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Category: information science – Page 270

This Robotic Chemist Does Over 600 Experiments a Week and Learns From Its Own Work

The 400 kilogram wheeled system moves about the lab guided by LIDAR laser scanners and has an industrial robotic arm made by German firm Kuka that it uses to carry out tasks like weighing out solids, dispensing liquids, removing air from the vessel, and interacting with other pieces of equipment.

In a paper in Nature, the team describes how they put the device to work trying to find catalysts that speed up reactions that use light to split water into hydrogen and oxygen. To do this, the robot used a search algorithm to decide how to combine a variety of different chemicals and updated its plans based on the results of previous experiments.

The robot carried out 688 experiments over 8 days, working for 172 out of 192 hours, and at the end it had found a catalyst that produced hydrogen 6 times faster than the one it started out with.

Plants Can Secretly Send Underground Electrical Signals. Here’s How They Do It

A new study offers a better understanding of the hidden network of underground electrical signals being transmitted from plant to plant – a network that has previously been shown to use the Mycorrhizal fungi in soil as a sort of electrical circuit.

Through a combination of physical experiments and mathematical models based on differential equations, researchers explored how this electrical signalling works, though it’s not clear yet exactly what messages plants might want to transmit to each other.

The work builds on previous experiments by the same team looking at how this subterranean messaging service functions, using electrical stimulation as a way of testing how signals are carried even when plants aren’t in the same soil.

New Algorithm Coordinates Complex Behaviors Between Hundreds of Robots in a Fraction of the Time

A new approach to designing motion plans for multiple robots grows “trees” in the search space to solve complex problems in a fraction of the time.

In one of the more memorable scenes from the 2002 blockbuster film Minority Report, Tom Cruise is forced to hide from a swarm of spider-like robots scouring a towering apartment complex. While most viewers are likely transfixed by the small, agile bloodhound replacements, a computer engineer might marvel instead at their elegant control system.

In a building several stories tall with numerous rooms, hundreds of obstacles and thousands of places to inspect, the several dozen robots move as one cohesive unit. They spread out in a search pattern to thoroughly check the entire building while simultaneously splitting tasks so as to not waste time doubling back on their own paths or re-checking places other robots have already visited.

Using astrocytes to change the behavior of robots controlled by neuromorphic chips

Neurons, specialized cells that transmit nerve impulses, have long been known to be a vital element for the functioning of the human brain. Over the past century, however, neuroscience research has given rise to the false belief that neurons are the only cells that can process and learn information. This misconception or ‘neurocomputing dogma’ is far from true.

An is a different type of cell that has recently been found to do a lot more than merely fill up spaces between neurons, as researchers believed for over a century. Studies are finding that these cells also play key roles in brain functions, including learning and central pattern generation (CPG), which is the basis for critical rhythmic behaviors such as breathing and walking.

Although astrocytes are now known to underlie numerous brain functions, most existing inspired by the only target the structure and function of neurons. Aware of this gap in existing literature, researchers at Rutgers University are developing brain-inspired algorithms that also account for and replicate the functions of astrocytes. In a paper pre-published on arXiv and set to be presented at the ICONS 2020 Conference in July, they introduce a neuromorphic central pattern generator (CPG) modulated by artificial astrocytes that successfully entrained several rhythmic walking behaviors in their in-house robots.

6 Dimensionality Reduction Algorithms With Python

Dimensionality reduction is an unsupervised learning technique.

Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms.

There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good idea to explore a range of dimensionality reduction algorithms and different configurations for each algorithm.

Can existing laws cope with the AI revolution?

Say something Eric Klien.

Given the increasing proliferation of AI, I recently carried out a systematic review of AI-driven regulatory gaps. My review sampled the academic literature on AI in the hard and social sciences and found fifty existing or future regulatory gaps caused by this technology’s applications and methods in the United States. Drawing on an adapted version of Lyria Bennett-Moses’s framework, I then characterized each regulatory gap according to one of four categories: novelty, obsolescence, targeting, and uncertainty.

Significantly, of the regulatory gaps identified, only 12 percent represent novel challenges that compel government action through the creation or adaptation of regulation. By contrast, another 20 percent of the gaps are cases in which AI has made or will make regulations obsolete. A quarter of the gaps are problems of targeting, in which regulations are either inappropriately applied to AI or miss cases in which they should be applied. The largest group of regulatory gaps are ones of uncertainty in which a new technology is difficult to classify, causing a lack of clarity about the application of existing regulations.

Novelty. In cases of novel regulatory gaps, a technology creates behavior that requires bespoke government action. Of the identified cases, 12 percent are novel. This includes, for example, the Food and Drug Administration’s (FDA) standard for certifying the safety of high-risk medical devices which is applicable to healthcare algorithms, also called black-box medicine.

Programmable balloons pave the way for new shape-morphing devices

Balloon shaping isn’t just for kids anymore. A team of researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) has designed materials that can control and mold a balloon into pre-programmed shapes. The system uses kirigami sheets—thin sheets of material with periodic cuts—embedded into an inflatable device. As the balloon expands, the cuts in the kirigami sheet guide the growth, permitting expansion in some places and constricting it in others. The researchers were able to control the expansion not only globally to make large-scale shapes, but locally to generate small features.

The team also developed an inverse design strategy, an algorithm that finds the optimum design for the kirigami inflatable device that will mimic a target shape upon inflation.

“This work provides a new platform for shape-morphing devices that could support the design of innovative medical tools, actuators and reconfigurable structures,” said Katia Bertoldi, the William and Ami Kuan Danoff Professor of Applied Mechanics at SEAS and senior author of the study.

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