AI & Robotics News and Discussions

[wjfox]

QAnon founder may have been identified thanks to machine learning

February 19th, 2022

With help from machine learning software, computer scientists may have unmasked the identity of Q, the founder of the QAnon movement. In a sprawling report published on Saturday, The New York Times shared the findings of two independent teams of forensic linguists who claim they’ve identified Paul Furber, a South African software developer who was one of the first to draw attention to the conspiracy theory, as the original writer behind Q. They say Arizona congressional candidate Ron Watkins also wrote under the pseudonym, first by collaborating with Furber and then later taking over the account when it eventually moved to post on his father’s 8chan message board.

The two teams of Swiss and French researchers used different methodologies to come to the same conclusion. The Swiss one, made up of two researchers from startup OrphAnalytics, used software to break down Q’s missives into patterns of three-character sequences. They then tracked how often those sequences repeated. The French team, meanwhile, trained an AI to look for patterns in Q’s writing.

https://www.engadget.com/qanon-machine- ... 18665.html

[weatheriscool]

Risks of using AI to grow our food are substantial and must not be ignored, warn researchers
https://techxplore.com/news/2022-02-ai- ... ntial.html
by University of Cambridge

Imagine a field of wheat that extends to the horizon, being grown for flour that will be made into bread to feed cities' worth of people. Imagine that all authority for tilling, planting, fertilizing, monitoring and harvesting this field has been delegated to artificial intelligence: algorithms that control drip-irrigation systems, self-driving tractors and combine harvesters, clever enough to respond to the weather and the exact needs of the crop. Then imagine a hacker messes things up.

A new risk analysis, published today in the journal Nature Machine Intelligence, warns that the future use of artificial intelligence in agriculture comes with substantial potential risks for farms, farmers and food security that are poorly understood and under-appreciated.

"The idea of intelligent machines running farms is not science fiction. Large companies are already pioneering the next generation of autonomous ag-bots and decision support systems that will replace humans in the field," said Dr. Asaf Tzachor in the University of Cambridge's Center for the Study of Existential Risk (CSER), first author of the paper.

"But so far no-one seems to have asked the question 'are there any risks associated with a rapid deployment of agricultural AI?'" he added.

[weatheriscool]

Researchers enhance human-robot interaction by merging mixed reality and robotics
https://techxplore.com/news/2022-03-hum ... otics.html
by Ingrid Fadelli , Tech Xplore

Over the past decades, engineers have created devices with increasingly advanced functions and capabilities. A device capability that was substantially improved in recent years is known as "spatial computing."

The term spatial computing essentially refers to the ability of computers, robots, and other electronic devices to be "aware" of their surrounding environment and to create digital representations of it. Cutting edge technologies, such as sensors and mixed reality (MR), can significantly enhance spatial computing, enabling the creation of sophisticated sensing and mapping systems.

Researchers at the Microsoft Mixed Reality and AI Lab and ETH Zurich have recently developed a new framework that combines MR and robotics to enhance spatial computing applications. They implemented and tested this framework, introduced in a paper pre-published on arXiv, on a series of systems for human-robot interaction.

"The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning, which offers exciting new possibilities for collaboration between humans and robots," the researchers wrote in their paper. "This paper presents several human-robot systems that utilize these capabilities to enable novel robot use cases: mission planning for inspection, gesture-based control, and immersive teleoperation."

[Yuli Ban]

[caltrek]

How AI Could Revolutionize COVID-19 Diagnosis
by Naeem Ramzan
March 3, 2022

https://www.sciencealert.com/how-a-19th ... -diagnosis

Extract:

(Science Alert) Investigations early on in the pandemic found that abnormalities showed up in the chest radiography images of patients with the virus, leading the World Health Organization to recommend using radiography for diagnosing COVID when PCR testing isn't available, especially for severe patients.

But there's a resource bottleneck here, too. Using X-rays and CT scans for diagnosis requires radiologists to carefully decipher the chest images, since COVID's visual pointers can be hard to spot. So, we created an artificial intelligence program to do this instead, to speed up diagnosis and allow radiologists to get on with their jobs.

The program is based on something called a deep convolutional neural network, a type of algorithm typically used to analyze images. Such algorithms can pick out the key features of images and classify those that have similarities and differences.
…
As well as being beneficial for patients, this could also speed up their passage onto suitable wards elsewhere in the hospital, and so relieve the strain on hard-pressed A&E departments.

The app could also be very effective at diagnosing COVID cases in low-income countries and remote areas where PCR is not readily available. So, as a next step, we're planning to test it out in Pakistan, as part of the EU-funded SAFE RH project, to see what impact it can have in the real world.

[Yuli Ban]

Ana-digital computer has been on my mind for half a decade now as being the most likely method for producing AGI.

[Yuli Ban]

[raklian]

[Yuli Ban]

[wjfox]

^ Exponential growth in action. I am seriously impressed with synthesia.io

[Yuli Ban]

Japan is the world´s number one industrial robot manufacturer – delivering 45% of the global supply. In recent years, the country’s robot suppliers have increased their production capacity considerably: Their export ratio rose to 78% in 2020, when 136,069 industrial robots were shipped. These are results published by the International Federation of Robotics (IFR) ahead of the International Robot Exhibition (iREX) in Tokyo, March 9 to 12, 2022.

“Exports of Japanese industrial robots on average had a compound annual growth rate of 6% in the last five years”, says Milton Guerry, President of the International Federation of Robotics (IFR). “At the same time, imports of robots have always been extremely low. In 2020, only 2% of Japanese installations were imported. The domestic Japanese robot market is the second largest in the world after China.”

[weatheriscool]

Mathematical paradoxes demonstrate the limits of AI

by University of Cambridge
https://techxplore.com/news/2022-03-mat ... ts-ai.html

Humans are usually pretty good at recognizing when they get things wrong, but artificial intelligence systems are not. According to a new study, AI generally suffers from inherent limitations due to a century-old mathematical paradox.

Like some people, AI systems often have a degree of confidence that far exceeds their actual abilities. And like an overconfident person, many AI systems don't know when they're making mistakes. Sometimes it's even more difficult for an AI system to realize when it's making a mistake than to produce a correct result.

Researchers from the University of Cambridge and the University of Oslo say that instability is the Achilles' heel of modern AI and that a mathematical paradox shows AI's limitations. Neural networks, the state of the art tool in AI, roughly mimic the links between neurons in the brain. The researchers show that there are problems where stable and accurate neural networks exist, yet no algorithm can produce such a network. Only in specific cases can algorithms compute stable and accurate neural networks.

The researchers propose a classification theory describing when neural networks can be trained to provide a trustworthy AI system under certain specific conditions. Their results are reported in the Proceedings of the National Academy of Sciences.

[Yuli Ban]

To make their Mini Cheetah better equipped to skillfully scramble across varying terrains, robotics researchers at MIT’s CSAIL used AI-powered simulations to quickly teach the bot to adapt its walking style as needed. That included learning how to run, which resulted in a new gait that allows the robot to move faster than it ever has before.

As much as robot designers strive to engineer and program a robot to handle any situation it might experience in the real world, it’s an impossible task. The world is endlessly chaotic. And when simply walking down a sidewalk, a robot could face a myriad of obstacles from smooth pavement to slippery patches of ice to areas covered in loose gravel to all of the above one after the other. It’s why bi-pedal robots and even quadrupeds usually have a very slow and careful gait. They’re designed and programmed to expect the worst-case scenario when it comes to the terrain they’re navigating and proceed very carefully, even when walking across smooth surfaces free of any debris or obstacles.

[Yuli Ban]

[caltrek]

How Eye Imaging Technology Could Help Robots and Cars See Better
March 29, 2022

https://www.eurekalert.org/news-releases/947991

Introduction:

(EurekAlert) DURHAM, N.C. – Even though robots don’t have eyes with retinas, the key to helping them see and interact with the world more naturally and safely may rest in optical coherence tomography (OCT) machines commonly found in the offices of ophthalmologists.

One of the imaging technologies that many robotics companies are integrating into their sensor packages is Light Detection and Ranging, or LiDAR for short. Currently commanding great attention and investment from self-driving car developers, the approach essentially works like radar, but instead of sending out broad radio waves and looking for reflections, it uses short pulses of light from lasers.

Traditional time-of-flight LiDAR, however, has many drawbacks that make it difficult to use in many 3D vision applications. Because it requires detection of very weak reflected light signals, other LiDAR systems or even ambient sunlight can easily overwhelm the detector. It also has limited depth resolution and can take a dangerously long time to densely scan a large area such as a highway or factory floor. To tackle these challenges, researchers are turning to a form of LiDAR called frequency-modulated continuous wave (FMCW) LiDAR.

“FMCW LiDAR shares the same working principle as OCT, which the biomedical engineering field has been developing since the early 1990s,” said Ruobing Qian, a PhD student working in the laboratory of Joseph Izatt, the Michael J. Fitzpatrick Distinguished Professor of Biomedical Engineering at Duke. “But 30 years ago, nobody knew autonomous cars or robots would be a thing, so the technology focused on tissue imaging. Now, to make it useful for these other emerging fields, we need to trade in its extremely high resolution capabilities for more distance and speed.”

In a paper appearing March 29 in the journal Nature Communications, the Duke team demonstrates how a few tricks learned from their OCT research can improve on previous FMCW LiDAR data-throughput by 25 times while still achieving submillimeter depth accuracy.

[Yuli Ban]

Artificial intelligence beats eight world champions at bridge

An artificial intelligence has beaten eight world champions at bridge, a game in which human supremacy has resisted the march of the machines until now.

The victory represents a new milestone for AI because in bridge players work with incomplete information and must react to the behaviour of several other players – a scenario far closer to human decision-making.

In contrast, chess and Go – in both of which AIs have already beaten human champions – a player has a single opponent at a time and both are in possession of all the information.

“What we’ve seen represents a fundamentally important advance in the state of artificial intelligence systems,” said Stephen Muggleton, a professor of machine learning at Imperial College London.

French startup NukkAI announced the news of its AI’s victory on Friday, at the end of a two-day tournament in Paris.

[Yuli Ban]

Scroll back up to restore default view.
Fortune
Musk wants to start selling Tesla’s A.I.-powered humanoid robot next year, but his A.I. chief just went on sabbatical
Christiaan Hetzner
Mon, March 28, 2022, 8:15 AM·4 min read
In this article:

TSLA
+0.88%
Watchlist

Watchlist
Fair Value Estimateyahoo plus badge
Overvalued
See why
Tesla’s director for artificial intelligence is taking a sabbatical from the company, but that has not stopped CEO Elon Musk from announcing plans for the possible market launch of its A.I.-heavy Optimus robot in 2023.

In an interview published on Sunday, the ambitious billionaire (with a penchant for setting out unrealistic goals) reaffirmed plans to present a working proof-of-concept for the Optimus—an all new product, for which there has not even been a physical mock-up.

“It could be ready towards the end of next year, at least for a moderate (scale) series production,” he told Germany's Welt am Sonntag, promising a “pretty good result on a prototype basis” before the end of this December.

Tesla appears to be in the process of rebranding itself as an A.I. company first, carmaker second as part of the next stage of growth—Musk’s Master Plan Part 3.

During Tesla’s product roadmap update in January, Musk said that this year’s top two priorities are not completing engineering work on several cars already promised, but finishing Full Self-Driving (FSD) and working on Optimus.

Musk has argued the humanoid bot he unveiled in virtual form for the first time in August is not unlike a Tesla car: motion is achieved through a series of actuators and motors operated by a central processor and imbued with A.I. — only instead of a four-wheels, its chassis is bipedal.

[raklian]

[raklian]

[Yuli Ban]

Serving as a robotic security guard might feel like a dystopic use of the amazing technology powering Boston Dynamics’ Spot, but the robotic dog is actually well suited to protecting a historically significant area like Pompeii, whose crumbling ruins still pose a safety hazard, especially when it comes to protecting what’s left of the city from relic hunters.

Although the remains of Pompeii, an ancient Italian city of roughly 20,000 people that was buried under ash and pumice during the Mount Vesuvius eruption in AD 79, have been undergoing excavations as far back as 1592, they were halted in 1960 after major projects left the remains of the site in decay. Further excavations were limited to smaller areas at a time, but as recently as 2018 there have been new discoveries made in Pompeii, which unfortunately also means there are new opportunities for relic hunters to search for illegal treasures.