Managing Business Costs with Data Streams and Automation
Cost Management automation overview (Microsoft 365 & Microsoft Azure)
You can use Cost Management automation and reporting to build a custom set of solutions to retrieve and manage cost data. The article covers what APIs are available for use and common scenarios for Cost Management automation
Generative Artificial Intelligence
Introduction
Generative AI represents a cutting-edge subset of artificial intelligence, focused on generating new content, be it text, images, sound, or even complex designs. This technology learns patterns from existing data and produces new, original outputs that fit within the observed patterns.
Key Concepts
1. Generative vs. Discriminative Models
Generative models create new content, whereas discriminative models differentiate between given data points, like classifying images.
2. Training Data
For a generative model to produce reliable outputs, it requires substantial amounts of training data to understand patterns.
Popular Techniques
1. **Generative Adversarial Networks (GANs):** GANs consist of two neural networks, the generator and discriminator, that train together. The generator tries to produce fake data, while the discriminator attempts to tell real from fake. The two networks improve through this competition, leading the generator to produce increasingly convincing data.
2. **Variational Autoencoders (VAEs):** VAEs learn to compress data into a low-dimensional space and then decode it back. This compression-decompression cycle allows them to generate new, similar data.
Applications
1. **Art & Media:** Create artwork, music, or even entire movies.
2. **Design:** Generate new product designs or architectural layouts.
3. **Text:** Produce coherent, relevant textual content.
4. **Gaming:** Create game environments or characters on the fly.
Challenges & Considerations
1. **Ethical Concerns:** The ability of generative AI to create realistic content can lead to misuse, such as deepfakes or misleading information.
2. **Computational Demands:** Training generative models requires significant computational resources.
3. **Bias:** If training data contains biases, the AI can perpetuate or even amplify those biases in its outputs.
Conclusion
Generative AI holds tremendous potential across various industries, offering innovative solutions and creative possibilities. However, its power comes with challenges, necessitating responsible and informed use.
Machine Learning
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Why is machine learning important?
Increasing interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage. All of these things means it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.