Right but that’s what I mean. If we think about what everyone is actually trying to do with AI recognition isn’t really the objective. Interpretation is. And interpretation is not fixed to a specific data medium.
News on China's scientific and technological development.
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AssassinsMace
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Again the US thought no one can do anything without them hence why they thought they can control the standards. But now they've realized China can do without them meaning if China has its own standard and it'll spread across the world because like I've been saying everything the West does costs more therefore will be out of reach of most in the world, that means by default China's standard becomes mainstream and the US loses out because their standard is not compatible with the rest of the world. The US easing curbs is all about hoping to get China to go along before it's too late to follow the US.
If im not mistaken Huawei has introduced a file system standard for mobile apps.
That recently got merged into android, wouldn't be surprised if Huawei will create application standards with open Harmony apps.
Kind of like flatpacks, AppImage etc you have right now for Linux.
It doesn't quite work like this. There are many complexities in each form of data, dimensionality alone is not the only challenge. For example, you can use raw RGB inputs for images and achieve good results but text requires extensive pre-processing to even work (tokenization, embeddings, etc.). Text and time series are also extremely tricky domains because they need context, both local and global - prior to emergence of transformers, it was very challenging to achieve good quality AND speed because recurrent neural networks were slow as heck and convolutional nets (even with dilations) handle global context poorly. Even now transformers are quite tricky because they require gigantic datasets to achieve decent performance, are prone to parameter bloat and vanilla transformers also have rather high complexity which makes them slow. This issue is intensified for mobile devices where CNNs are decently optimized but transformers still run poorly, so classic ML and even algorithmic approaches are still widely used for text. Another problem is with training such networks, there are multiple tricks on how to learn language models - e.g. classic "predict next token", BERT-style, GPT-styles etc.
So I would not say that one domain is easier or harder than the other one, there are complex issues and tricky subjects in all of them.
Interesting. I wonder if China should ban Huawei from working with the US on standards. Maybe that would lock the US out of standards setting in this area. Or Huawei can just refuse to work with US standards setting unless the US govt makes concessions in other areas. Unlikely but...
Not 100% related, but kind of. This shows how Alibaba is spreading its wings in Pakistan and other countries in the region. This is also a great way to link up exporters with consumers in China and other places. It's not easy to make in road against Amazon, but Alibaba is trying to do that in many countries. It will be expanding to Europe next with Lazada I think. The fact that it has a group of exporters from places like Pakistan allow them to show case products that are not available on Amazon.
Now that China has become the factory of the world, China is also trying to compete in the digital economy.
Now that China has become the factory of the world, China is also trying to compete in the digital economy.