The sentient Magic Carpet from Aladdin might have a new competitor. While it can't fly or speak, a new tactile sensing carpet from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) can estimate human poses without using cameras, in a step towards improving self-powered personalized healthcare, smart homes, and gaming. Many of our daily activities involve physical contact with the ground: walking, exercising, or resting. These embedded interactions contain a wealth of information that help us better understand people's movements. Previous research has leveraged use of single RGB cameras, (think Microsoft Kinect), wearable omnidirectional cameras, and even plain old off the shelf webcams, but with the inevitable byproducts of camera occlusions and privacy concerns.

The robot seen here can't see the human arm during the entire dressing process, yet it manages to successfully get a jacket sleeve pulled onto the arm. Robots are already adept at certain things, such as lifting objects that are too heavy or cumbersome for people to manage. Another application they're well suited for is the precision assembly of items like watches that have large numbers of tiny parts -- some so small they can barely be seen with the naked eye. "Much harder are tasks that require situational awareness, involving almost instantaneous adaptations to changing circumstances in the environment," explains Theodoros Stouraitis, a visiting scientist in the Interactive Robotics Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "Things become even more complicated when a robot has to interact with a human and work together to safely and successfully complete a task," adds Shen Li, a PhD candidate in the MIT Department of Aeronautics and Astronautics. Li and Stouraitis -- along with Michael Gienger of the Honda Research Institute Europe, Professor Sethu Vijayakumar of the University of Edinburgh, and Professor Julie A. Shah of MIT, who directs the Interactive Robotics Group -- have selected a problem that offers, quite literally, an armful of challenges: designing a robot that can help people get dressed.

If faced with the choice of sending a swarm of full-sized, distinct robots to space, or a large crew of smaller robotic modules, you might want to enlist the latter. Modular robots, like those depicted in films such as "Big Hero 6," hold a special type of promise for their self-assembling and reconfiguring abilities. But for all of the ambitious desire for fast, reliable deployment in domains extending to space exploration, search and rescue, and shape-shifting, modular robots built to date are still a little clunky. They're typically built from a menagerie of large, expensive motors to facilitate movement, calling for a much-needed focus on more scalable architectures -- both up in quantity and down in size. Scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) called on electromagnetism -- electromagnetic fields generated by the movement of electric current -- to avoid the usual stuffing of bulky and expensive actuators into individual blocks.

MIT CSAIL project shows that neural nets contain "subnetworks" 10x smaller that can just learn just as well - and often faster These days, nearly all AI-based products in our lives rely on "deep neural networks" that automatically learn to process labeled data. For most organizations and individuals, though, deep learning is tough to break into. To learn well, neural networks normally have to be quite large and need massive datasets. This training process usually requires multiple days of training and expensive graphics processing units (GPUs) - and sometimes even custom-designed hardware. But what if they don't actually have to be all that big after all?

Today's artificial intelligence technology is intended to mimic nature and replicate the same decision-making abilities that people develop naturally in a computer. Artificial neural networks, like living brains, are made up of many individual cells. When a cell becomes active, it transmits a signal to all other cells in the vicinity. The following cell's signals are added together to determine if it will become active as well. The system's behavior is determined by the way one cell influences the activity of the next.

Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) claim to have developed a system that can decipher a lost language without knowing its relation to other languages. The team says this is a step toward a system that's able to decipher lost languages using just a few thousand words. Lost languages are more than an academic curiosity. Without them, we risk losing a body of knowledge about the people who historically spoke them. Unfortunately, most lost languages left such minimal records that scientists can't decipher the languages using conventional machine-translation algorithms.

Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) say they've created an autonomous river vessel -- Roboat II -- that's capable of carrying passengers across fast-moving bodies of water. It's the latest addition to a fleet of autonomous boats developed by CSAIL, MIT Senseable City Lab, and the Amsterdam Institute for Advanced Metropolitan Solutions (AMS) over the last five years. As MIT's Rob Matheson explained in a recent blog post, the Roboats -- rectangular hulls packing sensors, thrusters, microcontrollers, cameras, and other hardware -- emerged from the ongoing project. The goal is to create robot fleets that can ferry people and goods through Amsterdam's 160 canals and self-assemble into bridges to help reduce pedestrian congestion. Roboat II measures 2 meters long (6 feet) and can carry up to six passengers at a time.

Deep learning, a subset of the broad field of AI, refers to the engineering of developing intelligent machines that can learn, perform and achieve goals as humans do. Over the last few years, deep learning models have been illustrated to outpace conventional machine learning techniques in diverse fields. The technology enables computational models of multiple processing layers to learn and represent data with manifold levels of abstraction, imitating how the human brain senses and understands multimodal information. A team of researchers from TU Wien (Vienna), IST Austria and MIT (USA) has developed a new artificial intelligence system based on the brains of tiny animals like threadworms. This new AI-powered system is said to have the potential to control a vehicle with just a few synthetic neurons. According to the researchers, the system has decisive advantages over previous deep learning models.

Artificial intelligence (AI) can become more efficient and reliable if it is made to mimic biological models. New approaches in AI research are hugely successful in experiments. Artificial intelligence has arrived in our everyday lives--from search engines to self-driving cars. This has to do with the enormous computing power that has become available in recent years. But new results from AI research now show that simpler, smaller neural networks can be used to solve certain tasks even better, more efficiently, and more reliably than ever before.

MIT CSAIL project shows that neural nets contain "subnetworks" 10x smaller that can just learn just as well - and often faster These days, nearly all AI-based products in our lives rely on "deep neural networks" that automatically learn to process labeled data. For most organizations and individuals, though, deep learning is tough to break into. To learn well, neural networks normally have to be quite large and need massive datasets. This training process usually requires multiple days of training and expensive graphics processing units (GPUs) - and sometimes even custom-designed hardware. But what if they don't actually have to be all that big after all?