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Fintech’s New Power Couple: AI And Trust

At Money20/20 I learned that Fintech has a new power couple, AI and Trust.

The combination of the two is the payment protocol of tomorrow. Come along with me as I share my findings from the world’s #1 fintech show.

There is bunch of interviews and images forthcoming from the event.

Thanks again to Tedd Huff of Fintech Confidential for inviting me to participate in the event. It allowed me to share My Instant AI with event attendees.

(https://www.linkedin.com/pulse/fintechs-new-power-couple-ai-…urke-eirte)


Tedd Huff asked me to be a confidential informant in Las Vegas recently. But, wait before you go down conspiracy theory rabbit hole, please let me explain.

Topographical sparse mapping: A neuro-inspired sparse training framework for deep learning models

AI models have been expanding dramatically in size and the number of trainable parameters. This rapid growth has introduced many challenges, including increased computational costs and inefficiencies. Dynamic sparse training has emerged as a novel approach to address overparameterization and achieve energy-efficient artificial neural network (ANN) architectures. The highly efficient neuro-inspired sparse design remains underexplored compared to the significant focus on random topology searches. We propose the Topographical Sparse Mapping (TSM) method, inspired by the vertebrate visual system and convergent units. TSM introduces a sparse input layer for MLPs, significantly reducing the number of parameters.

Artificial muscles use ultrasound-activated microbubbles to move

Researchers at ETH Zurich have developed artificial muscles that contain microbubbles and can be controlled with ultrasound. In the future, these muscles could be deployed in technical and medical settings as gripper arms, tissue patches, targeted drug delivery, or robots.

It might look like a simple material experiment at first glance, as a brief ultrasound stimulation induces a thin strip of silicone to start bending and arching. But that’s just the beginning. A team led by Daniel Ahmed, Professor of Acoustic Robotics for Life Sciences and Healthcare, has developed a new class of : flexible membranes that respond to ultrasound with the help of thousands of microbubbles.

The work is published in the journal Nature.

AI model identifies high-performing battery electrolytes by starting from just 58 data points

In an ideal world, an AI model looking for new materials to build better batteries would be trained on millions or even hundreds of millions of data points.

But for emerging next-generation battery chemistries that don’t have decades of research behind them, waiting for new studies takes time the world doesn’t have.

“Each experiment takes up to weeks, months to get ,” said University of Chicago Pritzker School of Molecular Engineering (UChicago PME) Schmidt AI in Science Postdoctoral Fellow Ritesh Kumar. “It’s just infeasible to wait until we have millions of data to train these models.”

AI: Physiognomy & Stereotypes ARE REAL

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Cyber-Securing the Connected Worlds of the Internet of Things, Smart Cities, and Space

In this latest edition of Security & Tech Insights newsletter, the topic of vulnerabilities of digital connectivity are analyzed in special regards to IoT, Smart Cities, and Space. Also included are articles reviewing Cybersecurity Awareness and Preparedness, and new threats to contend with from AI-enabled Ransomware. Thanks for reading and sharing! Chuck Brooks.

#cybersecurity #internetofthings #smartcities #space #ai #ransomware | on LinkedIn.

Reading vs. Playing on a Tablet: Do They have Different Effects on the Brain?

The difference between the brains of children who read books (left picture) and screen time (right picture) over 1 hour. Early childhood, screen time over 60 minutes, are vulnerable to emotional and focus disorders. Increasing the duration of screen time reduces brain connectivity in the language, visual and intelligence centres compared to reading books.


The school bell rang long ago, but Danny is still sitting in his chair, trying to finish copying from the board. “Why is this process so hard? Why does it take me so much longer to read than it takes my friends?” Danny is frustrated. The more he tries to read faster, the harder it is for him to understand what he is reading. Around the time when he finally finishes copying, his friends come back to the class from the break. Like 10–15% of the children in the world, Danny has dyslexia. Dyslexia is defined as difficulty in reading accurately or quickly and, most of the time; it affects the person’s ability to understand what is read and to spell words correctly. The reading difficulty continues into adulthood and does not disappear, even though most adults with dyslexia find ways to “bypass” this difficulty, sometimes using text-to-speech software. Children and adults with dyslexia have different brain activity than do people who are good readers. They have lower activity in the brain area responsible for vision and identification of words [ 1, 2 ] and in another brain area responsible for attention and recognition of errors during reading [ 3 ]. A question could then be asked: is this reading difficulty strange or is it actually the ability to read that is magical? How did the human brain learn to read? And does the daily use of technology, which sometimes “bypasses” the need to make an effort to read, help us to learn to read or make it more difficult? This article will discuss these subjects.

Reading is a relatively new human ability—about 5,000 years old. The Egyptians were among the first to use symbols to represent words within a spoken language, and they used drawings to transmit ideas via writing. As difficult as it is to draw each word in a language, it is still much easier to understand Egyptian hieroglyphs than to figure out what is written in an unfamiliar language. Today, 5,000 years later, we expect each child in first grade to immediately understand that the lines and circles that form letters have a unique sound corresponding to them. To do that, the brain has to rely on neural networks that were designed to perform other tasks, such as seeing, hearing, language comprehension, speech, attention, and concentration [ 4 ] (see Figure 1).

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