Augmenting Human Cognition and Decision Making with AI
TLDRJake Hofman from Microsoft Research discusses the project 'Augmenting Human Cognition and Decision Making with AI,' exploring how AI can enhance human decision-making and productivity. The talk uses a sports analogy to illustrate the varying impacts of AI tools, from short-term boosts like running shoes to long-term improvements akin to a coach. Two studies are highlighted: one on LLM-based search improving routine task speed and accuracy, and another on LLM-based tutoring that boosts learning when used after individual problem-solving attempts. The emphasis is on the importance of design choices and rigorous experimentation in optimizing AI tools for human benefit.
Takeaways
- 🚀 **AI for Enhanced Decision Making**: Jake discusses the use of AI at Microsoft Research to augment human cognition and decision-making, aiming to improve productivity and self-improvement.
- 🤖 **AI Interaction Spectrum**: The analogy of AI tools is presented, ranging from 'steroids' that offer temporary benefits but can lead to long-term deskilling, to 'running sneakers' that provide temporary boosts without negative consequences, and 'coaches' that offer sustainable improvement.
- 📈 **LLM-Based Search Study**: A study on LLM-based search tools shows that they can double the speed of routine tasks when accurate, but mistakes can lead to incorrect decisions if not detected.
- 🔍 **Improving LLM Search**: Adding confidence-based highlighting to LLM search results can reduce overreliance on incorrect information and improve task performance.
- 📚 **LLM-Based Tutoring Study**: A study on LLM-based tutoring for learning math problems indicates that explanations boost learning, especially when used after attempting problems independently.
- 📈 **Customized LLM Tutoring**: A customized LLM tutor with a pre-prompt to emulate human tutors can provide cognitively friendly strategies, suggesting a small but significant improvement over standard explanations.
- 🌟 **Design Choices Impact**: The design and use of AI tools can significantly impact how they affect people, and it's essential to make choices that optimize benefits and minimize risks.
- 🧪 **Importance of Experimentation**: Rigorous measurement and experimentation are crucial for prototyping and validating AI tools to ensure their effectiveness and safety.
- 📝 **Research and Learning**: The studies presented aim to optimize AI tools for decision-making and learning, emphasizing the importance of understanding how these tools interact with human cognition.
- 🔗 **Resource Sharing**: Jake provides links to the research papers discussed, encouraging further exploration and discussion on the topic of AI and human augmentation.
Q & A
What is the main focus of the research at Microsoft Research in New York City mentioned in the transcript?
-The main focus of the research is on 'Augmenting Human Cognition and Decision Making with AI,' which aims to understand how AI can assist people in making better decisions, reasoning about information, increasing productivity, and self-improvement.
What does the sports analogy in the script represent in the context of AI interaction?
-The sports analogy represents the spectrum of ways people might interact with AI tools. It ranges from the least desirable outcome, likened to steroids which provide temporary enhancement but can lead to long-term deskilling, to the ideal outcome, akin to a coach that helps in the moment and fosters sustainable self-improvement.
What is an example of a situation where over-reliance on AI could lead to long-term deskilling?
-An example is forgetting how to spell if one over-relies on spell check, as it might diminish the individual's spelling skills over time.
How did the study on LLM-based search affect decision-making?
-The study found that for routine tasks with accurate information from the LLM, people were twice as fast using LLM-based search compared to traditional search. However, when the LLM made mistakes, people often failed to notice and made incorrect decisions themselves.
What was the simple fix implemented in the LLM-based search study to reduce overreliance on incorrect information?
-The fix was the addition of confidence-based highlighting, similar to what is seen in spelling or grammar checks, which significantly reduced overreliance on incorrect information and improved task performance.
What were the three conditions tested in the LLM-based tutoring study?
-The three conditions were: 1) Answer-only condition where participants were told whether their attempt was right or wrong, 2) Stock LLM condition where participants were given a correct but esoteric formula by GPT 4, and 3) Customized LLM condition where a pre-prompted LLM suggested more cognitively friendly strategies.
How did the different types of assistance in the LLM-based tutoring study affect learning outcomes?
-The study found that LLM explanations boosted learning compared to only seeing answers. There were also benefits to using the tutor after attempting the problem first, rather than consulting it beforehand. Additionally, there was some evidence that the customized pre-prompt provided a small advantage over the stock explanations.
What is the significance of the two studies mentioned in the transcript?
-The significance of the two studies is that they demonstrate how design choices in AI tools can substantially impact their effectiveness and the benefits they provide to users. They also highlight the importance of rigorous measurement and experimentation in optimizing AI tools to maximize benefits and minimize risks.
What was the main takeaway from the speaker's presentation?
-The main takeaway is that the choices we make in designing and deploying AI tools are crucial. Experimentation and rigorous testing are key to ensuring that we maximize the positive impacts of AI on human cognition and decision-making while minimizing potential negative consequences.
How did the speaker suggest the audience could engage with the research discussed?
-The speaker offered links to the papers discussed in the presentation and expressed a willingness to engage with any comments and questions the audience might have, encouraging further interaction and discussion around the research findings.
Outlines
🤖 Augmenting Human Cognition with AI
Jake Hofman introduces the concept of using AI to enhance human decision-making and productivity at Microsoft Research in New York City. He discusses the potential of AI to improve individuals and presents a sports analogy to illustrate the varying levels of interaction between humans and AI tools. The least desirable outcome is compared to steroids, offering temporary benefits with long-term downsides, while the ideal outcome is likened to a coach that provides immediate assistance and long-term improvement. Two studies are briefly mentioned to demonstrate how AI tool design can significantly impact users: one on LLM-based search and another on LLM-based tutoring.
📈 Studies on AI Tool Design and Learning
The second paragraph delves into the specifics of two studies conducted to explore the design and use of AI tools. The first study examines the impact of LLM-based search on decision-making by having participants choose between car options using either traditional search or LLM-based search. The study finds that while LLM-based search is faster for routine tasks, it can lead to incorrect decisions when it provides inaccurate information. A solution involving confidence-based highlighting is presented to mitigate this issue. The second study focuses on LLM-based tutoring and its effect on learning, comparing different types of assistance provided during math problem practice. The results indicate that explanations from an LLM can enhance learning, and that customized pre-prompts can further improve outcomes. The importance of careful design and experimentation in AI tool development is emphasized to maximize benefits and minimize risks.
Mindmap
Keywords
💡AI
💡Augmenting Human Cognition
💡Decision Making
💡Productivity
💡LLM-based Search
💡Confidence-Based Highlighting
💡LLM-based Tutor
💡Cognitively Friendly Strategies
💡Rigorous Measurement
💡Design Choices
💡Self-Improvement
Highlights
Jake Hofman from Microsoft Research in New York City discusses 'Augmenting Human Cognition and Decision Making with AI'.
The goal is to use AI to help people make better decisions, reason about information, be more productive, and improve themselves.
A sports analogy is used to describe the spectrum of human-AI interaction, with steroids at one end and a coach at the other.
Over-reliance on AI tools can lead to long-term deskilling, such as forgetting how to spell due to reliance on spell check.
AI tools can act like a good pair of running sneakers, providing a temporary boost without long-term consequences.
An LLM-based search study shows that people can be twice as fast with accurate information from LLM-based search compared to traditional search.
When the LLM made a mistake, people often didn't notice and made incorrect decisions themselves.
Adding confidence-based highlighting to LLM-based search greatly reduced overreliance on incorrect information and improved task performance.
The second study examines the impact of LLM-based tutoring on learning, using standardized math problems.
LLM explanations significantly boosted learning compared to only seeing answers.
Using the tutor after attempting problems on their own led to substantial benefits over consulting the tutor before attempting the problem.
Customized pre-prompts in LLMs provided a small boost over stock explanations, suggesting cognitively friendly strategies.
Design choices in AI tools can substantially impact how they affect people, emphasizing the importance of experimentation and prototyping.
Rigorous measurement and experimentation are crucial to maximize benefits and minimize risks of AI tools.
The studies provide useful examples of the impact of designing and deploying AI tools.
The presenter shares links to the discussed papers and invites comments and questions.
