AI & Robotics News and Discussions

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caltrek
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CausaLens Obtains $45 Million No-code Technology That Introduces Cause and Effect into AI Decision Making
by Ingrid Lunden
January 28, 2022

https://techcrunch.com/2022/01/28/causa ... on-making/

Introduction:
(TechCrunch) One of the most popular applications of artificial intelligence to date has been to use it to predict things, using algorithms trained with historical data to determine a future outcome. But popularity doesn’t always mean success: predictive AI leaves out a lot of the nuance, context and cause-and-effect reasoning that goes into an outcome; and as some have pointed out (and as we have seen), this means that sometimes the “logical” answers produced by predictive AI can prove disastrous. A startup called causaLens has developed causal inference technology — presented as a no-code tool that doesn’t require a data scientist to use to introduce more nuance, reasoning and cause-and-effect sensibility into an AI-based system — which it believes can solve this problem.

CausaLens’s aim, CEO and co-founder Darko Matovski said, is for AI “to start to understand the world as humans understand it.”

Today the startup is announcing $45 million in funding after seeing some early success with its approach, growing revenues 500% since coming out of stealth a year ago. This is being described as a “first close” of the round, meaning it’s still open and potentially going to grow in size.

Dorilton Ventures and Molten Ventures (the VC that rebranded from Draper Esprit) led the round, with previous backers Generation Ventures and IQ Capital, and new backer GP Bullhound also participating. Sources tell us the round values London-based causaLens at around $250 million.

CausaLens’s customers and partners currently include organizations in healthcare, financial services and government, among a number of other verticals, where its technology is used not just for AI-based decision making but to bring in more cause-and-effect nuance when arriving at outcomes.
You can read more about AI and cause and effect reasoning here: https://www.causalens.com/white-paper/h ... nd-effect/
Last edited by caltrek on Tue Feb 01, 2022 6:27 pm, edited 1 time in total.
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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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Musk: Robots to be bigger business than Tesla cars
Elon Musk likes to have a focus - and this year, it looks like it might be robots.

He told investors on a Tesla earnings call his nascent robot plans had "the potential to be more significant than the vehicle business, over time".

And they would be the most important things Tesla worked on this year.

The robot in question, part of a project dubbed Optimus, was previewed last year - to raised eyebrows - by a human in a robot suit dancing on stage.

And the performance became a popular internet meme.

The Tesla Bot, as it was dubbed, would use the same artificial-intelligence (AI) systems that helped power Tesla vehicles, Mr Musk said at the event last August - but no prototype has yet been made.
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"Chain of Thought Prompting Elicits Reasoning in Large Language Models", Wei (LaMDA inner monologue)
Although scaling up language model size has reliably improved performance on a range of NLP tasks, even the largest models currently struggle with certain reasoning tasks such as math word problems, symbolic manipulation, and commonsense reasoning. This paper explores the ability of language models to generate a coherent chain of thought -- a series of short sentences that mimic the reasoning process a person might have when responding to a question. Experiments show that inducing a chain of thought via prompting can enable sufficiently large language models to better perform reasoning tasks that otherwise have flat scaling curves.
This, if you ask me, is going to be the next area of improvement for transformers: inner voices. It'll improve these models vastly more than merely adding more parameters will.
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How robots learn to hike
Steep sections on slippery ground, high steps, scree and forest trails full of roots: the path up the 1,098-​metre-high Mount Etzel at the southern end of Lake Zurich is peppered with numerous obstacles. But ANYmal, the quadrupedal robot from the Robotic Systems Lab at ETH Zurich, overcomes the 120 vertical metres effortlessly in a 31-minute hike. That’s 4 minutes faster than the estimated duration for human hikers – and with no falls or missteps.
This is made possible by a new control technology, which researchers at ETH Zurich led by robotics professor Marco Hutter recently presented in the journal Science Robotics ("Learning robust perceptive locomotion for quadrupedal robots in the wild").
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Artificial Intelligence System Rapidly Predicts How Two Proteins Will Attach
February 1, 2022

https://www.eurekalert.org/news-releases/942017

Introduction:
(EurekAlert) Antibodies, small proteins produced by the immune system, can attach to specific parts of a virus to neutralize it. As scientists continue to battle SARS-CoV-2, the virus that causes Covid-19, one possible weapon is a synthetic antibody that binds with the virus’ spike proteins to prevent the virus from entering a human cell.

To develop a successful synthetic antibody, researchers must understand exactly how that attachment will happen. Proteins, with lumpy 3D structures containing many folds, can stick together in millions of combinations, so finding the right protein complex among almost countless candidates is extremely time-consuming.

To streamline the process, MIT researchers created a machine-learning model that can directly predict the complex that will form when two proteins bind together. Their technique is between 80 and 500 times faster than state-of-the-art software methods, and often predicts protein structures that are closer to actual structures that have been observed experimentally.

This technique could help scientists better understand some biological processes that involve protein interactions, like DNA replication and repair; it could also speed up the process of developing new medicines.
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An Autonomous Delivery Robot Is the Next Big Thing, Experts Say
In a few years, it will probably be pretty ordinary to see robots delivering packages and transporting goods in cities.

Delivery companies are always on the lookout for the next big innovation in the sector.

In the US, companies like Starship Technologies have seen huge growth as a result of the pandemic, and others like Coco are also scaling quickly.

Some big names have also developed their own autonomous delivery robots, like Alibaba, whose robots are becoming ever more prominent in daily life in China.

Many companies have already launched their first prototypes, but the robots developed by the Polytechnic University of Catalonia (UPC) and CARNET have a standout feature — they're able to go up and down stairs, according to the press release.
Image
It has six wheels and can go up or down steps up to 7.9 inches high. Universitat Politècnica de Catalunya/upc.edu

I'd like to believe that this wasn't going to be the "Next Big Thing" in a world without the COVID-19 pandemic. Perhaps we'd see some delivery robot experiments in the 2020s, but there'd be no overwhelming demand for it driving rapid commercialization until at least the 2030s. Yet we're probably just 3 to 5 years away from this being a major thing in most major (and even some minor) cities.
As always, I might be wrong.
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Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block opponents while operating their vehicles at their traction limits1. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the non-linear control challenges of real race cars while also encapsulating the complex multi-agent interactions. Here we describe how we trained agents for Gran Turismo that can compete with the world’s best e-sports drivers. We combine state-of-the-art, model-free, deep reinforcement learning algorithms with mixed-scenario training to learn an integrated control policy that combines exceptional speed with impressive tactics. In addition, we construct a reward function that enables the agent to be competitive while adhering to racing’s important, but under-specified, sportsmanship rules. We demonstrate the capabilities of our agent, Gran Turismo Sophy, by winning a head-to-head competition against four of the world’s best Gran Turismo drivers. By describing how we trained championship-level racers, we demonstrate the possibilities and challenges of using these techniques to control complex dynamical systems in domains where agents must respect imprecisely defined human norms.
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Over the last two years, Sony AI trained a computer system to play Polyphony Digital's Gran Turismo Sport, a popular and realistic car racing game, and beat some of the world's best human competitors, Sony said on Wednesday.

The AI, named GT Sophy, defeated top humans only in time trials when there were no other cars on the track during a July competition. But by October, GT Sophy beat the humans even with a scrum of virtual race cars.
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Self-healing Materials for Robotics Made from ‘Jelly’ and Salt
February 18, 2022

https://www.eurekalert.org/news-releases/944019

Introduction:
(EurekAlert) Researchers have developed self-healing, biodegradable, 3D-printed materials that could be used in the development of realistic artificial hands and other soft robotics applications.

The low-cost jelly-like materials, developed by researchers at the University of Cambridge, can sense strain, temperature and humidity. And unlike earlier self-healing robots, they can also partially repair themselves at room temperature.

The results are reported in the journal NPG Asia Materials.

Soft sensing technologies could transform robotics, tactile interfaces and wearable devices, among other applications. However, most soft sensing technologies aren’t durable and consume high amounts of energy.

“Incorporating soft sensors into robotics allows us to get a lot more information from them, like how strain on our muscles allows our brains to get information about the state of our bodies,” said David Hardman from Cambridge’s Department of Engineering, the paper’s first author.
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