AI

AI has been advancing like never before. The underlying technologies like machine learning are driving innovations in almost every stream. Such as object identification, natural language processing, artificial photogeneration, computer vision, and voice recognition. But what about debugging? 

Machine learning could help in identifying the patterns in anomalies. Debugging is all about finding and predicting those inconsistencies. Machine learning can be an AI debugging tool if data is properly fed, and trained. If that works then the AI model could identify the bugs, and their fixes highlighting the suspected bug code from its past experiences. 

But to solve bugs like humans, AI needs to have human general reasoning, insights, and creativity. When a programmer explains to an Artificial Intelligence system what code it should be doing, then Artificial Intelligence tests all the possible inputs. If the programmer tells AI how code should work in all instances, then the AI debugger becomes some sort of meta-compiler and the programmer’s descriptions become a high-level form of coding. 

AI should be able to guess the root cause of common bugs from masses of training data and real-world experiences of code and live systems. Here is an Artificial Intelligence debugging open-source system that can do this.

AI debugging and visualization tool 

Microsoft’s TensorWatch, which is an open-source system and a debugging tool with advanced capabilities for researchers and engineers. TensorWatch provides the interactive debugging of real-time training processes using either the composable UI in Jupyter Notebook or live shareable dashboards in Jupyter Lab. As TensorWatch is a Python library, it can be used to build custom UIs or even for vast Python data science ecosystems.  This system supports stand visualization types such as bar charts, histograms, pie charts, and 3D variations. 

TensorWatch with a common interface, streams can listen to other streams that can enable the creation of custom data flow graphs. It allows the implementation of a variety of advanced functions. Microsoft also introduced a lazy logging mode along with TensorWatch to observe the variables. 

Let's wait and see how TensorWatch helps in contributing to further advances towards debugging and visualizing machine learning.