A crewless robotic boat retracing the 1620 sea voyage of the Mayflower has landed near Plymouth Rock. The sleek Mayflower Autonomous Ship met with an escort boat as it approached the Massachusetts shoreline Thursday, more than 400 years after its namesake's historic journey from England. It was towed into Plymouth Harbor -- per U.S. Coast Guard rules for crewless vessels -- and docked near a replica of the original Mayflower that brought the Pilgrims to America. Piloted by artificial intelligence technology, the 50-foot (15-meter) trimaran didn't have a captain, navigator or any humans on board. The solar-powered ship's first attempt to cross the Atlantic in 2021 was beset with technical problems, forcing it back to its home port of Plymouth, England -- the same place the Pilgrim settlers sailed from in 1620.

NASA's Perseverance rover has been aptly named because -- nearly two months after beginning its search into past life on Mars -- it has still yet to find any viable samples. The car-sized robot began its mission to find ancient biomarkers in the Martian clay on April 22, which could indicate if alien life ever existed on the Red Planet. It has been roaming around an ancient delta to look for sampling sites that might contain ancient microbes and organics. The rover then drills down to extract a specimen that it plans to leave at the base of the delta to be retrieved in future missions. However, NASA has since revealed that, so far, no samples have been successfully collected. The fragile clay materials the rover targets have been known to fracture, crack and crumble during the abrasion and coring process.

Artificial intelligence "acts" unethically in ways that are different from humans, even if the harms that both AI and humans can cause are similar. For example, even if both humans and AI can invade people's privacy, discriminate, or cause physical harm, artificial intelligence does not act with intention to cause such harm. Rather, the harm results from how artificial intelligence collects and processes data. Currently, artificial intelligence cannot achieve consciousness, though one Google engineer disagrees. Today, the type of artificial intelligence that companies are creating and incorporating into their operations and decision systems is artificial narrow intelligence, which refers to a computer's ability to perform a single task or limited tasks extremely well.

The system, called iBorderCtrl, analyzed facial movements to attempt to spot signs a person was lying to a border agent. The trial was propelled by nearly $5 million in European Union research funding, and almost 20 years of at Manchester Metropolitan University, in the UK. Polygraphs and other technologies built to detect lies from physical attributes have been widely declared unreliable by psychologists. Soon, errors were reported from iBorderCtrl, too. Media reports indicated that its [lie-prediction algorithm didn't and the project's own website that the technology "may imply risks for fundamental human rights."

Your semanticist is dissatisfied today. This time it is the buzz phrase "artificial intelligence"--or "AI." While casting large shadow ideas about computing power, the term "AI" does more to obscure than clarify. People who write about innovative uses of computing should probably avoid it. The inspiration for the present jeremiad is a recent Foreign Affairs article entitled "A Force for the Future: A High-Reward, Low-Risk Approach to AI Military Innovation."

The system, called iBorderCtrl, analyzed facial movements to attempt to spot signs a person was lying to a border agent. The trial was propelled by nearly $5 million in European Union research funding, and almost 20 years of research at Manchester Metropolitan University, in the UK. Polygraphs and other technologies built to detect lies from physical attributes have been widely declared unreliable by psychologists. Soon, errors were reported from iBorderCtrl, too. Media reports indicated that its lie-prediction algorithm didn't work, and the project's own website acknowledged that the technology "may imply risks for fundamental human rights."

A Google AI engineer recently stunned the world by announcing that one of the company's chatbots had become sentient. He was subsequently placed on paid administrative leave for his outburst. His name is Blake Lemoine and he sure seems like the right person to talk about machines with souls. Not only is he a professional AI developer at Google, but he's also a Christian priest. The only problem is that the whole concept is ridiculous and dangerous.

Artificial intelligence (AI) has been deployed across multiple industries to increase security, improve productivity or enhance user experiences. AI arrived in the cybersecurity space claiming to cyber leaders that it was the solution to detecting and stopping advanced attacks. According to a global survey released in September 2021, just under half of executives think artificial intelligence is the best tool to counter nation-state cyberattacks. It's true that AI technologies can continually learn and improve, generalizing observations from past attacks to discover new malicious behaviors. However, while AI is lathered across almost every marketing campaign involved in promoting cybersecurity products, the promise of AI is generally hollow until the models can meet or exceed human levels of intelligence.

Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find latent manifolds for classification and regression of complex experimental data. Like other ML problems, VAEs require hyperparameter tuning, e.g., balancing the Kullback Leibler (KL) and reconstruction terms. However, the training process and resulting manifold topology and connectivity depend not only on hyperparameters, but also their evolution during training. Because of the inefficiency of exhaustive search in a high-dimensional hyperparameter space for the expensive to train models, here we explored a latent Bayesian optimization (zBO) approach for the hyperparameter trajectory optimization for the unsupervised and semi-supervised ML and demonstrate for joint-VAE with rotational invariances. We demonstrate an application of this method for finding joint discrete and continuous rotationally invariant representations for MNIST and experimental data of a plasmonic nanoparticles material system. The performance of the proposed approach has been discussed extensively, where it allows for any high dimensional hyperparameter tuning or trajectory optimization of other ML models.

We study the problem of learning the parameters for the Hamiltonian of a quantum many-body system, given limited access to the system. In this work, we build upon recent approaches to Hamiltonian learning via derivative estimation. We propose a protocol that improves the scaling dependence of prior works, particularly with respect to parameters relating to the structure of the Hamiltonian (e.g., its locality $k$). Furthermore, by deriving exact bounds on the performance of our protocol, we are able to provide a precise numerical prescription for theoretically optimal settings of hyperparameters in our learning protocol, such as the maximum evolution time (when learning with unitary dynamics) or minimum temperature (when learning with Gibbs states). Thanks to these improvements, our protocol is practical for large problems: we demonstrate this with a numerical simulation of our protocol on an 80-qubit system.