon, et al. "FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets."
Artificial Intelligence has unleashed a wave of innovation, from effortlessly summarizing
articles to engaging in deep, thought-provoking conversations — with large language
models taking on the primary workload.
Enter the extraordinary realm of large language models (LLMs), the brainchild of deep
learning algorithms. These powerhouses not only decipher and grasp massive amounts
of data but also possess the uncanny ability to recognize, summarize, translate, predict,
and even generate a diverse range of textual and coding content.
‘Big models’: the success and pitfalls of Transformer models in natural langu...Leiden University
Abstract: Large Language Models receive a lot of attention in the media these days. We have all experienced that generative language models of the GPT family are very fluent and can convincingly answer complex questions. But they also have their limitations and pitfalls. In this presentation I will introduce Transformer-based language models, explain the relation between BERT, GPT, and the 130 thousand other models available on https://huggingface.co. I will discuss their use and applications and why they are so powerful. Then I will point out challenges and pitfalls of Large Language Models and the consequences for our daily work and education.
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...David Talby
An April 2023 presentation to the AMIA working group on natural language processing. The talk focuses on three current trends in NLP and how they apply in healthcare: Large language models, No-code, and Responsible AI.
Unleashing the Power of OpenAI GPT-3 in FME Data Integration WorkflowsSafe Software
Join us for an eye-opening webinar where we will demonstrate the incredible power and productivity of OpenAI GPT-3 in FME data integration scenarios. From natural language processing to automated workflow generation and predictive modeling, we will show how GPT-3 can tackle even the most complex and daunting data integration challenges without a single line of code. This is a must-attend event for anyone looking to unlock the full potential of their data and streamline their integration workflows. Be prepared to be amazed and astounded at the capabilities of GPT-3 and FME!
ProductTank HK #31 - Maximizing Product Ops Efficiency with Generative AIAmanda Lam
ProductTank Hong Kong #31 - Maximizing Product Ops Efficiency with Generative AI
by Mushroom Luk and Amanda Lam
In his presentation, Mushroom will share his secrets for using generative AI tools like ChatGPT and GitHub Copilot to generate code and streamline workflows. He'll also take you on a journey through the exciting new field of text-to-image AI generation, showing you how to turn text-based descriptions into stunning visual content.
Get ready to be spellbound by the power of generative AI technology, as Mushroom demonstrates how to use these tools to create real value for your organization. Don't miss this chance to learn from the data wizard himself!
In addition, Amanda will also share her experiences in using more generative AI tools such as Bing Chat, Google Bard, Dall-E, Bing Image Generator and MidJourney etc., to tackle real life product ops problems such as user story preparation, budget and timeline estimation, slide preparations etc. This session will be based on live demo and will be interactive with the audience.
About the speakers
Introducing Mushroom Luk, the data analyst who knows as much about programming as a fish knows about tightrope walking! He's found the ultimate cheat code in life: an AI sidekick that does all the heavy lifting.
Amanda Lam is a tech enthusiast and community activist. She organizes ProductTank Hong Kong, Women Techmakers Hong Kong and HKPUG events, and is a consistent host of the weekly Cantonese tech podcast, HKPUG Podcast.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
SharePoint "Moneyball" - The Art and Science of Winning the SharePoint Metric...Susan Hanley
Measurement is not just about looking for a bottom-line result to justify investments. It’s also a tool to provide feedback about where the organization is along the road to successfully leveraging investments in SharePoint and the business outcomes it provides. At every stage in the development of your solution, metrics provide a valuable means for focusing attention on desired behaviors and results. This presentation showcases a practical and realistic framework for SharePoint metrics based on real world examples and successes.
LangChain Intro, Keymate.AI Search Plugin for ChatGPT, How to use langchain library? How to implement similar functionality in programming language of your choice? Example LangChain applications.
The presentation revolves around the concept of "langChain", This innovative framework is designed to "chain" together different components to create more advanced use cases around Large Language Models (LLMs). The idea is to leverage the power of LLMs to tackle complex problems and generate solutions that are more than the sum of their parts.
One of the key features of the presentation is the application of the "Keymate.AI Search" plugin in conjunction with the Reasoning and Acting Chain of Thought (ReAct) framework. The presenter encourages the audience to utilize these tools to generate reasoning traces and actions. The ReAct framework, learned from an initial search, is then applied to these traces and actions, demonstrating the potential of LLMs to learn and apply complex frameworks.
The presentation also delves into the impact of climate change on biodiversity. The presenter prompts the audience to look up the latest research on this topic and summarize the key findings. This exercise not only highlights the importance of climate change but also demonstrates the capabilities of LLMs in researching and summarizing complex topics.
The presentation concludes with several key takeaways. The presenter emphasizes that specialized custom solutions work best and suggests a bottom-up approach to expert systems. However, they caution that over-abstraction can lead to leakages, causing time and money limits to hit early and tasks to fail or require many iterations. The presenter also notes that while prompt engineering is important, it's not necessary to over-optimize if the LLM is clever. The presentation ends on a hopeful note, expressing a need for more clever LLMs and acknowledging that good applications are rare but achievable.
Overall, the presentation provides a comprehensive overview of the LanGCHAIN framework, its applications, and the potential of LLMs in solving complex problems. It serves as a call to action for the audience to explore these tools and frameworks.
Build an LLM-powered application using LangChain.pdfAnastasiaSteele10
LangChain is an advanced framework that allows developers to create language model-powered applications. It provides a set of tools, components, and interfaces that make building LLM-based applications easier. With LangChain, managing interactions with language models, chaining together various components, and integrating resources like APIs and databases is a breeze. The platform includes a set of APIs that can be integrated into applications, allowing developers to add language processing capabilities without having to start from scratch.
How to fine-tune and develop your own large language model.pptxKnoldus Inc.
In this session, we will what are large language models, how we can fin-tune a pre-trained LLM with our data, including data preparation, model training, model evaluation.
Using Large Language Models in 10 Lines of CodeGautier Marti
Modern NLP models can be daunting: No more bag-of-words but complex neural network architectures, with billions of parameters. Engineers, financial analysts, entrepreneurs, and mere tinkerers, fear not! You can get started with as little as 10 lines of code.
Presentation prepared for the Abu Dhabi Machine Learning Meetup Season 3 Episode 3 hosted at ADGM in Abu Dhabi.
Automate your Job and Business with ChatGPT #3 - Fundamentals of LLM/GPTAnant Corporation
Episode 3: The LLM / GPT / AI Prompt / Data Engineer Roadmap
In this episode, we'll discuss the history, fundamentals, and the different flavors of LLMs available, beyond GPT/ChatGPT. This is a dry run of a session that will be on a LLM Bootcamp ( Fill out the survey on the link if you are interested in an in-person vs. virtual session)
Intro / Fundamentals of LLM
LLM Foundations
History of LLMs
Tuning, Training, or "In Context Learning" with LLMs
What is "Prompt Engineering"
Case for Augmenting LLMs
Series: Using AI / ChatGPT at Work - GPT Automation
Are you a small business owner or web developer interested in leveraging the power of GPT (Generative Pretrained Transformer) technology to enhance your business processes? If so, Join us for a series of events focused on using GPT in business. Whether you're a small business owner or a web developer, you'll learn how to leverage GPT to improve your workflow and provide better services to your customers.
GPT Automation: What it is and How it Works
How Time-Saving GPT Automation Can Improve Your Business
Cost-Effective GPT Automation: How it Can Save Your Business Money
Using GPT Automation for Customer Service: Benefits and Best Practices
The Power of GPT Automation for Content Creation
Data Analysis Made Easy with GPT Automation
Top GPT-3 Automation Tools for Businesses
The Ethical Considerations of GPT Automation
Overcoming Bias in GPT Automation: Best Practices
The Future of GPT Automation: Trends and Predictions
Since we focus on "no code" here, we'll explore the tools that are already out there such as ChatGPT plugins for Chrome, OpenAI GPT API, low-code/no-code platforms like Make/Integromat and Zapier, existing apps like Jasper/Rytr, and ecosystem tools like Everyprompt. We'll also discuss the resources available for those interested in learning more about GPT, including other people’s prompts.
Benchmark comparison of Large Language ModelsMatej Varga
Benchmark comparison of Large Language Models. Check out which one performed the best, according to a study by
Ye, Seonghyeon, et al. "FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets."
Artificial Intelligence has unleashed a wave of innovation, from effortlessly summarizing
articles to engaging in deep, thought-provoking conversations — with large language
models taking on the primary workload.
Enter the extraordinary realm of large language models (LLMs), the brainchild of deep
learning algorithms. These powerhouses not only decipher and grasp massive amounts
of data but also possess the uncanny ability to recognize, summarize, translate, predict,
and even generate a diverse range of textual and coding content.
‘Big models’: the success and pitfalls of Transformer models in natural langu...Leiden University
Abstract: Large Language Models receive a lot of attention in the media these days. We have all experienced that generative language models of the GPT family are very fluent and can convincingly answer complex questions. But they also have their limitations and pitfalls. In this presentation I will introduce Transformer-based language models, explain the relation between BERT, GPT, and the 130 thousand other models available on https://huggingface.co. I will discuss their use and applications and why they are so powerful. Then I will point out challenges and pitfalls of Large Language Models and the consequences for our daily work and education.
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...David Talby
An April 2023 presentation to the AMIA working group on natural language processing. The talk focuses on three current trends in NLP and how they apply in healthcare: Large language models, No-code, and Responsible AI.
Unleashing the Power of OpenAI GPT-3 in FME Data Integration WorkflowsSafe Software
Join us for an eye-opening webinar where we will demonstrate the incredible power and productivity of OpenAI GPT-3 in FME data integration scenarios. From natural language processing to automated workflow generation and predictive modeling, we will show how GPT-3 can tackle even the most complex and daunting data integration challenges without a single line of code. This is a must-attend event for anyone looking to unlock the full potential of their data and streamline their integration workflows. Be prepared to be amazed and astounded at the capabilities of GPT-3 and FME!
ProductTank HK #31 - Maximizing Product Ops Efficiency with Generative AIAmanda Lam
ProductTank Hong Kong #31 - Maximizing Product Ops Efficiency with Generative AI
by Mushroom Luk and Amanda Lam
In his presentation, Mushroom will share his secrets for using generative AI tools like ChatGPT and GitHub Copilot to generate code and streamline workflows. He'll also take you on a journey through the exciting new field of text-to-image AI generation, showing you how to turn text-based descriptions into stunning visual content.
Get ready to be spellbound by the power of generative AI technology, as Mushroom demonstrates how to use these tools to create real value for your organization. Don't miss this chance to learn from the data wizard himself!
In addition, Amanda will also share her experiences in using more generative AI tools such as Bing Chat, Google Bard, Dall-E, Bing Image Generator and MidJourney etc., to tackle real life product ops problems such as user story preparation, budget and timeline estimation, slide preparations etc. This session will be based on live demo and will be interactive with the audience.
About the speakers
Introducing Mushroom Luk, the data analyst who knows as much about programming as a fish knows about tightrope walking! He's found the ultimate cheat code in life: an AI sidekick that does all the heavy lifting.
Amanda Lam is a tech enthusiast and community activist. She organizes ProductTank Hong Kong, Women Techmakers Hong Kong and HKPUG events, and is a consistent host of the weekly Cantonese tech podcast, HKPUG Podcast.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
SharePoint "Moneyball" - The Art and Science of Winning the SharePoint Metric...Susan Hanley
Measurement is not just about looking for a bottom-line result to justify investments. It’s also a tool to provide feedback about where the organization is along the road to successfully leveraging investments in SharePoint and the business outcomes it provides. At every stage in the development of your solution, metrics provide a valuable means for focusing attention on desired behaviors and results. This presentation showcases a practical and realistic framework for SharePoint metrics based on real world examples and successes.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
Content will range start with why does Text Analytics need a special session on convincing boss, followed by a role play summarizing current mistakes, a sample elevator pitch for your boss and a proposed execution plan. The content is tailored for Mid to Senior Level Managers trying to convince Leaders/Executives/Heads. It doesn’t provide any technical details –methodologies, tools, vendors or hardware investments.
This was presented at Text Analytics West Summit 2014 at San Francisco. Questions? Reach out at Ramkumar Ravichandran @ Linkedin.
Business process management - Selected casesPeter Trkman
A guest lecture at Pforzheim University in December 2021. It focuses on my cases related to business process management, customer service, business model innovation and supply chain management.
An overview of what digital transformation is, the main changes (in terms of customer experience, business models, and operational processes) and the opportunities brought by COVID-19.
(in Slovenian language)
Research presentation on supply chain management (October, 2019, WU, Vienna)Peter Trkman
The presentation of our School of Economics and Business, University of Ljubljana (SEB LU) followed by my past and current research on supply chain management (including processes, risk management, business analytics). The final part presents the ongoing research on the role of coopetition in supply chains
1.Business process management in organisations and supply chains 2. Cultural ...Peter Trkman
My presentation for Erasmus+ International Staff Week, 8 - 12 April 2019. It includes the presentation of the teaching at Academic unit for business informatics and logistics; the presentation of my past research and - most important for the audience - some of the things I have learnt as a guest professor at various universities around the world.
As always, the presentation is not entirely self-explanatory. If you want to learn more: peter.trkman@ef.uni-lj.si
A case study of the use of social media to promote university's study programs. The case is used as an in-class assignment for Electronic Business course
Digital transformation does not matterPeter Trkman
Presentation for CIO Forum, the leading Slovenian conference for information systems experts.
The talk will discuss reasons for the popularity of digital transformation buzzword, and the real changes that have happened in the business world.
The slides for the lecture before promotion to full professor for information systems management at triple crown accredited FELU - business school in Slovenia, Central Europe.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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
TESDA TM1 REVIEWER FOR NATIONAL ASSESSMENT WRITTEN AND ORAL QUESTIONS WITH A...
Large language models in higher education
1. LLM orodja so tukaj. Kaj sedaj?
Peter Trkman
Luka Tomat
2. Disclaimer
• This set of slides is intended for a “pedagogical conference” at School of
Economics and Business, University of Ljubljana on September, 11 2023.
• The slides are not meant as a stand-alone study resource but as a companion to
the workshop/starting point for the debate.
• All examples are generated by us with the available large-language model tools;
no completeness or accuracy claimed.
• For any further questions, feel free to contact us at peter.trkman@ef.uni-lj.si and
luka.tomat@ef.uni-lj.si
Feel free to use the slides under the
CC BY-SA license
3. Izhodišča
• Orodja umetne inteligence obstajajo že desetletja. Zakaj so tako popularna zdaj?
• Nova orodja so izjemno enostavna za uporabo brez kakršnega koli informacijskega znanja
• Ukvarjajo se s temami, ki so delavcem z znanjem in splošni javnosti bližje kot npr. optimizacija
skladišč
• Obstaja izjemna raznolikost načinov uporabe
• Ustvarjanje vsebin/slik ni le poceni, temveč ima (praktično) ničelne mejne stroške (tako finančne kot
časovne)
• odpirajo temeljna "filozofska" vprašanja, kaj je ustvarjalnost in "človečnost” in kako poučevati
Kaj to pomeni za nas
• Pripovedni del je manj pomemben. Vsak lahko zdaj piše lepa besedila. Skrbno napisan tekst ni več
signal česar koli
• "small talk" bo izumrl: v preteklosti je bilo skrbno napisan tekst, veliko število reference, lepi stavki,
dolg magisterij...znak truda in sposobnosti avtorjev.
• Kako sedaj izobraževati?
Povzeto po: https://www.linkedin.com/posts/peter-trkman_workshop-on-chatgpt-and-large-language-
models-activity-7081614403489476608-DNQL
5. Out-of-scope za danes
Uporaba LLM izven pedagoško-študijskega procesa
Uporaba drugih orodij generativne intelligence
Več na: https://www.cpoef.si/open-programs/generativna-umetna-inteligenca/
6. Vsebina
• Kaj LLM že znajo
• Preprečevanje uporabe LLM
• Učenje uporabe LLM
• Uporaba LLM pri konkretnih primerih
10. Chat GPT za strukturo mag. dela
ChatGPT
Certainly! Writing a thesis on digital transformation requires a structured approach to ensure that you cover all relevant aspects of the topic. Here's a suggested structure for your thesis:
Title Page:
•Title of the Thesis
•Your Name
•Institutional Affiliation
•Date
Abstract:
•A brief summary of your thesis (typically 150-250 words) that highlights the key points and findings.
Acknowledgments:
•Optional section where you can express gratitude to those who supported you during your research.
Table of Contents:
•List of chapters and sections with page numbers for easy navigation.
List of Figures and Tables:
•If applicable, include a list of figures and tables used in your thesis.
List of Abbreviations and Acronyms:
•Include any abbreviations or acronyms used in your thesis along with their definitions.
List of Symbols:
•If applicable, include a list of symbols used in your thesis.
Chapter 1: Introduction
•Introduction to the topic of digital transformation.
•Background and context.
•Problem statement and research questions.
•Purpose and significance of the study.
•Structure of the thesis (briefly outline what each chapter covers).
Chapter 2: Literature Review
•Overview of the concept of digital transformation.
•Historical context and evolution.
•Theoretical frameworks and models related to digital transformation.
•Key concepts, terms, and definitions.
•Previous research and studies on digital transformation.
Chapter 3: Theoretical Framework
•Select and discuss relevant theoretical frameworks or models that will guide your research.
•Explain how these frameworks relate to digital transformation.
Chapter 4: Methodology
•Explain your research methodology (qualitative, quantitative, mixed methods, case study, etc.).
•Data collection methods (interviews, surveys, data analysis, etc.).
•Data analysis techniques.
•Ethical considerations.
Chapter 5: Case Studies or Empirical Research
•Present your findings based on your chosen methodology.
•Analyze and discuss the data.
•Include case studies or real-world examples to illustrate digital transformation concepts.
Chapter 6: Discussion
•Interpret the results and findings.
•Discuss how your research answers the research questions.
•Analyze the implications of your findings for digital transformation in practice.
•Compare and contrast your results with existing literature.
Chapter 7: Conclusion
•Summarize the key findings of your research.
•Reiterate the significance of your study.
•Provide practical recommendations for organizations undergoing digital transformation.
•Discuss the limitations of your study.
•Suggest areas for future research.
References
•List all the sources you cited in your thesis following a specific citation style (APA, MLA, Chicago, etc.).
Appendices
•Include any supplementary material that supports your thesis (e.g., survey questionnaires, interview transcripts, additional data, etc.).
Index (Optional):
•If your thesis is particularly long and detailed, you may consider including an index to help readers navigate specific topics or concepts.
Remember to follow the formatting and citation guidelines required by your institution or department. Good luck with your thesis on digital transformation!
14. Kaj pa akademske reference – ChatGPT je slabši
Title: The Positive Impact of Business Analytics on Non-Financial Performance
Introduction: In the rapidly evolving landscape of modern business, the utilization of data-driven insights has become an indispensable tool for organizations aiming to achieve sustainable growth and success. Business
analytics, a comprehensive approach to extracting insights from data, has emerged as a powerful catalyst for informed decision-making across various sectors. This support paper aims to present a compelling
argument for the hypothesis that "business analytics positively affect non-financial performance." By delving into the ways in which business analytics empowers organizations to optimize their operations,
enhance customer experiences, and foster innovation, we will demonstrate how it goes beyond financial metrics to substantially impact non-financial dimensions of performance.
Optimized Operational Efficiency: Business analytics provides organizations with the means to optimize operational efficiency, leading to improvements in non-financial performance areas. By analyzing operational data,
organizations can identify bottlenecks, inefficiencies, and areas of improvement in their processes. This leads to streamlined workflows, reduced operational costs, and enhanced resource allocation. For instance,
predictive analytics can help organizations anticipate demand patterns, enabling them to optimize inventory levels and reduce stockouts. Such improvements directly impact non-financial metrics like production
efficiency, customer satisfaction, and employee morale.
Enhanced Customer Experience: A core pillar of non-financial performance is the customer experience. Business analytics empowers organizations to gain a comprehensive understanding of customer preferences,
behaviors, and needs. Through advanced analytics techniques, such as sentiment analysis and customer segmentation, organizations can tailor their products, services, and marketing strategies to align with
customer expectations. Consequently, improved customer experiences result in higher customer loyalty, increased brand advocacy, and positive word-of-mouth. These non-financial outcomes not only contribute
to enhanced performance but also have the potential to generate long-term value for the organization.
Data-Driven Innovation: Innovation is a critical driver of non-financial performance, and business analytics plays a pivotal role in fostering innovation within organizations. By analyzing market trends, consumer insights,
and competitive landscapes, organizations can identify opportunities for new product development, service enhancements, and process innovations. Furthermore, data-driven insights enable organizations to
make informed decisions about potential expansion into new markets or the introduction of novel business models. As innovation directly impacts brand perception, employee engagement, and overall
competitiveness, business analytics indirectly enhances non-financial performance.
Agile Decision-Making: Business analytics equips organizations with real-time, data-driven insights that facilitate agile decision-making. In today's dynamic business environment, the ability to respond swiftly to changing
circumstances is paramount. Analytics-driven decision-making enables organizations to proactively address challenges, capitalize on emerging trends, and exploit market opportunities. This agility in decision-
making translates into non-financial benefits, such as improved risk management, better adaptability to market fluctuations, and increased operational resilience.
Employee Performance and Engagement: The impact of business analytics on non-financial performance extends to the realm of human resources. By leveraging data analytics, organizations can gain insights into
employee performance, satisfaction, and engagement. This information allows for targeted interventions, such as personalized training and development programs, which lead to improved employee productivity
and job satisfaction. A motivated and engaged workforce contributes to non-financial outcomes like reduced employee turnover, enhanced team collaboration, and a positive organizational culture.
Conclusion: In conclusion, the hypothesis that "business analytics positively affect non-financial performance" is strongly supported by a wealth of evidence. Through optimized operational efficiency, enhanced customer
experiences, data-driven innovation, agile decision-making, and improved employee performance and engagement, business analytics serves as a multifaceted enabler of non-financial success. Organizations
that effectively harness the power of data analytics are poised to not only achieve financial growth but also to thrive across various non-financial dimensions, securing a competitive advantage in today's data-
centric business landscape.
Operational Efficiency:
Davenport, T. H. (2006). Competing on Analytics. Harvard Business Review.
Gupta, J. N. D., & Jain, A. K. (2018). Impact of Business Analytics on Operational Performance: An Empirical Study. International Journal of Information Management.
Customer Experience:
Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer Experience Creation: Determinants, Dynamics, and Management Strategies. Journal of
Retailing.
Luo, X., & Ba, S. (2012). Perspective: The Impact of Information Technology Investments on Customer Satisfaction, Loyalty, and Retention: Evidence from Franchised Firms. Information Systems Research.
Data-Driven Innovation:
Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading Digital: Turning Technology into Business Transformation. Harvard Business Review Press.
Berman, S. J., Bell, R., & Kim, K. (2019). The Use of Big Data Analytics in New Product Development: Drivers and Performance Implications. Journal of Business Research.
Agile Decision-Making:
Lacity, M., & Willcocks, L. P. (2013). Nine Ways to Reduce Cognitive Biases in Decision Making. MIT Sloan Management Review.
Serrano, A., & Karahanna, E. (2016). The Compensatory Interaction between User Capabilities and Technology Capabilities in Influencing Task Performance: An Empirical Investigation of Business Analytics.
Information Systems Research.
Employee Performance and Engagement:
Hameed, T., & Waheed, A. (2018). Impact of Business Analytics on Employee Performance: The Mediating Role of Employee Engagement. International Journal of Information Management.
Koelemeijer, K., van Deursen, A., & van der Horst, A. (2019). How Business Analytics Contributes to Organizational Performance: An Exploratory Study. International Journal of Information Management.
16. Elicit obvlada tudi to
The papers provide different definitions of
business analytics. Delen (2018) states that
business analytics is the encapsulation of
all mechanisms that help convert data into
actionable insight for better and faster
decision-making. Camm (2019) defines
business analytics as a set of tools and
techniques used to analyze data and make
informed business decisions. Yin (2020)
defines business analytics as a system
enabled by machine learning techniques
that aims to promote the efficiency and
performance of an organization by
supporting the decision-making process.
Acito (2014) describes business analytics
as a revolution that leverages value from
data and aligns strategy and desirable
behaviors to business performance
management in conjunction with analytic
tasks and capabilities.
Kaj je poslovna analitika
Kaj je poslovni model?
17. Kako deluje Elicit
Hm, pravzaprav zelo podobno kot mi
ali študenti, ko delamo pregled
literature
18. Podrobnejša vprašanja: “How does business
analytics affect performance?”
The papers suggest that business analytics can positively affect performance.
Ashrafi (2019) found that business analytics capabilities can increase a firm's agility,
which in turn can improve performance. Kakhki (2016) also found a positive
relationship between the extent of business intelligence and analytics implementation
and business performance. Trkman (2010) found a statistically significant
relationship between analytical capabilities and supply chain performance. Shanks
(2010) proposed a theoretical framework for understanding how business analytics
can lead to value-creating actions that improve firm performance and competitive
advantage. Overall, the papers suggest that business analytics can have a positive
impact on performance, but the extent of this impact may depend on factors such as
the type of business analytics used and the level of technological support.
22. Preprečevanje uporabe LLM
Orodja za zaznavanje obstajajo, vendar so “po definiciji” nezanesljiva.
De facto študentom v nekontroliranem okolju ne morete (in ne smete) preprečiti
uporabe LLM
Razmislite, kaj jih hočete naučiti
24. Enostavno pravilo
Če morate za preverjanje študentskega dela (domačih nalog/seminarskih) uporabljati
detektorje AI, spremenite nalogo in/ali način ocenjevanja.
26. Rešitve?
Obstajajo številne tehnične rešitve
(detektorji AI), ki so preproste za
uporabo, vendar ne rešujejo širšega
izziva trenutnega spreminjajočega
okolja.
Tehnologija pri tem ni ključna, pomembno
je razumavanje širše slike, želene
spremembe pa je potrebno razumeti na
konceptualnem nivoju, preden jih
uvedemo v prakso.
Vir: https://www.compilatio.net/en/blog/best-ai-detectors
27. Zahtevati uporabo AI
V kolikor je smiselna
Primer: pri predmetu e-business morajo študenti poslovnih ved razviti spletno stran
Nesmiselno bi jih bilo učiti html, css ali grafično oblikovanje itd.
Morajo pa razmisliti o vsebini, temeljnih sporočilih, se naučiti uporabe novih orodij
ter v predstavitvi povedati, zakaj so se tako odločili in katera orodja so se naučili
28. Primer naloge (pred Chat GPT)
Select a process within a chosen company (it can be a real or a fictional example)
Imagine you are developing a new software application for this process. What specifically would you do in
each phase of SDLC ? Be as concrete as possible.
How about Scrum - what would you do if using this method?
Which approach would you recommend?
Naloga vredna 4 točke (od 100) pri predmetu Informatics (1. letnik UPEŠ, English track)
Pedagoški cilj (neformalen): študente naučiti, kako hitro googleati, razmisliti, kako bi to aplicirali na nek
praktičen primer, kritično razmišljanje (prednosti/slabosti metode) ter jih navdušiti za nadaljnje učenje o
razvoju programskih rešitev (vsaj v vlogi poslovnih uporabnikov)
31. Še vedno možne naloge
Ni vse na Internetu:
“Specifically, students were asked to assess the current state of the carbonated soft drink
industry in 2023 and analyze significant changes in the industry forces (based on Porter’s
Five Forces Framework) since the end of the case study discussed in class”
ali pa
“Describe the main lessons of the Hamapol case study”
“Reflect on the guest lecture of Ms. Butina and how her lessons could be applied to the
case that Mr. Novak has presented in his guest lecture”
Možen nov poskus: date vprašanje/področje, ki ga niste obravnavali pri predmetu.
Študentje imajo 10 minut časa za odgovor.
32. Še vedno možne naloge
How do you plan to control for Cronbach
common method variance?
How about Cronbach common method
traceability?
33. Učenje, kako uporabljati LLM
“Helping students to become better prompt engineers” ni smiselno
Generično znotraj številnih predmetov ni smiselno (tudi uporabe Googlea ne učimo)
Po potrebi lahko delavnice na RPS
(glej npr. poglavje 3 tule : https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4516801
in še mnogo drugih virov)
34. Kaj morajo študenti znati
Manj pomembno: branje in povzemanje velikega nabora virov
Bolj pomembno: hitro učenje; sposobnost postavljanja pravih vprašanj
Tipičen primer: črkovalne napake pri predmetu Poslovna angleščina vplivajo na
oceno. Pri izpitu Informatics ne vplivajo. Pri seminarski nalogi pa kažejo površnost
študenta (niti črkovalnika ni uporabl).
Svojemu sinu (12 let) za domače naloge pri angleščini ne pustim uporabljati niti MS
Worda. Hčerka (14 let) za zgodovino in DKE lahko uporablja Chat GPT.
35. Možne spremembe: maksimalna transparentnost
“Opravil sem 3 intervjuje v podjetju Mercator” ali “zbral sem odgovore 190
respondentov” ni več OK
“Dne 11. 9. 2023 sem v podjetju Špedko opravil intervju z vodjo službe za informatiko
ga. Jožico Novak. Intervju je potekal v sejni sobi podjetja na Vojkovi 32, trajal pa
je med 9.00 in 10.30. Zvočni posnetek intervjuja je na razpolago pri avtorju
magistrske”
36. Možne spremembe: magistrske naloge
Radikalno zmanjšati število citiranih referenc v zaključnih/seminarskih nalogah na neko smiselno količino (ostalo v “nadaljnje
branje”).
Radikalno povečati zahtevo po študentovem poznavanju vsebine vsega, kar je citiral. Teme iz “nadaljnje branje” mora okvirno
poznati.
Zagovor se iz “pogovora” spremeni v de facto “diplomski izpit”.
Možen potek zagovora
V: Kje pa je tale Ashrafi zbiral podatke?
O: hm, ne vem
[over & out - pade zagovor, “se vidimo čez 1 mesec”]
V: hm, kje pa je tale Ashrafi zbiral podatke?
O: v Iranu
V: pa je ta iranska analiza sploh relevantna za vaš primer slovenskega podjetja?
O: hm, ne vem [ima še šanso, da naredi, ampak samo če res dobro odgovori na ostalo]
O: ja, ker...[zelo natančen odgovor, ki povzame omejitve raziskave Ashrafija in pove tudi, zakaj so Ashrafijeve ugotovitve vseeno
relevantne za njegov konkreten primer in kako so mu pomagale pri implementaciji orodja].... [naredi s super oceno. V takem
primeru “So what if ChatGPT wrote it?”]
37. Tipično vprašanje študenta
As for the purpose and goals I used a bit of let's say "inspiration" from
ChatGPT, and I saw in the guidelines that we should reference the AI tool.
Can you tell me whether I am on the right path, should I include the in-cite
reference there?
In terms of Chat-GPT: Take Chat-GPT as if you would discuss the topic with me or e.g.
your mother (if she were the Chief Information Office in Skopje) - it is perfectly OK to talk to
people (and computers 🙂), get ideas for them, interact.
Take ChatGPT as if you were discussing topics with an expert who will help point you in
the right direction, give you some ideas, entertain you, or an assistant who will summarise
some thoughts. But do not take it as a "credible source". Also, in the thesis, don't cite
ChatGPT (in the same way as you would not cite your mother's thoughts or e.g. "as
professor Trkman said, smart mobility is important").
38. Možna politika pri predmetu
It is perfectly OK to use ChatGPT (partly) for this particular task... but mainly to get ideas
on what to explore further/how to structure your answer.
Your answer should show that you are personally well versed in the topic and should not
be a copy & paste answer from ChatGPT.
Take ChatGPT as if you were discussing topics with an expert who will help point you in
the right direction, or an assistant who will summarise some thoughts.
It is perfectly OK to talk to people (and computers 🙂), get ideas for them
Also: be careful, ChatGPT makes things up (especially the list of references). Any
mistake is your mistake.
You must fully understand your answer - selected students may be chosen to give further
oral explanations of their answers.
39. Sporočilo za študente
Example: Let’s say that you will be a consultant for WMS implementation and your
customer will ask you: “what is the support for order picking by voice offered by your
WMS? Which are the main expected problems in its implementation in my company?
What is the pay-back period?”
Your response can’t be: “Hm, wait for a few minutes for me to ask ChatGPT ver. 10
what WMS, payback period and order picking are”.
You will need a profound theoretical knowledge, practical experience, ability to
analyse and apply your insights into a specific context of a company, ability to
communicate with diverse audiences...and then the ability to get things done.
40. Končna misel
#chatgpt je velika tema.Vendar ga ne uporabljajte preveč. Sami pišite osebna e-
poštna sporočila.
Trenutno lahko dolgi, lepo napisani ChatGPT maili še delujejo
Bolj ko postaja naš svet digitalen, bolj so majhni, človeški koraki ključni
Prošnja nekomu za uslugo (ali ljubezensko pismo) naj ne bo dolgo, generično
besedilo z umetno inteligenco, temveč kratko sporočilo "ni mi vseeno zate".