This is how we lost control of our faces
Deborah Raji, a fellow at nonprofit Mozilla, and Genevieve Fried, who advises members of the US Congress on algorithmic accountability, examined over 130 facial-recognition data sets compiled over 43 years. They found that researchers, driven by the exploding data requirements of deep learning, gradually abandoned asking for people’s consent. This has led more and more of people’s personal photos to be incorporated into systems of surveillance without their knowledge.
It has also led to far messier data sets: they may unintentionally include photos of minors, use racist and sexist labels, or have inconsistent quality and lighting. The trend could help explain the growing number of cases in which facial-recognition systems have failed with troubling consequences, such as the false arrests of two Black men in the Detroit area last year.
People were extremely cautious about collecting, documenting, and verifying face data in the early days, says Raji. “Now we don’t care anymore. All of that has been abandoned,” she says. “You just can’t keep track of a million faces. After a certain point, you can’t even pretend that you have control.”
A history of facial-recognition data
The researchers identified four major eras of facial recognition, each driven by an increasing desire to improve the technology. The first phase, which ran until the 1990s, was largely characterized by manually intensive and computationally slow methods.
But then, spurred by the realization that facial recognition could track and identify individuals more effectively than fingerprints, the US Department of Defense pumped $6.5 million into creating the first large-scale face data set. Over 15 photography sessions in three years, the project captured 14,126 images of 1,199 individuals. The Face Recognition Technology (FERET) database was released in 1996.
The following decade saw an uptick in academic and commercial facial-recognition research, and many more data sets were created. The vast majority were sourced through photo shoots like FERET’s and had full participant consent. Many also included meticulous metadata, Raji says, such as the age and ethnicity of subjects, or illumination information. But these early systems struggled in real-world settings, which drove researchers to seek larger and more diverse data sets.
In 2007, the release of the Labeled Faces in the Wild (LFW) data set opened the floodgates to data collection through web search. Researchers began downloading images directly from Google, Flickr, and Yahoo without concern for consent. LFW also relaxed standards around the inclusion of minors, using photos found with search terms like “baby,” “juvenile,” and “teen” to increase diversity. This process made it possible to create significantly larger data sets in a short time, but facial recognition still faced many of the same challenges as before. This pushed researchers to seek yet more methods and data to overcome the technology’s poor performance.
Then, in 2014, Facebook used its user photos to train a deep-learning model called DeepFace. While the company never released the data set, the system’s superhuman performance elevated deep learning to the de facto method for analyzing faces. This is when manual verification and labeling became nearly impossible as data sets grew to tens of millions of photos, says Raji. It’s also when really strange phenomena start appearing, like auto-generated labels that include offensive terminology.
The emergent industrial metaverse
Annika Hauptvogel, head of technology and innovation management at Siemens, describes the industrial metaverse as “immersive, making users feel as if they’re in a real environment; collaborative in real time; open enough for different applications to seamlessly interact; and trusted by the individuals and businesses that participate”—far more than simply a digital world.
The industrial metaverse will revolutionize the way work is done, but it will also unlock significant new value for business and societies. By allowing businesses to model, prototype, and test dozens, hundreds, or millions of design iterations in real time and in an immersive, physics-based environment before committing physical and human resources to a project, industrial metaverse tools will usher in a new era of solving real-world problems digitally.
“The real world is very messy, noisy, and sometimes hard to really understand,” says Danny Lange, senior vice president of artificial intelligence at Unity Technologies, a leading platform for creating and growing real-time 3-D content. “The idea of the industrial metaverse is to create a cleaner connection between the real world and the virtual world, because the virtual world is so much easier and cheaper to work with.”
While real-life applications of the consumer metaverse are still developing, industrial metaverse use cases are purpose-driven, well aligned with real-world problems and business imperatives. The resource efficiencies enabled by industrial metaverse solutions may increase business competitiveness while also continually driving progress toward the sustainability, resilience, decarbonization, and dematerialization goals that are essential to human flourishing.
This report explores what it will take to create the industrial metaverse, its potential impacts on business and society, the challenges ahead, and innovative use cases that will shape the future. Its key findings are as follows:
• The industrial metaverse will bring together the digital and real worlds. It will enable a constant exchange of information, data, and decisions and empower industries to solve extraordinarily complex real-world problems digitally, changing how organizations operate and unlocking significant societal benefits.
• The digital twin is a core metaverse building block. These virtual models simulate real-world objects in detail. The next generation of digital twins will be photorealistic, physics-based, AI-enabled, and linked in metaverse ecosystems.
• The industrial metaverse will transform every industry. Currently existing digital twins illustrate the power and potential of the industrial metaverse to revolutionize design and engineering, testing, operations, and training.
The Download: China’s retro AI photos, and experts’ AI fears
Across social media, a number of creators are generating nostalgic photographs of China with the help of AI. Even though these images get some details wrong, they are realistic enough to trick and impress many of their followers.
The pictures look sophisticated in terms of definition, sharpness, saturation, and color tone. Their realism is partly down to a recent major update of image-making artificial-intelligence program Midjourney that was released in mid-March, which is better not only at generating human hands but also at simulating various photography styles.
It’s still relatively easy, even for untrained eyes, to tell that the photos are generated by an AI. But for some creators, their experiments are more about trying to recall a specific era in time than trying to trick their audience. Read the full story.
Zeyi’s story is from China Report, his weekly newsletter giving you the inside track on tech in China. Sign up to receive it in your inbox every Tuesday.
Read more of our reporting on AI-generated images:
+ These new tools let you see for yourself how biased AI image models are. Bias and stereotyping are still huge problems for systems like DALL-E 2 and Stable Diffusion, despite companies’ attempts to fix it. Read the full story.
Evolutionary organizations reimagine the future
The global technology consultancy Thoughtworks describes organizations that can respond to marketplace changes with continuous adaptation as “evolutionary organizations.” It argues that, instead of focusing only on technology change, organizations should focus on building capabilities that support ongoing reinvention. While many organizations recognize the benefit of adopting agile approaches in their technology capabilities and architectures, they have not extended these structures and ways of thinking throughout the operating model, which would allow their impact to extend beyond that of a single transformation project.
Global spending on digital transformation is growing at a brisk pace: 16.4% per year according to IDC. The firm’s 2021 “Worldwide Digital Transformation Spending Guide” forecasts that annual transformation expenditures will reach $2.8 trillion in 2025, more than double the spending in 2020.1 At the same time, research from Boston Consulting Group shows that 7 out of 10 digital transformation initiatives fall short of their objectives. Organizations that succeed, however, achieve almost double the earnings growth of those that fail and more than double the growth in the total value of their enterprises.2 Understanding how to make these transitions successful, then, should be of key interest to all business leaders.
This MIT Technology Review Insights report is based on a survey of 275 corporate leaders, supplemented by interviews with seven experts in digital transformation. Its key findings include the following:
• Digital transformation is not solely a technology issue. Adopting new technology for its own sake does not set the organization up to continue to adapt to changing circumstances. Among survey respondents, however, transformation is still synonymous with tech, with 70% planning to adopt a new technology in the next year, but only 41% pursuing changes to their business model.
• The business environment is changing faster than many organizations think. Most survey respondents (81%) believe their organization is more adaptable than average and nearly all (89%) say that they’re keeping up with or ahead of their competitors—suggesting a wide gap between the rapidly evolving reality and executives’ perceptions of their preparedness.
• All organizations must build capabilities for continuous reinvention. The only way to keep up is for organizations to continuously change and evolve, but most traditional businesses lack the strategic flexibility necessary to do this. Nearly half of business leaders outside the C-suite (44%), for example, say organizational structure, silos, or hierarchy are the biggest obstacle to transformation at their firm.
• Focusing on customer value and empowering employees are keys to organizational evolution. The most successful transformations prioritize creating customer value and enhancing customer and employee experience. Meeting evolving customer needs is the constant source of value in a world where everything is changing, but many traditional organizations fail to take this long view, with only 15% of respondents most concerned about failing to meet customer expectations if they fail to transform.
• Rapid experimentation requires the ability to fail and recover quickly. Organizations agree that iterative, experimental processes are essential to finding the right solutions, with 81% saying they have adopted agile practices. Fewer are confident, however, in their ability to execute decisions quickly (76%)—or to shut down initiatives that aren’t working (60%).