AI & Robotics News and Discussions

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weatheriscool
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Samsung's latest robot vac uses AI to recognize small obstacles
By Paul Ridden
June 25, 2021

https://newatlas.com/around-the-home/sa ... ot-vacuum/
Samsung is claiming a few world firsts with its latest robot vacuum cleaner, which was originally launched at CES 2021 back in January. The Jet Bot AI+ is said to be the first to feature an active stereo-type 3D sensor for object detection, and the first equipped with Intel AI for object recognition.

"We’re truly excited to introduce a robot vacuum that comes with industry-leading object detection and recognition technologies," said Samsung's Hyesoon Yang. "Consumers can now clean their homes more intelligently and efficiently using the AI-powered Jet Bot that makes cleaning more personalized and convenient."

The company's design team wanted to equip the automated cleaning assistant with enough smarts that it could "actively and efficiently clean like humans" and do so without much intervention from users.
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Yuli Ban
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SoftBank shrinks robotics business, stops Pepper production
SoftBank Group Corp is slashing jobs at its global robotics business and has stopped producing its Pepper robot, according to sources and documents reviewed by Reuters, as the conglomerate downgrades its industry ambitions.

Production of the humanoid Pepper, touted as the first robot with "a heart", was stopped last year, according to three sources familiar with the matter and the documents. It would be costly to restart production, two of the sources said.

Built by Foxconn (2317.TW) in China, Pepper was meant to help plug labour shortages but struggled to find a global customer base. Only 27,000 were produced, one of the sources said.

The pullback reflects the fading of Chief Executive Masayoshi Son's plan to make SoftBank the leader in the robotics industry, producing human-like machines that could serve customers and babysit kids.
Slightly disappointing because I liked Pepper's sleek design. However, it's clear that SoftBank isn't going to lead this field. They already lost Boston Dynamics, and as much as I liked the aesthetics of Pepper... it's really the last straggler of that embarrassing wave of social robots of the 2010s. The entire point of it being "emotional" left it doomed from the start because it's using 2010s-era chatbot technology.

Those social robots would actually stand a chance this coming decade, actually. If Pepper could use LaMDA instead of Watson, it'd have gone so much further. The technology is almost there. All these companies just started it off too soon.
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waitingforthe2030s
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Microsoft and OpenAI have a new A.I. tool that will give coding suggestions to software developers
Microsoft on Tuesday announced an artificial intelligence system that can recommend code for software developers to use as they write code.

Microsoft is looking to simplify the process of programming, the area where the company got its start in 1975. That could keep programmers who already use the company’s tools satisfied and also attract new ones.

The system, called GitHub Copilot, draws on source code uploaded to code-sharing service GitHub, which Microsoft acquired in 2018, as well as other websites. Microsoft and GitHub developed it with help from OpenAI, an AI research start-up that Microsoft backed in 2019.

Researchers at Microsoft and other institutions have been trying to teach computers to write code for decades. The concept has yet to go mainstream, at times because programs to write programs have not been versatile enough. The GitHub Copilot effort is a notable attempt in the field, relying as it does on a large volume of code in many programming languages and vast Azure cloud computing power.
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Yuli Ban
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And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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And remember my friend, future events such as these will affect you in the future
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AI Designs Quantum Physics Experiments Beyond What Any Human Has Conceived
Originally built to speed up calculations, a machine-learning system is now making shocking progress at the frontiers of experimental quantum physics
Quantum physicist Mario Krenn remembers sitting in a café in Vienna in early 2016, poring over computer printouts, trying to make sense of what MELVIN had found. MELVIN was a machine-learning algorithm Krenn had built, a kind of artificial intelligence. Its job was to mix and match the building blocks of standard quantum experiments and find solutions to new problems. And it did find many interesting ones. But there was one that made no sense.

“The first thing I thought was, ‘My program has a bug, because the solution cannot exist,’” Krenn says. MELVIN had seemingly solved the problem of creating highly complex entangled states involving multiple photons (entangled states being those that once made Albert Einstein invoke the specter of “spooky action at a distance”). Krenn and his colleagues had not explicitly provided MELVIN the rules needed to generate such complex states, yet it had found a way. Eventually, he realized that the algorithm had rediscovered a type of experimental arrangement that had been devised in the early 1990s. But those experiments had been much simpler. MELVIN had cracked a far more complex puzzle.
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caltrek
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Robots Were Supposed to Take Our Jobs. Instead, They’re Making Them Worse.
by Emily Stewart
July 2, 2021

https://www.vox.com/the-goods/22557895/ ... -uber-lyft

Introduction:
(Vox) The robot revolution is always allegedly just around the corner. In the utopian vision, technology emancipates human labor from repetitive, mundane tasks, freeing us to be more productive and take on more fulfilling work. In the dystopian vision, robots come for everyone’s jobs, put millions and millions of people out of work, and throw the economy into chaos.

Such a warning was at the crux of Andrew Yang’s ill-fated presidential campaign, helping propel his case for universal basic income that he argued would become necessary when automation left so many workers out. It’s the argument many corporate executives make whenever there’s a suggestion they might have to raise wages: $15 an hour will just mean machines taking your order at McDonald’s instead of people, they say. It’s an effective scare tactic for some workers.

But we often spend so much time talking about the potential for robots to take our jobs that we fail to look at how they are already changing them — sometimes for the better, but sometimes not. New technologies can give corporations tools for monitoring, managing, and motivating their workforces, sometimes in ways that are harmful. The technology itself might not be innately nefarious, but it makes it easier for companies to maintain tight control on workers and squeeze and exploit them to maximize profits.
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Yuli Ban
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Nvidia benchmark tests show impressive gains in training AI models
Nvidia announced that systems based on its graphics processor units (GPUs) are delivering 3 to 5 times better performance when it comes to training AI models than they did a year ago, according to the latest MLPerf benchmarks published yesterday
Nvidia announced that systems based on its graphics processor units (GPUs) are delivering 3 to 5 times better performance when it comes to training AI models than they did a year ago, according to the latest MLPerf benchmarks published yesterday.

The MLPerf benchmark is maintained by the MLCommons Association, a consortium backed by Alibaba, Facebook AI, Google, Intel, Nvidia, and others that acts as an independent steward.

The latest set of benchmarks span eight different workloads covering a range of use cases for AI model training, including speech recognition, natural language processing, object detection, and reinforcement learning. Nvidia claims its OEM partners were the only systems vendors to run all the workloads defined by the MLPerf benchmark across a total of 4,096 GPUs. Dell, Fujitsu, Gigabyte Technology, Inspur, Lenovo, Nettrix, and Supermicro all provided on-premises systems certified by Nvidia that were used to run the benchmark.
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Yuli Ban
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OpenAI Launches GitHub Copilot: AI Focused On Code Generation. Should We Be Worried Now?
What can a $1 billion investment buy?
On Tuesday this week, OpenAI and GitHub answered this question boldly with the preview of a new AI tool — GitHub Copilot. It can write user-compatible code and is much better at the task than its predecessor — GPT-3.
Copilot autocompletes code snippets, suggests new lines of code, and can even write whole functions based on the description provided. According to the GitHub blog, the tool is not just a language-generating algorithm based on user input — it is a virtual pair programmer.
It learns and adapts to the user’s coding habits, analyzes the available codebase, and generates suggestions backed by billions of lines of public code it has been trained on.
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Yuli Ban
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How MUM improved Google Searches for vaccine information [Google has already been using MUM to improve search, apparently.]
Soda, pop; sweater, jumper; soccer, football. So many things go by different names. Sometimes it’s a function of language, but sometimes it’s a matter of cultural trends or nuance, or simply where you are in the world.

One very relevant example is COVID-19. As people everywhere searched for information, we had to learn to identify all the different phrases people used to refer to the novel coronavirus to make sure we surfaced high quality and timely information from trusted health authorities like the World Health Organization and Centers for Disease Control and Prevention. A year later, we’re encountering a similar challenge with vaccine names, only this time, we have a new tool to help: Multitask Unified Model (MUM).
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