How Does The Author Describe Machine Intelligence

How Does The Author Describe Machine Intelligence – Caption: MIT researchers have created a technique that can automatically describe the roles of individual neurons in a neural network with natural language. In this illustration, the technique was able to identify the “upper limit of horizontal objects” in the photographs, which are highlighted in white.

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How Does The Author Describe Machine Intelligence

How Does The Author Describe Machine Intelligence

MIT researchers have created a technique that can automatically describe the roles of individual neurons in a neural network with natural language. In this illustration, the technique was able to identify the “upper limit of horizontal objects” in the photographs, which are highlighted in white.

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Neural networks are sometimes called black boxes because while they can outperform humans at certain tasks, even the researchers who design them often don’t understand how and why they work so well. But if a neural network is used outside the lab, perhaps to classify medical images that can help diagnose heart disease, knowing how the model works helps researchers predict how it will actually behave.

MIT researchers have now developed a method that sheds some light on the inner workings of black box neural networks. Modeled after the human brain, neural networks are organized into layers of connected nodes, or “neurons”, that process data. The new system can automatically generate descriptions of these individual neurons, created in English or another natural language.

For example, in a neural network trained to recognize animals in images, your method might describe a specific neuron as recognizing fox ears. Its scalable technique is able to generate more precise and specific descriptions for individual neurons than other methods.

In a new paper, the team shows that this method can be used to test a neural network to determine what it has learned, or even edit a network by identifying and shutting down useless or incorrect neurons.

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“We wanted to create a method where a machine learning practitioner could give this system its model and it would say everything it knows about this model, from the point of view of the neurons in the model, in language. This helps you answer the question fundamental question, ‘Is there something my model knows about it that I wouldn’t expect it to know?'” says Evan Hernandez, a graduate student at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper. .

Co-authors include Sarah Schottman, a postdoctoral fellow at CSAIL; David Bau, a recent CSAIL graduate and assistant professor of computer science at Northeastern University; Teona Bagashvili, visiting former student at CSAIL; Antonio Turalba, Delta Electronics Professor of Electrical Engineering and Computer Science and member of CSAIL; and senior author Jacob Andreas, assistant professor in the X Consortium at CSAIL. The study will be presented at the International Conference on Learning Representations.

Most existing techniques that help machine learning practitioners understand how a model works describe the entire neural network or require researchers to identify concepts that they think individual neurons can focus on.

How Does The Author Describe Machine Intelligence

The system developed by Hernandez and his collaborators, called MILAN (Mutual Information Guided Linguistic Annotation of Neurons), improves on these methods because it does not require a list of concepts in advance and can automatically generate natural language descriptions of all neurons in the network. This is especially important because a single neural network can contain hundreds of thousands of individual neurons.

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MILAN produces descriptions of neurons in neural networks trained for computer vision tasks such as object recognition and image synthesis. To describe a particular neuron, the system first examines the behavior of that neuron in thousands of images to find the set of image regions where the neuron is most active. It then selects a natural language description for each neuron to maximize a so-called amount of point mutual information between the image regions and the descriptions. This encourages descriptions that capture the unique role of each neuron within the larger network.

“In a neural network trained to classify images, there will be tons of different neurons that recognize dogs. But there are many different types of dogs and many different parts of dogs. Of these neurons, it’s not very informative. We want descriptions that are very specific to what the neuron does. It’s not just dogs, it’s the left side of the German Shepherd’s ears,” says Hernandez.

The team compared MILAN to other models and found that it produced richer and more accurate descriptions, but the researchers were more interested in seeing how it could help answer specific questions about computer vision models.

First, they used MILAN to analyze which neurons are most important in a neural network. They created descriptions for each neuron and named them based on the words in the descriptions. They slowly removed neurons from the network to see how their accuracy changed and found that neurons that had two very different words in their descriptions (vessels and fossils, for example) were less important to the network.

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They also used MILAN to test models to see if they learned anything unexpected. The researchers used image classification models trained on datasets in which human faces were blurred, ran MILAN and counted how many neurons were sensitive to human faces.

“Blurring faces in this way reduces the number of face-sensitive neurons, but is far from eliminating them. In fact, we hypothesized that some of these neurons in faces are highly sensitive to specific demographic groups, which is quite surprising. These models never I’ve seen a human face before, but all kinds of facial processing takes place inside them,” says Hernandez.

In a third experiment, the team used MILAN to edit a neural network, finding and removing neurons that detected bad correlations in the data, leading to a 5% increase in the network’s accuracy on inputs that showed the problematic correlation.

How Does The Author Describe Machine Intelligence

Although the researchers were impressed with the performance of MILAN in these three applications, the model sometimes provides descriptions that are still too vague, or makes a wrong guess when it doesn’t know the concept it should recognize.

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They plan to address these limitations in future work. They also want to continue to improve the richness of descriptions that MILAN is able to produce. They hope to apply MILAN to other types of neural networks and use it to describe what groups of neurons do, as neurons work together to produce an output.

“It’s a bottom-up approach to interpretation. The goal is to create open compositional descriptions of function with natural language. We want to harness the expressive power of human language to create much more natural and rich descriptions of what neurons do. ability to generalize this approach to different types of models That’s the thing I’m most excited about,” says Whitman.

“The ultimate test of any explainable AI technique is whether it can help researchers and users make better decisions about when and how to deploy AI systems,” says Andreas. “We’re still a long way from being able to do that in general. But I’m optimistic that Milan – and the use of language as a broader explanatory tool – will be a useful part of the toolbox.”

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How Does The Author Describe Machine Intelligence

At MIT, “weak loop” social networks, which help promote new ideas, have declined during the Covid-19 pandemic, researchers report. Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to think like humans and mimic their actions. The term can also be applied to any machine that displays features associated with the human brain, such as learning and problem solving.

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The ideal characteristic of artificial intelligence is its ability to rationalize and carry out actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning (ML), which refers to the concept that computer programs can learn and adapt to new data automatically without the help of humans. Deep learning techniques enable this automatic learning by absorbing large amounts of unstructured data such as text, images or video.

When most people hear the term artificial intelligence, the first thing they usually think of is robots. This is because big-budget movies and novels weave stories about human characters.

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