AI AI AI

Table of Contents

AI, AI, AI …….. the many different areas …. and growing!

One of the key achievements of AI is the rise of Human Productivity. One of the best toolset mankind can use to improve human civilization.

Given the numbers of AI publications has increased from 162,444 in 2010 to 334,497 in 2021, this is a fast moving disruptive field of Technology with rapid acceleration. It is very difficult to stay on top of the latest research in this field.

Time to pay attention to the relevant information and keep S/N ratio under control. AI has to be applied to real-world problems, applied to businesses, healthcare & research.

Here are three generally broad areas to look at:

  • Machine Learning
  • Deep Learning
  • Generative AI

Machine Learning

This is the classic approach to AI that most of us have experienced in our career in the last 30 years. AI that is trained on data. Data keeps growing and AI learning keeps improving. Data needs to be engineered, organized and human respose needs to be collected. Predictions, recommendations needs to be connected back to human responses and AI efficacy improves.

The volume and complexity of Data keeps increasing.

Here is a list of Algorithms commonly used in this area:

  • Linear regression
  • Logistic regression
  • Decision tree
  • SVM algorithm
  • Naive Bayes algorithm
  • KNN algorithm
  • K-means
  • Random forest algorithm
  • Gradient Descent

Deep Learning

Deep learning is primarily focused around Neural Networks. Neural networks have been around for a long time but it’s accuracy and level of use with real data grew exponentialy in the last decade. It is now able to use broad range of data types like ( images, video, audio, in addition to text data that ML relied heavily on.

Here are the list of Algorithms commonly used in this area:

  • Peceptron
  • Feed Forward Neural Network
  • Radial Basis Network
  • Convolutional neural networks ( CNN)
  • Recurrent Neural Networks ( RNN )
  • LSTM – Long Short-Term Memory

Generative AI

It is 2023 and thanks to the rise of tools like DALL-E, ChatGPT there is a nature of AI that ties strongly to generating content in response to prompts. A prompt nature is very human behavior this area is very relevant to Applied AI.

It’s clear that generative-AI tools have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks.

The challenge for society is to figure out the best positive impact that this form of Applied AI can make to humanity.

Here is a list of Algorithms commonly used in this area:

  • Generative Adversariel Networks ( GAN )
  • Generative Pre-Trained Transformers ( GPT )
  • Large Language Models ( LLMs )