Course introduction

Summary

The video script from 'Applying AI Principles with Google Cloud' introduces the course focused on responsible AI practices. It highlights the rapid development of AI, its integration into daily life, and the importance of addressing potential biases and ethical issues. The script emphasizes Google's commitment to AI principles that ensure inclusivity, accountability, privacy, and scientific excellence. It underscores the human-centric approach in AI development, stressing the role of values in decision-making throughout the AI lifecycle.

Takeaways

• 🧠 AI is increasingly integrated into daily life, from traffic predictions to TV show recommendations.
• 🚀 Generative AI is becoming more common, making non-AI technologies seem outdated.
• 🛑 Historically, AI development was limited to a small group of specialty engineers, but barriers to entry are now lower.
• 🌐 AI systems are developing rapidly, enabling computers to interact with the world in new ways.
• 📊 The pace of AI development has accelerated, with computing power doubling every 3.5 months according to Stanford University’s 2019 AI index report.
• 📉 Error rates for AI tasks, such as image classification, have significantly decreased, with ImageNet's error rate dropping from 26% in 2011 to 2% by 2020.
• 🤖 Despite advancements, AI is not perfect and developing responsible AI requires understanding potential issues and unintended consequences.
• 🔍 AI reflects societal issues and without good practices, it may replicate and amplify existing biases.
• 📜 There is no universal definition of 'responsible AI,' but common themes include transparency, fairness, accountability, and privacy.
• 🏢 Google's approach to responsible AI is guided by principles that aim for inclusivity, accountability, safety, privacy, and scientific excellence.
• 🛠 Google incorporates responsibility by design into its products and organization, using AI principles to guide decision-making.
• 🤝 Google shares insights and lessons to promote responsible AI practices within the wider community, as demonstrated by this course.

Q & A

What is the main focus of the course 'Applying AI Principles with Google Cloud'?

The course focuses on the practice of responsible AI, exploring the development and use of AI technologies while emphasizing ethical considerations and principles.

How have daily interactions with AI become more common?

Daily interactions with AI have become more common through its integration in various aspects of life, such as traffic and weather predictions, and personalized TV show recommendations.

Why might non-AI-enabled technologies start to seem inadequate as AI becomes more prevalent?

As AI continues to advance and provide more sophisticated solutions, technologies that do not incorporate AI may be perceived as less efficient or advanced, leading to a sense of inadequacy.

What historical barriers have been lowered to allow more people to build AI systems?

The barriers to entry have been lowered, making AI more accessible to a wider range of people, including those without AI expertise, due to increased availability of tools and resources.

What is Moore's Law and how does it relate to the development of AI?

Moore's Law is the observation that the number of transistors on a microchip doubles about every two years, which historically tracked closely with AI results. However, since 2012, compute power for AI has been doubling at an even faster rate, approximately every 3.5 months.

How has the accuracy of Vision AI technologies improved over time, as exemplified by the ImageNet dataset?

The error rate for ImageNet, an image classification dataset, has significantly declined from 26% in 2011 to 2% by 2020, demonstrating the rapid improvement in the accuracy of Vision AI technologies.

What is the significance of the error rate for human performance in the context of AI advancements?

The human error rate, which is around 5% for the same tasks AI performs, serves as a benchmark to compare AI performance, highlighting the remarkable progress made in AI accuracy.

Why is it important to understand the limitations and unintended consequences of AI despite its advancements?

Understanding the limitations and potential unintended consequences of AI is crucial for developing responsible AI, as it helps to mitigate risks and ensure that AI technologies are used ethically and effectively.

What common themes are found in various organizations' AI principles?

Common themes in AI principles across organizations include transparency, fairness, accountability, and privacy, reflecting a commitment to responsible AI practices.

How does Google approach responsible AI in terms of its principles and practices?

Google's approach to responsible AI is rooted in a commitment to develop AI that is inclusive, accountable, safe, respects privacy, and is driven by scientific excellence. They have developed AI principles, practices, governance processes, and tools that embody these values.

What is the role of human decision-making in the development and application of AI technologies?

Human decision-making is central to AI development and application. People design, build, and decide how AI systems are used, with their values influencing every decision point from concept to deployment and maintenance.

Outlines

00:00

🧠 Introduction to Responsible AI with Google Cloud

The video script introduces the course 'Applying AI Principles with Google Cloud,' focusing on the responsible practice of AI. Narrators Marcus and Katelyn set the stage for the importance of AI in daily life and its rapid development. They discuss the historical inaccessibility of AI and the lowering barriers that now allow a broader audience to engage with it. The script highlights the exponential growth of AI capabilities, using the ImageNet challenge as an example of AI's progress and accuracy. Despite advancements, the course emphasizes that AI is not perfect and that responsible AI development requires understanding potential issues and biases. It also touches on the lack of a universal definition for 'responsible AI,' but notes common themes such as transparency, fairness, accountability, and privacy. Google's approach to AI is presented, with a commitment to building AI for everyone, ensuring it is safe, accountable, respects privacy, and is driven by scientific excellence. The course aims to share Google's learnings and promote responsible practices within the AI community.

05:06

🤖 The Human Role in AI Development

This paragraph emphasizes the human element in AI development, countering the misconception that machines are the central decision-makers in AI. It clarifies that people design, build, and decide the usage of AI systems, making human decision-making integral throughout AI's lifecycle. The paragraph stresses that every human decision introduces personal values, which is why each decision point in AI development must be considered and evaluated for responsible choices. The focus is on the importance of human judgment in shaping technology products and the ethical implications of these choices, from the collection of data to the deployment and application of AI in various contexts.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence, or AI, refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is presented as a technology that has become increasingly accessible and powerful, impacting daily life through traffic predictions, weather forecasts, and entertainment recommendations. The script emphasizes the rapid development and deployment of AI systems and the importance of responsible AI practices to ensure these technologies are used ethically and safely.

💡Generative AI

Generative AI is a subset of AI that involves creating new content, such as images, text, or music, that is not simply a replication of existing data. The script mentions generative AI as a rapidly advancing field that is becoming more common, which can lead to the perception that non-AI technologies may seem inadequate in comparison. The development of generative AI raises questions about the responsible use of such powerful technologies.

💡Responsible AI

Responsible AI is an approach to the development and deployment of AI technologies that emphasizes ethical considerations, transparency, fairness, and accountability. The video script discusses the importance of understanding the potential issues and unintended consequences of AI. It highlights that responsible AI requires a commitment to good practices to prevent the replication and amplification of societal biases within AI systems.

💡Bias

Bias, in the context of AI, refers to the systematic errors or unfairness that can be introduced by the data, algorithms, or decisions made during the development and deployment of AI systems. The script warns that without responsible practices, AI may replicate existing societal issues or biases, emphasizing the need for vigilance in the design and use of AI to prevent such outcomes.

💡Transparency

Transparency in AI involves making the processes, algorithms, and decision-making criteria of AI systems clear and understandable to both users and stakeholders. The video script identifies transparency as one of the key themes in responsible AI, suggesting that clear communication about how AI systems work is essential for building trust and ensuring accountability.

💡Fairness

Fairness in AI is about ensuring that AI systems do not discriminate or favor certain groups over others and that they treat all individuals equally. The script mentions fairness as a common theme in AI principles, indicating that organizations strive to develop AI systems that are impartial and do not perpetuate existing inequalities.

💡Accountability

Accountability in the context of AI means that there is a clear understanding of who is responsible for the decisions made by AI systems and the consequences of those decisions. The video script discusses the importance of accountability as a core principle in responsible AI, emphasizing that organizations must be held responsible for the AI systems they develop and deploy.

💡Privacy

Privacy in relation to AI refers to the protection of personal data and ensuring that AI systems respect and maintain the confidentiality of user information. The script identifies privacy as a key theme in Google's approach to responsible AI, highlighting the company's commitment to respecting privacy in the development and use of AI technologies.

💡Scientific Excellence

Scientific Excellence is the pursuit of high standards in research and development, ensuring that AI technologies are built on a foundation of rigorous scientific inquiry and innovation. The video script mentions that Google's approach to responsible AI is driven by scientific excellence, indicating a commitment to advancing AI in a way that is both innovative and grounded in solid research.

💡ImageNet

ImageNet is a large-scale image classification dataset used in machine learning and AI research, particularly for training and evaluating visual recognition algorithms. The script uses ImageNet as an example to illustrate the significant improvements in AI accuracy over time, noting the substantial decrease in error rates from 26% in 2011 to 2% by 2020.

💡Human Decision Making

Human Decision Making is the process by which people make choices and decisions throughout the development and deployment of AI systems. The video script emphasizes that it is humans, not machines, who play the central role in decision-making in AI. This includes decisions about data collection, algorithm design, and the application of AI in various contexts, all of which are influenced by human values and choices.

Highlights

AI is becoming more common in daily life, influencing areas such as traffic predictions and TV show recommendations.

Generative AI is making non-AI technologies seem inadequate as it becomes more prevalent.

AI development was historically limited to a small, expensive group of specialty engineers.

Barriers to AI development are decreasing, allowing more individuals to build AI systems.

AI systems are enabling computers to interact with the world in unprecedented ways.

The pace of AI development is extraordinary, with compute power doubling every 3.5 months according to the 2019 AI index report.

Vision AI technologies have become significantly more accurate and powerful over time.

The error rate for ImageNet, an image classification dataset, has dramatically decreased from 26% in 2011 to 2% by 2020.

Human error rate in image classification is 5%, showing AI's advancement in accuracy.

AI is not infallible and requires understanding of potential issues, limitations, and unintended consequences.

AI may replicate and amplify societal issues and biases without proper practices.

There is no universal definition of 'responsible AI', and its implementation varies by organization.

Common themes in responsible AI include transparency, fairness, accountability, and privacy.

Google's approach to responsible AI is driven by principles that reflect its mission and values.

Google has developed AI principles, practices, governance processes, and tools to guide responsible AI.

Google incorporates responsibility by design into its products and organizational structure.

Google uses its AI principles as a framework for responsible decision-making in AI development.

Google acknowledges that the work on responsible AI is never finished and seeks to collaborate and share insights.

The course aims to provide insights into Google Cloud's journey toward responsible AI development and use.

The goal is for participants to use shared information to shape their organization's responsible AI strategy.

There is a lack of consensus on the definition of AI, but this has not stopped technical advancement.

Google's AI Principles apply to advanced technology development, including various types of technologies.

Human decision-making is central to AI development, not just the machines themselves.

Every decision in AI development involves human values and requires responsible consideration.

Transcripts

Hi there, and welcome to Applying AI Principles with Google Cloud,

a course focused on the practice of responsible AI.

My name is Marcus.

And I’m Katelyn.

We’ll be your narrators throughout this course.

Many of us already have daily interactions with artificial intelligence (or AI), from

predictions for traffic and weather,

to recommendations of TV shows you might like to watch next.

As AI, especially generative AI, becomes more common

many technologies that aren’t AI-enabled may start to seem inadequate.

And such powerful, far-reaching technology raises

equally powerful questions about its development and use.

Historically, AI was not accessible to Ordinary people.The vast majority of those

trained and capable of developing AI were specialty engineers,

who were scarce in number, and expensive.

But the barriers to entry are being lowered allowing more people to build AI, even those

without AI expertise .

Now, AI systems are enabling computers to see, understand, and interact with the

world in ways that were unimaginable just a decade ago.

And these systems are developing at an extraordinary pace.

According to Stanford University’s 2019 AI index report, before 2012,

AI results tracked closely with Moore’s Law, with compute doubling every two years.

The report states that, since 2012, compute has been doubling approximately every 3 and

a half months.

To put this in perspective, over this time, Vision AI technologies have only become more

accurate and powerful.

For example, the error rate for ImageNet, an image classification dataset, has declined

significantly.

in 2011 the error rate was 26%.

The error rate for the annual ImageNet large-scale visual recognition challenge

has declined significantly.

By 2020, that number was 2%.

For reference, the error rate of people performing the same task is 5%.

And yet, despite these remarkable advancements, AI is not infallible.

Developing responsible AI requires an understanding of the possible issues, limitations, or unintended

consequences.

Technology is a reflection of what exists in society, so without good practices, AI

may replicate existing issues or bias, and amplify them.

But there is not a universal definition of “responsible AI,”

nor is there a simple checklist

or formula that defines how responsible AI practices should be implemented.

Instead, organizations are developing their own AI principles, that reflect their mission

and values.

While these principles are unique to every organization,

if you look for common themes, you find a consistent

set of ideas across transparency,

fairness, accountability,

and privacy.

At Google, our approach to responsible AI is rooted in a commitment to strive towards

AI that is built for everyone,

that it is accountable and safe, that respects privacy,

and that is driven by scientific excellence.

We’ve developed our own AI principles,

practices, governance processes,

and tools that together embody our values and guide

our approach to responsible AI.

We’ve incorporated responsibility by design into our products,

and even more importantly, our organization.

Like many companies, we use our AI principles as a framework to

guide responsible decision making.

We’ll explore how we do this in detail later in this course.

It’s important to emphasize here that we don’t pretend to have all of the answers.

We know this work is never finished, and we want to share what we’re learning to collaborate

and help others on their own journeys.

We all have a role to play in how responsible AI is applied.

Whatever stage in the AI process you are involved with,

from design to deployment

or application, the decisions you make have an impact.

It's important that you too have a defined and repeatable process for using AI responsibly.

Google is not only committed to building socially-valuable advanced technologies, but also to promoting

responsible practices by sharing our insights and lessons learned with the wider community.

This course represents one piece of these efforts.

The goal of this course is to provide a window into Google and, more specifically, Google

Cloud’s journey toward the responsible development and use of AI.

Our hope is that you’ll be able to take the information and resources we’re sharing

and use them to help shape your organization’s own responsible AI strategy.

But before we get any further, let’s clarify what we mean when we talk about AI.

Often, people want to know the differences between artificial intelligence, machine learning,

and deep learning.

However, there is no universally agreed-upon definition of AI.

Critically, this lack of consensus around how AI should be defined has not stopped technical

advancement, underscoring the need for ongoing dialogue

about how to responsibly create and use these systems.

At Google, we say our AI Principles apply to advanced technology development as an umbrella

to encapsulate all kinds of technologies.

Becoming bogged down in semantics can distract from the central goal: to develop technology

responsibly.

As a result, we’re not going to do a deep dive into the definitions of these technologies,

and instead we’ll focus on the importance of human decision making in technology development.

There is a common misconception with artificial intelligence that machines play the central

decision-making role.

In reality, it’s people who design and build these machines

and decide how they are used.

People are involved in each aspect of AI development.They collect or create the data that the model

is trained on.

They control the deployment of the AI and how it is applied in a given context.

Essentially, human decisions are threaded throughout our technology products.

And every time a person makes a decision, they are actually making a choice based on

their values.

Whether it's the decision to use generative AI to solve a problem, as opposed to other

methods [right top], or anywhere throughout the machine learning lifecycle, they introduce

their own set of values.

This means that every decision point requires consideration and evaluation to ensure that

choices have been made responsibly from concept through deployment and maintenance.