Author Archives: Fei-Fei Li

How to make AI that’s good for people

For a field that was not well known outside of academia a decade ago, artificial intelligence has grown dizzyingly fast. Tech companies from Silicon Valley to Beijing are betting everything on it, venture capitalists are pouring billions into research and development, and start-ups are being created on what seems like a daily basis. If our era is the next Industrial Revolution, as many claim, AI is surely one of its driving forces.

It is an especially exciting time for a researcher like me. When I was a graduate student in computer science in the early 2000s, computers were barely able to detect sharp edges in photographs, let alone recognize something as loosely defined as a human face. But thanks to the growth of big data, advances in algorithms like neural networks and an abundance of powerful computer hardware, something momentous has occurred: AI has gone from an academic niche to the leading differentiator in a wide range of industries, including manufacturing, health care, transportation and retail.

I worry, however, that enthusiasm for AI is preventing us from reckoning with its looming effects on society. Despite its name, there is nothing “artificial” about this technology—it is made by humans, intended to behave like humans and affects humans. So if we want it to play a positive role in tomorrow’s world, it must be guided by human concerns.

I call this approach “human-centered AI.” It consists of three goals that can help responsibly guide the development of intelligent machines.

First, AI needs to reflect more of the depth that characterizes our own intelligence. Consider the richness of human visual perception. It’s complex and deeply contextual, and naturally balances our awareness of the obvious with a sensitivity to nuance. By comparison, machine perception remains strikingly narrow.

Sometimes this difference is trivial. For instance, in my lab, an image-captioning algorithm once fairly summarized a photo as “a man riding a horse” but failed to note the fact that both were bronze sculptures. Other times, the difference is more profound, as when the same algorithm described an image of zebras grazing on a savanna beneath a rainbow. While the summary was technically correct, it was entirely devoid of aesthetic awareness, failing to detect any of the vibrancy or depth a human would naturally appreciate.

That may seem like a subjective or inconsequential critique, but it points to a major aspect of human perception beyond the grasp of our algorithms. How can we expect machines to anticipate our needs—much less contribute to our well-being—without insight into these “fuzzier” dimensions of our experience?

Making AI more sensitive to the full scope of human thought is no simple task. The solutions are likely to require insights derived from fields beyond computer science, which means programmers will have to learn to collaborate more often with experts in other domains.

Such collaboration would represent a return to the roots of our field, not a departure from it. Younger AI enthusiasts may be surprised to learn that the principles of today’s deep-learning algorithms stretch back more than 60 years to the neuroscientific researchers David Hubel and Torsten Wiesel, who discovered how the hierarchy of neurons in a cat’s visual cortex responds to stimuli.

Likewise, ImageNet, a data set of millions of training photographs that helped to advance computer vision, is based on a project called WordNet, created in 1995 by the cognitive scientist and linguist George Miller. WordNet was intended to organize the semantic concepts of English.

Reconnecting AI with fields like cognitive science, psychology and even sociology will give us a far richer foundation on which to base the development of machine intelligence. And we can expect the resulting technology to collaborate and communicate more naturally, which will help us approach the second goal of human-centered AI: enhancing us, not replacing us.

Imagine the role that AI might play during surgery. The goal need not be to automate the process entirely. Instead, a combination of smart software and specialized hardware could help surgeons focus on their strengths—traits like dexterity and adaptability—while keeping tabs on more mundane tasks and protecting against human error, fatigue and distraction.

Or consider senior care. Robots may never be the ideal custodians of the elderly, but intelligent sensors are already showing promise in helping human caretakers focus more on their relationships with those they provide care for by automatically monitoring drug dosages and going through safety checklists.

These are examples of a trend toward automating those elements of jobs that are repetitive, error-prone and even dangerous. What’s left are the creative, intellectual and emotional roles for which humans are still best suited.

No amount of ingenuity, however, will fully eliminate the threat of job displacement. Addressing this concern is the third goal of human-centered AI: ensuring that the development of this technology is guided, at each step, by concern for its effect on humans.

Today’s anxieties over labor are just the start. Additional pitfalls include bias against underrepresented communities in machine learning, the tension between AI’s appetite for data and the privacy rights of individuals and the geopolitical implications of a global intelligence race.

Adequately facing these challenges will require commitments from many of our largest institutions. Universities are uniquely positioned to foster connections between computer science and traditionally unrelated departments like the social sciences and even humanities, through interdisciplinary projects, courses and seminars. Governments can make a greater effort to encourage computer science education, especially among young girls, racial minorities and other groups whose perspectives have been underrepresented in AI. And corporations should combine their aggressive investment in intelligent algorithms with ethical AI policies that temper ambition with responsibility.

No technology is more reflective of its creators than AI. It has been said that there are no “machine” values at all, in fact; machine values are human values. A human-centered approach to AI means these machines don’t have to be our competitors, but partners in securing our well-being. However autonomous our technology becomes, its impact on the world—for better or worse—will always be our responsibility.

This article was originally published in the New York Times.

Cloud AutoML: Making AI accessible to every business

When we both joined Google Cloud just over a year ago, we embarked on a mission to democratize AI. Our goal was to lower the barrier of entry and make AI available to the largest possible community of developers, researchers and businesses.

Our Google Cloud AI team has been making good progress towards this goal. In 2017, we introduced Google Cloud Machine Learning Engine, to help developers with machine learning expertise easily build ML models that work on any type of data, of any size. We showed how modern machine learning services, i.e., APIs—including Vision, Speech, NLP, Translation and Dialogflow—could be built upon pre-trained models to bring unmatched scale and speed to business applications. Kaggle, our community of data scientists and ML researchers, has grown to more than one million members. And today, more than 10,000 businesses are using Google Cloud AI services, including companies like Box, Rolls Royce Marine, Kewpie and Ocado.

But there’s much more we can do. Currently, only a handful of businesses in the world have access to the talent and budgets needed to fully appreciate the advancements of ML and AI. There’s a very limited number of people that can create advanced machine learning models. And if you’re one of the companies that has access to ML/AI engineers, you still have to manage the time-intensive and complicated process of building your own custom ML model. While Google has offered pre-trained machine learning models via APIs that perform specific tasks, there's still a long road ahead if we want to bring AI to everyone.

To close this gap, and to make AI accessible to every business, we’re introducing Cloud AutoML. Cloud AutoML helps businesses with limited ML expertise start building their own high-quality custom models by using advanced techniques like learning2learn and transfer learning from Google. We believe Cloud AutoML will make AI experts even more productive, advance new fields in AI and help less-skilled engineers build powerful AI systems they previously only dreamed of.

Our first Cloud AutoML release will be Cloud AutoML Vision, a service that makes it faster and easier to create custom ML models for image recognition. Its drag-and-drop interface lets you easily upload images, train and manage models, and then deploy those trained models directly on Google Cloud. Early results using Cloud AutoML Vision to classify popular public datasets like ImageNet and CIFAR have shown more accurate results with fewer misclassifications than generic ML APIs.

Here’s a little more on what Cloud AutoML Vision has to offer:

  • Increased accuracy: Cloud AutoML Vision is built on Google’s leading image recognition approaches, including transfer learning and neural architecture search technologies. This means you’ll get a more accurate model even if your business has limited machine learning expertise.

  • Faster turnaround time to production-ready models: With Cloud AutoML, you can create a simple model in minutes to pilot your AI-enabled application, or build out a full, production-ready model in as little as a day.

  • Easy to use: AutoML Vision provides a simple graphical user interface that lets you specify data, then turns that data into a high quality model customized for your specific needs.

AutoML

“Urban Outfitters is constantly looking for new ways to enhance our customers’ shopping experience," says Alan Rosenwinkel, Data Scientist at URBN. "Creating and maintaining a comprehensive set of product attributes is critical to providing our customers relevant product recommendations, accurate search results and helpful product filters; however, manually creating product attributes is arduous and time-consuming. To address this, our team has been evaluating Cloud AutoML to automate the product attribution process by recognizing nuanced product characteristics like patterns and neckline styles. Cloud AutoML has great promise to help our customers with better discovery, recommendation and search experiences."

Mike White, CTO and SVP, for Disney Consumer Products and Interactive Media, says: “Cloud AutoML’s technology is helping us build vision models to annotate our products with Disney characters, product categories and colors. These annotations are being integrated into our search engine to enhance the impact on Guest experience through more relevant search results, expedited discovery and product recommendations on shopDisney.”

And Sophie Maxwell, Conservation Technology Lead at the Zoological Society of London, tells us: "ZSL is an international conservation charity devoted to the worldwide conservation of animals and their habitats. A key requirement to deliver on this mission is to track wildlife populations to learn more about their distribution and better understand the impact humans are having on these species. In order to achieve this, ZSL has deployed a series of camera traps in the wild that take pictures of passing animals when triggered by heat or motion. The millions of images captured by these devices are then manually analysed and annotated with the relevant species, such as elephants, lions and giraffes, etc., which is a labour-intensive and expensive process. ZSL’s dedicated Conservation Technology Unit has been collaborating closely with Google’s Cloud ML team to help shape the development of this exciting technology, which ZSL aims to use to automate the tagging of these images—cutting costs, enabling wider-scale deployments and gaining a deeper understanding of how to conserve the world’s wildlife effectively."

If you’re interested in trying out AutoML Vision, you can request access via this form.

AutoML Vision is the result of our close collaboration with Google Brain and other Google AI teams, and is the first of several Cloud AutoML products in development. While we’re still at the beginning of our journey to make AI more accessible, we’ve been deeply inspired by what our 10,000+ customers using Cloud AI products have been able to achieve. We hope the release of Cloud AutoML will help even more businesses discover what’s possible through AI.

References

Cloud AutoML: Making AI accessible to every business

When we both joined Google Cloud just over a year ago, we embarked on a mission to democratize AI. Our goal was to lower the barrier of entry and make AI available to the largest possible community of developers, researchers and businesses.

Our Google Cloud AI team has been making good progress towards this goal. In 2017, we introduced Google Cloud Machine Learning Engine, to help developers with machine learning expertise easily build ML models that work on any type of data, of any size. We showed how modern machine learning services, i.e., APIs—including Vision, Speech, NLP, Translation and Dialogflow—could be built upon pre-trained models to bring unmatched scale and speed to business applications. Kaggle, our community of data scientists and ML researchers, has grown to more than one million members. And today, more than 10,000 businesses are using Google Cloud AI services, including companies like Box, Rolls Royce Marine, Kewpie and Ocado.

But there’s much more we can do. Currently, only a handful of businesses in the world have access to the talent and budgets needed to fully appreciate the advancements of ML and AI. There’s a very limited number of people that can create advanced machine learning models. And if you’re one of the companies that has access to ML/AI engineers, you still have to manage the time-intensive and complicated process of building your own custom ML model. While Google has offered pre-trained machine learning models via APIs that perform specific tasks, there's still a long road ahead if we want to bring AI to everyone.

To close this gap, and to make AI accessible to every business, we’re introducing Cloud AutoML. Cloud AutoML helps businesses with limited ML expertise start building their own high-quality custom models by using advanced techniques like learning2learn and transfer learning from Google. We believe Cloud AutoML will make AI experts even more productive, advance new fields in AI and help less-skilled engineers build powerful AI systems they previously only dreamed of.

Our first Cloud AutoML release will be Cloud AutoML Vision, a service that makes it faster and easier to create custom ML models for image recognition. Its drag-and-drop interface lets you easily upload images, train and manage models, and then deploy those trained models directly on Google Cloud. Early results using Cloud AutoML Vision to classify popular public datasets like ImageNet and CIFAR have shown more accurate results with fewer misclassifications than generic ML APIs.

Here’s a little more on what Cloud AutoML Vision has to offer:

  • Increased accuracy: Cloud AutoML Vision is built on Google’s leading image recognition approaches, including transfer learning and neural architecture search technologies. This means you’ll get a more accurate model even if your business has limited machine learning expertise.

  • Faster turnaround time to production-ready models: With Cloud AutoML, you can create a simple model in minutes to pilot your AI-enabled application, or build out a full, production-ready model in as little as a day.

  • Easy to use: AutoML Vision provides a simple graphical user interface that lets you specify data, then turns that data into a high quality model customized for your specific needs.

AutoML

“Urban Outfitters is constantly looking for new ways to enhance our customers’ shopping experience," says Alan Rosenwinkel, Data Scientist at URBN. "Creating and maintaining a comprehensive set of product attributes is critical to providing our customers relevant product recommendations, accurate search results and helpful product filters; however, manually creating product attributes is arduous and time-consuming. To address this, our team has been evaluating Cloud AutoML to automate the product attribution process by recognizing nuanced product characteristics like patterns and neckline styles. Cloud AutoML has great promise to help our customers with better discovery, recommendation and search experiences."

Mike White, CTO and SVP, for Disney Consumer Products and Interactive Media, says: “Cloud AutoML’s technology is helping us build vision models to annotate our products with Disney characters, product categories and colors. These annotations are being integrated into our search engine to enhance the impact on Guest experience through more relevant search results, expedited discovery and product recommendations on shopDisney.”

And Sophie Maxwell, Conservation Technology Lead at the Zoological Society of London, tells us: "ZSL is an international conservation charity devoted to the worldwide conservation of animals and their habitats. A key requirement to deliver on this mission is to track wildlife populations to learn more about their distribution and better understand the impact humans are having on these species. In order to achieve this, ZSL has deployed a series of camera traps in the wild that take pictures of passing animals when triggered by heat or motion. The millions of images captured by these devices are then manually analysed and annotated with the relevant species, such as elephants, lions and giraffes, etc., which is a labour-intensive and expensive process. ZSL’s dedicated Conservation Technology Unit has been collaborating closely with Google’s Cloud ML team to help shape the development of this exciting technology, which ZSL aims to use to automate the tagging of these images—cutting costs, enabling wider-scale deployments and gaining a deeper understanding of how to conserve the world’s wildlife effectively."

If you’re interested in trying out AutoML Vision, you can request access via this form.

AutoML Vision is the result of our close collaboration with Google Brain and other Google AI teams, and is the first of several Cloud AutoML products in development. While we’re still at the beginning of our journey to make AI more accessible, we’ve been deeply inspired by what our 10,000+ customers using Cloud AI products have been able to achieve. We hope the release of Cloud AutoML will help even more businesses discover what’s possible through AI.

References

Source: Google Cloud


Opening the Google AI China Center

Since becoming a professor 12 years ago and joining Google a year ago, I’ve had the good fortune to work with many talented Chinese engineers, researchers and technologists. China is home to many of the world's top experts in artificial intelligence (AI) and machine learning. All three winning teams of the ImageNet Challenge in the past three years have been largely composed of Chinese researchers. Chinese authors contributed 43 percent of all content in the top 100 AI journals in 2015—and when the Association for the Advancement of AI discovered that their annual meeting overlapped with Chinese New Year this year, they rescheduled.

I believe AI and its benefits have no borders. Whether a breakthrough occurs in Silicon Valley, Beijing or anywhere else, it has the potential to make everyone’s life better for the entire world. As an AI first company, this is an important part of our collective mission. And we want to work with the best AI talent, wherever that talent is, to achieve it.

That’s why I am excited to launch the Google AI China Center, our first such center in Asia, at our Google Developer Days event in Shanghai today. This Center joins other AI research groups we have all over the world, including in New York, Toronto, London and Zurich, all contributing towards the same goal of finding ways to make AI work better for everyone.

Focused on basic AI research, the Center will consist of a team of AI researchers in Beijing, supported by Google China’s strong engineering teams. We’ve already hired some top experts, and will be working to build the team in the months ahead (check our jobs site for open roles!). Along with Dr. Jia Li, Head of Research and Development at Google Cloud AI, I’ll be leading and coordinating the research. Besides publishing its own work, the Google AI China Center will also support the AI research community by funding and sponsoring AI conferences and workshops, and working closely with the vibrant Chinese AI research community.

Humanity is going through a huge transformation thanks to the phenomenal growth of computing and digitization. In just a few years, automatic image classification in photo apps has become a standard feature. And we’re seeing rapid adoption of natural language as an interface with voice assistants like Google Home. At Cloud, we see our enterprise partners using AI to transform their businesses in fascinating ways at an astounding pace. As technology starts to shape human life in more profound ways, we will need to work together to ensure that the AI of tomorrow benefits all of us. 

The Google AI China Center is a small contribution to this goal. We look forward to working with the brightest AI researchers in China to help find solutions to the world’s problems. 

Once again, the science of AI has no borders, neither do its benefits.