To the living we owe respect, but to the dead we owe only the truth.
Voltaire
College students pitted against ChatGPT to boost writing (insidehighered.com)
The University of Nevada, Reno has introduced two innovative online courses aimed at education majors, where students are challenged to compete against ChatGPT in writing assignments. This initiative, led by professors Leping Liu and Rod Case, is designed to help future educators understand the limitations and potential of AI in educational settings. By directly comparing their writing to that of ChatGPT, students not only improve their writing skills but gain a deeper understanding of AI-generated content. The approach includes gamification elements, where students advance through levels inspired by the book Dune if they outperform ChatGPT, thus making the learning process engaging and interactive.
Key Points
Course Introduction:
The University of Nevada, Reno, has launched two online courses where students compete against ChatGPT in writing assignments.
The courses help future educators understand AI's capabilities and limitations.
Instructors’ Perspectives:
Professors Leping Liu and Rod Case designed the courses to combine their information technology and educational studies expertise.
The initiative began in response to the widespread use of ChatGPT and aims to augment teaching and learning methods.
Course Design and Mechanics:
The courses incorporate gamification, analysis, and competition.
Students complete writing prompts and aim to earn higher grades than ChatGPT, which answers the same prompts.
Educational Impact:
The approach helps students understand how ChatGPT generates content and its limitations.
Students' writing improved significantly after comparing their work with ChatGPT’s outputs.
Gamification Element:
Success against ChatGPT is rewarded through a gamified system where students advance through cities named in the book Dune.
This approach aims to increase engagement in online courses.
Broader Implications:
The initiative reflects a growing trend of integrating AI and gamification into education.
It addresses concerns that ignoring AI technology could lead to students creating their own, possibly less effective, rules for its use.
Key Quotes
Leping Liu: “ChatGPT comes out and everyone is using it, talking about it, whether or not we’d like them to. We have to deal with it, so we [wanted to] find a way to augment our teaching and learning and not just focus on cheating [concerns].”
Rod Case: “They begin to understand the nature of ChatGPT and how it writes and puts content together and understanding its limits as well. They’re future teachers and need to understand its limits in the way it writes and what they can and can’t use it for.”
Marshall Jones: “One of the things that happened with cell phones is that grownups didn’t understand them, so kids made up their own rules. My theory is if we remain reactive to this, it’ll remain a parallel where they make up their own rules.”
Robert Seamans: “If you’re in a classroom setting or at home and it’s, ‘Play this game against ChatGPT,’ there’s no harm in that; it’s the equivalent of saying, ‘Go home tonight and research XYZ using the internet.’”
Why It Matters
This initiative is significant for several reasons:
Educational Innovation:
By integrating AI into coursework, the University of Nevada, Reno is at the forefront of educational innovation. This approach enhances students' writing skills and prepares them to navigate and leverage AI technologies in their future careers.
Understanding AI Limitations:
Future educators gain firsthand experience with AI, helping them understand its strengths and limitations. This knowledge is crucial as AI increasingly integrates into various educational tools and platforms.
Encouraging Critical Thinking:
Comparing their work with AI-generated content encourages students to think critically about their writing and improves their analytical skills. This process of reflection and improvement is essential for academic growth.
Engagement Through Gamification:
Gamification makes the learning process more engaging and interactive, which is particularly important in online education. This method can lead to higher levels of student motivation and participation.
Preparing for Future Challenges:
Understanding their implications and applications will be vital for educators as AI technologies continue to evolve. This initiative ensures that future teachers are well-equipped to handle these advancements professionally.
In summary, the University of Nevada, Reno's approach to integrating AI and gamification in education represents a forward-thinking strategy that enhances learning outcomes and prepares future educators for a technology-driven world.
RAG vs. GAR: A primer on Generation Augmented Retrieval (luk.sh)
The article by Oliver Lukesch compares two advanced approaches in the field of machine learning and information retrieval: Retrieval-Augmented Generation (RAG) and Generation-Augmented Retrieval (GAR). While RAG is an effective method to "ground" large language models (LLMs) in specific datasets, it has several drawbacks that make it less suitable for certain applications. The author explains these drawbacks and introduces GAR as an alternative that leverages the reasoning capabilities of LLMs to enhance the retrieval process and provide rich, interactive results. GAR is particularly useful for domain-specific applications where precise and customizable outputs are crucial.
Retrieval-Augmented Generation (RAG)
Overview:
RAG is a technique that combines retrieval-based methods with generative models. It works by retrieving relevant documents or pieces of information from a pre-existing database and then using a generative model to craft responses based on that information. This approach aims to ground the generative model's outputs in factual data, thus improving the accuracy and relevance of the generated text.Strengths:
Factual Grounding: By leveraging a retrieval step, RAG can ground its outputs in real, verifiable data.
Flexibility: It can be applied to a wide range of tasks, including Q&A, summarization, and content generation.
Enhancement of LLMs: It enhances the performance of large language models (LLMs) by providing them with specific, relevant context from a fixed dataset.
Weaknesses:
Repetitive Outputs: The generative model might produce repetitive text, especially if the dataset contains similar or redundant information.
Responsibility for Outputs: The use of conversational interfaces can lead to potential PR problems if the generated content is inappropriate or incorrect.
Dependency on Data Quality: The performance of RAG heavily depends on the quality and relevance of the retrieved documents.
Generation-Augmented Retrieval (GAR)
Overview:
GAR flips the paradigm by using LLMs primarily for their reasoning capabilities rather than for text generation. In GAR, the LLMs analyze and reason over query inputs to determine which database entries to retrieve and present. This approach aims to provide more accurate, context-aware, and interactive results.Process:
User Query: The user inputs a query, which can include plain text and additional filters or parameters.
Data Preprocessing: The system pre-filters relevant database entries based on essential properties.
LLM Reasoning: The LLM uses its reasoning capabilities to select the best matching entries.
Output Generation: The selected entries are returned as IDs in JSON format, which the application then converts into a rich, interactive display.
Strengths:
Precise Results: By focusing on reasoning, GAR can provide highly accurate results tailored to complex queries.
Interactive Outputs: The approach supports the creation of feature-rich and interactive results, enhancing user experience.
Control Over Outputs: GAR allows for tighter control over what is displayed to the user, reducing the risk of inappropriate or incorrect information.
Weaknesses:
Speed: Complex reasoning processes can be slow, although this can be mitigated with faster models and streaming techniques.
Context Limitations: The limited context size of models can be a bottleneck, necessitating efficient filtering and retrieval methods.
Less Conversational: GAR is less suited for purely conversational interfaces but can complement them by providing interactive content.
Applications and Implications
Applications:
Domain-Specific Search Engines: Both RAG and GAR can be used to improve the accuracy and relevance of search results in specialized fields.
Customer Support: Implementing these technologies in chatbots can enhance the quality of automated customer service.
Content Generation: RAG can be particularly useful in generating summaries, articles, and other content based on existing data.
Legal and Medical Fields: GAR's precise and controlled outputs make it suitable for applications where accuracy and reliability are crucial.
Implications:
User Experience: The choice between RAG and GAR can significantly impact the user experience, depending on the application's needs for accuracy, interactivity, and speed.
Ethical Considerations: Ensuring that the generated content is appropriate and accurate is critical, especially in sensitive domains.
Technological Advancements: As LLMs continue to evolve with larger context sizes and faster inference speeds, the practical applications of both RAG and GAR will expand.
Key Quotes
On RAG's Limitations:
"Conversational interfaces do not always offer the best UX: Many people try to opt out of the newly launched Google AI search for a reason."
"Whenever you launch a chatbot under your name or brand, you are responsible for the answers it generates."
On the Benefits of GAR:
"Generation Augmented Retrieval turns LLMs into smart, context-aware query engines that tell the application what objects to load from the database in order to display them as rich, interactive UI elements."
"The main difference from RAG is that in GAR, LLMs are primarily used for their reasoning capabilities rather than text generation."
Final Thoughts
RAG and GAR represent two sophisticated approaches to leveraging LLMs for information retrieval and generation. While RAG focuses on grounding generative models in factual data, GAR emphasizes the reasoning capabilities of LLMs to provide precise and interactive results. Understanding the strengths and weaknesses of each approach is essential for selecting the right tool for specific applications, ultimately enhancing the effectiveness and user experience of AI-powered systems.
Frontiers, knowledge work, 2024++ | shyamal’s space
Shyamal Anadkat’s article explores the transformative impact of AI on knowledge work as we move into 2024 and beyond. He reflects on the evolution of AI, particularly noting the advancements in large language models like GPT-4, and how these technologies are reshaping various industries. Anadkat categorizes the emerging startups into three groups: those that distribute knowledge, those that create new knowledge, and those that develop AI assistants for knowledge work. He highlights the potential of these AI tools to democratize access to knowledge, optimize resources, and enhance productivity. The piece also discusses the economic implications of this shift, suggesting a movement from a knowledge-based economy to a resource-based one by 2035. Anadkat emphasizes the importance of user experience (UX) in AI development, arguing that successful AI integration will depend on creating tools that augment human abilities rather than replace them.
Key Points
Advancements in AI:
GPT-4 has significantly impacted developers, startups, students, and economies globally.
AI models have become more intelligent, multi-modal, faster, fine-tunable, interpretable, and safer.
Categories of AI Startups:
Distributing Knowledge: Startups that help in spreading and accessing knowledge.
Creating New Knowledge: Tools that assist in generating new insights and information.
Developing AI Assistants: Companies focusing on building AI copilots/assistants for various knowledge work tasks.
Real-world Applications and Impact:
Klarna's AI assistant has significantly reduced customer service times and matched human agents in satisfaction scores.
Perplexity.ai is reimagining search with real-time web searching and advanced summarization.
ChatGPT enterprise solutions are revolutionizing work across different business functions.
Economic Shifts:
AI is democratizing access to knowledge, which will shift competitive advantage towards resource optimization.
By 2035, there may be a transition from a knowledge-based economy to a resource-based economy.
Focus on User Experience (UX):
Effective AI tools must hide complexity behind simple, user-friendly interfaces.
The real challenge is designing complete user experiences that deliver sustained value.
Future of Knowledge Work:
AI will augment human capabilities, allowing workers to focus on higher-value activities like creativity and critical thinking.
Companies that effectively integrate AI will gain a competitive edge.
Key Quotes
On GPT-4's Impact:
“I’m genuinely humbled by gpt-4’s impact - how it shifted worldviews and landscapes, sparking new opportunities for developers, startups, students, and economies globally while driving critical progress on AI alignment and safety.”
On AI Startups:
“Founders thinking deeply about knowledge work from the first principle basis and ones chasing to accelerate that vision by deploying knowledge work assistants are worth taking a bet on.”
On Real-world Applications:
“Klarna announce its AI assistant which has had 2.3 million conversations, two-thirds of Klarna’s customer service chat, doing the equivalent work of 700 full-time agents... customers now resolve their errands in less than 2 mins compared to 11 mins previously.”
On Economic Shifts:
“As AI systems/agents become more capable of performing various knowledge tasks, such as research, software engineering, analysis, and content creation, the barriers to accessing and utilizing knowledge will be lowered.”
On User Experience (UX):
“Often the best solutions hide complexity behind simple interfaces that provide long-term value.”
On the Future of Work:
“The wisest path for knowledge workers may be to quickly become experts at using AI tools themselves to amplify their skills, to stake out new high ground in roles that make the most of human strengths like empathy, strategy, and managing ambiguity.”
Why It Matters
The discussion on the frontiers of knowledge work and AI is crucial as it highlights the transformative potential of AI in reshaping industries and economies. Anadkat’s insights emphasize the importance of user-centric AI development and the need for strategic integration of AI tools to enhance human capabilities. Understanding these dynamics is essential for businesses, policymakers, and workers as they navigate the evolving landscape of AI and its implications for productivity, economic shifts, and the future of work. The article also underscores the importance of democratizing knowledge and optimizing resources, which could lead to more equitable and efficient economic structures.
The Root Cause - Philip Jacob (whirlycott.com)
Philip Jacob's article "The Root Cause" explores the complexities of identifying the root cause in incident analysis through a narrative involving a couple, Alice and Bob, and their frustrating grocery shopping experience. The story illustrates how multiple factors and decisions contribute to an incident where a grocery bag rips, causing the contents to spill. Jacob challenges the traditional approach of pinpointing a single root cause, arguing that such efforts are often unproductive and can impede learning. Instead, he advocates for a model that distinguishes between the trigger and contributing factors, emphasizing the importance of understanding the interplay between various elements in a complex system. This approach aims to foster a learning environment where individuals can adapt and improve the overall system's resilience.
Key Points
Narrative Setup:
Alice and Bob's grocery shopping experience is disrupted when a single bag overloaded with groceries rips, causing a mess.
Multiple factors contribute to the incident, including Bob's distraction with work, Alice's reluctance to speak up, and the cashier's indifference due to job dissatisfaction.
Critique of Root Cause Analysis:
Jacob critiques the traditional root cause analysis, arguing that it often leads to overly abstract discussions and fails to foster meaningful learning.
He suggests that focusing on a single root cause can be unhelpful and misleading in complex systems.
Alternative Model:
Jacob proposes a model that separates the trigger (the immediate cause of the incident) from contributing factors (underlying elements that played a role).
This model encourages a holistic view of incidents, recognizing the interplay of multiple factors.
Learning and Adaptation:
The goal is to identify opportunities for learning and adaptation for all parties involved, rather than assigning blame.
By understanding the contributing factors, individuals and organizations can make changes to improve the system's resilience.
Complex Systems:
Jacob emphasizes that incidents in complex systems are usually the result of multiple co-occurring factors.
He argues that recognizing this complexity is crucial for effective incident analysis and improvement.
Key Quotes
On the Incident:
“This is the incident.”
On Root Cause Analysis:
“The reason I don’t find the root cause model helpful in incident analysis is because it gets in the way of learning.”
“Establishing a root cause in a complex system is a valueless exercise.”
On the Alternative Model:
“A better model is one that relies on two simple concepts: the trigger and contributing factors.”
“When they [factors] co-occur, that’s when things go wrong. You can think of this as an opportunity to learn.”
On Learning and Adaptation:
“If we learn, we can adapt. If we can adapt, we can drive better outcomes.”
Why It Matters
Philip Jacob’s article is important because it challenges the conventional approach to incident analysis, which often focuses on identifying a single root cause. By illustrating the complexity of real-world incidents through a relatable narrative, Jacob highlights the limitations of this method and advocates for a more nuanced approach that recognizes the interplay of multiple factors. This perspective encourages a culture of continuous learning and adaptation, which is crucial for improving systems’ resilience and preventing future incidents. Understanding this approach can help organizations and individuals move beyond blame and towards constructive solutions, ultimately leading to better outcomes and more robust systems.
Processes don't create ownership, people do | Florian Bellmann | Be curious, explore and meditate.
Florian Bellmann's article "Processes don't create ownership, people do" emphasizes the importance of fostering a sense of ownership among team members through leadership and mentorship rather than relying solely on processes and guidelines. Bellmann argues that while processes can make ownership behaviors more measurable, they often fail to address the underlying issue and may even frustrate those who do not naturally take ownership. Instead, he advocates for leaders to create an environment where team members feel safe to take risks and make decisions. He highlights the necessity of continuous effort in mentoring and leading by example to nurture a culture of ownership. Bellmann concludes by challenging readers to reflect on their own actions regarding ownership and leadership.
Key Points
The Misconception of Processes:
Companies often implement processes and guidelines to enforce ownership, making such behaviors measurable for management.
These processes address only the symptoms and not the root cause, potentially leading to frustration among team members who do not naturally take ownership.
The Role of Leadership:
Ownership is a behavioral and mindset change, making it a leader’s task to mentor and guide team members.
Leaders should create an environment that encourages risk-taking and decision-making.
Continuous Development:
Developing ownership requires continuous effort and cannot be achieved through shortcuts.
Leaders must repeatedly explain the vision and each team member’s contribution to it.
Leading by Example:
Leaders should mentor, take time to guide their teams, and lead by example to build the desired culture.
They must inspire and influence their team members to cultivate ownership.
Personal Reflection:
Bellmann encourages readers to reflect on their own experiences with taking ownership and inspiring others.
Key Quotes
On Processes:
"The tendency to create processes and guidelines to enforce ownership is a common pitfall."
"Having only processes in place though, that are potentially tied to performance evaluations, will solely reinforce the behaviours of people who already take ownership and frustrate the rest of the team."
On Leadership and Mentorship:
"It's a mentoring task."
"It's about creating an environment in which people feel safe to take risks and make decisions on their own."
"Mentoring, truly taking time and leading by example are the way to build the desired culture."
On Continuous Effort:
"Ownership must be developed and nurtured continuously. It's a prolonged effort. There is no shortcut."
On Personal Reflection:
"When was the last time you took ownership, led by example and maybe even inspired someone?"
Why It Matters
Florian Bellmann's article is significant because it challenges the common approach of relying on processes to instill ownership in teams. By highlighting the limitations of process-driven strategies and emphasizing the critical role of leadership and mentorship, Bellmann provides valuable insights into building a culture of ownership. This perspective is crucial for organizations seeking sustainable success, as it encourages leaders to invest in their team’s development and create environments where ownership can thrive. Understanding these principles can help leaders foster more engaged, proactive, and empowered teams, ultimately leading to better organizational outcomes.
In his keynote address at City Week 2024, Randall S. Kroszner, an external member of the Bank of England’s Financial Stability Committee (FPC) and Financial Market Infrastructure Committee (FMIC), discussed the dual role of financial technology and artificial intelligence (AI) in promoting productivity and posing potential financial stability risks. He emphasized the necessity for the UK to embrace technological innovation while maintaining financial stability. Kroszner highlighted the importance of distinguishing between incremental and fundamentally disruptive innovations, and the different regulatory challenges they present. He also outlined a framework for considering AI's impact, focusing on model interpretability and potential misalignment.
Key Points
Technological Potential and Risks:
Recent advances in technology and AI offer significant opportunities for innovation and productivity growth but also introduce new financial stability risks.
The balance between fostering innovation and ensuring financial stability is crucial.
Regulatory Challenges:
Incremental innovations are easier to regulate due to their predictable impacts.
Disruptive innovations pose greater challenges as their consequences are less predictable and require more proactive regulatory approaches.
Role of Financial Policy Committees:
The FPC is tasked with identifying, monitoring, and mitigating systemic risks.
The FMIC focuses on supervising financial market infrastructures and facilitating innovation in central counterparties and central securities depositories.
Importance of Productivity:
Higher productivity leads to stronger economic growth, higher wages, increased profitability, and more tax revenues.
The UK has experienced weak productivity growth in recent years compared to other countries.
AI and Productivity Growth:
AI and technology can significantly enhance productivity. For instance, Goldman Sachs estimates that generative AI could raise annual labor productivity growth by 1.5 percentage points.
The impact of AI depends on the speed and scale of its adoption.
Framework for AI:
Kroszner proposed a framework for thinking about AI, emphasizing the interpretability of models and the risk of misalignment.
Large language models (LLMs) and their complex algorithms pose challenges in understanding and explaining outcomes.
Historical Lessons and Future Actions:
Policymakers can draw on past experiences to manage the challenges posed by new technologies.
There are existing areas where actions can be taken to ensure a conducive environment for both innovation and financial stability.
Key Quotes
On the dual role of committees: "I have approached this subject with my two Bank of England hats on: as an external member of both the Bank’s Financial Stability Committee (FPC) and the Financial Market Infrastructure Committee (FMIC)."
On productivity: "Higher productivity means stronger economic growth, higher real wages, increased profitability and a boost to tax revenues."
On incremental vs. disruptive innovation: "Ensuring both financial stability and innovation is particularly challenging when we are dealing with the potential for fundamentally disruptive innovation that AI could bring versus the more traditional case when innovation and change is more incremental."
On the importance of AI: "There are many estimates of the boost AI and technology can give to productivity growth. A recent report by Goldman Sachs suggests that generative AI could raise annual US and UK labour productivity growth by just under 1.5 percentage points."
Lessons from History’s Most Misunderstood Philosopher [Recs] (substack.com)
The article "Lessons from History’s Most Misunderstood Philosopher [Recs]" by Devansh on the Artificial Intelligence Made Simple Substack explores the philosophy of Epicurus and its relevance to modern life. Here are some key takeaways:
Highlights
Misunderstanding of Epicurus: Often associated with hedonism, Epicurus's philosophy is actually about achieving happiness by removing sources of unhappiness.
Epicurean Hedonism: True pleasure comes from simplicity and self-sufficiency, not from indulgence or material excess.
Introspection: Much suffering is self-inflicted through irrational fears and societal pressures. Epicurus emphasizes the importance of critical thinking and self-reflection.
Friendship: Strong social connections are crucial for happiness and well-being, often neglected in modern society.
Main Themes
Epicurus the Hedonist:
Happiness is achieved by removing unnecessary desires and indulgences.
Modern relevance: In a world of social media and constant advertising, Epicurean philosophy can help us focus on genuine pleasures rather than societal pressures.
Epicurus the Introspector:
Superstitions and irrational fears lead to suffering.
Importance of mental space for critical thinking, away from societal distractions.
Epicurus the Friend:
Emphasizes the importance of friendship for a happy life.
Modern society often undervalues friendships in favor of career and personal achievements.
Misconceptions about Epicureanism
Not about Lavish Indulgence: Epicureanism is about achieving tranquility (ataraxia) through simple pleasures and self-sufficiency.
Pleasures and Pains: Epicurus ranked pleasures and pains based on their impact on well-being, advocating for the elimination of unnecessary desires.
Practical Applications
Social Media and Consumption: Encourages questioning and pruning desires influenced by societal pressures.
Community and Introspection: Close-knit communities can aid in self-reflection and critical thinking.
Friendship and Health: Strong social connections significantly contribute to happiness and health, reducing risks of depression and premature death.
The article suggests reading Epicurus's work to form personal conclusions and critically evaluate his philosophy's applicability to modern life.