Blue Flower

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 This is a must-listen for anyone who’s even remotely interested in implementing AI in their work and organisation. Ethan Mollick is an Associate Professor at the Wharton School of the University of Pennsylvania, where he studies and teaches innovation, entrepreneurship, and AI’s impact on work and education. I think of him as Jacques Cousteau of deep divers into the world of AI chatbots — he has gone deeper in his experiments with tools like ChatGPT than most people.

His upcoming book Co-Intelligence: Living and Working with AI (out on 2 April) is a detailed playbook for using AI in work and everyday life.

Here’s what you’ll learn in the conversation with Azeem Azhar : - Ethan and Azeem Azhar share how they use ChatGPT and other tools in their work — and simple practices to achieve better results in your day-to-day use, - What is the observed impact of AI in work, as studied by Ethan and his colleagues, and published in their 2023 study Navigating the Jagged Technological Frontier (see EV441) - How businesses and individuals can develop strategies for harnessing the power of AI. - How to maintain the balance between exploration and exploitation as you implement AI in your organisation — and how to prepare for the unknown future.

 

Where to find Ethan: Newsletter: https://www.oneusefulthing.org

 

How to unleash the power of AI, with Ethan Mollick

Timestamps:

0:00 Introduction

03:01 Centaur vs. Cyborg approaches to AI

04:22 Being polite to AI & setting the context

11:23 Ethan's students create GPTs to automate their jobs

19:05 The jagged frontier of AI

23:59 What should the kids be learning in the age of AI?

28:55 How to set yourself up for success with generative AI

38:49 Anthropomorphising LLMs and their personas

42:33 Factuality and hallucinations

44:36 AI's impact on entrepreneurship

48:39 Emerging characteristics

50:17 What happens if jobs become obsolete?

56:13 How should organisations balance exploitation vs exploration

01:02:24 How to implement AI in your work if you're busy?

01:03:25 What if it works out really well for us?

 

Published by Azeem Azhar

Azeem Azhar is a global expert on artificial intelligence and exponential technologies. He writes one of the top technology newsletters Exponential View, and hosts the Bloomberg Originals series Exponentially.

Exponentially TV Show youtube.com/watch?v=HzlsKrMO5M4

 

 

Summary:

In this insightful discussion, Ethan Mollick, a professor at the Wharton School, explores the vast potential and limitations of AI technologies like ChatGPT. The conversation delves into practical applications, the evolution of AI, and the personal experiences of those who utilize these technologies for various tasks. Here are some key points:

  • Usage and Impact: Mollick spends a significant amount of time interacting with AI, particularly ChatGPT, applying it to a variety of tasks. He highlights its role as a "Swiss army knife of the mind," aiding in tasks ranging from writing to data analysis.

  • Authorial Process: In writing his book "Co-Intelligence," Mollick extensively used AI, benefiting from its capability to offer diverse sentence structures and analogies, thereby making the writing process smoother and more creative.

  • AI as a Tool: The conversation underscores the distinction between two types of AI usage: 'Cyborg' and 'Centaur'. Mollick’s approach to writing the book was a blend of these, integrating AI insights while maintaining a clear division of tasks.

  • Interaction Dynamics: Mollick’s interaction with AI is casual, treating it as a collaborator rather than just a tool. This approach, coupled with understanding the AI’s personality, enhances the output quality.

  • Effectiveness and Limitations: Despite its utility, AI has its limitations. Mollick discusses instances where AI’s output could be less than ideal, highlighting the importance of understanding and navigating these limitations.

  • Insights on Flexibility and Creativity: The discussion also touches on the importance of flexibility and creativity in leveraging AI for tasks such as academic research, showcasing how AI can serve as a collaborative partner in intellectual endeavors.

  • Future Perspectives: Mollick expresses optimism about the evolving capabilities of AI, suggesting that future developments could significantly enhance productivity and creativity in various fields.

This conversation sheds light on the nuanced relationship between humans and AI, emphasizing the potential of AI to augment human capabilities when used thoughtfully and creatively.

Insights based on numbers:

  • Mollick mentions a ratio of ChatGPT and Perplexity sessions to Google, highlighting a shift in his research and information-gathering habits due to AI's effectiveness.
  • The 40% improvement in quality and 26% improvement in speed when consulting professionals incorporate AI into their workflow, showcasing AI's impact on efficiency and output quality.

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The Turing Lectures are back for 2023, at London's Royal Institute. In this series, we'll explore the various angles of large-language models and generative AI in the public eye. Across three lectures, we aim to provide a comprehensive, thoughtful and engaging understanding of this rapidly emerging field and its impact on society.
 
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 Generative AI refers to a type of artificial intelligence that involves creating new and original data or content. Unlike traditional AI models that rely on large datasets and algorithms to classify or predict outcomes, generative AI models are designed to learn the underlying patterns and structure of the data and generate novel outputs that mimic human creativity. ChatGPT is perhaps the most well-known example, but the field is far larger and more varied than text generation. Other applications of generative AI include image and video synthesis, speech generation, music composition, and virtual reality.
In this lecture, Professor Mirella Lapata will present an overview of this exciting—sometimes controversial—and rapidly evolving field. Mirella Lapata is professor of natural language processing in the School of Informatics at the University of Edinburgh.
Her research focuses on getting computers to understand, reason with, and generate natural language. She is the first recipient (2009) of the British Computer Society and Information Retrieval Specialist Group (BCS/IRSG) Karen Sparck Jones award and a Fellow of the Royal Society of Edinburgh, the ACL, and Academia Europaea.
 
This lecture is part of a series of events - How AI broke the internet - that explores the various angles of large-language models and generative AI in the public eye. This series of Turing Lectures is organised in collaboration with The Royal Institution
 
 
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 As generative AI becomes increasingly sophisticated, its potential to revolutionise the way we interact with data is clear. It has already shown its ability to assist with tasks such as image and video synthesis, text and speech generation, and music composition. However, the rapid development of generative AI models has also raised concerns about their misuse, particularly in the context of disinformation campaigns, deepfakes and online harassment. Are generative AI models moving too fast for regulation to keep up? Will the development of generative AI outpace our ability to ensure their responsible use?
 
In this lecture, Dr Mhairi Aitken will examine what this means for online and offline safety and discuss how society might be able to mitigate these risks. Mhairi Aitken is an Ethics Fellow in the Public Policy Programme at The Alan Turing Institute, and an Honorary Senior Fellow at Australian Centre for Health Engagement, Evidence and Values (ACHEEV) at the University of Wollongong in Australia.
 
She is a Sociologist whose research examines social and ethical dimensions of digital innovation particularly relating to uses of data and AI. She was included in the 2023 international list of “100 Brilliant Women in AI Ethics”.
 
This lecture is part of a series of events - How AI broke the internet - that explores the various angles of large-language models and generative AI in the public eye. This series of Turing Lectures is organised in collaboration with The Royal Institution.
 
 
 
 
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 With their ability to generate human-like language and complete a variety of tasks, generative AI has the potential to revolutionise the way we communicate, learn and work. But what other doors will this technology open for us, and how can we harness it to make great leaps in technology innovation? Have we finally done it? Have we cracked AI?
 
Join Professor Michael Wooldridge for a fascinating discussion on the possibilities and challenges of generative AI models, and their potential impact on societies of the future. Michael Wooldridge is Director of Foundational AI Research and Turing AI World-Leading Researcher Fellow at The Alan Turing Institute. His work focuses on multi-agent systems and developing techniques for understanding the dynamics of multi-agent systems. His research draws on ideas from game theory, logic, computational complexity, and agent-based modelling. He has been an AI researcher for more than 30 years and has published over 400 scientific articles on the subject.
 
This lecture is part of a series of events - How AI broke the internet - that explores the various angles of large-language models and generative AI in the public eye.
 
This series of Turing Lectures is organised in collaboration with The Royal Institution of Great Britain.
 
 
 
 
 
 
 

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Presentations of the MIT Generative AI Week

 

MIT Generative AI Week reflects our conviction that MIT has a special responsibility to help society come to grips with the tectonic forces of generative AI – to understand its potential, contain its risks, and harness its power for good. Our objective is to spotlight the insights of our MIT researchers, stimulate thoughtful analysis, and engage in critical dialogues on the implications and possibilities of generative AI in our ever-shifting landscape.

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Generative AI Shaping The Future: Opening Remarks by President Kornbluth

MIT President Sally Kornbluth highlights several projects faculty and students have undertaken to use generative AI to make a positive impact in the world.
 
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MIT Professor of Electrical Engineering and Computer Science and CSAIL Director Daniela Rus discusses a future where generative AI exists as both a technological marvel and a source of hope and a force for good . 
 
3
MIT Professor of Literature and Distinguished Chair of the Humanities Joshua Bennett reads his poem about what it means to be human, drawing inspiration from his daughter and childhood .
 
 
 
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MIT Electrical Engineering and Computer Science Professor and CSAIL member Jacob Andreas explores the capabilities and applications of generative models of language.  
 
 
 
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Professor and Head of AI + Decision-Making Faculty, MIT Electrical Engineering and Computer Science, and CSAIL member Antonio Torralba looks at the potential of generative models in analyzing images.
 
 
 
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MIT Associate Professor and MIT Brain and Cognitive Sciences and McGovern Institute Investigator Ev Fedorenko on applying large language models to help us better understand language and the human brain. 
 
 
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MIT Professor, Schwarzman College of Computing and Associate Director, and COO of CSAIL Armando Solar-Lezama presents how AI will program itself.  
 
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William T. Freeman, Professor, MIT Electrical Engineering and Computer Science and CSAIL, sits down for a roundtable talk with all four speakers about some potential future research directions for generative AI. 
 
 
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Joy Ma, MIT Major in Physics and Computer Science, Class of 2024, reads an excerpt from the 1925 science fiction novel “Metropolis."  
 
 
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In a roundtable about the future of generative AI, moderator Daniela Rus sits down with MIT professors Joshua Tenenbaum, (Computational Cognitive Science, CSAIL, and Center for Brains, Minds, and Machines), Dina Katabi, (Electrical Engineering and Computer Science and CSAIL) and Max Tegmark (Physics).  
 
 
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Cathy Wu, Assistant Professor, MIT Civil and Environmental Engineering, IDSS, and LIDS, describes how generative AI could help create synthetic data to help prepare self-driving cars for rare events, better model traffic patterns, and improve zoning regulations to ease housing shortages.
 
 
 
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Generative AI Applications: John Hart

John Hart, Professor and Department Head, MIT Mechanical Engineering, on how AI and computing can connect design to future production infrastructure.
 
 
 
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Generative AI Applications: Andrew Lo

Andrew Lo, Professor, MIT Sloan School of Management, CSAIL, and Director of the MIT Laboratory for Financial Engineering, talks about how AI can impact finance, including whether large language models can provide sound financial advice.
 
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Tod Machover, Professor, MIT Media Lab and MIT Opera of the Future Group Director, presents potential avenues where generative AI can help musicians discover new sounds. 
 
 
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The Impact of AI on Developer Productivity

Listen to Laura Tacho, CTO at DX, and Dr. Philippa Hardman, AI expert and Cambridge University Scholar, discuss AI’s impact on developer team productivity and how we think about development team performance. This conversation covers how AI tools like coding assistants and predictive analytics are changing both the ways we write and deploy software and the ways that we think about evaluating the productivity and performance of development organizations and individual engineers. Laura and Philippa also cover the risks of not adopting AI, and how to think about choosing better measures of team performance.

 

The Impact of AI on Developer Productivity

 

Published by DX is a developer insights platform, designed by the researchers behind DORA and SPACE. DX is the only solution that provides both qualitative and quantitative measures, helping developer productivity leaders pinpoint their biggest opportunities and translate their impact into dollars. DX is built specifically for leaders of internal developer productivity and/or platform teams. We’ve helped leaders at companies of all stages — from startups to Fortune 500 — get clarity into where to focus and measure the positive impact of their efforts.

 

 

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AI has entered the public sphere, catalyzed by the recent wave of generative AI. But there is much more that constitutes AI research than just language models and image generation. In the Stanford HAI fall conference, we bring to the fore the broader spectrum of AI research: AI in the sciences, AI in creative disciplines, and AI in society.
 
 
1
 
 Welcome & Introduction
Surya Ganguli Associate Professor of Applied Physics, and by courtesy, of Neurobiology, of Electrical Engineering, and of Computer Science, Stanford University; Faculty Associate Director, Stanford HAI
Percy Liang Associate Professor of Computer Science, Stanford University; Director, Stanford Center for Research on Foundation Models; Faculty Affiliate, Stanford HAI
 
Opening Keynote Shakir Mohamed Senior Research Scientist, Google DeepMind
 
 
 
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AI has entered the public sphere, catalyzed by the recent wave of generative AI. But there is much more that constitutes AI research than just language models and image generation. During the Stanford HAI fall conference, we bring to the fore the broader spectrum of AI research: AI in the sciences, AI in creative disciplines, and AI in society.
Session 1: Sciences
Speakers
Alex Rives | Evolutionary Scale Language Models Computer scientist focused on language models for biology
Pratyusha Sharma | Understanding Sperm Whale Communication PhD Candidate, EECS, MIT
Aditi Sheshadri Assistant Professor of Earth System Science and, by courtesy, Senior Fellow at the Woods Institute for the Environment, Stanford University
 
Moderator Surya Ganguli 
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AI has entered the public sphere, catalyzed by the recent wave of generative AI. But there is much more that constitutes AI research than just language models and image generation. In this keynote conversation,
Daphne Koller explains the potentials of generative AI in drug discovery.
 
Speaker: Daphne Koller CEO and Founder, insitro; Adjunct Professor of Computer Science, Stanford University
 
 
 
4
AI has entered the public sphere, catalyzed by the recent wave of generative AI. But there is much more that constitutes AI research than just language models and image generation. During the Stanford HAI fall conference, we bring to the fore the broader spectrum of AI research: AI in the sciences, AI in creative disciplines, and AI in society.
 
Session 2: Challenging, redefining, and expanding notions of human creativity.
 
Speakers
Chris Donahue | Music Generation with Precise Control and Composable Outputs Assistant Professor, Carnegie Mellon University; Part-time Research Scientist, Google Magenta
Angjoo Kanazawa Assistant Professor, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley; Director, Kanazawa AI Research (KAIR);
 
Advisory Board:
Wonder Dynamics and Luma
AI Been Kim | Leveraging Alphazero to Improve our Understanding & Creativity in Chess Senior Staff Research Scientist, Google DeepMind Lisa Schut | Leveraging Alphazero to Improve our Understanding & Creativity in Chess Doctoral Candidate in Machine Learning, University of Oxford; Research Scientist Intern, Google DeepMind
 
Ge Wang | Music & AI: What Do We (Really) Want? Associate Professor Center for Computer Research in Music and Acoustics (CCRMA) Department of Music and, by courtesy, of Computer Science, Stanford University
 
Moderator Diyi Yang Assistant Professor, Computer Science Department, Stanford University 
 
 
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Stanford HAI
 
AI has entered the public sphere, catalyzed by the recent wave of generative AI. But there is much more that constitutes AI research than just language models and image generation. In this conference, we bring to the fore the broader spectrum of AI research: AI in the sciences, AI in creative disciplines, and AI in society.
 
Session 3: Society
 
Speakers
Erik Brynjolfsson | The Turing Trap Director, Stanford Digital Economy Lab; Jerry Yang and Akiko Yamazaki Professor and Senior Fellow, Stanford HAI;
Ralph Landau Senior Fellow, Stanford Institute for Economic Policy Research Jaron Lanier | Data Dignity Prime Unifying Scientist, Microsoft 
Joon Park |Generative Agents: Interactive Simulacra of Human Behavior Ph.D. Candidate of Computer Science, Stanford University
Ashia Wilson Assistant Professor, Department of Electrical Engineering and Computer Science, MIT
 
Moderator Rishi Bommasani Society Lead, Stanford Center for Research on Foundation Models; Ph.D. Candidate of Computer Science, Stanford University
 
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