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GPT-3 can be eerily human-like—for better and for worse.

Science & technology Aug 8th 2020 edition.

Not all vaccines for COVID-19 will cover everyone, in fact many may have large gaps. A novel, large-scale machine learning project at MIT designed one that might protect many more people.

The context: Studies show that when people and AI systems work together, they can outperform either one acting alone. Medical diagnostic systems are often checked over by human doctors, and content moderation systems filter what they can before requiring human assistance. But algorithms are rarely designed to optimize for this AI-to-human handover. If they were, the AI system would only defer to its human counterpart if the person could actually make a better decision.

The research: Researchers at MIT’s Computer Science and AI Laboratory (CSAIL) have now developed an AI system to do this kind of optimization based on strengths and weaknesses of the human collaborator. It uses two separate machine-learning models; one makes the actual decision, whether that’s diagnosing a patient or removing a social media post, and one predicts whether the AI or human is the better decision maker.

The latter model, which the researchers call “the rejector,” iteratively improves its predictions based on each decision maker’s track record over time. It can also take into account factors beyond performance, including a person’s time constraints or a doctor’s access to sensitive patient information not available to the AI system.

Deepfakes are the most concerning use of AI for crime and terrorism, according to a new report from University College London.

The research team first identified 20 different ways AI could be used by criminals over the next 15 years. They then asked 31 AI experts to rank them by risk, based on their potential for harm, the money they could make, their ease of use, and how hard they are to stop.

Deepfakes — AI-generated videos of real people doing and saying fictional things — earned the top spot for two major reasons. Firstly, they’re hard to identify and prevent. Automated detection methods remain unreliable and deepfakes also getting better at fooling human eyes. A recent Facebook competition to detect them with algorithms led researchers to admit it’s “very much an unsolved problem.”

Swarm intelligence (SI) is concerned with the collective behaviour that emerges from decentralised self-organising systems, whilst swarm robotics (SR) is an approach to the self-coordination of large numbers of simple robots which emerged as the application of SI to multi-robot systems. Given the increasing severity and frequency of occurrence of wildfires and the hazardous nature of fighting their propagation, the use of disposable inexpensive robots in place of humans is of special interest. This paper demonstrates the feasibility and potential of employing SR to fight fires autonomously, with a focus on the self-coordination mechanisms for the desired firefighting behaviour to emerge. Thus, an efficient physics-based model of fire propagation and a self-organisation algorithm for swarms of firefighting drones are developed and coupled, with the collaborative behaviour based on a particle swarm algorithm adapted to individuals operating within physical dynamic environments of high severity and frequency of change. Numerical experiments demonstrate that the proposed self-organising system is effective, scalable and fault-tolerant, comprising a promising approach to dealing with the suppression of wildfires – one of the world’s most pressing challenges of our time.

According to Forbes, payroll costs consume up to 25 per cent of a restaurant’s profit. Restaurateurs in Sydney and other parts of Australia hope to combat that expense by following in the footsteps of venues in Asia that have used drone waiters instead of human wait staff.

Faster and Human-Free Waiter drones are robotic devices that soar through the air with platters of food and glasses of beverages perched on top. Customers place their orders via electronic devices or other means, then the kitchen sends out their food on trays carried by machines rather than humans. Each drone can carry up to 4.4 pounds of cargo.

Sensors on the sides of the drones prevent them from crashing into objects or people as they navigate busy restaurants. While this strategy eliminates the human element that many experts believe is essential to the hospitality industry, the waiter drones’ success in Asia suggests they might prove a valuable contribution to restaurants in Australia.

In the movie “Ant-Man,” the title character can shrink in size and travel by soaring on the back of an insect. Now researchers at the University of Washington have developed a tiny wireless steerable camera that can also ride aboard an insect, giving everyone a chance to see an Ant-Man view of the world.

The camera, which streams video to a smartphone at 1 to 5 frames per second, sits on a mechanical arm that can pivot 60 degrees. This allows a viewer to capture a high-resolution, panoramic shot or track a moving object while expending a minimal amount of energy. To demonstrate the versatility of this system, which weighs about 250 milligrams—about one-tenth the weight of a playing card—the team mounted it on top of live beetles and insect-sized robots.

The results will be published July 15 in Science Robotics.

face_with_colon_three yay closer to foglet bodies: 3.

Is the T-1000 no longer science fiction?

It is a human dream to realize a robot with automatic mechanical functions similar to the robots presented in several science-fiction movies and series such as “Ex Machina”, “Black Mirror”, “The Terminator”, etc.

Continue reading “Liquid-Metal-Driven Micromachines for the Next Cutting-edge Technology” »

The presence of outliers in a classification or regression dataset can result in a poor fit and lower predictive modeling performance.

Identifying and removing outliers is challenging with simple statistical methods for most machine learning datasets given the large number of input variables. Instead, automatic outlier detection methods can be used in the modeling pipeline and compared, just like other data preparation transforms that may be applied to the dataset.

In this tutorial, you will discover how to use automatic outlier detection and removal to improve machine learning predictive modeling performance.