Noisy ships and industry are impacting critically endangered Kemp's ridley sea turtles. Breakthroughs, discoveries, and DIY tips sent six days a week. For the world's smallest sea turtles, life in the ocean is getting pretty noisy. These relatively little turtles (on average they're still 75 to 100 pounds) mostly found in the Gulf of Mexico already face fishing gear accidents, seacraft collisions, plastic pollution, and habitat deterioration, and now excess noise may be harming the critically endangered and rare Kemp's ridley sea turtles (). We say because even though these sea turtles share waters with extremely busy shipping lanes, scientists know very little about their underwater hearing.

In 2002, at age 40, Henry had a massive stroke, which left him with quadriplegia and an inability to speak. Since then, he's learned how to communicate by moving his eyes over a letter board, but he is highly reliant on caregivers and his wife, Jane. Henry got a glimmer of a different kind of life when he saw Charlie Kemp on CNN in 2010. Kemp, a robotics professor at Georgia Tech, was on TV talking about PR2, a robot developed by the company Willow Garage. PR2 was a massive two-armed machine on wheels that looked like a crude metal butler.

Take a look around your home and chances are you have one, or at least you have considered the convenience of having one. They are the devices and appliances that can be remotely controlled – otherwise known as smart devices – which over the past decade have become core features of the modern home. Think of the TVs that allow you to flick through various streaming services, the smart fridges that can have their temperatures moderated and contents checked from afar, the robot vacuum, air purifiers, or one of the big tech companies' virtual helpers to play music or dim the lights. But as the technologies gather, share, aggregate and analyse the data collected, that convenience has come at a cost: privacy. Experts say consumers should be aware of how much personal information they are trading, and what that information is used for.

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through the multi-head attention based intention fusion module to capture the speaker's intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human's psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multi-task learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression.

Most of us don't think of power-hungry killer robots as an imminent threat to humanity, especially when poverty and the climate crisis are already ravaging the Earth. This wasn't the case for Sam Bankman-Fried and his followers, powerful actors who have embraced a school of thought within the effective altruism movement called "longtermism". In February, the Future Fund, a philanthropic organization endowed by the now-disgraced cryptocurrency entrepreneur, announced that it would be disbursing more than $100m – and possibly up to $1bn – this year on projects to "improve humanity's long-term prospects". The slightly cryptic reference might have been a bit puzzling to those who think of philanthropy as funding homelessness charities and medical NGOs in the developing world. In fact, the Future Fund's particular areas of interest include artificial intelligence, biological weapons and "space governance", a mysterious term referring to settling humans in space as a potential "watershed moment in human history".

Mobile manipulators for indoor human environments can serve as versatile devices that perform a variety of tasks, yet adoption of this technology has been limited. Reducing size, weight, and cost could facilitate adoption, but risks restricting capabilities. We present a novel design that reduces size, weight, and cost, while supporting a variety of tasks. The core design consists of a two-wheeled differential-drive mobile base, a lift, and a telescoping arm configured to achieve Cartesian motion at the end of the arm. Design extensions include a 1 degree-of-freedom (DOF) wrist to stow a tool, a 2-DOF dexterous wrist to pitch and roll a tool, and a compliant gripper. We justify our design with anthropometry and mathematical models of static stability. We also provide empirical support from teleoperating and autonomously controlling a commercial robot based on our design (the Stretch RE1 from Hello Robot Inc.) to perform tasks in real homes.

He based the design of this device on complete randomness. He took a radioactive element that radiates a random charged particle, found in smoke detectors and used it as the input to his device because it too would be tied to the conscious field like all other things in our universe. The field if it was conscious and existed outside of our time he thought it might know how to deliberately make the element radiate a helium particle to then choose a random character on his computer. He had a friend of his design a random generating character program that when triggered displayed a random character from all the characters available on the computer. He set up a random pathway from the Geiger counter.

If you have ever used Google Maps on your phone without fiddling with the location settings, it goes without saying that the tech giant knows everywhere you've been. The really bad news is that even if you have previously tried to stop Google tracking your every movement, the company may have done so anyway. On Friday the Australian Competition and Consumer Commission (ACCC) won a legal action in the federal court, which ruled that, thanks to a peculiar set-up that required a user to check "No" or "Do Not Collect" to both "Location History" and "Web & App Activity" on some Android and Pixel phones, someone who ticked "No" to just one would still end up being tracked. We asked Dr Katharine Kemp, a legal academic from the University of New South Wales whose focus is consumer law, and the Australian cryptographer Vanessa Teague for their thoughts on the significance of the decision and how a person might go about securing their devices. Kemp, an Apple user herself, says that for many consumers, today's decision may not actually mean much, as the decision only related to Android users and Google has since updated the settings that formed the basis of the ACCC's complaint.

Companies looking to protect their innovations in artificial intelligence and machine learning face a dilemma. Data and algorithms are essential components to developing products based on these technologies, but it is notoriously difficult to protect them from an intellectual property perspective. There are two possible approaches here. One is to apply for a patent. Another is to seek trade secret protection.

Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people worldwide. Yet, conducting research in this area presents numerous challenges, including the risks of physical interaction between people and robots. Physics simulations have been used to optimize and train robots for physical assistance, but have typically focused on a single task. In this paper, we present Assistive Gym, an open source physics simulation framework for assistive robots that models multiple tasks. It includes six simulated environments in which a robotic manipulator can attempt to assist a person with activities of daily living (ADLs): itch scratching, drinking, feeding, body manipulation, dressing, and bathing. Assistive Gym models a person's physical capabilities and preferences for assistance, which are used to provide a reward function. We present baseline policies trained using reinforcement learning for four different commercial robots in the six environments. We demonstrate that modeling human motion results in better assistance and we compare the performance of different robots. Overall, we show that Assistive Gym is a promising tool for assistive robotics research.