In artificial intelligence, knowledge is the information required by an intelligent system to accomplish tasks. While traditional knowledge bases use discrete, symbolic representations, detecting knowledge encoded in the continuous representations learned from data has received increasing attention recently. In this work, we propose a method for building a continuous knowledge base that can store knowledge imported from multiple, diverse neural networks. The key idea of our approach is to define an interface for each neural network and cast knowledge transferring as a function simulation problem. Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original RNN model. With the continuous knowledge base, it is also easy to achieve knowledge distillation and transfer learning. Our work opens the door to building a universal continuous knowledge base to collect, store, and organize all continuous knowledge encoded in different neural networks trained for different AI tasks.

Claimed to be the first architecture to enable concurrent execution of multiple neural networks and entire workflows on a single system, while supporting heterogeneous compute intensive workloads, the Graph Streaming Processor (GSP) architecture will be demonstrated at CES 2020 by Blaize. The computing architecture offers advances in energy efficiency, flexibility, and usability, says the company for existing and new artificial intelligence (AI) in the automotive, smart vision, and enterprise computing segments. The Blaize GSP architecture and Blaize Picasso software development platform blend dynamic data flow methods and graph computing models with fully programmable, proprietary SoCs. This allows Blaize computing platforms to exploit the native graph structure inherent in neural network workloads all the way through runtime, says the company. The massive efficiency multiplier is delivered via a data streaming mechanism, where non-computational data movement is minimised or eliminated for what Blaize claims is the lowest possible latency and it reduces both memory requirements and energy demand at the chip, board and system levels.

Start-up Blaize (formerly known as Thinci) has announced details of the first true Graph-Native silicon architecture and software built to process neural networks and enable AI applications. The Blaize Graph Streaming Processor (GSP) architecture enables concurrent execution of multiple neural networks and workflows on a single system. It also supports a range of heterogeneous compute-intensive workloads, says Blaize. According to Blaize, the computing architecture meets the demands and complexity of new computational workloads found in artificial intelligence (AI) applications in automotive, smart vision and enterprise computing segments. The Blaize GSP architecture and Blaize Picasso software development platform blends dynamic data flow methods and graph computing models with fully programmable proprietary SoCs.

Whenever we train our own Neural Networks, we need to take care of something called the generalization of the Neural Network. This essentially means how good our model is at learning from the given data and applying the learnt information elsewhere. When training a neural network, there's going to be some data which the Neural Network trains on, and there's going to be some data reserved for checking the performance of the Neural Network. If the Neural Network performs well on the data which it has not trained on, we can say it has generalized well on the given data. Let's understand this with an example.

The SparkFun JetBot AI Kit is a robot platform powered by the Jetson Nano Developer Kit by NVIDIA. This SparkFun kit is based on the open-source NVIDIA JetBot! We understand that not everyone has access to multiple 3D printers on each floor, and a whole warehouse of electronics so we wanted to build a kit from ready to assemble parts to get you up and running as quickly as possible. The SparkFun JetBot AI Kit is a great launchpad for creating entirely new AI projects for makers, students and enthusiasts who are interested in learning AI and building fun applications. It's straightforward to set up and use and is compatible with many popular accessories.