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Category: robotics/AI – Page 1,072

As impersonation scams in the United States rise, Card’s ordeal is indicative of a troubling trend. Technology is making it easier and cheaper for bad actors to mimic voices, convincing people, often the elderly, that their loved ones are in distress. In 2022, impostor scams were the second most popular racket in America, with over 36,000 reports of people being swindled by those pretending to be friends and family, according to data from the Federal Trade Commission. Over 5,100 of those incidents happened over the phone, accounting for over $11 million in losses, FTC officials said.

Advancements in artificial intelligence have added a terrifying new layer, allowing bad actors to replicate a voice with just an audio sample of a few sentences. Powered by AI, a slew of cheap online tools can translate an audio file into a replica of a voice, allowing a swindler to make it “speak” whatever they type.

Experts say federal regulators, law enforcement and the courts are ill-equipped to rein in the burgeoning scam. Most victims have few leads to identify the perpetrator and it’s difficult for the police to trace calls and funds from scammers operating across the world. And there’s little legal precedent for courts to hold the companies that make the tools accountable for their use.

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An artificial intelligence (AI) tool can accurately identify normal and abnormal chest X-rays in a clinical setting, according to a study published in Radiology.

Chest X-rays are used to diagnose a wide variety of conditions to do with the heart and lungs. An abnormal chest X-ray can be an indication of a range of conditions, including cancer and chronic lung diseases.

An AI tool that can accurately differentiate between normal and abnormal chest X-rays would greatly alleviate the heavy workload experienced by globally.

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I recently read an interesting book on reality, entitled The Fabric of Reality. In the book, David Deutsch constructs a unified theory of reality by combining four fundamental theories: 1. Quantum mechanics (multiverse interpretation). 2. Turing principle of computers and artificial intelligence. 3. Popperian epistemology. 4. Darwinian evolution. Deutsch says: In all cases the theory […].

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Summary: Utilizing a classic neural network, researchers have created a new artificial intelligence model based on recent biological findings that shows improved memory performance.

Source: OIST

Computer models are an important tool for studying how the brain makes and stores memories and other types of complex information. But creating such models is a tricky business. Somehow, a symphony of signals – both biochemical and electrical – and a tangle of connections between neurons and other cell types creates the hardware for memories to take hold. Yet because neuroscientists don’t fully understand the underlying biology of the brain, encoding the process into a computer model in order to study it further has been a challenge.

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As the use of machine learning (ML) algorithms continues to grow, computer scientists worldwide are constantly trying to identify and address ways in which these algorithms could be used maliciously or inappropriately. Due to their advanced data analysis capabilities, in fact, ML approaches have the potential to enable third parties to access private data or carry out cyberattacks quickly and effectively.

Morteza Varasteh, a researcher at the University of Essex in the U.K., has recently identified new type of inference attack that could potentially compromise confidential user data and share it with other parties. This attack, which is detailed in a paper pre-published on arXiv, exploits vertical federated learning (VFL), a distributed ML scenario in which two different parties possess different information about the same individuals (clients).

“This work is based on my previous collaboration with a colleague at Nokia Bell Labs, where we introduced an approach for extracting private user information in a data center, referred to as the passive party (e.g., an ),” Varasteh told Tech Xplore. “The passive party collaborates with another , referred to as the active party (e.g., a bank), to build an ML algorithm (e.g., a credit approval algorithm for the bank).”

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To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/.
The first 200 of you will get 20% off Brilliant’s annual premium subscription.

My name is Artem, I’m a computational neuroscience student and researcher. In this video we talk about the concept of critical point – how the brain might optimize information processing by hovering near a phase transition.

Patreon: https://www.patreon.com/artemkirsanov.
Twitter: https://twitter.com/ArtemKRSV

OUTLINE:
00:00 Introduction.
01:11 — Phase transitions in nature.
05:05 — The Ising Model.
09:33 — Correlation length and long-range communication.
13:14 — Scale-free properties and power laws.
20:20 — Neuronal avalanches.
25:00 — The branching model.
31:05 — Optimizing information transmission.
34:06 — Brilliant.org.
35:41 — Recap and outro.

The book: https://mitpress.mit.edu/9780262544030/the-cortex-and-the-critical-point/

REFERENCES (in no particular order):

Continue reading “Brain Criticality — Optimizing Neural Computations” | >

In recent years, the field of artificial intelligence has made tremendous strides, but what happens when #AI systems become #selfaware? In this video, we’ll explore the concept of AI self-awareness, its #scary implications for society, and what it means for the #future of AI.

AI self-awareness is the ability of an #artificialintelligence system to recognize its own existence and understand the consequences of its actions. While there are different levels of self-awareness that an AI system could potentially exhibit, it generally involves the system being able to recognize and respond to changes in its own state.
One way that researchers are exploring AI self-awareness is by using neural networks and other machine learning algorithms. For example, researchers have created AI systems that can recognize and respond to their own errors, which is an important first step in developing higher-order self-awareness.

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📺 Watch the entire video for more information!

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(True) Scary AI Topics, AI disasters, AI Threats, Artificial Intelligence and other thrilling AI eventsevents.

Continue reading “WHAT happens When AI Becomes SELF-AWARE…” | >

Despite AI’s impressive track record, its computational power pales in comparison with a human brain. Now, scientists unveil a revolutionary path to drive computing forward: organoid intelligence, where lab-grown brain organoids act as biological hardware.

Artificial intelligence (AI) has long been inspired by the human brain. This approach proved highly successful: AI boasts impressive achievements – from diagnosing medical conditions to composing poetry. Still, the original model continues to outperform machines in many ways. This is why, for example, we can ‘prove our humanity’ with trivial image tests online. What if instead of trying to make AI more brain-like, we went straight to the source?

Scientists across multiple disciplines are working to create revolutionary biocomputers where three-dimensional cultures of brain cells, called brain organoids, serve as biological hardware. They describe their roadmap for realizing this vision in the journal Frontiers in Science.

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Summary: Scientists have developed a way to use artificial intelligence (AI) to find signs of life on other planets. They combined statistical ecology and machine learning to map the patterns and rules of how life survives in harsh environments on Earth, and then trained the AI to recognize those same patterns and rules in data from other planets. This method can help guide rovers and other exploration missions to places with the highest probability of containing life.

Source: SETI Institute.

Wouldn’t finding life on other worlds be easier if we knew exactly where to look? Researchers have limited opportunities to collect samples on Mars or elsewhere or access remote sensing instruments when hunting for life beyond Earth.

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