Facebook is spending a lot of time and money on augmented reality, including building its own AR glasses using Ray-Ban. Currently, these gadgets can only record and share images, but how does the company think such devices will be used in the future?
A new research project led by Facebook’s AI team suggests a range of corporate ambitions. Imagine an AI system that constantly analyzes people’s lives using first-person video. Record what they see, do, and hear to help them with their daily work. Facebook researchers want these systems to develop a range of skills, such as episodic memory (answering questions such as where the keys were placed) and audiovisual dialiation (remembering who said what when). I gave an overview.
May take advantage of this kind of research
Currently, the above tasks cannot be reliably accomplished with any AI system, and Facebook emphasizes that this is a research project, not a commercial development. But it’s clear that the company sees these features as the future of AR computing. Indeed, given augmented reality and what it can do, Facebook AI research scientist Kristen Gromann told The Verge that it could take advantage of this type of research.
Such ambitions have a huge impact on privacy. Privacy experts are already worried about how to use Facebook’s AR glasses to allow the wearer to secretly record the general public. Such concerns are exacerbated only when future versions of the hardware not only record the footage, but also analyze and post it, turning the wearer into a gait monitor.
Facebook’s first commercial AR glasses can only record and share videos and photos, not analysis.Photo by Amanda Lopez of The Verge
The name of Facebook’s research project is Ego4D, which refers to first-person or self-centered video analysis. It consists of two main components. An open dataset of self-centered video and a set of benchmarks that Facebook believes AI systems can work on in the future.
Facebook helped collect 3,205 hours of first-person footage from around the world
The dataset is the largest ever created, and Facebook has partnered with 13 universities around the world to collect the data. A total of about 3,205 hours of footage was recorded by 855 participants from nine countries. The university was responsible for collecting the data, not Facebook. Some of the participants were paid and wore GoPro cameras and AR glasses to record videos of unscripted activities. This can range from construction work to making sweets, playing with pets, and socializing with friends. All footage was anonymized by the university, including blurring the faces of bystanders and removing personally identifiable information.
Grauman says the dataset is the first of its kind, both in size and variety. According to her, the closest equivalent project includes 100 hours of first-person footage shot entirely in the kitchen. We have opened the eyes of these AI systems to more than kitchens in the UK and Sicily, [to footage from] Saudi Arabia, Tokyo, Los Angeles, Colombia.
The second component of Ego4D is a set of benchmarks or tasks that Facebook wants researchers around the world to solve using dataset-trained AI systems. The company describes these as follows:
Episodic memory: When and what happened (for example, where did you put the key?)?
Prediction: What could you do next (for example, wait, you’ve already added salt to this recipe)?
Manipulating hands and objects: what am I doing (for example, tell me how to play the drums)?
Audiovisual Diarization: Who said what when (for example, what were the main topics in class?)?
Social Interaction: Who is interacting with whom (for example, help me better hear people talking to me in this noisy restaurant)?
Currently, AI systems find it very difficult to tackle these issues, but creating datasets and benchmarks is a proven way to accelerate development in the field of AI.
In fact, the creation of one particular dataset and its associated annual competition, known as ImageNet, is considered the kickstart of the recent AI boom. The ImagetNet dataset consists of photographs of a wide variety of objects that researchers have trained to identify AI systems. In 2012, the contest’s winning entries began the era of current research, blasting past rivals using certain methods of deep learning.
Facebook’s Ego4D dataset should help spur research into AI systems that can analyze first-person data. Image: Facebook
Facebook wants the Ego4D project to have a similar effect on the augmented reality world. According to the company, Ego4D-trained systems could one day be used not only in wearable cameras, but also in home assistant robots that rely on first-person cameras to navigate the world around them.
Grauman says the project has the opportunity to actually facilitate work in this area in ways that weren’t really possible yet. Our field needs to be understood in the context of ongoing activities of AR systems, robots, from the ability to analyze piles of photographs and videos taken by humans for very special purposes, this fluid and continuous. Move to a first-person visual stream.
The development of Facebook’s AI surveillance system bothers many people
The tasks that Facebook outlines certainly seem to be practical, but companies interested in this area are worried about many. Facebook’s privacy record is terrible, straddling data breaches and $ 5 billion fines from FTC. It has also been repeatedly shown that the company emphasizes growth and engagement over user well-being in many domains. With this in mind, I’m worried that the benchmarks for this Ego4D project don’t include significant privacy protection. For example, the audiovisual dialiation task (copying what different people say) doesn’t mention deleting data about people you don’t want to record.
When asked about these issues, a Facebook spokesperson told The Verge that he hopes more privacy protections will be introduced in the future. To the extent that companies develop commercial applications using this dataset and benchmarks, a spokeswoman said they expect to develop safeguards for such applications. For example, before AR glasses emphasize someone’s voice, you can set a protocol to ask for permission from someone else’s glasses, or limit the range of your device so that only the other person’s voice can be picked up. can. Someone I’m already talking to or in the immediate vicinity of me.
So far, such safeguards are only fictitious.
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