Lifeboat Foundation

Three times in the coming month or so, rockets will light their engines and set course for Mars. A trio of nations — the United States, China and the United Arab Emirates (UAE) — will be sending robotic emissaries to the red planet, hoping to start new chapters of exploration there.

Each mission is a pioneer in its own right. The United States is sending its fifth rover, NASA’s most capable ever, in the hope of finding evidence of past life on Mars and collecting a set of rocks that will one day be the first samples flown back to Earth. China aims to build on its lunar-exploration successes by taking one of its rovers to Mars for the first time. And the UAE will be launching an orbiter — the first interplanetary mission by any Arab nation — as a test of its young but ambitious space agency.

It is far from a given that all these missions will make it; Mars is notorious as a graveyard for failed spacecraft. But if they do, they will substantially rewrite scientific understanding of the planet. The two rovers are heading for parts of Mars that have never been explored(see ‘Landing sites’), and the UAE’s orbiter will track the changing Martian atmosphere.

Researchers at MIT are working with Ava Robotics to provide a sanitary solution to high foot traffic spaces.

A mobile autonomous robotic lab assistant developed by researchers from the University of Liverpool is 1,000 times faster than human researchers at performing experiments. It uses LIDAR to navigate the lab and can handle equipment like a human.

NASA is about to begin building its latest spacecraft. Called “Psyche” it will explore a 140 miles/226 kilometers-wide asteroid called “16 Psyche.” Today it’s passed a major milestone.

Why is NASA going to ‘16 Psyche?’

Located in the Solar System’s main asteroid belt between Mars and Jupiter, metal-rich 16 Psyche is thought to be the exposed metallic iron, nickel and gold core of a protoplanet. Most asteroids are rocky or icy.

Team members at Adobe have built a new way to use artificial intelligence to automatically personalize a blog for different visitors.

This tool was built as part of the Adobe Sneaks program, where employees can create demos to show off new ideas, which are then showcased (virtually, this year) at the Adobe Summit. While the Sneaks start out as demos, Adobe Experience Cloud Senior Director Steve Hammond told me that 60% of Sneaks make it into a live product.

Hyman Chung, a senior product manager for Adobe Experience Cloud, said that this Sneak was designed for content creators and content marketers who are probably seeing more traffic during the coronavirus pandemic (Adobe says that in April, its own blog saw a 30% month-over-month increase), and who may be looking for ways to increase reader engagement while doing less work.

Quantum information scientists have introduced a new method for machine-learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.

The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.

Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.

China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars.

The move has led — at least in the West — to warnings of a global AI arms race and concerns about the growing reach of China’s authoritarian surveillance state. But treating China as a “villain” in this way is both overly simplistic and potentially costly. While there are undoubtedly aspects of the Chinese government’s approach to AI that are highly concerning and rightly should be condemned, it’s important that this does not cloud all analysis of China’s AI innovation.

The world needs to engage seriously with China’s AI development and take a closer look at what’s really going on. The story is complex and it’s important to highlight where China is making promising advances in useful AI applications and to challenge common misconceptions, as well as to caution against problematic uses.

In fact, in a recent paper in Royal Society Open Science, researchers showed that AI tasked with maximizing returns is actually disproportionately likely to pick an unethical strategy in fairly general conditions. Fortunately, they also showed it’s possible to predict the circumstances in which this is likely to happen, which could guide efforts to modify AI to avoid it.

The fact that AI is likely to pick unethical strategies seems intuitive. There are plenty of unethical business practices that can reap huge rewards if you get away with them, not least because few of your competitors dare use them. There’s a reason companies often bend or even break the rules despite the reputational and regulatory backlash they could face.

Those potential repercussions should be of considerable concern to companies deploying AI solutions, though. While efforts to build ethical principles into AI are already underway, they are nascent and in many contexts there are a vast number of potential strategies to choose from. Often these systems make decisions with little or no human input and it can be hard to predict the circumstances under which they are likely to choose an unethical approach.

Using their artificial wings made out of foam, the robots are able to stay afloat simply by mimicking the flapping motion of real birds.