Mann, J., Meshkin, H., Zirkle, J. et al. Mechanism-based organization of neural networks to emulate systems biology and pharmacology models. Sci Rep 14, 12,082 (2024). https://doi.org/10.1038/s41598-024-59378-9
Researchers at the Princeton Plasma Physics Laboratory are harnessing artificial intelligence and machine learning to enhance fusion energy production, tackling the challenge of controlling plasma reactions. Their innovations include optimizing the design and operation of containment vessels and using AI to predict and manage instabilities, significantly improving the safety and efficiency of fusion reactions. This technology has been successfully applied in tokamak reactors, advancing the field towards viable commercial fusion energy. Credit: SciTechDaily.com.
The intricate dance of atoms fusing and releasing energy has fascinated scientists for decades. Now, human ingenuity and artificial intelligence are coming together at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) to solve one of humankind’s most pressing issues: generating clean, reliable energy from fusing plasma.
Unlike traditional computer code, machine learning — a type of artificially intelligent software — isn’t simply a list of instructions. Machine learning is software that can analyze data, infer relationships between features, learn from this new knowledge, and adapt. PPPL researchers believe this ability to learn and adapt could improve their control over fusion reactions in various ways. This includes perfecting the design of vessels surrounding the super-hot plasma, optimizing heating methods, and maintaining stable control of the reaction for increasingly long periods.
Less than two weeks after Google introduced “AI Overview” in its search engine, the feature is facing public criticism due to ‘nonsensical and inaccurate’ responses without an option for users to opt-out. Social media users have highlighted numerous instances where the tool has given incorrect, even controversial answers. Why is this happening?
Small businesses are an important part of society and AI is becoming just as important. Here is how artificial intelligence can help them.
Language models (LMs) are a cornerstone of artificial intelligence research, focusing on the ability to understand and generate human language. Researchers aim to enhance these models to perform various complex tasks, including natural language processing, translation, and creative writing. This field examines how LMs learn, adapt, and scale their capabilities with increasing computational resources. Understanding these scaling behaviors is essential for predicting future capabilities and optimizing the resources required for training and deploying these models.
The primary challenge in language model research is understanding how model performance scales with the amount of computational power and data used during training. This scaling is crucial for predicting future capabilities and optimizing resource use. Traditional methods require extensive training across multiple scales, which is computationally expensive and time-consuming. This creates a significant barrier for many researchers and engineers who need to understand these relationships to improve model development and application.
Existing research includes various frameworks and models for understanding language model performance. Notable among these are compute scaling laws, which analyze the relationship between computational resources and model capabilities. Tools like the Open LLM Leaderboard, LM Eval Harness, and benchmarks like MMLU, ARC-C, and HellaSwag are commonly used. Moreover, models such as LLaMA, GPT-Neo, and BLOOM provide diverse examples of how scaling laws can be practiced. These frameworks and benchmarks help researchers evaluate and optimize language model performance across different computational scales and tasks.
Neuromorphic computing represents an exciting crossover between technology and biology, a frontier where computer science meets the mysteries of the human brain. Designed to mimic the way humans process information, this technology holds the promise to stir a revolution everywhere, from artificial intelligence to robotics. But what exactly is neuromorphic computing and why is it taking the center stage?
Agent-computer interfaces enable software automated software engineering.
SWE-agent is our new system for autonomously solving issues in GitHub repos.
MIT scientists train shape-shifting, slimy robots that can seamlessly squeeze through narrow spaces by contorting their bodies on command.
Researchers have tested a range of neuroprosthetic devices, from wheelchairs to robots to advanced limbs, that work with their users to intelligently perform tasks.
They work by decoding brain signals to determine the actions their users want to take, and then use advanced robotics to do the work of the spinal cord in orchestrating the movements. The use of shared control — new to neuroprostheses — “empowers users to perform complex tasks,” says José del R. Millán, who presented the new work at the Cognitive Neuroscience Society (CNS) conference in San Francisco today.
Millán, of the Swiss Federal Institute of Technology in Lausanne, Switzerland, began working on “brain-computer interfaces” (BCIs), designing devices that use people’s own brain activity to restore hand grasping and locomotion, or provide mobility via wheelchairs or telepresence robots, using people’s own brain activity.
The world is at a critical juncture where the choices we make regarding AI, education, healthcare, and decentralized systems will determine whether we move towards a future of chaos and despair or one of abundance and higher consciousness Questions to inspire discussion What is the potential future of AI? —The speaker.
