This Paper Proposes RWKV: A New AI Approach that Combines the Efficient Parallelizable Training of Transformers with the Efficient Inference of Recurrent Neural Networks
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Advancements in deep learning have influenced a wide variety of scientific and industrial applications in artificial intelligence. Natural language processing, conversational AI, time series analysis, and indirect sequential formats (such as pictures and graphs) are common examples of the complicated sequential data processing jobs involved in these. Recurrent Neural Networks (RNNs) and Transformers are the most common methods; each has advantages and disadvantages. RNNs have a lower memory requirement, especially when dealing with lengthy sequences. However, they can’t scale because of issues like the vanishing gradient problem and training-related non-parallelizability in the time dimension.
As an effective substitute, transformers can handle short-and long-term dependencies and enable parallelized training. In natural language processing, models like GPT-3, ChatGPT LLaMA, and Chinchilla demonstrate the power of Transformers. With its quadratic complexity, the self-attention mechanism is computationally and memory-expensive, making it unsuitable for tasks with limited resources and lengthy sequences.
A group of researchers addressed these issues by introducing the Acceptance Weighted Key Value (RWKV) model, which combines the best features of RNNs and Transformers while avoiding their major shortcomings. While preserving the expressive qualities of the Transformer, like parallelized training and robust scalability, RWKV eliminates memory bottleneck and quadratic scaling that are common with Transformers. It does this with efficient linear scaling.
A central challenge for systems neuroscience and artificial intelligence is to understand how cognitive behaviors arise from large, highly interconnected networks of neurons. Digital simulation is linking cognitive behavior to neural activity to bridge this gap in our understanding at great expense in time and electricity. A hybrid analog-digital approach, whereby slow analog circuits, operating in parallel, emulate graded integration of synaptic currents by dendrites while a fast digital bus, operating serially, emulates all-or-none transmission of action potentials by axons, may improve simulation efficacy. Due to the latter’s serial operation, this approach has not scaled beyond millions of synaptic connections (per bus). This limit was broken by following design principles the neocortex uses to minimize its wiring. The resulting hybrid analog-digital platform, Neurogrid, scales to billions of synaptic connections, between up to a million neurons, and simulates cortical models in real-time using a few watts of electricity. Here, we demonstrate that Neurogrid simulates cortical models spanning five levels of experimental investigation: biophysical, dendritic, neuronal, columnar, and area. Bridging these five levels with Neurogrid revealed a novel way active dendrites could mediate top-down attention.
K.B. and N.N.O. are co-founders and equity owners of Femtosense Inc.
As much as id love to see it. Not until someone solves human level hands, i believe will cost about 10+ billion USD. And, a battery can run 8 to 12 hours, and be changed or re charged in under 15 minutes.
HOUSTON/AUSTIN, Texas, Dec 27 (Reuters) — Standing at 6 feet 2 inches (188 centimeters) tall and weighing 300 pounds (136 kilograms), NASA’s humanoid robot Valkyrie is an imposing figure.
Valkyrie, named after a female figure in Norse mythology and being tested at the Johnson Space Center in Houston, Texas, is designed to operate in “degraded or damaged human-engineered environments,” like areas hit by natural disasters, according to NASA.
But robots like her could also one day operate in space.
The science of predicting chaotic systems lies at the intriguing intersection of physics and computer science. This field delves into understanding and forecasting the unpredictable nature of systems where small initial changes can lead to significantly divergent outcomes. It’s a realm where the butterfly effect reigns supreme, challenging the traditional notions of predictability and order.
Central to the challenge in this domain is the unpredictability inherent in chaotic systems. Forecasting these systems is complex due to their sensitive dependence on initial conditions, making long-term predictions highly challenging. Researchers strive to find methods that can accurately anticipate the future states of such systems despite the inherent unpredictability.
Prior approaches in chaotic system prediction have largely centered around domain-specific and physics-based models. These models, informed by an understanding of the underlying physical processes, have been the traditional tools for tackling the complexities of chaotic systems. However, their effectiveness is often limited by the intricate nature of the systems they attempt to predict.
Slime robots can go places that other robots can’t and could have valuable applications in everything from healthcare to the utilities sector.