It seems these robots could be used to spy on you from home. 😃

A team of researchers demonstrated that popular robotic household vacuum cleaners can be remotely hacked to act as microphones.

The researchers—including Nirupam Roy, an assistant professor in the University of Maryland’s Department of Computer Science—collected information from the laser-based navigation system in a popular vacuum robot and applied signal processing and deep learning techniques to recover speech and identify television programs playing in the same room as the device.

The research demonstrates the potential for any device that uses light detection and ranging (Lidar) technology to be manipulated for collecting sound, despite not having a microphone. This work, which is a collaboration with assistant professor Jun Han at the University of Singapore was presented at the Association for Computing Machinery’s Conference on Embedded Networked Sensor Systems (SenSys 2020) on November 18, 2020.

I highly recommend checking this fantastic look at Deep Blue and the fascinating role chess has played in the ongoing development of artificial intelligence.

After an electrical engineer enters the field of computer chess, his creation captures the attention of the world as he attempts to defeat the world chess champion.

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Music by Ryan Probert: https://twitter.com/ProbeComposer
Graphic Design by Christopher “Arcaxon” Malouin-Monjaraz: https://twitter.com/Arcaxon
Saxophone by Naomi Sullivan: https://twitter.com/naomisullivan9
Vocals by Rachel Nicholas: https://twitter.com/_rachnicholas
“Chess Personality” Paintings by Anton Oxenuk: https://twitter.com/antonoxenuk
“Gambit” Chess Robot by DerEineSchwarzeRabe: https://twitter.com/D_E_S_R

Transformer model, a deep learning framework, has achieved state-of-the-art results across diverse domains, including natural language, conversation, images, and even music. The core block of any Transformer architecture is the attention module, which computes similarity scores for all pairs of positions in an input sequence. Since it requires quadratic computation time and quadratic memory size of the storing matrix, with the increase in the input sequence’s length, its efficiency decreases.

Thus, for long-range attention, one of the most common methods is sparse attention. It reduces the complexity by computing selective similarity scores from the sequence, based on various methods. There are still certain limitations like unavailability of efficient sparse-matrix multiplication operations on all accelerators, lack of theoretical guarantees, insufficiency to address the full range of problems, etc.

Cafe X Robot Coffee Bar in San Francisco employs assembly line-style robots to build your coffee orders for you. This robot barista can make two drinks in under a minute and will get your order right every time.

For More Info:

https://cafexapp.com

Music Credits:

Spring by Ikson: https://soundcloud.com/ikson
Music promoted by Audio Library: https://youtu.be/5WPnrvEMIdo

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