Drones on Strings as People Puppeteers?

 Quite new thought to me.  Though our look at group tasks for drones might have used this.

Drones on Strings Could Puppeteer People in VR

New Scientist, Matthew Sparkes, November 25, 2022

Researchers at Germany's Saarland University and Canada's University of Toronto have tested a system that uses a drone attached to a user’s finger via string to mimic the action of button-pushing in virtual reality. Saarland's Martin Feick said the challenges of maintaining the drone's stability while pulling the string include its tendency to oscillate or drift, while coordinating multiple drones so they do not crash or tangle up will be problematic. Feick acknowledged testing the drones safely with people is currently infeasible, so the researchers deployed nets to catch the drones. The drones also can produce distracting sounds and drafts, although Feick said soundless blade-free drones capable of ultrasonic levitation show potential.

Full article.

Explanations and Contexts

An example of the need for explain-ability. Like in a conversation with a human, we could want the option of getting an explanation of a solution.  But the nature of an explanation does often depend on context. Is is for management or an engineer?  Is it for a current set of data or a generalization?    Does it depend on some regulation or special constraints?  Context is often key.  Often occurred in our work. 

Researchers Develop 'Explainable' Algorithm

University of Toronto (Canada), Matthew Tierney, March 31, 2021

An "explainable" artificial intelligence (XAI) algorithm developed by researchers at Canada's University of Toronto (U of T) and LG AI Research was designed to find and fix defects in display screens. XAI addresses issues with the "black box" approach of machine learning strategies, in which the artificial intelligence makes decisions entirely on its own. With XAI's "glass box" approach, XAI algorithms are run simultaneously with traditional algorithms to audit the validity and level of their learning performance, perform debugging, and identify training efficiencies. U of T's Mahesh Sudhakar said LG "had an existing [machine learning] model that identified defective parts in LG products with displays, and our task was to improve the accuracy of the high-resolution heat maps of possible defects while maintaining an acceptable run time." The new XAI algorithm, Semantic Input Sampling for Explanation (SISE), outperformed comparable approaches on industry benchmarks.... ' 

Alphabet Sidewalk Labs Abandon's Toronto Smart-Neighborhood

Heard a report early on on this, because it linked to some of our own smart store efforts, details will be interesting to see.   Where will the result be published?

Alphabet's Sidewalk Labs abandons its Toronto smart neighborhood project
Just like that, it's over.

Nick Summers, @nisummers in Engadget

Alphabet subsidiary Sidewalk Labs will no longer pursue its dream of a smart neighbourhood in Toronto.

In a Medium blog post, CEO Daniel Doctoroff said “unprecedented economic uncertainty” meant it was “too difficult” to achieve its dreams for Quayside, a proposed redevelopment on the city’s waterfront. If the company pushed forward with its vision — which was yet to receive sign-off from the Canadian government — “core parts of the plan” would need to be scarified, he said. “And so, after a great deal of deliberation, we concluded that it no longer made sense to proceed with the Quayside project,” Doctoroff added. ... "

Testing Self-Driving Cars in Extreme Conditions

Extremes of data can be important.  Now how are they folded into the tests being proposed?

Research Data Puts Self-Driving Cars to the Ultimate Test: Canadian Winter   By U of T News

Driving in snowy conditions.

A new dataset will train future autonomous vehicles to drive in winter conditions.

Researchers at Canada's universities of Toronto (U of T) and Waterloo collaborated with San Francisco-based artificial intelligence (AI) infrastructure firm Scale AI to create a dataset for training future autonomous vehicles to drive in winter conditions.

The Canadian Adverse Driving Conditions dataset uses real-world scans of icy, snow-covered Canadian roads as a virtual training course for self-driving cars' algorithms.

U of T's Steven Waslander said most driving datasets are collected in summer, and self-driving algorithms trained on such data tend to be confounded in adverse conditions.

Waslander and Waterloo's Krzysztof Czarnecki compiled the new dataset over the past two winters, using a Lincoln MKZ hybrid equipped with cameras, a LiDAR scanner, and a global-positioning system tracker to record conditions across more than 1,000 kilometers (621 miles) of roads.

Scale AI labeled the data through computer and human image recognition, and further analysis and processing converted the data into a software-parsable format.

From U of T News

Smarter Smart City

Alphabet and Toronto to design a better Smart. Starting with a blighted and underused area.

A Smarter Smart city

An ambitious project by Alphabet subsidiary Sidewalk Labs could reshape how we live, work, and play in urban neighborhoods.
by Elizabeth Woyke

In Toronto’s waterfront, where the eastern part of the city meets Lake Ontario, is a patchwork of cement and dirt. It’s home to plumbing and electrical supply shops, parking lots, winter boat storage, and a hulking silo built in 1943 to store soybeans—a relic of the area’s history as a shipping port.

Torontonians describe the site as blighted, underutilized, and contaminated. Alphabet’s Sidewalk Labs wants to transform it into one of the world’s most innovative city neighborhoods. It will, in the company’s vision, be a place where driverless shuttle buses replace private cars; traffic lights track the flow of pedestrians, bicyclists, and vehicles; robots transport mail and garbage via underground tunnels; and modular buildings can be expanded to accommodate growing companies and families. ... "

Fairness in Decision Making: Deferring to Humans

Intriguing effort.  We worked with systems that ultimately required high level executive agreement.  how might those decisions be trained for fairness?   And consider the inherent risk involved in such approaches.   Uncertainty is always inherent in such methods,  how is that integrated?

Predict Responsibly: Fairness Needed in Algorithmic Decision-Making, U of T Experts Say  in U of T News   by Nina Haikara

David Madras at the University of Toronto (U of T) in Canada believes machine learning algorithms could handle uncertainty better by adding fairness in their decision-making processes. Madras worked with U of T professors Toniann Pitassi and Richard Zemel to develop an algorithmic model that includes fairness. The researchers note in situations where there is a degree of uncertainty, an algorithm must have the option to admit its lack of certainty and defer its decision to a human user. "In order to train up our model, we have to use historical decisions that are made by decision-makers," Zemel says. "The outcomes of those decisions, created by existing decision-makers, can be themselves biased or in a sense incomplete." Madras thinks greater concentration on algorithmic fairness alongside issues of privacy, security, and safety will help make machine learning more conducive to high-stakes applications. ... "