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Category: robotics/AI

WASHINGTON—The Biden administration launched an initiative Thursday aiming to make more government data available to artificial intelligence researchers, part of a broader push to keep the U.S. on the cutting edge of the crucial new technology.

The National Artificial Intelligence Research Resource Task Force, a group of 12 members from academia, government, and industry led by officials from the White House Office of Science and Technology Policy and the National Science Foundation, will draft a strategy for creating an AI research resource that could, in part, give researchers secure access to stores of anonymous data about Americans, from demographics to health and driving habits.

They would also look to make available computing power to analyze the data, with the goal of allowing access to researchers across the country.

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(Bloomberg) — On a Wednesday afternoon in May, an Uber driver in San Francisco was about to run out of charge on his Nissan Leaf. Normally this would mean finding a place to plug in and wait for a half hour, at least. But this Leaf was different.

Instead of plugging in, the driver pulled into a swapping station near Mission Bay, where a set of robot arms lifted the car off of the ground, unloaded the depleted batteries and replaced them with a fully charged set. Twelve minutes later the Leaf pulled away with 32 kilowatt hours of energy, enough to drive about 130 miles, for a cost of $13.

A swap like this is a rare event in the U.S. The Leaf’s replaceable battery is made by Ample, one of the only companies offering a service that’s more popular in markets in Asia. In March, Ample announced that it had deployed five stations around the Bay Area. Nearly 100 Uber drivers are using them, the company says, making an average of 1.3 swaps per day. Ample’s operation is tiny compared to the 100000 public EV chargers in the U.S.—not to mention the 150000 gas stations running more than a million nozzles. Yet Ample’s founders Khaled Hassounah and John de Souza are convinced that it’s only a matter of time before the U.S. discovers that swapping is a necessary part of the transition to electric vehicles.

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COVID 19 pandemic, automation and 6G could end the metropolitan era from building high sky scrapers for companies. Companies can operate like a network from home to home without going to office. This will help a lot to bring down Urban Heat Islands and make our cities more efficient in transportation and communication to send the data even faster.

Tom Marzetta is the director of NYU Wireless, New York University’s research center for cutting-edge wireless technologies. Prior to joining NYU Wireless, Marzetta was at Nokia Bell Labs, where he developed massive MIMO. Massive MIMO (short for “multiple-input multiple-output”) allows engineers to pack dozens of small antennas into a single array. The high number of antennas means more signals can be sent and received at once, dramatically boosting a single cell tower’s efficiency.

Massive MIMO is becoming an integral part of 5G, as is an independent development that came out of NYU Wireless by the center’s founding director Ted Rappaport: Millimeter waves. And now the professors and students at NYU Wireless are already looking ahead to 6G and beyond.

Marzetta spoke with IEEE Spectrum about the work happening at NYU Wireless, as well as what we all might expect from 6G when it arrives in the next decade. The conversation below has been edited for clarity and length.

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Glow is an iconic interesting research about deep neural networks that can generalize with small training sets.

Since the early days of machine learning, artificial intelligence scenarios have faced with two big challenges in order to experience mainstream adoption. First, we have the data efficiency problem that requires machine or deep learning models to be trained using large and accurate datasets which, as we know, are really expensive to build and maintain. Secondly, we have the generalization problem which AI agents face in order to build new knowledge that is different from the training data. Humans, by contrast, are incredibly efficient learning with minimum supervision and rapidly generalizing knowledge from a few data examples.

Generative models are one of the deep learning disciplines that focuses on addressing the two challenges mentioned above. Conceptually, generative models are focused on observing an initial dataset, like a set of pictures, and try to learn how the data was generated. Using more mathematical terms, generative models try to infer all dependencies within very high-dimensional input data, usually specified in the form of a full joint probability distribution. Entire deep learning areas such as speech synthesis or semi-supervised learning are based on generative models. Recently, generative models such as generative adversarial networks(GANs) have become extremely popular within the deep learning community. Recently, OpenAI experimented with a not-very well-known technique called Flow-Based Generative Models in order to improve over existing methods.

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In recent years, roboticists have developed a wide variety of robots with human-like capabilities. This includes robots with bodies that structurally resemble those of humans, also known as humanoid robots.

Testing the performance of can sometimes be challenging, as there are numerous measures to consider when trying to determine their applicability in real-world scenarios. Two features that are particularly important for robots are posture control and , as these robot’s body structures can sometimes make them prone to falling or stumbling, especially in complex environments.

Researchers at Technische Universität Berlin and the University Clinic of Freiburg recently created a system to evaluate the posture control and balance of both humans and humanoid robots. This system, presented in a paper pre-published on arXiv, is designed to assess balance and posture control of robots or humans as they perform different movements on a moving surface.

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China’s going all in on deep learning. The Beijing Academy of Artificial Intelligence (BAAI) recently released details concerning its “Wu Dao” AI system – and there’s a lot to unpack here.

Up front: Wu Dao is a multi-modal AI system. That means it can do a bunch of different things. It can generate text, audio, and images, and, according to Engadget, it can even “power virtual idols.”

The reason for all the hullabaloo surrounding Wu Dao involves its size. This AI model is huge. It was trained using a whopping 1.75 trillion parameters. For comparison, OpenAI’s biggest model, GPT-3, was trained with just 175 billion.

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Last month, self-driving technology company TuSimple shipped a truckload of watermelons across the state of Texas ten hours faster than normal. They did this by using their automated driving system for over 900 miles of the journey. The test drive was considered a success, and marked the beginning of a partnership between TuSimple and produce distributor Guimarra. This is one of the first such partnerships announced, but TuSimple may soon have some competition from another big player in the driverless vehicles game: Alphabet Inc. subsidiary Waymo.

Yesterday, Waymo announced a partnership with transportation logistics company JB Hunt to move cargo in automated trucks in Texas. The first route they’ll drive is between Houston and Fort Worth, which Waymo claims is “one of the most highly utilized freight corridors in the country.”

At around 260 miles long, much of the route is a straight shot on Interstate 45. The trucks will have human safety drivers on board who will likely take over some of the city driving portions, but the goal is to use the automated system as much as possible. A software technician will be on board as well, which makes sense given software will be doing the bulk of the driving.

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Floorplanning is the process by which an integrated circuit is designed using a top-down view. Rather like the architectural plan of a home, garden, and walkways, each of the major functional blocks is placed in a schematic representation that provides a guide for where everything needs to be. This layout can include transistors, capacitors, resistors, wires and other components, all packed into extremely tiny spaces.

Determining the optimal configuration for processing speed and power efficiency is a detailed and lengthy task, involving many iterations. It can often take weeks or even months for expert human engineers. Attempts to fully automate the process have been unsuccessful.

However, researchers from Google have this week reported a new machine-learning approach to floorplanning. Not only does it reduce the design workload to a matter of hours, it also results in chips with superior designs.

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