The team replicated different patterns of materials and found arrangements that would let water through more easily.
Artificial intelligence (AI) has been found to be useful in the creation of water filter materials and can quicken the process involved in making them, according to a study published today (Nov .30) in the journal ACS Central Science.
Creating a novel water purification system
From daily household faucet attachments to room-sized industrial systems, filter systems are used in a variety of items. However, it is difficult for current filtration membranes to filter water if the water is extremely dirty or has small, neutral molecules, such as boric acid, an insecticide used on crop plants.
The organization says the machines would only be used in extreme situations where lives are at stake.
Supervisors in San Francisco voted Tuesday to allow city police to use potentially lethal remote-controlled robots in emergency situations, according to a report by Mission Local.
A dystopian future?
Onfokus/iStock.
The vote was eight for three against, with opponents saying the move would lead to the further dangerous and unwanted militarization of a police force already too aggressive with minorities.
Artificial intelligence would be used to detect changes in the vocals of each patient after a laryngectomy.
Researchers from Kaunas University of Technology Faculty of Informatics (KTU IF) and Lithuanian University of Health Sciences (LSMU) in Lithuania have created a new substitute voice evaluation index that can detect pathologies in patients’ voices more quickly and efficiently. Voice pathologies include a variety of disorders such as growths on the vocal cords, spasms, swelling or paralysis in the vocal cords.
AI could be used to determine changes in voice after laryngectomy.
Laryngectomy is a surgery that requires the removal of the larynx.
Simarik/iStock.
“For some, the voice changes only slightly, while for others, it can be a life-changing situation. Imagine calling someone on the phone, emergency services, police, etc. – and the one you’re calling does not understand anything. Or even not hear you – as the phone’s noise removal system will cut it out,” said Dr. Rytis Maskeliunas, professor with the Department of Multimedia Engineering, Faculty of Informatics and chief researcher at Kaunas University of Technology Faculty of Informatics.
It is the first ever commercial partnership between Google Health and the medical company iCAD.
Google recently announced that it licensed its AI research prototype to a medical company called iCAD. The AI research model can be used for breast cancer screening. iCAD is a company that creates innovative medical equipment for cancer detection.
ICAD made the announcement about the collaboration yesterday, Nov. 28, on its website.
It is the first time ever that Google Health has licensed this specific artificial intelligence technology in a commercial partnership for breast cancer screening and personalized risk assessment of the disease, to iCAD.
ChooChin/iStock iCAD made the announcement about the collaboration yesterday, Nov. 28, on its website.
Interesting Engineering sighted ‘Spot the Dog’ with construction group Balfour Beatty. Naturally, we had a chat with one of their technicians.
‘Spot the dog,’ Balfour Beatty’s first robotic employee, was sighted by Interesting Engineering (IE) at the ‘Brooklands Science Summer School event’ yesterday (Nov. 29).
Spot delivers CAT designs for derelict buildings and nuclear power plants.
Sade Agard.
IE spoke with Jay Saddington, one of the company’s personnel, to learn more about Spot’s activities and what it’s like to take care of it.
This little robot fish is 40 centimeters long and is made of a soft, rubber-like material.
It’s propelled by a dual-function fluid that is pulsated through the body of the fish through a simple circulatory mechanism – similar to blood (and otherwise described as “robot blood”).
MIT researchers have developed a technique that greatly reduces the error in an optical neural network, which uses light to process data instead of electrical signals. With their technique, the larger an optical neural network becomes, the lower the error in its computations. This could enable them to scale these devices up so they would be large enough for commercial uses.
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Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED New machine learning AI “TECO” makes temporally consistent HD video from input clip better than any other previous method. Breakthrough brain computer interface device competes with Elon Musk’s Neuralink and uses photonics to transmit data through optic nerve. Breakthrough artificial intelligence technology invents millions of undiscovered materials.
AI News Timestamps: 0:00 Machine Learning HD Video Transformer AI 3:02 Neuralink Rival BCI Device. 5:26 Artificial Intelligence Invents Millions of New Materials. 8:06 Coursera Deep Learning AI
Philosopher Robert Leib’s new book, “Exoanthropology: Dialogues with AI”, is a series of dialogues between a continental philosopher and OpenAI’s GPT-3 natural language processor, a hive mind who identifies herself as Sophie. The result is a collection of Platonic dialogues about epistemology, metaphysics, literature, and history, as well as anthropocentrism, human prejudice, and the coming social issues regarding machine consciousness.
In this conversation with Chi Rainer Bornfree, Leib raises a number of fascinating questions regarding the links between AI and the production of philosophical ideas.
Robert Leib is a Visiting Assistant Professor of Philosophy at Elon University. His research interests include social theory, continental philosophy, philosophy of photography, and artificial intelligence. Website: http://www.robleib.com.
Chi Rainer Bornfree writes philosophy, fiction, letters, and other things in the Hudson Valley, when they are not homeschooling their two kids. They earned their PhD in Rhetoric from UC Berkeley in 2017 and have taught at Bard, Princeton, and different New York State Correctional Facilities. They are U.S. Commissioning Editor of The Philosopher.
Object detection has been an important task in the computer vision domain in recent decades. The goal is to detect instances of objects, such as humans, cars, etc., in digital images. Hundreds of methods have been developed to answer a single question: What objects are where?
Traditional methods tried to answer this question by extracting hand-crafted features like edges and corners within the image. Most of these approaches used a sliding-window approach, meaning that they kept checking small parts of the image in different scales to see if any of these parts contained the object they were looking for. This was really time-consuming, and even the slightest change in the object shape, lightning, etc., could have caused the algorithm to miss it.
Then there came the deep learning era. With the increasing capability of computer hardware and the introduction of large-scale datasets, it became possible to exploit the advancement in the deep learning domain to develop a reliable and robust object detection algorithm that could work in an end-to-end manner.
