
YFCC100M-HNfc6
YFCC100M-HNfc6: A Large-Scale Deep Features Benchmark for Similarity Search
Physical AI refers to using AI techniques to solve problems that involve direct interaction with the physical world, e.g., by observing the world through sensors or by modifying the world through actuators. The data is generated from various sources, including physical sensors and ”human sources,” such as social networks or smartphones. Actuation may range from support to human decisions to managing automated devices(e.g., traffic lights, gates) and actively directing autonomous cars, drones, etc.
One intrinsic feature of Physical AI is the uncertainty associated with the acquired information, its incompleteness, and the uncertainty about the effects of actions over (physical) systems that share the environment with humans. In other words, Physical AI deals with unreliable, heterogeneous, and high-dimensional sources of data/information and a significant set of actuation variables/actions to learn models, detect events, or classify situations, to name just a few cases. In some cases, a decision-making loop is closed over physical systems with their dynamics, often complicated and challenging to model (e.g., weather dynamics, human crowd behavior).
To tackle such large physical problems, existing techniques for data processing and decision-making are not tractable. Thus, one should develop and improve methods that exploit redundancy, combine/infer partial/missing data, transfer knowledge (e.g., through learning) and exploit low-rank characteristics of data to reduce the several relevant dimensions of the problems (in terms of observation, state and action spaces).
YFCC100M-HNfc6: A Large-Scale Deep Features Benchmark for Similarity Search
Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
Detection of physical objects in still images or videos
The model developed called SLIDE for SateLlite Images prediction with Deep Learning is a combination of state-of-the-art AI techniques that is designed to forecast irradiance maps. It uses up to four satellite-derived irradiance maps in order to genera...
3 Clinical Use Cases with supporting databases are offered to the community
A ROS (Robot Operation System) tool to simulate side-by-side accompainment of a person by a robot in a dynamic environment.
A collection of 250 images taken in a vineyard in Ribera de Duero, annotated using bounding boxes, to train and validate object detection models.
CUSUM RLS filter contains a change detection algorithm for multiple sensors, using the Recursive Least Squares (RLS) and Cumulative Sum (CUSUM) methods [F. Gustafsson. Adaptive Filtering and Change Detection. John Willey & Sons, LTD 2000].
Link to a Git repository that includes the syntethic Data set and a Docker container that implements an API Gateway based on python Tornado framework, that implement the AI algorithm based on syntethic data, and the method useful to train and predict da...
While reinforcement learning algorithms converge towards a single policy, it may be useful to generate multiple policies instead of just one.
Stable Baselines 3 provides open-source implementations of deep reinforcement learning (RL) algorithms in Python.