Data-driven discovery of governing equations has kindled significant interests in many science and engineering areas. Existing studies primarily focus on uncovering equations that govern nonlinear dynamics based on direct measurement of the system states (e.g., trajectories). Limited efforts have been placed on distilling governing laws of dynamics directly from videos for moving targets in a 3D space. To this end, we propose a vision-based approach to automatically uncover governing equations of nonlinear dynamics for 3D moving targets via raw videos recorded by a set of cameras. The approach is composed of three key blocks: (1) a target tracking module that extracts plane pixel motions of the moving target in each video, (2) a Rodrigues' rotation formula-based coordinate transformation learning module that reconstructs the 3D coordinates with respect to a predefined reference point, and (3) a spline-enhanced library-based sparse regressor that uncovers the underlying governing law of dynamics. This framework is capable of effectively handling the challenges associated with measurement data, e.g., noise in the video, imprecise tracking of the target that causes data missing, etc. The efficacy of our method has been demonstrated through multiple sets of synthetic videos considering different nonlinear dynamics.

The Emo robot mimics people's facial expressions A humanoid robot can predict whether someone will smile a second before they do, and match the smile on its own face. The creators hope the technology could make interactions with robots more lifelike. Although artificial intelligence can now mimic human language to an impressive degree, interactions with physical robots often fall into the "uncanny valley", in part because robots can't replicate the complex non-verbal cues and mannerisms that are vital for communication. Now, Hod Lipson at Columbia University in New York and his colleagues have created a robot called Emo that uses AI models and high-resolution cameras to predict people's facial expressions and try to replicate them. It can anticipate whether someone will smile about 0.9 seconds before they do, and smile itself in sync.

Evolving virtual creatures is a field with a rich history and recently it has been getting more attention, especially in the soft robotics domain. The compliance of soft materials endows soft robots with complex behavior, but it also makes their design process unintuitive and in need of automated design. Despite the great interest, evolved virtual soft robots lack the complexity, and co-optimization of morphology and control remains a challenging problem. Prior work identifies and investigates a major issue with the co-optimization process -- fragile co-adaptation of brain and body resulting in premature convergence of morphology. In this work, we expand the investigation of this phenomenon by comparing learnable controllers with proprioceptive observations and fixed controllers without any observations, whereas in the latter case, we only have the optimization of the morphology. Our experiments in two morphology spaces and two environments that vary in complexity show, concrete examples of the existence of high-performing regions in the morphology space that are not able to be discovered during the co-optimization of the morphology and control, yet exist and are easily findable when optimizing morphologies alone. Thus this work clearly demonstrates and characterizes the challenges of optimizing morphology during co-optimization. Based on these results, we propose a new body-centric framework to think about the co-optimization problem which helps us understand the issue from a search perspective. We hope the insights we share with this work attract more attention to the problem and help us to enable efficient brain-body co-optimization.

Thousands of cold cases could be solved thanks to an breakthrough in fingerprint analysis by artificial intelligence. A computer using artificial intelligence system has shattered the received wisdom of decades that each fingerprint from a person's finger is unique. So if a criminal left a thumbprint at one crime scene, and a print from his index finger at another, there would be no way to link the two. The breakthrough came about when a Columbia University student attempted to see if artificial intelligence could find links between apparently very different fingerprints from the same person. To test the idea, Gabe Guo, an engineering graduate with no background in forensics presented a computer with images of some 60,000 fingerprints in pairs.

For the past 40 years, our physicists have run only into dead ends. This great stagnation in the field of physics has seen almost no new discoveries in recent times. If you take a closer look, you'll find no real progress being made after the standard model of particle physics was completed in the 1970s. We have only been able to confirm pre-existing theories but have not found anything beyond them. Concepts that have been known for more than 80 years, like Quantum Gravity, Dark Matter, and Quantum Measurement problems, still remain unsolved. This might not sound alarming, but if we fail to consistently progress in scientific fields, the development of the human race will reach a standstill.

Scientists have long considered robot consciousness a subject fraught with ethical--maybe even moral--pitfalls, and so they left it out of the artificial intelligence equation. But that's no longer the case, says a Columbia University engineer. Hod Lipson, Columbia's director of the Creative Machines Lab, recently told the New York Times that the idea of a robot with a conscious was traditionally taboo. "We were almost forbidden from talking about it," Lipson said. "Don't talk about the c-word; you won't get tenure. So in the beginning I had to disguise it, like it was something else."

In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger than any existing database of planar mechanisms and is not limited to specific kinds of mechanisms such as four-bars, six-bars, \etc which are typically what most databases include. LINKS is made up of various components including 100 million mechanisms, the simulation data for each mechanism, normalized paths generated by each mechanism, a curated set of paths, the code used to generate the data and simulate mechanisms, and a live web demo for interactive design of linkage mechanisms. The curated paths are provided as a measure for removing biases in the paths generated by mechanisms that enable a more even design space representation. In this paper, we discuss the details of how we can generate such a large dataset and how we can overcome major issues with such scales. To be able to generate such a large dataset we introduce a new operator to generate 1-DOF mechanism topologies, furthermore, we take many steps to speed up slow simulations of mechanisms by vectorizing our simulations and parallelizing our simulator on a large number of threads, which leads to a simulation 800 times faster than the simple simulation algorithm. This is necessary given on average, 1 out of 500 candidates that are generated are valid~(and all must be simulated to determine their validity), which means billions of simulations must be performed for the generation of this dataset. Then we demonstrate the depth of our dataset through a bi-directional chamfer distance-based shape retrieval study where we show how our dataset can be used directly to find mechanisms that can trace paths very close to desired target paths.

The ability of robots to model themselves without being assisted by engineers is important for many reasons: Not only does it save labor, but it also allows the robot to keep up with its own wear-and-tear, and even detect and compensate for damage. The authors argue that this ability is important as we need autonomous systems to be more self-reliant. A factory robot, for instance, could detect that something isn't moving right, and compensate or call for assistance. "We humans clearly have a notion of self," explained the study's first author Boyuan Chen, who led the work and is now an assistant professor at Duke University. "Close your eyes and try to imagine how your own body would move if you were to take some action, such as stretch your arms forward or take a step backward. Somewhere inside our brain we have a notion of self, a self-model that informs us what volume of our immediate surroundings we occupy, and how that volume changes as we move."

An artificial intelligence tool has examined physical systems and not surprisingly, found new ways of describing what it found. How do we make sense of the universe? At its most basic, physics helps us understand the relationships between "observable" variables – these are things we can measure. Some variables like acceleration can be reduced to more fundamental variables. These are all variables in physics which shape our understanding of the world.

A study on the physics discovery titled "Automated discovery of fundamental variables hidden in experimental data" has been published in the journal Nature Computational Science. Researchers from Columbia Engineering have developed a new artificial intelligence (AI) program that could derive the fundamental variables of physics from video footage of physical phenomena. The program analyzed videos of systems like the swinging double pendulum, which researchers already know four "state variables" exist for; the angle and angular velocity of each arm. Within a few hours, the AI determined there were 4.7 variables at play. "We thought this answer was close enough. Especially since all the AI had access to was raw video footage, without any knowledge of physics or geometry. But we wanted to know what the variables actually were, not just their number," said Hod Lipson, director of the Creative Machines Lab in the Department of Mechanical Engineering.