I asked GPT-NEO if AI is as good at reading as a human. This is the response.

I have been spending some time building a GPT-NEO model that can generate coherent, long form content of around 750 words in length. Over the weekend I fed it two newspaper headlines lauding the ability of AI to read and asked my GPT model if AI was as good at reading as humans.

This is what I got back:

“Or does it? The answer to both questions is that AI is still at the beginning stages of its journey toward human-level reading skills, which will be a significant leap for AI when it is able to read books. I have not yet seen any AI who reads at a human reading level yet.

At the same time that AI is making its way toward human-level reading, it is also working to replace humans with AI systems, starting with the development of AI systems in various areas such as speech recognition (in particular, voice recognition), natural language processing, and image recognition.

To be sure, AI development has many achievements in the AI space, including the development of AI systems that can read books. I will give a couple examples of AI systems that are capable of reading a book in a short period of time.

The first example, from the research of a Japanese research team, is a deep learning-based model that can read and understand books in a short amount of time. The model is based on a deep neural network and a convolutional neural network.

The neural network is built on a pretrained Google neural network, which the researchers used to build their model. The CNN-based model is able to classify the sentences of a book and analyze them in detail.

The whole process used deep learning techniques, which are known to be highly effective in AI systems. Here is how the researchers describe the process:

In the beginning, the neural network is pre-trained on books from a database that it has. Then, it goes through the training phase to optimize the neural network for specific tasks and tasks, including the extraction of the essential features of the material, and the classification of the extracted features.

The results of the training are used to define the weights of the learned neural network, and the result is fed back into the neural network for fine tuning. The process repeats until the network is optimized for the desired task.

The researchers say that their model was able to classify the sentences of a Japanese book “in less than a second.” This is quite remarkable when compared with the current state of the art (two seconds), which is a rate that is not fast enough to read a book in a short amount of time.

The research team believes that their model has made “significant progress” in reading a book within a few minutes. In the next phase, the model is going to be fine-tuned using the novel text that will be used in the future.

The researchers believe that the next step in the learning process is to be able to read the material at a rate of several hundred words per minute. The researchers are optimistic that their research will make “significant progress” in achieving this goal.”