Learn more about what comes next. Facebook this morning announced ReSkin, an open source touch-sensing synthetic "skin" created by researchers at the company in collaboration with Carnegie Mellon University. Leveraging machine learning and magnetic sensing, ReSkin is designed to offer an inexpensive, versatile, durable, and replaceable solution for long-term use, employing an unsupervised learning algorithm to help auto-calibrate the sensor. Alongside ReSkin, and perhaps timed in effort to distract from exposes detailing its internal turbulence, Facebook also today outlined its broader progress in developing hardware, simulators, libraries, benchmarks, and datasets for touch sensing, which the company says form the foundation for AI systems that can understand and interact through touch. "We typically think of touch as a way to convey warmth and care, but it's also a key sensing modality for perceiving the world around us," Facebook research scientist Roberto Calandra and hardware engineer Mike Lambeta said in a blog post.

"VICReg could be used to model the dependencies between a video clip and the frame that comes after, therefore learning to predict the future in a video." Humans have an innate capability to identify objects in the wild, even from a blurred glimpse of the thing. We do this efficiently by remembering only high-level features that get the job done (identification) and ignoring the details unless required. In the context of deep learning algorithms that do object detection, contrastive learning explored the premise of representation learning to obtain a large picture instead of doing the heavy lifting by devouring pixel-level details. But, contrastive learning has its own limitations.

When disaster or disease strikes, relief agencies respond more effectively when they have detailed mapping tools to know exactly where to deliver assistance. But extremely reliable and precise maps often are not available. So, our team, composed of artificial intelligence researchers and data scientists in Facebook's Boston office, used our computer vision expertise to create and share population density maps that are more accurate and higher resolution than any of their predecessors. Building on our previous publication of similar high-resolution population maps for 22 countries, we're now releasing new maps of the majority of the African continent, and the project will eventually map nearly the whole world's population. When it is completed, humanitarian agencies will be able to determine how populations are distributed even in remote areas, so that health care workers can better reach households and relief workers can better distribute aid.

Facebook researchers have developed what they claim is the largest automatic speech recognition (ASR) model of its kind -- a model that learned to understand words in 51 languages after training on over 16,000 hours of voice recordings. In a paper published on the preprint server Arxiv.org, the coauthors say the system, which contains around a billion parameters, improves speech recognition performance up to 28.8% on one benchmark compared with baselines. Designing a single model to recognize speech in multiple languages is desirable for several reasons. It simplifies the backend production pipeline, for one thing, and studies have shown training multilingual models on similar languages can decrease overall word error rate (WER). Facebook's model -- a so-called joint sequence-to-sequence (Seq2Seq) model -- was trained while sharing the parameters from an encoder, decoder, and token set across all languages. The encoder maps input audio sequences to intermediate representations while the decoder maps the representations to output text, and the token set simplifies the process of working with many languages by sampling sentences at different frequencies.

Facebook researchers believe the game NetHack is well-tailored to training, testing, and evaluating AI models. To this end, they today released the NetHack Learning Environment, a research tool for benchmarking the robustness and generalization of reinforcement learning agents. For decades, games have served as benchmarks for AI. But things really kicked into gear in 2013 -- the year Google subsidiary DeepMind demonstrated an AI system that could play Pong, Breakout, Space Invaders, Seaquest, Beamrider, Enduro, and Q*bert at superhuman levels. Rather, they're informing the development of systems that might one day diagnose illnesses, predict complicated protein structures, and segment CT scans.

In May, the same week Facebook announced Shops, a way for businesses to set up online stores for customers across Facebook, WhatsApp, Messenger, and Instagram, the tech giant detailed the AI and machine learning systems behind its ecommerce experiences. Facebook said its goal is to one day develop an assistant that can serve up product recommendations on the fly, and that can learn preferences by analyzing images of what's in a person's wardrobe while allowing the person to try new items on self-replicas and sell apparel that others can preview. A flurry of Facebook-authored papers accepted to the Conference on Computer Vision and Pattern Recognition (CVPR) 2020 suggest the company is on its way to developing the components of this assistant. One paper describes an algorithm that uncovers and quantifies fashion influences from images taken around the world. Another demonstrates an AI model that generates 3D models of people from single images.

Navigating a new indoor space without any prior knowledge or even a map is a challenging task for a human, let alone a robot. To help develop intelligent machines that interact more effectively with complex 3D environments, Facebook researchers developed a GPU-accelerated deep reinforcement learning model that achieves near 100 percent success in navigating a variety of virtual environments without a pre-provided map. To achieve this breakthrough, the team focused their work on developing an efficient approach to scaling RL models, which require a significant number of training samples, using multi-node distribution. "A single parameter server and thousands of (typically CPU) workers may be fundamentally incompatible with the needs of modern computer vision and robotics communities," the researchers explained in their post, Near-perfect point-goal navigation from 2.5 billion frames of experience. "Unlike Gym or Atari, 3D simulators require GPU acceleration…. The desired agents operate from high-dimensional inputs (pixels) and use deep networks, such as ResNet50, which strain the parameter server. Thus, existing distributed RL architectures do not scale and there is a need to develop a new distributed architecture."

Facebook researchers taught the company's artificial intelligence software how to play a Solitaire-like card game that requires players to work together. Researchers at Facebook have taught the company's artificial intelligence (AI) software how to play the game Hanabi, a Solitaire-like card game that requires players to work together. Hanabi is viewed as a capable testbed for AI because it requires teamwork and strategy. The Facebook researchers had to find a way to give the Hanabi bots a method to understand the hints of their teammate bots, based on the limited information they have about their own cards. The researchers helped the bots using a variant of the Monte Carlo "search" technology technique to help them evaluate their possible moves.

Facebook researchers have developed Fashion, a system that uses artificial intelligence to recommend how to make an outfit more stylish. AI has been slow to penetrate subjective fields like fashion, so Fashion relied on humans to evaluate its suggestions. The technology could be used by digital assistants to provide personal styling or shopping advice. Can a computer learn to tell what is fashionable and what is not? Artificial intelligence researchers at Facebook believe it can.

In a recent blog post, Facebook has announced they have open-sourced AI Habitat, an Artificial Intelligence (AI) simulation platform that is designed to train embodied agents, such as virtual robots. Using this technology, robots can learn how to grab an object from an adjacent room or assist a visually-impaired person in navigating an unfamiliar transit system. The technology leverages embodied AI which focuses on interactive environments to train real-world systems. This is a different approach than relying upon static data sets which other researchers have traditionally used. A team of Facebook researchers, including Manos Savva, Abhishek Kadian, Oleksandr Maksymets and Dhruv Batra, have released a research paper that demonstrates the capabilities of Al Habitat.