Blog

Archive for the ‘information science’ category: Page 6

Aug 28, 2024

Researchers develop a new humanoid platform for robotics research

Posted by in categories: information science, robotics/AI

Advancements in the field of robotics are fueled by research, which in turn heavily relies on effective platforms to test algorithms for robot control and navigation. While numerous robotics platforms have been developed over the past decades, most of them have shortcomings that limit their use in research settings.

Researchers at the University of California (UC) Berkeley recently developed Berkeley Humanoid, a new robotic platform that could be used to train and test algorithms for the control of humanoid robots. This new humanoid , introduced in a paper posted to the preprint server arXiv, addresses and overcomes some of the limitations of previously introduced robotics research platforms.

Continue reading “Researchers develop a new humanoid platform for robotics research” »

Aug 28, 2024

D-Wave’s Quantum Computer Serves as Brains Behind Study That Connects Neural Activity to Academic Performance

Posted by in categories: computing, education, information science, neuroscience, quantum physics

The study, published by a multi-institutional team of researchers…


Researchers used D-Wave’s quantum computing technology to explore the relationship between prefrontal brain activity and academic achievement, particularly focusing on the College Scholastic Ability Test (CSAT) scores in South Korea.

The study, published by a multi-institutional team of researchers across Korea in Scientific Reports, relied on functional near-infrared spectroscopy (fNIRS) to measure brain signals during various cognitive tasks and then applied a quantum annealing algorithm to identify patterns correlating with higher academic performance.

Continue reading “D-Wave’s Quantum Computer Serves as Brains Behind Study That Connects Neural Activity to Academic Performance” »

Aug 26, 2024

Organoid intelligence: a new biocomputing frontier | Frontiers in Science

Posted by in categories: biological, information science, robotics/AI, science

Organoid intelligence (OI) is an emerging scientific field aiming to create biocomputers where lab-grown brain organoids serve as ‘biological hardware’

In their article, published in Frontiers in Science, Smirnova et al., outline the multidisciplinary strategy needed to pursue this vision: from next-generation organoid and brain-computer interface technologies, to new machine-learning algorithms and big data infrastructures.

Continue reading “Organoid intelligence: a new biocomputing frontier | Frontiers in Science” »

Aug 25, 2024

Neuromorphic computing with memristors: from device to system — Professor Huaqiang Wu

Posted by in categories: information science, robotics/AI

Recently, computation in memory becomes very hot due to the urgent needs of high computing efficiency in artificial intelligence applications. In contrast to von-neumann architecture, computation in memory technology avoids the data movement between CPU/GPU and memory which could greatly reduce the power consumption. Memristor is one ideal device which could not only store information with multi-bits, but also conduct computing using ohm’s law. To make the best use of the memristor in neuromorphic systems, a memristor-friendly architecture and the software-hardware collaborative design methods are essential, and the key problem is how to utilize the memristor’s analog behavior. We have designed a generic memristor crossbar based architecture for convolutional neural networks and perceptrons, which take full consideration of the analog characteristics of memristors. Furthermore, we have proposed an online learning algorithm for memristor based neuromorphic systems which overcomes the varation of memristor cells and endue the system the ability of reinforcement learning based on memristor’s analog behavior.

Full abstract and speaker details can be found here: https://nus.edu/3cSFD3e.

Continue reading “Neuromorphic computing with memristors: from device to system — Professor Huaqiang Wu” »

Aug 25, 2024

This AI Learns Continuously From New Experiences—Without Forgetting Its Past

Posted by in categories: information science, robotics/AI

Algorithms like OpenAI’s GPT-4 are like brains frozen in time. A new study shows how future AIs could learn continuously in response to a changing world.

Aug 25, 2024

In Leaked Audio, Amazon Cloud CEO Says AI Will Soon Make Human Programmers a Thing of the Past

Posted by in categories: information science, robotics/AI

The CEO of Amazon Web Services suggested in leaked audio that human devs may not be coding in as little as two years.

Aug 25, 2024

The testing of AI in medicine is a mess. Here’s how it should be done

Posted by in categories: biotech/medical, information science, robotics/AI

Hundreds of medical algorithms have been approved on basis of limited clinical data. Scientists are debating who should test these tools and how best to do it.

Aug 24, 2024

The Ethics, Challenges, and Future of Whole Brain Emulation & AGI | Deep Interview with Randal Koene

Posted by in categories: blockchains, ethics, information science, neuroscience, robotics/AI, singularity

Join Randal Koene, a computational neuroscientist, as he dives into the intricate world of whole brain emulation and mind uploading, while touching on the ethical pillars of AI. In this episode, Koene discusses the importance of equal access to AI, data ownership, and the ethical impact of AI development. He explains the potential future of AGI, how current social and political systems might influence it, and touches on the scientific and philosophical aspects of creating a substrate-independent mind. Koene also elaborates on the differences between human cognition and artificial neural networks, the challenge of translating brain structure to function, and efforts to accelerate neuroscience research through structured challenges.

00:00 Introduction to Randal Koene and Whole Brain Emulation.
00:39 Ethical Considerations in AI Development.
02:20 Challenges of Equal Access and Data Ownership.
03:40 Impact of AGI on Society and Development.
05:58 Understanding Mind Uploading.
06:39 Randall’s Journey into Computational Neuroscience.
08:14 Scientific and Philosophical Aspects of Substrate Independent Minds.
13:07 Brain Function and Memory Processes.
25:34 Whole Brain Emulation: Current Techniques and Challenges.
32:12 The Future of Neuroscience and AI Collaboration.

Continue reading “The Ethics, Challenges, and Future of Whole Brain Emulation & AGI | Deep Interview with Randal Koene” »

Aug 23, 2024

Researchers propose a smaller, more noise-tolerant quantum factoring circuit for cryptography

Posted by in categories: computing, encryption, information science, quantum physics

The most recent email you sent was likely encrypted using a tried-and-true method that relies on the idea that even the fastest computer would be unable to efficiently break a gigantic number into factors.

Quantum computers, on the other hand, promise to rapidly crack complex cryptographic systems that a classical computer might never be able to unravel. This promise is based on a quantum factoring proposed in 1994 by Peter Shor, who is now a professor at MIT.

But while researchers have taken great strides in the last 30 years, scientists have yet to build a quantum computer powerful enough to run Shor’s algorithm.

Aug 23, 2024

The circle of life, publish or perish edition: Two journals retract more than 40 papers

Posted by in categories: information science, robotics/AI

The team has released the width-pruned version of the model on Hugging Face under the Nvidia Open Model License, which allows for commercial use. This makes it accessible to a wider range of users and developers who can benefit from its efficiency and performance.

“Pruning and classical knowledge distillation is a highly cost-effective method to progressively obtain LLMs [large language models] of smaller size, achieving superior accuracy compared to training from scratch across all domains,” the researchers wrote. “It serves as a more effective and data-efficient approach compared to either synthetic-data-style fine-tuning or pretraining from scratch.”

This work is a reminder of the value and importance of the open-source community to the progress of AI. Pruning and distillation are part of a wider body of research that is enabling companies to optimize and customize LLMs at a fraction of the normal cost. Other notable works in the field include Sakana AI’s evolutionary model-merging algorithm, which makes it possible to assemble parts of different models to combine their strengths without the need for expensive training resources.

Page 6 of 318First345678910Last