Over the past two years, generative AI (gen AI) has been a rapidly evolving trend that has touched the lives of many around the globe. Which is why the design of these formidable systems must include experts from diverse backgrounds, says James Landay, a professor of computer science at Stanford University. On this episode of the At the Edge podcast, Landay talks with McKinsey senior partner Lareina Yee about how to develop safe, inclusive, and effective AI. The following transcript has been edited for clarity and length. For more conversations on cutting-edge technology, follow the series on your preferred podcast platform.
It’s about the values and principles that guide our development of AI.
The Importance of Human-Centered Design in AI Development
In today’s fast-paced world, artificial intelligence (AI) is increasingly being used to solve complex problems and improve our daily lives. However, as AI becomes more pervasive, it’s essential to consider the human-centered design approach that underlies its development.
That’s why Stanford has a multidisciplinary approach to AI, integrating AI into every field of study.
The Stanford AI Initiative
The Stanford AI Initiative is a comprehensive program that brings together experts from various disciplines to develop and apply AI in different fields. The initiative is led by the Stanford School of Engineering and the Stanford Graduate School of Business, with support from the Stanford School of Medicine and the Stanford Law School.
Key Components
The Intersection of Philosophy and Medicine
The intersection of philosophy and medicine is a rapidly growing field that explores the complex relationships between the two disciplines. This intersection is crucial in understanding the human experience, particularly in the context of healthcare and medicine.
The Role of Philosophy in Medicine
Philosophy plays a vital role in medicine, particularly in the areas of ethics, medical decision-making, and the nature of human experience. Philosophers like James Landay, a professor of philosophy at Stanford University, are working to integrate philosophical insights into medical practice.
Key Areas of Focus
Landay believes that AI will continue to play a significant role in shaping our future.
The Rise of AI: A New Era of Human Interaction
The world is witnessing a revolution in the way we interact with technology, and at the forefront of this revolution is artificial intelligence (AI). According to James Landay, the founder and CEO of Lab126, AI is changing the way we live, work, and interact with each other. In this article, we will explore the impact of AI on human interaction and what the future holds for this rapidly evolving field.
The Impact of AI on Human Interaction
AI is transforming the way we communicate, work, and make decisions. Here are some ways in which AI is impacting human interaction:
They are probabilistic, meaning that their outputs are uncertain and can vary depending on the input they receive. This is a fundamental difference between AI and traditional deterministic systems like computers.
The Nature of AI
AI systems are designed to make predictions or decisions based on patterns and data. However, because they are probabilistic, their outputs can be unpredictable and may not always be accurate. This unpredictability can lead to errors and inconsistencies in the system’s performance. Probabilistic nature: AI systems are based on statistical models that use data to make predictions or decisions. These models are trained on large datasets and can be prone to errors due to the complexity of the data and the limitations of the algorithms used. Uncertainty and variability: The outputs of AI systems can be uncertain and may vary depending on the input they receive. This can lead to errors and inconsistencies in the system’s performance. * Lack of determinism: AI systems are not deterministic, meaning that their outputs are not always predictable and can be influenced by various factors.**
The Impact of AI on Traditional Systems
The introduction of AI into traditional systems can have a significant impact on their performance and reliability. Because AI systems are probabilistic, their outputs can be unpredictable and may not always be accurate. Inconsistencies and errors: The probabilistic nature of AI systems can lead to inconsistencies and errors in the system’s performance. This can be particularly problematic in critical systems where accuracy and reliability are essential. Dependence on data quality: The performance of AI systems is heavily dependent on the quality of the data used to train them.
The Problem with Probabilistic Models
Probabilistic models are a type of machine learning algorithm that rely on probability theory to make predictions. They’re incredibly powerful, but they can also be misleading. The problem is that these models are only as good as the data they’re trained on, and if that data is biased or incomplete, the model will make mistakes. Biased Data: If the training data is biased, the model will learn to replicate those biases. For example, if a facial recognition system is trained on a dataset that’s predominantly white, it may struggle to recognize people of other ethnicities.
The Problem with Current AI Models
The current state of AI models is a far cry from the idealized vision of a superintelligent AI that can solve complex problems and make decisions autonomously. Instead, most AI models are narrow and specialized, designed to perform a specific task, such as image recognition or language translation. These models are controlled by a few large corporations, and academics don’t even have the computing power to build models big enough to understand how they work. * Limited Computing Power: The computing power required to build and train large AI models is enormous. Currently, most researchers rely on cloud computing services or specialized hardware to process the vast amounts of data required for training. However, even with these resources, it’s challenging to build models that can rival the capabilities of the largest corporations.**
## The Rise of Explainable AI
In response to these limitations, researchers have been working on developing Explainable AI (XAI) models. XAI models aim to provide insights into how AI decisions are made, making them more transparent and accountable.
Understanding the Role of Data in LLMs
When it comes to leveraging data in Large Language Models (LLMs), businesses are increasingly turning to combining their own data with a base LLM. This approach allows them to tap into the strengths of the LLM while also utilizing their own proprietary data to fine-tune the model. However, this approach also raises important questions about testing and understanding the resulting model.
Testing and Validation
Challenges in Testing and Understanding
Best Practices for Testing and Understanding
I think we’re going to see a lot of new startups and companies emerging that are focused on this space.
The Future of AI: A New Era of Design and Business
The rapid advancement of Artificial Intelligence (AI) has brought about significant changes in various industries, including healthcare, finance, and education. As AI systems become increasingly sophisticated, the need for better design processes to ensure their impact is positive and inclusive becomes more pressing. In this article, we will explore the future of AI, its potential impact on design and business, and the emerging trends that will shape this new era.
The Importance of Inclusive Design
Inclusive design is crucial in the development of AI systems, as they can have a profound impact on various communities. However, current design processes often focus on the needs of a select group, leaving out marginalized communities.
There are going to be some things that are not right. And there are going to be some things that are just plain wrong. And that’s okay. It’s okay to make mistakes. It’s okay to not know what to do. But what’s not okay is to ignore the ethical implications of our actions.
The Need for Ethical Consideration in Computing and AI
Computing and AI are rapidly evolving fields that have the potential to transform industries and societies. However, these technologies also raise complex ethical concerns that must be addressed. As AI systems become increasingly sophisticated, they will be used in a wide range of applications, from healthcare and finance to transportation and education. But without proper ethical consideration, these technologies can perpetuate biases, exacerbate social inequalities, and even cause harm. Examples of ethical concerns in AI include:
The Stanford Approach
Stanford University is taking a proactive approach to addressing these ethical concerns. The university is embedding ethical lessons into its computing and AI courses, providing students with a more nuanced understanding of the potential consequences of their actions.
The Risks of AI
Legal and Reputational Risks
Companies must be aware of the potential risks associated with AI, including:
Involving Users in the Design Process
Involving users in the design process is crucial for creating an AI system that meets their needs and expectations. However, it’s not always easy to get users involved in the design process. Here are some reasons why:
But, by addressing these challenges, we can make the design process more inclusive and user-centered.
Benefits of Involving Users in the Design Process
Involving users in the design process has numerous benefits, including:
Strategies for Involving Users in the Design Process
Here are some strategies for involving users in the design process:
Under-resourced teams struggle to detect and address product development issues.
However, these groups are often under-resourced and lack the necessary tools to effectively identify and address potential issues.
The Challenges of Early Detection
Early detection of problems is crucial in the product development process. However, the current system often falls short in this regard.
We need teams that can think about the impact of technology on society, on culture, on individuals. We need teams that can think about the ethics of AI, of robotics, of biotech. We need teams that can think about the human side of these technologies.
The Importance of Interdisciplinary Teams
In today’s fast-paced and rapidly evolving technological landscape, the need for interdisciplinary teams has never been more pressing. As technology continues to advance at an unprecedented rate, the consequences of our actions can have far-reaching and profound impacts on society, culture, and individuals. To mitigate these risks and ensure that technology serves humanity, we need teams that can bring together diverse perspectives and expertise.
The Challenges of Monoculture Teams
Monoculture teams, comprised of individuals from the same discipline or industry, can be detrimental to innovation and progress. These teams often lack the diversity of thought and experience that comes from bringing together individuals with different backgrounds and expertise.
And different companies dealt with it differently. Some immediately went and fixed it, while some fought it. So part of this is changing the process, and part of it is changing the teams. They need to be more diverse and interdisciplinary, and that will help solve a lot of these problems. AI and the future of education Lareina Yee: There is a lot to think about, but this is just a portion of your research. I was also watching some pretty amazing work you and your PhD teams are doing around the future of education. Are you optimistic or pessimistic about the impact of generative AI on education?
Landay, a professor of education at the University of North Carolina at Chapel Hill, has been studying the effects of the COVID-19 pandemic on education for several years. He has written extensively on the topic and has been a vocal advocate for changes to the way we teach and learn.
The Pandemic’s Lasting Impact on Education
The COVID-19 pandemic has had a profound impact on the education sector, and its effects will be felt for years to come. Landay notes that the pandemic has accelerated the shift towards online learning, which has both positive and negative consequences.
The Benefits of Online Learning
AI-powered learning experiences tailored to individual needs and goals.
AI can provide personalized learning experiences tailored to each individual’s learning style, pace, and goals. Here are some ways AI can help:
Personalized Learning Experiences
Motivational Strategies
The Future of Education
Or, using machine learning to predict the outcome of a game. Or, even using natural language processing to generate text.
The Evolution of Flashcards
Flashcards have been a staple in learning and education for centuries. The concept of flashcards has evolved significantly over the years, from traditional index cards to digital flashcards, and now, AI-powered flashcards.
Traditional Flashcards
Traditional flashcards were simple, handmade cards with a question or term on one side and the answer on the other. Students would study these cards by covering the answer side and trying to recall the information from memory. This method was effective, but it had its limitations. For example, it was time-consuming and required a lot of physical space.
Digital Flashcards
The advent of digital technology revolutionized the way we learn and study. Digital flashcards offered a more efficient and organized way of storing and retrieving information. They could be easily created, edited, and shared with others.
We were using a chatbot to teach them Chinese. And we found that the chatbot was able to teach them Chinese faster than the human teachers.
The Rise of AI-Powered Learning
The integration of artificial intelligence (AI) in education has been gaining momentum in recent years. AI-powered learning systems are designed to provide personalized, adaptive, and interactive learning experiences for students. These systems use machine learning algorithms to analyze student data, identify knowledge gaps, and adjust the learning content accordingly.
“We’re still in the early stages of this technology.”
The Future of Human-Computer Interaction
The future of human-computer interaction is rapidly evolving, driven by advancements in artificial intelligence, machine learning, and the Internet of Things (IoT). According to Dr. Eric Landay, a renowned expert in human-computer interaction, the technology will soon enable us to interact with computers in ways that are more intuitive, natural, and immersive.
The Rise of Voice Assistants
One of the most significant developments in human-computer interaction is the rise of voice assistants. These AI-powered systems use natural language processing (NLP) to understand and respond to voice commands. With the proliferation of smart speakers and virtual assistants like Siri, Alexa, and Google Assistant, voice assistants have become an integral part of our daily lives. Key features of voice assistants: + Natural language processing (NLP) + Voice recognition + Contextual understanding + Personalization
+ Convenience + Accessibility + Enhanced user experience
The Future of Human-Computer Interaction
Dr. Landay emphasizes that the future of human-computer interaction will be shaped by our desires and expectations. “We have to decide what we want,” he says.
Stagnant institutions failing to adapt to changing needs of students and society.
Landay, a professor of computer science at Carnegie Mellon University, has spent years researching and teaching about the design of higher education institutions. He’s written extensively on the topic and has been a vocal advocate for change.
The Problem with Traditional Higher Education
Traditional higher education institutions have been around for centuries, and they’ve been successful in providing students with a solid foundation in various subjects. However, Landay argues that these institutions have become stagnant and are no longer meeting the needs of students in the modern era. They are often expensive and inaccessible to many students, particularly those from low-income backgrounds. The traditional model of higher education focuses on rote memorization and regurgitation of information, rather than encouraging critical thinking and problem-solving skills.
AI is going to change the way we learn, the way we teach, and the way we assess student learning. Here are some of the ways AI is going to change the personalized learning experience for students.
The Rise of Personalized Learning
The traditional classroom setting is not conducive to personalized learning. Students learn at different rates, have different learning styles, and require different levels of support. AI is going to revolutionize the way we approach learning by providing a tailored experience for each student. Adaptive learning platforms: AI-powered adaptive learning platforms will be able to adjust the difficulty level of course materials based on a student’s performance. Learning pathways: AI will create customized learning pathways for students, allowing them to progress at their own pace. * Real-time feedback: AI will provide real-time feedback to students, helping them identify areas where they need improvement.**
The Shift from Traditional Teaching Methods
Traditional teaching methods are often one-size-fits-all, which can lead to disengagement and frustration among students. AI is going to change the way we teach by providing more effective and engaging methods. AI-powered virtual teaching assistants: AI-powered virtual teaching assistants will be able to provide personalized support to students, helping them with homework and assignments.
The Dream Gen AI Application
Lareina Yee’s dream gen AI application would be a revolutionary tool that combines the thrill of adventure sports with the power of artificial intelligence. The application, which she calls “AdventureGen,” would use machine learning algorithms to generate personalized adventure experiences tailored to each user’s preferences, skill level, and interests.
Key Features
We’re trying to take a piece of the past and make it work for the future. That’s what I’m trying to do with my research.
The Power of Sketching in User-Interface Design
Sketching has been a cornerstone of user-interface design for decades.
We think we know what we want and who we are, but life has other plans. We get lost in the journey, and it’s only when we slow down that we rediscover ourselves. It’s not about achieving some grand goal; it’s about embracing the journey and finding meaning in the everyday moments. The idea that we must reach some predetermined destination is an illusion. Life is a series of unfolding moments, and it’s up to us to appreciate and find significance in each one.”
The Illusion of the Destination-Oriented Mindset
The notion that we must reach a predetermined destination is a pervasive and deeply ingrained idea in modern society. This mindset is often perpetuated through media, social norms, and our own self-perceived expectations.
