AI Today Podcast

Transformer models have proven to be especially powerful for Natural Language Processing applications and image generation, and have been popularized by models such as GPT-3, Stable diffusion, and BERT. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelze...

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the term Boltzmann Machine, explain how these terms relate to AI and why it’s important to know about them.
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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Decoder, AutoEncoder, Generative Adversarial Network (GAN), explain how these terms relate to AI and why it’s important to know about them.
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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the term Convolutional Neural Network (CNN), explain how these terms relate to AI and why it’s important to know about them.
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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM), explain how these terms relate to AI and why it’s important to know about them.
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Usually used as a simple example for how deep learning neural networks work, a feed-forward neural network is the most basic, “vanilla” general kind of neural network. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the term Feed-Forward Neur...

In order for machine learning systems to work, they need to be trained on data. But there are different types of data and depending on the situation you may want to use one type of data over another, or a combination of different types. In this episode of the AI Today podcast hos...

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Pre-Trained Model and Transfer Learning, explain how these terms relate to AI and why it’s important to know about them.
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As AI applications become more complex and data-intensive, the need for scalable and efficient storage solutions becomes increasingly important. AI models require vast amounts of data to be processed, and as the size and complexity of these models continue to grow, so does the ne...

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms CPU, GPU, TPU, and Federated Learning, explain how these terms relate to AI and why it’s important to know about them.
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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Tokenization and Vectorization, explain how these terms relates to AI and why it’s important to know about them.
Want to dive deeper into an understanding of artificial intelligence, m...

In order for machine learning systems to work they need to be trained on data. But as the old saying goes “garbage in is garbage out” so you need to make sure you have a dataset of prepared data that is cleaned so you can incrementally train a machine learning model to perform a ...