The killer ground drone revolution is here.
Category: robotics/AI – Page 1126
Just like a pianist who learns to play their instrument without looking at the keys or a basketball player who puts in countless hours to throw a seemingly effortless jump shot, UCLA mechanical engineers have designed a new class of material that can learn behaviors over time and develop a “muscle memory” of its own, allowing for real-time adaptation to changing external forces.
The material is composed of a structural system made up of tunable beams that can alter its shape and behaviors in response to dynamic conditions. The research finding, which boasts applications in the construction of buildings, airplanes and imaging technologies among others, was published Wednesday in Science Robotics.
“This research introduces and demonstrates an artificial intelligent material that can learn to exhibit the desired behaviors and properties upon increased exposure to ambient conditions,” said mechanical and aerospace engineering professor Jonathan Hopkins of the UCLA Samueli School of Engineering who led the research. “The same foundational principles that are used in machine learning are used to give this material its smart and adaptive properties.”
The new innovative features allow for advanced image editing using artificial intelligence.
Adobe announced new advancements in its Photoshop at its annual Adobe Max conference for technology. These new innovations make the image editing application even smarter in its abilities, and more collaborative. Along with these announcements came a whole new wave of AI advancements and capabilities incorporated into the software.
The flagship desktop app powered by Adobe Sensei AI features numerous improvements, including the one click Delete option and the Fill tool to remove and replace objects with a single click. Along with the AI feature, these improvements were made in time to be introduced at the fall conference. It allows users to remove unwanted elements in their pictures quickly with a shortcut, using Shift + Delete. Another updated feature is the photo restoration neural filter that uses machine learning to detect and get rid of scratches and other small flaws on old photographs.
‘You’re in a world made of marshmallows!’
A Google app that allows people to communicate with artificial intelligence (AI) systems has been made available in the United Kingdom (U.K.) for a limited trial period.
You’re in a world made of marshmallows! As you take a step, a gentle ‘squish’ comes out under your feet. The marshmallow horizon stretches out in all directions. The sky is a gooey, sticky pink.
Andrey Suslov/iStock.
Researchers from Trinity College Dublin have developed a new, machine learning-based technique to accurately classify the state of macrophages, which are key immune cells. Classifying macrophages is important because they can modify their behaviour and act as pro-or anti-inflammatory agents in the immune response. As a result, the work has a suite of implications for research and has the potential to one day make major societal impact.
For example, this new approach could be of use to drug designers looking to create therapies targeting diseases and auto-immune conditions such as diabetes, cancer and rheumatoid arthritis – all of which are impacted by cellular metabolism and macrophage function.
Because classifying macrophages allows scientists to directly distinguish between macrophage states – based only on their metabolic response under certain conditions – this new information could be used as a diagnosis tool, or to highlight the role of a particular cell type in a disease environment.
The project is aided by a $3.6 million grant from the National Science Foundation to the Living and Working With Robots program at UT Austin, under the umbrella of Good Systems, a broad research initiative at the university focused on leveraging the human benefits of AI.
MySA has reached out to UT Austin’s Cockrell School of Engineering for more information on the autonomous robots program.
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With the rise of Snowflake and other cloud data warehouses, enterprises finally have a simple way to mobilize their data assets at scale. They can easily connect data from different sources and start driving efficiencies while keeping upfront investments (or CapEx) on the lower side.
The benefits of the solutions are unparalleled, but cloud data services also come with the challenge of high operating expenses. Essentially, due to constantly growing datasets, companies have to deal with high compute costs and query performance latencies. Without a solution, their teams have to give about 30–40% of their time to manually develop features that could optimize the warehouse for the required performance and budget constraints.
Given the rapid pace at which technology is developing, it comes as no surprise that quantum technologies will become commonplace within decades. A big part of ushering in this new age of quantum computing requires a new understanding of both classical and quantum information and how the two can be related to each other.
Before one can send classical information across quantum channels, it needs to be encoded first. This encoding is done by means of quantum ensembles. A quantum ensemble refers to a set of quantum states, each with its own probability. To accurately receive the transmitted information, the receiver has to repeatedly ‘guess’ the state of the information being sent. This constitutes a cost function that is called ‘guesswork.’ Guesswork refers to the average number of guesses required to correctly guess the state.
The concept of guesswork has been studied at length in classical ensembles, but the subject is still new for quantum ensembles. Recently, a research team from Japan—consisting of Prof. Takeshi Koshiba of Waseda University, Michele Dall’Arno from Waseda University and Kyoto University, and Prof. Francesco Buscemi from Nagoya University—has derived analytical solutions to the guesswork problem subject to a finite set of conditions. “The guesswork problem is fundamental in many scientific areas in which machine learning techniques or artificial intelligence are used. Our results trailblaze an algorithmic aspect of the guesswork problem,” says Koshiba. Their findings are published in IEEE Transactions on Information Theory.
Artificial Intelligence has the potential to cause nuclear war-like catastrophic events, a new study says.
This work studies how a lattice of tunable beams can learn desired behaviors and what factors affect mechanical learning.