Interact with some of the old AI systems.
https://www.nature.com/immersive/d41586-023-02822-z/index.html
A test of artificial intelligence
As debate rages over the abilities of modern AI systems, scientists are still struggling to effectively assess machine intelligence.
No expectation of privacy, except when viewed by a drone? (And we’re not even talking AI!)
EFF to Michigan Court: Governments Shouldn’t Be Allowed to Use a Drone to Spy on You Without a Warrant
Hannah Zhao of EFF writes:
Should the government have to get a warrant before using a drone to spy on your home and backyard? We think so, and in an amicus brief filed last Friday in Long Lake Township v. Maxon, we urged the Michigan Supreme Court to find that warrantless drone surveillance of a home violates the Fourth Amendment.
In this case, Long Lake Township hired private operators to repeatedly fly drones over Todd and Heather Maxon’s home to take aerial photos and videos of their property in a zoning investigation. The Township did this without a warrant and then sought to use this documentation in a court case against them. In our brief, we argue that the township’s conduct was governed by and violated the Fourth Amendment and the equivalent section of the Michigan Constitution.
The Township argued that the Maxons had no reasonable expectation of privacy based on a series of cases from the U.S. Supreme Court in the 1980s. In those cases, law enforcement used helicopters or small planes to photograph and observe private backyards that were thought to be growing cannabis. The Court found there was no reasonable expectation of privacy—and therefore no Fourth Amendment issue—from aerial surveillance conducted by manned aircraft.
But, as we pointed out in our brief, drones are fundamentally different from helicopters or airplanes. Drones can silently and unobtrusively gather an immense amount of data at only a tiny fraction of the cost of traditional aircraft. In other words, the government can buy thousands of drones for the price of one helicopter and its hired pilot. Drones are also smaller and easier to operate. They can fly at much lower altitudes, and they can get into spaces—such as under eaves or between buildings—that planes and helicopters can never enter. And the noise created by manned airplanes and helicopters functions as notice to those who are being watched—it’s unlikely you’ll miss a helicopter circling overhead when you’re sunbathing in your yard, but you may not notice a drone.
Drone prevalence has soared in recent years, fueled by both private and governmental use. We have documented more than 1,471 law enforcement agencies across the United States that operate drones. In some cities, police have begun implementing “drone as first responder” programs, in which drones are constantly flying over communities in response to routine calls for service. It’s important to remember that communities of color are more likely to be the targets of governmental surveillance. And authorities have routinely used aerial surveillance technologies against individuals participating in racial justice movements. Under this backdrop, states like Florida, Maine, Minnesota, Nevada, North Dakota, and Virginia have enacted statutes requiring warrants for police use of drones.
Warrantless drone surveillance represents a formidable threat to privacy and it’s imperative for courts to recognize the danger that governmental drone use poses to our Fourth Amendment rights.
This article originally appeared at EFF.
Perspective.
https://www.bespacific.com/google-data-commons-ai/
Data Commons is using AI to make the world’s public data more accessible and helpful
Google Paper on Data Commons, September 12, 2023: “Publicly available data from open sources (e.g., United States Census Bureau (Census) [1], World Health Organization (WHO) [2], Intergovernmental Panel on Climate Change (IPCC) [3]) are vital resources for policy makers, students and researchers across different disciplines. Combining data from different sources requires the user to reconcile the differences in schemas, formats, assumptions, and more. This data wrangling is time consuming, tedious and needs to be repeated by every user of the data. Our goal with Data Commons (DC) is to help make public data accessible and useful to those who want to understand this data and use it to solve societal challenges and opportunities. We do the data processing and make the processed data widely available via standard schemas and Cloud APIs. Data Commons is a distributed network of sites that publish data in a common schema and interoperate using the Data Commons APIs. Data from different Data Commons can be ‘joined’ easily. The aggregate of these Data Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can then be searched over using Natural Language questions utilizing advances in Large Language Models. This paper describes the architecture of Data Commons, some of the major deployments and highlights directions for future work.”
Data Sources Data in the Data Commons Graph comes from a variety of sources, each of which often includes multiple surveys. Some sources/surveys include a very large number of variables, some of which might not yet have been imported into Data Commons. The sources have been grouped by category and are listed alphabetically within each category.
We also maintain a list of upcoming data imports
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