Course introduction

Summary

The video script from 'Applying AI Principles with Google Cloud' introduces the course on responsible AI practice, highlighting the rapid advancements in AI and its impact on society. It emphasizes the importance of developing AI ethically, with transparency, fairness, accountability, and privacy. Google's approach to AI is discussed, including their principles and commitment to building AI for everyone, ensuring safety, respecting privacy, and driving scientific excellence. The course aims to share Google's journey and insights to help shape organizations' responsible AI strategies.

Takeaways

• 🧠 Artificial Intelligence (AI) is increasingly integrated into daily life, from traffic predictions to TV show recommendations.
• 🚀 AI, particularly generative AI, is evolving rapidly, making non-AI technologies seem outdated and raising significant questions about its development and use.
• 🛠 Historically, AI was accessible only to a select few engineers, but barriers to entry are now lower, allowing more individuals to engage with AI development.
• 📈 AI systems are advancing at an extraordinary pace, with computational capabilities doubling every 3.5 months according to Stanford University’s 2019 AI index report.
• 📊 The accuracy of AI technologies, exemplified by the ImageNet challenge, has seen a dramatic improvement, with error rates dropping from 26% in 2011 to 2% by 2020.
• 🤖 Despite advancements, AI is not perfect and developing responsible AI requires understanding potential issues, limitations, and unintended consequences.
• 🔍 AI reflects societal biases if developed without good practices, potentially replicating and amplifying existing issues.
• 📋 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 is committed to sharing insights and lessons to promote responsible AI practices within the wider community, as part of its social commitment.
• 🔑 Human decision-making is central to AI development, with every choice reflecting values and impacting the responsible use of AI from concept to deployment.

Q & A

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

The course focuses on the practice of responsible AI, discussing its development, use, and the ethical considerations involved.

Who are the narrators of the course?

Marcus and Katelyn are the narrators who guide the audience throughout the course.

Why is generative AI becoming more common?

As AI technologies advance and become more accessible, non-AI-enabled technologies may start to seem inadequate, leading to the rise of generative AI.

What has historically limited the accessibility of AI to ordinary people?

Historically, AI was limited to specialty engineers who were scarce and expensive, creating a barrier to entry for ordinary people.

How has the pace of AI development accelerated according to the 2019 AI index report from Stanford University?

The report states that before 2012, AI results tracked closely with Moore’s Law, with compute doubling every two years. Since 2012, compute has been doubling approximately every 3.5 months.

What is the significance of the ImageNet dataset in the context of AI advancements?

ImageNet is an image classification dataset that has been used to measure the accuracy and power of Vision AI technologies, showing a significant decline in error rates over the years.

What was the error rate for ImageNet in 2011, and how does it compare to human performance?

In 2011, the error rate for ImageNet was 26%, which is significantly higher than the error rate of 5% for humans performing the same task.

What is the error rate for ImageNet in 2020, and how does it compare to human performance?

By 2020, the error rate for ImageNet had declined to 2%, which is lower than the human error rate of 5%.

What are some of the key themes found in organizations' AI principles?

Common themes in AI principles across organizations include transparency, fairness, accountability, and privacy.

How does Google approach responsible AI?

Google's approach to responsible AI is rooted in a commitment to build AI that is for everyone, accountable, safe, respects privacy, and is driven by scientific excellence.

What is the purpose of the course 'Applying AI Principles with Google Cloud' in terms of sharing knowledge?

The course aims to share Google's insights and lessons learned about responsible AI practices to help others shape their own AI strategies.

What is the importance of human decision-making in AI development according to the script?

Human decision-making is central to AI development as people design, build, and decide how AI systems are used, with their decisions reflecting their values and impacting the responsible use of technology.

Outlines

00:00

🤖 Introduction to Responsible AI with Google Cloud

The script introduces the course 'Applying AI Principles with Google Cloud', focusing on responsible AI practices. The narrators, Marcus and Katelyn, set the stage by discussing the prevalence of AI in daily life and the rapid advancements in the field. They highlight the historical inaccessibility of AI and the recent democratization of AI development. The course aims to address the powerful questions surrounding AI's development and use, emphasizing the importance of understanding potential issues and biases. Google's approach to responsible AI is outlined, including the company's principles that guide AI development, such as inclusivity, accountability, privacy, and scientific excellence. The course intends to share Google's learnings to foster collaboration and guide others in their AI journey.

05:06

🧠 Human Decision-Making in AI Development

This paragraph delves into the misconception that machines are the central decision-makers in AI, clarifying that it is actually humans who design, build, and decide the usage of AI systems. The paragraph underscores the importance of human involvement at every stage of AI development, from data collection to deployment. It stresses that human decisions are value-laden and must be made responsibly throughout the AI lifecycle. The paragraph concludes by emphasizing the significance of considering values and making ethical choices at every decision point in AI development, from concept to deployment and maintenance.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. In the video, AI is described as a transformative technology enabling computers to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. The theme of the video centers on the responsible development and use of AI, highlighting its increasing accessibility and impact on various aspects of daily life.

💡Responsible AI

Responsible AI involves developing and deploying AI systems in a manner that is ethical, transparent, and accountable. The video emphasizes the importance of understanding the potential issues and unintended consequences of AI, advocating for practices that prevent bias and promote fairness. Google's commitment to responsible AI is discussed, including their AI principles, practices, governance processes, and tools designed to ensure that AI technologies are developed and used responsibly.

💡AI Principles

AI Principles are guidelines or frameworks that organizations develop to ensure that their AI systems are built and used responsibly. The video explains that while there is no universal definition of responsible AI, common themes include transparency, fairness, accountability, and privacy. Google's AI principles reflect their mission and values, guiding their approach to responsible AI development and usage.

💡Generative AI

Generative AI refers to AI systems that can create new content, such as images, text, or music, based on the data they have been trained on. The video mentions that generative AI is becoming more common, making non-AI-enabled technologies seem inadequate. The responsible use of generative AI is crucial, as it can have significant implications for creativity, privacy, and ethics.

💡Moore's Law

Moore's Law is the observation that the number of transistors on a microchip doubles approximately every two years, leading to an exponential increase in computing power. The video references this law to highlight the rapid advancements in AI technology, noting that since 2012, compute power for AI has been doubling approximately every three and a half months. This accelerated pace has significantly enhanced the capabilities and accuracy of AI systems.

💡ImageNet

ImageNet is a large-scale visual recognition dataset used to train and evaluate image classification algorithms. The video uses ImageNet to illustrate the advancements in Vision AI technologies, noting that the error rate for classifying images has dramatically decreased from 26% in 2011 to 2% by 2020. This improvement showcases the progress and increased accuracy of AI in visual recognition tasks.

💡Bias in AI

Bias in AI refers to the systematic and unfair discrimination that can occur when AI systems reflect or amplify existing societal prejudices. The video stresses the importance of developing responsible AI to avoid replicating or exacerbating biases. By incorporating good practices and ethical considerations, organizations can mitigate bias and ensure fair outcomes from their AI systems.

💡Transparency

Transparency in AI involves being open and clear about how AI systems are developed, how they work, and how decisions are made by these systems. The video identifies transparency as a key theme in responsible AI practices, essential for building trust and accountability. Transparent AI development helps stakeholders understand and scrutinize the technology, leading to more informed and ethical usage.

💡Accountability

Accountability in AI means that individuals and organizations developing and deploying AI systems are responsible for their actions and the outcomes of their technologies. The video highlights accountability as a critical aspect of responsible AI, ensuring that those who create and use AI are answerable for their decisions and the impacts on society. This includes implementing processes and governance structures to oversee AI activities.

💡Privacy

Privacy in AI refers to the protection of individuals' personal information and ensuring that AI systems respect user confidentiality. The video mentions privacy as a fundamental component of Google's AI principles, emphasizing the need to safeguard data and prevent unauthorized access or misuse. Responsible AI development involves implementing privacy-preserving techniques and practices to maintain user trust and comply with legal standards.

Highlights

Introduction to the course on responsible AI with Google Cloud by Marcus and Katelyn.

Daily interactions with AI, such as traffic predictions and TV show recommendations.

The growing prevalence of AI, especially generative AI, and its impact on technology standards.

Historical context of AI being inaccessible to ordinary people, but barriers to entry are lowering.

AI systems now enable computers to see, understand, and interact with the world in unprecedented ways.

Stanford University's 2019 AI index report on the rapid increase in compute power since 2012.

Significant improvement in Vision AI technologies, with ImageNet error rates dropping from 26% in 2011 to 2% in 2020.

AI's fallibility and the importance of understanding its limitations and potential consequences.

The necessity of good practices to prevent AI from replicating and amplifying societal biases.

Lack of a universal definition of responsible AI and the need for organizations to develop their own AI principles.

Google's AI principles focus on transparency, fairness, accountability, and privacy.

Google's commitment to responsible AI through scientific excellence and responsible decision-making frameworks.

The course aims to share Google's journey and insights on responsible AI development.

Clarification of AI, machine learning, and deep learning without delving into strict definitions.

Emphasis on human decision-making throughout the AI development process and its impact on the technology.

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.