AI & Robotics News and Discussions

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wjfox
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With post-pandemic AI, we’ve now stepped into the Age of Acceleration

July 25, 2021 6:21 AM

As the IBM Watson experience shows, the path to AI success is fraught with challenges. Yet overall, it has been a very good year for AI and the companies developing it. So much so that Alphabet CEO Sundar Pichai, in a recent podcast recorded by BBC, says: “I view [AI] as a very profound enabling technology. If you think about fire or electricity or the internet, it is like that, but I think even more profound.”

That profound impact is becoming more pronounced as AI is showing up in more industries, ranging from semiconductor design to software development to voiceovers, farming, distribution, music creation, and even classical sculpting. In all instances, AI is augmenting and possibly replacing human activities while dramatically speeding up development of the final product. In biology, determining the structure of just one protein can take years of laboratory work, but new AI released to the public by the University of Washington can reduce this time to as little as 10 minutes. In the sculpture example, a replica of “Psyche Revived by Cupid’s Kiss,” produced by ABB2, an industrial robot developed by ABB Robotics, required just over 11 days to produce, while the original by 18th-century sculptor Canova required roughly five years. And due to the pandemic, demand for industrial robots has surged in the last year across many industries.

Read more: https://venturebeat.com/2021/07/25/with ... eleration/
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Yuli Ban
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Re: AI & Robotics News and Discussions

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And remember my friend, future events such as these will affect you in the future
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And remember my friend, future events such as these will affect you in the future
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Re: AI & Robotics News and Discussions

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The Future of Deep Learning Is Photonic
Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

While machine learning has been around a long time, deep learning has taken on a life of its own lately. The reason for that has mostly to do with the increasing amounts of computing power that have become widely available—along with the burgeoning quantities of data that can be easily harvested and used to train neural networks.

The amount of computing power at people's fingertips started growing in leaps and bounds at the turn of the millennium, when graphical processing units (GPUs) began to be harnessed for nongraphical calculations, a trend that has become increasingly pervasive over the past decade. But the computing demands of deep learning have been rising even faster. This dynamic has spurred engineers to develop electronic hardware accelerators specifically targeted to deep learning, Google's Tensor Processing Unit (TPU) being a prime example.

Here, I will describe a very different approach to this problem—using optical processors to carry out neural-network calculations with photons instead of electrons. To understand how optics can serve here, you need to know a little bit about how computers currently carry out neural-network calculations. So bear with me as I outline what goes on under the hood.
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And remember my friend, future events such as these will affect you in the future
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OSU Dynamic Robotics Laboratory's research team, led by Agility Robotics’ Jonathan Hurst, combined expertise from biomechanics and robot controls with new machine learning tools to accomplish something new: train a bipedal robot to run a full 5K on a single battery charge.

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And remember my friend, future events such as these will affect you in the future
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Kitchen robot in Riga cooks up new future for fast food
A pasta order comes in and the robotic arm springs into action at the Roboeatz eatery in Riga. After five minutes of gyrations, a piping hot plate is ready.

The Riga cafe, located under a crumbling concrete bridge, is designed in such a way that customers can observe the robotic arm at work.

It also has a seating area, although most customers prefer take away since vaccination certificates are required to be able to eat indoors in Latvia.

A Roboeatz app allows customers to order and pay for their dish before picking it up at the cafe.
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A talking robot is serving restaurant customers in California because the manager can't find enough workers during the labor shortage
A restaurant in Stockton, California, is using a robot helper to serve customers as it struggles to hire workers amid the US labor shortage.

"We are struggling to find people to come in and work, just like every other business right now," Ana Ortiz, general manager of the Sugar Mediterranean Bistro, told NBC News.

"I don't have enough employees to be running around food and serving tables," she added.

The robot was created by Richtech Robotics, and has been given the name Matradee by the company. It's capable of opening kitchen doors, allowing deliveries to go from the kitchen to the table, Richtech's website states.

The Matradee also uses LiDAR, among other technology, to maneuver and detect its surroundings up to 20 feet.

This helps it to carry up to four trays of food and dishes to hungry customers. "So, let's say I'm at table two, I'm taking the order for table two while the robot is running the food for me to table seven. I load up the robot with dirty dishes, and it takes it right back to the dishwasher," Ortiz told NBC News.
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[2107.12544] Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning
Reinforcement learning (RL) studies how an agent comes to achieve reward in an environment through interactions over time. Recent advances in machine RL have surpassed human expertise at the world's oldest board games and many classic video games, but they require vast quantities of experience to learn successfully -- none of today's algorithms account for the human ability to learn so many different tasks, so quickly. Here we propose a new approach to this challenge based on a particularly strong form of model-based RL which we call Theory-Based Reinforcement Learning, because it uses human-like intuitive theories -- rich, abstract, causal models of physical objects, intentional agents, and their interactions -- to explore and model an environment, and plan effectively to achieve task goals. We instantiate the approach in a video game playing agent called EMPA (the Exploring, Modeling, and Planning Agent), which performs Bayesian inference to learn probabilistic generative models expressed as programs for a game-engine simulator, and runs internal simulations over these models to support efficient object-based, relational exploration and heuristic planning. EMPA closely matches human learning efficiency on a suite of 90 challenging Atari-style video games, learning new games in just minutes of game play and generalizing robustly to new game situations and new levels. The model also captures fine-grained structure in people's exploration trajectories and learning dynamics. Its design and behavior suggest a way forward for building more general human-like AI systems.
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