A research team has used a machine learning approach to investigate the evolution of shell structure for nuclei far from the stability valley. The study, published in Physics Letters B and conducted by researchers from the Institute of Modern Physics (IMP) of the Chinese Academy of Sciences, Huzhou University, and the University of Paris-Saclay, reveals the double-magic nature of tin-100 and the disappearance of the magic number 20 in oxygen-28.
A small-N comparative analysis of six different areas of applied artificial intelligence (AI) suggests that the next period of development will require a merging of narrow-AI and strong-AI approaches. This will be necessary as programmers seek to move beyond developing narrowly defined tools to developing software agents capable of acting independently in complex environments. The present stage of artificial intelligence development is propitious for this because of the exponential increases in computer power and in available data streams over the last 25 years, and because of better understanding of the complex logic of intelligence. Applied areas chosen for examination were heart pacemakers, socialist economic planning, computer-based trading, self-driving automobiles, surveillance and sousveillance and artificial intelligence in medicine.
Researchers at the Indian Institute of Science (IISc) have developed a brain-inspired analog computing platform capable of storing and processing data in an astonishing 16,500 conductance states within a molecular film. Published today in the journal Nature, this breakthrough represents a huge step forward over traditional digital computers in which data storage and processing are limited to just two states.
Such a platform could potentially bring complex AI tasks, like training Large Language Models (LLMs), to personal devices like laptops and smartphones, thus taking us closer to democratizing the development of AI tools. These developments are currently restricted to resource-heavy data centers, due to a lack of energy-efficient hardware. With silicon electronics nearing saturation, designing brain-inspired accelerators that can work alongside silicon chips to deliver faster, more efficient AI is also becoming crucial.
“Neuromorphic computing has had its fair share of unsolved challenges for over a decade,” explains Sreetosh Goswami, Assistant Professor at the Centre for Nano Science and Engineering (CeNSE), IISc, who led the research team. “With this discovery, we have almost nailed the perfect system—a rare feat.”
After about 6 prompts, ChatGPT o1’s preview and mini create a running version of the code described from the methods section of my research paper. I do want to emphasize that while the skeletal code does emulate what my code does, it did use its own synthetic data I asked for it to create as opposed to real astronomical data that would be used in a real paper. Nevertheless, the potential it has is incredible, to effectively accomplish what I struggled for about 10 months in my first year of my PhD. I am excited to apply o1 for other use cases. Thank you to everyone who tuned in live last night! #ai #openaio1 ##chatgpt
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SambaNova Systems has just unveiled a new demo on Hugging Face, offering a high-speed, open-source alternative to OpenAI’s o1 model.
This demonstration is important because it shows that freely available AI models can…
SambaNova challenges OpenAI’s o1 model with Llama 3.1-powered demo on HuggingFace https://venturebeat.com/ai/sambanova-challenges-openais-o1-m…ggingface/
SambaNova Systems has just unveiled a…
Power the most demanding generative and agentic AI workloads with the most performant and capable processor, purpose-built for AI.
Not only will these export controls be increasingly difficult to implement, but they would also unlikely be in the best interests of the United States. Indeed, the current trajectory of export policies risks unintended consequences for little long-term strategic benefit. These include a decline in the competitiveness of the United States, a decoupling from U.S.-developed technology, and uncertainty for the domestic tech industry, amongst other risks.
A Better Way Forward
For the United States to maintain its global AI leadership, it must focus on competition and outcompeting its geopolitical rivals in the development, implementation, and diffusion of AI-based systems domestically and internationally instead of an expert-control-first approach. Defending against the rise of digital authoritarianism requires embracing competition and openness, enabling effective market access, and supporting the diffusion of U.S. AI-enabled technology and governance standards.