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Identifying the Neural Bases of Math Competence Based on Structural and Functional Properties of the Human Brain

It is well known that the human brain is a complex system that comprises not only individual brain regions but also distributed neural networks. The human brain is efficiently organized by integrating information across various brain regions to minimize the cost of information processing while maximizing the overall efficiency of the brain networks. Modern neuroimaging techniques allow us to identify distinct local cortical regions and investigate large-scale neural networks underlying math competence both structurally and functionally. To gain insights into the neural bases of math competence, this review aims to find answers based on structural and functional properties of the human brain in both typical and atypical populations of children and adults. Specifically, for atypical populations, we will focus on individuals with math learning deficits. Math learning deficits are neurodevelopmental disorders that impair an individual’s ability to learn and perform math-related tasks. Dyscalculia is a specific type of math learning deficit that affects the development of arithmetical skills and other basic numerical skills (Kuhl, Sobotta, Legascreen Consortium, & Skeide, 2021; Kucian et al., 2014; Rykhlevskaia, Uddin, Kondos, & Menon, 2009). When reviewing findings from atypical population, we will focus on individuals with math learning deficits including those with dyscalculia.

As math competence encompasses many different skills, for studies involving adults, this review will selectively examine the neural bases of relatively complex math skills, such as evaluation of mathematical statements (e.g., “Any equilateral triangle can be divided into two right triangles”; Amalric & Dehaene, 2016, 2019). For studies involving children, we will also include fundamental math abilities such as arithmetic skills that are commensurate with the math skills young children master. However, basic number comprehension and number comparison skills are outside the scope of this review. Moreover, we will consider whether neural markers associated with math competence are unique to math or may be reflective of academic achievement and cognitive abilities more generally.

Pea-Sized Human Brain Stimulator Invented

Summary: Researchers developed a groundbreaking pea-sized brain stimulator, the Digitally Programmable Over-brain Therapeutic (DOT), capable of wireless operation through magnetoelectric power transfer. This implantable device promises to revolutionize treatment for neurological and psychiatric disorders by enabling less invasive and more autonomous therapeutic options compared to traditional neurostimulation methods.

The DOT’s ability to stimulate the brain through the dura without implanted batteries represents a significant advancement in medical technology, offering potential treatments for conditions like drug-resistant depression directly from the comfort of one’s home. This innovation could change the landscape of how brain-related disorders are managed, emphasizing patient comfort and control.

Australian writers have been envisioning AI for a century. Here are 5 stories to read as we grapple with rapid change

I found this on NewsBreak:


Australians are nervous about AI. Efforts are underway to put their minds at ease: advisory committees, consultations and regulations. But these actions have tended to be reactive instead of proactive. We need to imagine potential scenarios before they happen.

Of course, we already do this – in literature.

There is, in fact, more than 100 years’ worth of Australian literature about AI and robotics. Nearly 2,000 such works are listed in the AustLit database, a bibliography of Australian literature that includes novels, screenplays, poetry and other kinds of literature.

Simultaneous Performance Improvement and Energy Savings with an Innovative Algorithm for 6G Vision Services

Professor Jeongho Kwak’s from the Department of Electrical Engineering and Computer Science at DGIST has developed a learning model and resource optimization technology that combines accuracy and efficiency for 6G vision services. This technology is expected to be utilized to address the high levels of computing power and complex learning models required by 6G vision services.

6G mobile vision services are associated with innovative technologies such as augmented reality (AR) and autonomous driving, which are receiving significant attention in modern society. These services enable quick capturing of videos and images, and efficient understanding of their content through deep learning-based models.

However, this requires high-performance processors (GPUs) and accurate learning models. Previous technologies treated learning models and computing/networking resources as separate entities, failing to optimize performance and mobile device resource utilization.

Artificial Intelligence Index

The Stanford Institute for Human-Centered AI publishes its Artificial Intelligence Index Report 2024, one of the most authoritative sources for data and insights on #AI.

Link to the full report:

Below are its top 10 takeaways:

1.


We provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI.

Tesla and Hyundai advance plans to replace taxis with driverless EVs

Self-driving cars do not get drunk, they do not fall asleep, they do not get distracted by text messages, and experts and manufacturers agree they could be the answer to slashing the road toll.

It’s one of the reasons why autonomous vehicles are in the spotlight again, with Tesla promising to unveil a robotaxi in August and Hyundai showing off the results of its driverless car trial in Las Vegas.

But debate is raging in the industry over whether the technology is or will ever be ready to drive in busy, unpredictable environments without any human oversight.