The'Waymo of the sea' tracks sperm whale conversations More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The Project CETI glider can autonomously follow sperm whale vocalizations. Breakthroughs, discoveries, and DIY tips sent six days a week. Sperm whales () go deep. They can dive 1,300 to 4,000 feet-deep and also travel as much as 15,000 miles per year.

We present the problem of reinforcement learning with exogenous termination. We define the Termination Markov Decision Process (TerMDP), an extension of the MDP framework, in which episodes may be interrupted by an external non-Markovian observer.

We present the problem of reinforcement learning with exogenous termination. We define the Termination Markov Decision Process (TerMDP), an extension of the MDP framework, in which episodes may be interrupted by an external non-Markovian observer. This formulation accounts for numerous real-world situations, such as a human interrupting an autonomous driving agent for reasons of discomfort. We learn the parameters of the TerMDP and leverage the structure of the estimation problem to provide state-wise confidence bounds. We use these to construct a provably-efficient algorithm, which accounts for termination, and bound its regret. Motivated by our theoretical analysis, we design and implement a scalable approach, which combines optimism (w.r.t. termination) and a dynamic discount factor, incorporating the termination probability. We deploy our method on high-dimensional driving and MinAtar benchmarks. Additionally, we test our approach on human data in a driving setting. Our results demonstrate fast convergence and significant improvement over various baseline approaches.

LAS VEGAS (Reuters) - As vehicles get smarter, your car will be keeping eyes on you. This week at CES, the international consumer electronics show in Las Vegas, a host of startup companies will demonstrate to global automakers how the sensor technology that watches and analyzes drivers, passengers and objects in cars will mean enhanced safety in the short-term, and revenue opportunities in the future. Whether by generating alerts about drowsiness, unfastened seat belts or wallets left in the backseat, the emerging technology aims not only to cut back on distracted driving and other undesirable behavior, but eventually help automakers and ride-hailing companies make money from data generated inside the vehicle. In-car sensor technology is deemed critical to the full deployment of self-driving cars, which analysts say is still likely years away in the United States. Right now, self-driving cars are still mainly at the testing stage.

In autonomous vehicle (AV) control, allowing mistakes can be quite dangerous and costly in the real world. For this reason we investigate methods of training an AV without allowing the agent to explore and instead having a human explorer collect the data. Supervised learning has been explored for AV control, but it encounters the issue of the covariate shift. That is, training data collected from an optimal demonstration consists only of the states induced by the optimal control policy, but at runtime, the trained agent may encounter a vastly different state distribution with little relevant training data. To mitigate this issue, we have our human explorer make sub-optimal decisions. In order to have our agent not replicate these sub-optimal decisions, supervised learning requires that we either erase these actions, or replace these action with the correct action. Erasing is wasteful and replacing is difficult, since it is not easy to know the correct action without driving. We propose an alternate framework that includes continuous scalar feedback for each action, marking which actions we should replicate, which we should avoid, and how sure we are. Our framework learns continuous control from sub-optimal demonstration and evaluative feedback collected before training. We find that a human demonstrator can explore sub-optimal states in a safe manner, while still getting enough gradation to benefit learning. The collection method for data and feedback we call "Backseat Driver." We call the more general learning framework ReNeg, since it learns a regression from states to actions given negative as well as positive examples. We empirically validate several models in the ReNeg framework, testing on lane-following with limited data. We find that the best solution is a generalization of mean-squared error and outperforms supervised learning on the positive examples alone.

Cars are getting smarter - and while many focus on seeing the road ahead, they are also set to begin analyzing drivers and passengers. This week at CES, the international consumer electronics show in Las Vegas, a host of startup companies are showing off inward facing cameras that watch and analyze drivers, passengers and objects in cars. Carmakers say they will boost safety - but privacy campaigners warn they could be used to make money by analyzing every movement - even being able to track a passenger's gaze to see what ads they are looking at, and monitor the emotions of people through their facial expressions. Occupants, inside a car, are seen on a monitor using technology by Silicon Valley company Eyeris, which uses cameras and AI to track drivers and passengers for safety benefits, shown during an interview in San Jose, California, U.S., December 28, 2018. Carmakers could gather anonymized data and sell it.

When Nvidia popped the bonnet on its Co-Pilot "backseat driver" AI at this year's Consumer Electronics Show, most onlookers were struck by its ability to lip-read while tracking CES-going "motorists'" actions within the "car". A slide taken at CES shows the Co-Pilot AI assistant performing four features: facial recognition, head tracking, gaze tracking and lip-reading. The @nvidia AI co-pilot analyzes you through face recognition, head and gaze tracking and lip reading to assist you. The automative AI is part of the GPU-flinger's DRIVE PX 2 platform, which uses sensors and multiple neural networks powered by the grunt of Nvidia's processors. An Nvidia spokesperson has since confirmed in an email to The Register that the lip-reading component was based on research paper [PDF] written by academics from the University of Oxford, Google DeepMind and the Canadian Institute for Advanced Research.