In a paper published recently in the Journal of Applied Physics, an international team of scientists from Lawrence Livermore National Laboratory (LLNL), Argonne National Laboratory and Deutsches Elektronen-Synchrotron have developed a new sample configuration that improves the reliability of equation of state measurements in a pressure regime not previously achievable in the diamond anvil cell.
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 robot, introduced in a paper posted to the preprint server arXiv, addresses and overcomes some of the limitations of previously introduced robotics research platforms.
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.
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.
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.
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.
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.
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.
