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Category: robotics/AI – Page 1,731

Machine learning algorithms have gained fame for being able to ferret out relevant information from datasets with many features, such as tables with dozens of rows and images with millions of pixels. Thanks to advances in cloud computing, you can often run very large machine learning models without noticing how much computational power works behind the scenes.

But every new feature that you add to your problem adds to its complexity, making it harder to solve it with machine learning algorithms. Data scientists use dimensionality reduction, a set of techniques that remove excessive and irrelevant features from their machine learning models.

Dimensionality reduction slashes the costs of machine learning and sometimes makes it possible to solve complicated problems with simpler models.

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Interesting as I recall Aubrey lamenting that he had met Bezos several times over the years but never got a dime from him. Also I wonder where he would put the cash. Just donor all h by is SENS? Pick a company like Age-x?

Jeff Bezos is said to get into the Longevity Industry next month according to Aubrey De Grey. Having a billionaire invest into finding a cure for aging is both amazing and worrisome.
The field of longevity research was long underfunded but recently, with more and more results coming in, investors like Jeff Bezos are getting more and more interested in the field.

Last week, the most prominent figure in the longevity-research community, Aubrey The gray, has announced that one of the biggest event of this community will transpire in around a month. Previous investors were other tech entrepreneur like Peter Thiel or Googles Larry Page.
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Every day is a day closer to the Technological Singularity. Experience Robots learning to walk & think, humans flying to Mars and us finally merging with technology itself. And as all of that happens, we at AI News cover the absolute cutting edge best technology inventions of Humanity.

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TIMESTAMPS:
00:00 A Secret Investor?
00:53 Aubrey De Grey Interview.
01:49 The History of Longevity Investors.
04:08 Why invest in Longevity Research.
06:49 Last Words.
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#aubreydegrey #longevity #jeffbezos

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Got milk?

Hoping to capitalise on a surge in demand for home deliveries, a Singapore technology company has deployed a pair of robots to bring residents their groceries in one part of the city state.

Developed by OTSAW Digital and both named “Camello”, the robots’ services have been offered to 700 households in a one-year trial.

Users can book delivery slots for their milk and eggs, and an app notifies them when the robot is about to reach a pick-up point — usually the lobby of an apartment building.

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ETH Computer scientists have developed a new AI solution that enables touchscreens to sense with eight times higher resolution than current devices. Thanks to AI, their solution can infer much more precisely where fingers touch the screen.

Quickly typing a message on a smartphone sometimes results in hitting the wrong letters on the small keyboard or on other input buttons in an app. The touch that detect finger input on the have not changed much since they were first released in mobile phones in the mid-2000s.

In contrast, the screens of smartphones and tablets are now providing unprecedented visual quality, which is even more evident with each new generation of devices: higher color fidelity, higher resolution, crisper contrast. A latest-generation iPhone, for example, has a display resolution of 2532×1170 pixels. But the it integrates can only detect input with a resolution of around 32×15 pixels—that’s almost 80 times lower than the display resolution: “And here we are, wondering why we make so many typing errors on the small keyboard? We think that we should be able to select objects with pixel accuracy through touch, but that’s certainly not the case,” says Christian Holz, ETH computer science professor from the Sensing, Interaction & Perception Lab (SIPLAB) in an interview in the ETH Computer Science Department’s “Spotlights” series.

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Launching this summer, NASA’s Laser Communications Relay Demonstration (LCRD) will showcase the dynamic powers of laser communications technologies. With NASA’s ever-increasing human and robotic presence in space, missions can benefit from a new way of “talking” with Earth.

Since the beginning of spaceflight in the 1950s, NASA missions have leveraged to send data to and from space. Laser communications, also known as optical communications, will further empower missions with unprecedented data capabilities.

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Scientists from the Institute of Scientific and Industrial Research at Osaka University have used machine-learning methods to enhance the signal-to-noise ratio in data collected when tiny spheres are passed through microscopic nanopores cut into silicon substrates. This work may lead to much more sensitive data collection when sequencing DNA or detecting small concentrations of pathogens.

Miniaturization has opened the possibility for a wide range of diagnostic tools, such as point-of-care detection of diseases, to be performed quickly and with very small samples. For example, unknown particles can be analyzed by passing them through nanopores and recording tiny changes in the . However, the intensity of these signals can be very low, and is often buried under random noise. New techniques for extracting the useful information are clearly needed.

Now, scientists from Osaka University have used to “denoise” nanopore data. Most machine learning methods need to be trained with many “clean” examples before they can interpret noisy datasets. However, using a technique called Noise2Noise, which was originally developed for enhancing images, the team was able to improve resolution of noisy runs even though no clean data was available. Deep neural networks, which act like layered neurons in the brain, were utilized to reduce the interference in the data.

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It will serve as a backbone network for the China Environment for Network Innovations (CENI), a national research facility connecting the largest cities in China, to verify its performance and the security of future network communications technology before commercial use.

Experimental network connects 40 leading universities to prepare for an AI-driven society five to 10 years down the track.

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