Lifeboat Foundation

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.

On Oct. 9, an unimaginably powerful influx of X-rays and gamma rays infiltrated our solar system. It was likely the result of a massive explosion that happened 2.4 billion light-years away from Earth, and it has left the science community stunned.

In the wake of the explosion, astrophysicists worldwide turned their telescopes toward the spectacular show, watching it unfold from a variety of cosmic vantage points — and as they vigilantly studied the event’s glimmering afterglow over the following week, they grew shocked by how utterly bright this gamma-ray burst seems to have been.

Eventually, the spectacle’s sheer intensity earned it a fitting (very millennial) name to accompany its robotic title of GRB221009A: B.O.A.T. — the “brightest of all time.”