How Is AI Transforming Cloud Management?

In 1927, the world was introduced to the origins of Artificial Intelligence (AI) in the form of a robot in the movie Metropolis. Throughout nearly a century since then, movies have continued to iterate on the complexities of AI, as both a fun take on it and serious commentary on the potential concerns and consequences. This is all well and good, but as AI has continued to evolve, we find ourselves asking, “how can we actually use this to make our lives easier?”

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Audio Classification with PyTorch's Ecosystem Tools

Audio signals are all around us. As such, there is an increasing interest in audio classification for various scenarios, from fire alarm detection for hearing impaired people, through engine sound analysis for maintenance purposes, to baby monitoring. Though audio signals are temporal in nature, in many cases it is possible to leverage recent advancements in the field of image classification and use popular high performing convolutional neural networks for audio classification.


IT just got smarter

Every company in the world needs to reduce risk and uncertainty in its IT operations, ITOM. The best way to do that is by combining AI and digital workflows. It’s all about applying machine learning to operational data so that you can generate insights about potential system issues, and then launch automated workflows that resolve problems fast—ideally, before they impact customers. Today’s partnership announcement between IBM and ServiceNow is great news for enterprise customers.

allegro AI

Accelerate your Hyperparameter Optimization with PyTorch's Ecosystem Tools

The design and training of neural networks are still challenging and unpredictable procedures. The difficulty of tuning these models makes training and reproducing more of an art than a science, based on the researcher’s knowledge and experience. One of the reasons for this difficulty is that the training procedure of machine learning models includes multiple hyperparameters that affect how the training process fits the model to the data.