[European AI Highlights: Development of NAIS-Net's New Neural Network Architecture, Training Depth 10 to 20 Times More Extensive than Ever) Researchers at the European artificial intelligence startup NNAISENSE have published on NAIS-Net (non-automatic input-output stable network) This is a new type of neural network architecture. They claim that the training depth of this architecture is 10 to 20 times that of other networks (such as residual network, Highway network), and it can guarantee stability.
Physics + Artificial Intelligence: The design of this network was inspired by control theory and physics. Their combination allows designers to build a system that can guarantee different types of input data and perform deeper convergence training for specific tasks. NAIS-Nets has reduced the size of the target and is able to complete the training quickly. This makes it possible to reduce the variance in the network training process and improve repeatability.
Scale: "Without increasing the total number of network parameters, NAIS-Nets can also be 10 to 20 times deeper than ResNet, and by stacking multiple stable NAIS-Net modules, graphics processing can be done without any depth-related models. Training is done on the premise of regularization,†the researchers wrote.
Results: After testing the CIFAR-100, the researchers found that NAIS-Net has the same performance as the residual network, but the variance is significantly lower. The architecture has not been tested on ImageNet, which is larger and more like the gold standard for evaluation models.
Why is it important: At present, one of the unsolved problems in artificial intelligence technology is that we do not really understand the principles of their work. From our past experience, we can see that we can hardly guarantee variance, summarization and Loss of performance. Methods like NAIS-Nets seem to reduce some of our uncertainty in these areas, which shows that we have made progress in designing systems with sufficient mathematical proofs to better guarantee some performance parameters. In addition, this also means that we can create a system with more transparent mechanisms, which seems to be the necessary foundation for building more elaborate systems in the future.
European Countries Join the AI ​​Revolution to Avoid Missing the Historical Revolution of the Cloud Computing Revolution
Twenty-five European countries have signed a contract and announced their intention to "join together" to develop artificial intelligence. The general purpose of this letter is that the signatories promised to collaborate with other member states in the implementation of their respective national development plans.
"The cooperation will focus on strengthening the construction of the European Artificial Intelligence Research Center, synergies in R&D and funding programs across Europe, and exchange of views on the social and economic impact of artificial intelligence. This alliance member states will work with the committee as coordinator. Keep the dialogue,†said European Commissioners Andrews Asp and Maria Gabriel.
Why it's important: Because China and the United States are large (hundreds of millions use verbal and written exchanges in the same language), and have sufficient funds to carry out research at the national level, both China and the United States have the structural advantage of developing artificial intelligence. A single European country cannot be compared with its investment capacity, so it needs to be united, otherwise it will be like missing the cloud computing revolution, and ultimately it will not be able to form a large AI company, resulting in their lack of political and economic influence in the era of AI.
The future of artificial intelligence is SpatialAI. What does this mean for robots, drones, etc.?
SLAM researcher Andrew Davison published a paper that studies the current state of SLAM (real-time location and map building) technology and predicts how it will evolve in the future based on current algorithmic trends. Existing artificial intelligence systems want to implement preset functions. They need Spatial Artificial Intelligence and a series of cognitive capabilities that the machine needs to understand and classify the surrounding environment so that it can effectively Take action. He assumes that this spatial artificial intelligence system will become the core of real-world artificial intelligence in the future, because it can “build and maintain a common, close-measuring scene representation in real time, mainly based on visual input and quantifiable performance. †This allows people to develop richer AI applications.
The gap between the status quo and spatial AI: With the application of learning methods and key functional handwriting rules, today's SLAM systems are undergoing changes, especially systems that build a map of the surrounding environment. Future spatial artificial intelligence systems may have more learning capabilities, especially in solving ambiguity or predicting changes in the world, and they need to achieve this on various chip architectures to maximize performance.
Many globally shared maps produced by "spatial artificial intelligence": Once there are a few systems in the world that have such spatial artificial intelligence capabilities, they may concentrate their insights on the world into a globally shared map that will be adopted. All connected devices are constantly updated. This means that once the system recognizes its location, it may not need to perform a lot of device processing because it can get context information from the cloud.
What kind of equipment is this? Cameras and sensor devices will vary depending on the target, for example, "Future home robots may install a navigation camera in the center of the fuselage and install a dedicated camera on the wrist to assist in the operation." These cameras will contain a global model to provide the system Continuously updated location information and semantic information around the world. The system will also use the forward-prediction scenario model to continuously check for new information to help it predict and respond to changes in the environment. In computing, these systems will mark the world around them and track them, map everything into the same space, conduct self-supervised learning, and integrate new sensory input data. Ultimately, if this global model becomes good enough, then the system only needs to collect and predict different information from its sensors, thereby further improving efficiency.
Test: A thorny issue raised by this idea is how we evaluate the performance of this spatial AI system. SLAM benchmarking is often limited, and some researchers tend to subjectively and qualitatively evaluate SLAM. Davison recommends using benchmarks such as SlamBench, which can measure the accuracy and computational cost performance of various processor platforms. It is also necessary to test SLAM performance on the SLAM system deployment platform. Therefore, the same system needs different tests to be deployed on drones or robots. In the future, evaluating performance with different parameters within the same system will be a good thing, such as segmenting objects, tracking environmental changes, assessing power usage, and measuring relocation robustness.
Why it's important: Such a paper gives a general overview of specific AI areas. The SLAM function is very important for the deployment of AI systems in the real world. It is likely that many contemporary AI components will be used in future SLAM systems. The design of these systems will also become more specialized and learnable, and Will be applied to heterogeneous computer substrates.
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