Artificial Intelligence

AI Sientists

AI Scientists build software with algorithms that can do tasks that normally require human intelligence.

AI Sientists can be experts in multiple AI disciplines:

• Applied mathematics
• Computational statistics
• Computer Science
• Machine learning
• Deep learning

Some AI Scientists also have significant big data experience:

• Business Inteligence
• Data Base Design
• Data Warehouse Design
• Data Mining
• SQL Queries
• SQL Reporting

Weak AI

Weak Artificial Intelligence is limited to specific or narrow areas like most of the AI we have around us today:

• Search Engines
• Apple's Siri
• Microsoft's Cortana
• Amazon's Alexa
• IBM's Watson

Weak AI is also called Narrow AI.

Weak AI simulates human cognition in contrast to Strong AI that have human cognition.

Strong AI

Strong Artificial Intelligence is the type of AI that mimics to human intelligence.

Strong AI indicates the ability to think, plan, learn, and communicate.

Strong AI is the theoretical next level of AI, also called True Intelligence.

Machine Learning (ML)

Classical programming uses programs to create results:

Machine Learning uses results to create programs (algorithms):

Neural Networks (NN)

One of the most significant discoveries in history is the power of Neural Networks (NN).

In Neural Networks, many layers of data called Neurons are added together or stacked on top of each other to compute new levels of data.

Commonly used short names:

• DNN Deep Neural Network
• CNN Convolutional Neural Network
• RNN Recurrent Neural Network

Deep Learning (DL)

Deep Learning are algorithms that use Neural Networks to extract higher-level data.

Each successive layer uses the preceding layer as input.

For instance, optical reading uses low layers to identify edges, and higher layers to identify letters.

Deep Learning has two phases:

1. Training: Input data are used to calculate the parameters of the model.

2. Inference: The "trained" model outputs data from any given input.

The Deep Learning Revolution

The deep learning revolution is here!

The deep learning revolution started around 2010. Since then, Deep Learning has been used to solve many "unsolvable" problems.

Examples

Convolutional Neural Networks (CNNs)

Deep CNNs such as ResNeta and Inception have reduced the error rate in the ImageNet classification from 25% in 2011 to 5% in 2017.

ImageNet is an image database organized according to the WordNet hierarchy, in which each node of the hierarchy contains hundreds and thousands of images. ImageNet is a useful resource for researchers, educators, students and everyone else with a passion for pictures.

WordNet is a lexical database of semantic relations between words in 200+ languages. It is organized as a combination of a dictionary and thesaurus, linking words together into semantic relations using synonyms, hyponyms, and meronyms.

Recurrent Neural Networks (RNNs)

RNNs are helping create music scores and novel instrument sounds:
https://magenta.tensorflow.org/demos.

History of AI