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Stanford Webinar - Create a Better User Experience through AI, Michael Bernstein

Stanford Online2020-08-17
webinar#stanford#artificial intelligence#user experience#user interface#ux#ui#ai#michael bernstein
4K views|3 years ago
💫 Short Summary

The video explores the intersection of AI and human-computer interaction, emphasizing the importance of designing user-centric systems while addressing challenges like bias and errors. It discusses the impact of AI on user interaction, the limitations of full automation, and the concept of intelligence augmentation. The video also delves into machine learning models, intelligibility, and simplifying explanations for complex models. It highlights the advancements in AI technology, such as personalization, behavior change interfaces, and challenges in designing interactive AI. Additionally, it touches on bias in AI systems, ethical implications, and the widespread impact of AI across various industries.

✨ Highlights
📊 Transcript
The importance of designing computational systems that cater to human needs in the intersection of AI and human-computer interaction.
Addressing challenges such as bias and errors in AI applications is crucial for creating user-centric experiences.
Potential pitfalls of AI technology include gender bias and stereotyping in automated systems.
Emphasis on creating AI-driven innovations while being mindful of ethical considerations and societal impact.
The impact of AI on user interaction is discussed, emphasizing the need for co-designing AI systems.
MIT's personal robotics group is studying social cues in robots to improve user interactions with AI.
UC Berkeley has developed grasping algorithms that predict intuitive paths for grabbing objects, enhancing user experience.
Interactive AI is prevalent in everyday tools such as Siri, Alexa, and search engines, showcasing the importance of built-in interactions for user experience.
Limitations of full automation in self-driving cars.
Handoff between automation and human control can be dangerous, demonstrated in a simulation.
Designing seamless transitions between automation and human intervention is crucial for safety.
Introduction of intelligence augmentation to enhance human capabilities alongside automation.
Douglas Engelbart's vision for enhancing human intellect through technology, rather than replacing it.
He invented hypertext, the mouse, and a key chord set to make our brains smarter with technology.
This vision contrasts with the concept of artificial intelligence replacing human intelligence.
Many user interfaces are designed with unrealistic AI capabilities, such as the infamous Clippy from Microsoft Office.
It is important to ensure that user interface expectations align with actual AI capabilities.
Progress in AI is mainly achieved through functions that transform input into simple responses.
Tasks that can be completed subconsciously in under a second can likely be automated by AI.
More complex tasks requiring extensive thought will remain outside AI's capabilities for the foreseeable future.
Tasks like checking if a car is in its lane can already be automated, showcasing the potential of AI in performing quick and straightforward tasks.
Technological advancements in intelligence augmentation have led to hardware that monitors water usage and provides visualizations for sustainable practices.
Customizable ability-based interfaces cater to individuals with different motor abilities, enhancing user experience and reducing errors.
Artificial Intelligence has the potential to aid people with disabilities in performing tasks more effectively.
Technology advancements are leading to the creation of tools in the style and fashion space that can understand user preferences and provide relevant items.
AI models are being developed for visual designers to analyze where people focus their attention, allowing for immediate design adjustments.
The tight feedback loop improves the ability to predict what captures attention and optimize accordingly.
Challenges exist in designing interactive artificial intelligence, particularly in dealing with uncertainty and errors.
Further exploration is needed to address core issues in this evolving space.
Challenges with AI implementation include errors and uncertainty from misinterpreting commands.
Systems with high certainty can automatically take action, such as Gmail prompting users to attach files.
Adaptive interaction involves adjusting interfaces to bring useful items closer to users, acting as a helpful assistant.
Microsoft Word adjusts layout based on potential user actions, showcasing adaptive interaction.
Being adaptive ensures tools are easily accessible and layouts are optimized for user interaction.
The importance of accelerators in providing autocomplete and faster routes in interfaces.
User control is crucial in decision-making processes and should be emphasized by default.
Uncertain AIs and recommender systems are utilized by platforms like Amazon and Netflix to suggest content based on user preferences.
The use of collaborative filtering by companies like Stitch Fix to personalize recommendations for users based on style preferences.
The recommender systems aim to provide a variety of suggestions to increase the likelihood of appealing to users.
Introduction of the mixed initiative interaction framework, where the computer and user take turns leading the recommendation process.
Emphasizing the importance of understanding user goals and providing value through personalized recommendations.
The concept of utility in user decision-making is explored based on different scenarios and goals.
Benefits and harms of taking action or doing nothing are discussed, with a focus on certainty levels.
In cases of low certainty, the utility of inaction is higher, indicating a preference for not taking action.
The video delves into reasoning about user interactions and decision-making processes in machine learning models.
Emphasis is placed on understanding and improving model effectiveness and interpretability.
Importance of Intelligibility in Machine Learning Models.
Machine learning models can produce non-intuitive results due to their opacity.
Introducing small amounts of noise can significantly alter model outputs, as demonstrated by a panda image being misclassified as a gibbon.
Understanding the reasons behind model predictions is essential for enhancing intelligibility and improving transparency.
Intelligibility is crucial for predicting the impact of input changes on model outputs, ensuring reliability in machine learning models.
Contrasting linear relationships with convolutional neural networks in terms of intelligibility.
Linear relationships are straightforward, while convolutional neural networks in computer vision are complex and less transparent.
Challenges of predicting outcomes in neural networks and the importance of visualizing model interpretations.
Demonstrating how neural networks interpret images of labrador retrievers and tiger cats.
Emphasizing the need for human-machine collaboration to improve model interpretation.
Simplifying machine learning models using LIME algorithm.
LIME simplifies complex machine learning models by sampling nearby points and learning a linear separator.
The analogy of explaining bedtime to a child is used to illustrate the concept of providing simple explanations for specific scenarios.
Simplifying explanations can be useful, but may not always capture the full complexity of the model.
Discussion on using influence functions to visualize how training data impacts model decisions.
Dilemma between simplifying models for clarity and providing full details for accuracy in representing model reasoning.
Drawing on psychology research for effective explanations, including the use of contrastive explanations.
Rise of AI techniques in voice and vision-based interactions beyond desktop applications.
Examples of AI integration in products like Fitbit and Apple Watch for activity monitoring.
Advancements in artificial intelligence enable devices to adapt to user behavior through machine learning.
Research projects at Georgia Tech and Carnegie Mellon showcase the capabilities of AI in devices like the Nest thermostat and smartwatches.
AI technology can be used to unlock doors, control stoves, and personalize interfaces based on user actions.
Despite its benefits, AI poses challenges in user interaction that must be overcome for optimal functionality.
Issues around bias in justice models and uncertainty in AI systems are discussed.
Thoughtful interaction design is crucial for managing uncertainty and creating a positive user experience.
Building AI models without effective user interfaces can have negative consequences.
People tend to avoid intelligent interfaces unless they are highly accurate due to their aversion to errors over benefits.
Further conversation and questions are needed to address the challenges posed by bias and uncertainty in AI systems.
Challenges of using pre-pandemic data in machine learning algorithms post-pandemic.
Google's AlphaGo algorithm failed when encountering a new move, highlighting the limitations of pre-trained models.
Caution advised in deploying AI in a changed world, stressing the importance of human oversight.
Emphasis on preventing unusual or poor decisions by ensuring human involvement in AI implementation.
Addressing bias in AI related to race, disabilities, and gender.
Defining fairness and bias in AI poses a significant challenge, with examples of algorithmic bias in predicting recidivism.
Despite efforts to tackle bias, there is no universal definition of fairness.
Attending a conference on fairness, accountability, and trust is recommended for cutting-edge technical work in addressing bias.
Fair algorithms could still be utilized for unjust purposes, highlighting the complexities of capturing fairness in AI.
Ethical implications of AI and computing in relation to power and oppression.
Importance of assessing goals, potential abuses, and system design to prevent misuse.
Questioning who controls AI systems and discomfort with potential outcomes.
AI's diffusion into various industries beyond tech fields, such as supply chain management, fashion, farming, and political polling.
Impact of AI applications on diverse sectors and the evolving nature of its integration.
Impact of AI on Various Domains and Power Dynamics.
AI influences business transformation and organizational power dynamics.
Melissa Valentine's studies emphasize the importance of designing power dynamics in AI-driven businesses.
AI shifts decision-making power from individuals to algorithms, leading to resistance from those losing power.
Proper management of AI integration is essential to prevent organizational disruption and combustion.
Discussion on managing data quality, building machine learning models, and determining error rates for product launch.
Mention of programs like digital transformations at SCPD for organizational transformation.
Managing organizational transformation is easier for small startups due to less baggage.
Exploration of communication strategies for AI error rates, including hiding errors or operating in forgiving domains.
Success in AI often seen in soft edge domains with multiple correct solutions, like GPT-3 generating human-like text without a single right answer.
Exposing confidence values in AI to users can be misleading as errors may not be AI-related.
Launching products with high error rates should be avoided.
Designing user experiences around AI requires caution and avoiding unfulfilled promises.
Vetting machine predictions by humans before sharing externally can increase certainty.
Microsoft's calendar.help exemplifies this approach by providing vetted AI-generated content to users.
Benefits of combining human and machine interaction in algorithms.
Human involvement for complex requests and algorithm for simple ones.
Data generated from human interactions improves algorithm over time.
Allows people to focus on more challenging tasks as algorithm evolves.
Webinar concludes with a thank you message to the audience and encourages sharing the recording.