“AI is creating tremendous economic value today.” – Andrew Ng
This page has been dedicated to my own studies into AI.
I have worked in computer automation for some 30 years, and have dabbled in AI for the past few decades (I was a LISP programmer in the 90s). My background is in computer automation, and mathematics, specifically discrete mathematics. I have been awarded several company awards for computer automation, and recognized for my task automation skills numerous times. Tinkering with AI code to evaluate concepts is challenging and fascinating. I love testing the boundaries of AI, both in the lab and online. Something I was working on back in 2012 with text-to-speech analysis and desktop automation (I wrote all the code): AI Operator. Just a note on how new the technology was back in 2012, Siri was released in 2011 (and it was terrible at the time).
Part of My Home Lab
Need to verify AI content? Try:
Artificial Intelligence is a philosophical and technological approach to make a computer, a robot, or a product think like a human. The field of AI studies how the human brain thinks, learns, decides and works, when it tries to solve a problem. The purpose of AI is to improve computer functions, which are related to human knowledge. For example, because human intelligence consists of reasoning, learning, and problem-solving, those conceptual ideas are translated into algorithmic components.
The digital intelligence bits of AI consists of abstract, often subjective concepts of:
- Problem Solving
- Linguistic Intelligence
The objective of AI research is to explore reasoning, knowledge representation, planning, learning, natural language processing, realization, and ability to move and manipulate objects.
Technological approaches include statistical methods, computational intelligence, and traditional coding in AI. During AI research, the practical utility of AI is applied to search and mathematical optimization, artificial neural networks, and methods based on statistics, probability, and economics, just to name a few. Computer science attracts AI in the fields of science, mathematics, psychology, linguistics, philosophy and so forth. Something to note, what many people associate with being intelligent in AI, such as playing a game of chess, is nothing more than elementary algorithms leveraging computational power. Consequently, due to how these algorithmic processes are employed, all modern applications of AI cannot meet the adaptability and efficiency of human intelligence. But, there is always a but, this is exactly why AI is such an explosive industry right now. The goal is to shorten the intelligence gap between human and machine. It is important to note, no technology on the planet thinks like a human, to date. Therein lies some of the real challenges in the fields of AI. And, as for the fear associated with the rise of killer AI, completely false. All AI leads back to people. People can and will be held accountable. The idea that machines will take over isn’t new, either. Take a look at this video from 1936:
Articles by Me
E-Learning Courses and Videos
Miscellaneous AI Sites
AI – C# – Chatbot with Random Responses
AI – C# – Neural Net
AI – C# – Chatbot With Built-in Responses
AI – Batch – Chatbot With Text File Responses
AI – PowerShell – Toying With Removing Hyperlinks; Pattern
AI – PowerShell – Search for Pattern in Multiple Files
AI – VBScript – Finding Indexes – Pattern Recognition
Python Prep for AI
Download: Here (you want the latest)
python -m pip install –upgrade pip
pip install openai