Astonishing Innovation ? Artificial Intelligence

Breaking News: The next revolution, "ChatGPT" made its debut on 11.30.23?

Visionaries are calling ChatGPT the next revolution and comparing the potential breakthrough to the parabolic internet boom in 1999. The New York Times reported this modern technology could change the world. Five days after ChatGPT made its debut (48 days ago), over one million users sign-up (following the release of ChatGPT, OpenAI was reportedly valued at $29 billion). As I type this post, the system is pushing its capacity barriers.

Forecasters are dubbing the OpenAI release a historic, revolutionary event. Skeptics and doubters are raising questions about users' propensity to plagiarise its outputs and A.I. as a potential scourge to society; naysayers urge caution and careful deliberation before adoption. It appears that millions of users currently utilizing ChatGPT never got the memo. So, what is ChatGPT?

Wikipedia: ChatGPT (Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI's GPT-3 family of large language models, and has fine-tuned an approach to transfer learning with both supervised and reinforcement learning techniques.

ABCs Newsletter: The A.I. Ultramarathon Mile-1

Over the holiday weekend, I did a deep dive headfirst down the proverbial rabbit hole on this subject and spent far more hours than expected. Still, I felt an urgency to read and try to digest everything I could about OpenAI and what the future may entail regarding artificial intelligence.

Over the following months, I will share with ABC followers what I'm learning about A.I., how I'm using it myself to increase productivity, and what I think is coming around the corner next. Each article will have links to previously discussed points for us to stay on course.

A.I. → Mile-1

Many years ago, when I was an institutional broker at Merrill Lynch, I would get frustrated that I didn't know as much as I needed to navigate the ever-changing financial market seas effectively. There were always highs and lows, ebb and flows, calm and rough oceans, and I struggled to try and understand what I did not understand or know.

I had been a tireless worker burning both ends of the candle for years. I would go into the office no later than 4 am, call on European Institutional accounts, and then work the American hours until around 6 pm. Why? Because I wanted the best for myself and my family financially, and I was making good money.

During this period, I had my life-learning aha moment; I finally found the missing piece to the puzzle as to why I was sometimes frustrated, confused, and indecisive about my professional ongoings. The missing piece was VOCABULARY!

It is estimated that around 150-200 new words are added to the English language daily. The world of macroeconomics, business, money, investments, and financial markets is an ever-evolving glossary of terms, definitions, and concepts. For example, I've spent the past decade keeping up with FED SPEAK.

Vocabulary lesson →

So, we will begin this A.I. ultramarathon's first step by assembling a glossary of need-to-know terms. Let's call it a lexAIcon that will list words explaining our subject matter.

A.I. LexAIcon

1. Algorithm: A set of rules that a machine can follow to learn how to do a task.
2. Artificial Intelligence (A.I.): The simulation of human intelligence in machines programmed to think and learn like humans.
3. Chatbot: A?chatbot?is designed to communicate with people through text or voice commands that mimic human-to-human conversation.
4. GPT: Generative Pre-trained Transformer, a language model developed by OpenAI.
5. Machine Learning (ML): A type of A.I. that involves training computer algorithms to learn from data without being explicitly programmed.
6. Data mining: Analyzing datasets to discover new patterns that might improve the model.
7. Data science: Drawing from statistics, computer science, and information science, this interdisciplinary field aims to use a variety of scientific methods, processes, and systems to solve problems involving data.
8. Deep Learning: A subfield of machine learning that involves training deep neural networks with multiple layers of neurons.
9. Supervised Learning: A type of machine learning where the model is trained on labeled data to predict an output given an input.
10. Unsupervised Learning: A type of machine learning where the model is trained on unlabeled data to identify patterns or structures in the data.
11. Reinforcement Learning: A type of machine learning where the model learns from interacting with its environment and receiving feedback as rewards or penalties.
12. Natural Language Processing (NLP): A subfield of A.I. that uses computational techniques to analyze, understand, and generate human language.
13. Neural Network: A machine learning model inspired by the structure of the human brain, composed of layers of interconnected nodes or "neurons."
14. OpenAI: An artificial intelligence research lab that developed GPT and other language models.
15. Semantic annotation: Tagging different search queries or products to improve a search engine's relevance.
16. Transformer: A neural network architecture used in natural language processing tasks.
17. Tokenization is breaking down a sentence or piece of text into individual words or phrases.
18. Turing test: Named after Alan Turing, famed mathematician, computer scientist, and logician, this tests a machine's ability to pass for a human, particularly in language and behavior. After being graded by a human, the machine passes if its output is indistinguishable from that of human participants.
19. Variation: Also called?queries?or?utterances, these work in tandem with intents for natural language processing. Variation is what a person might say to achieve a specific purpose or goal. For example, if the intent is "pay by credit card," the variation might be "I'd like to pay by card, please."
20. Weak A.I.: Also called?narrow A.I., this model has a set range of skills and focuses on one particular set of tasks. Most A.I. currently in use is weak A.I., unable to learn or perform tasks outside of its specialist skill set.

A.I. Mile-2 → Use Cases

Coming Soon ?In Mile-2, we will discuss "use cases," and I will share my experience using OpenAI.

I began researching A.I. solutions in 2021. I did not find commercial products available at that time suitable. I revisited my search for a product in early 2022 and began using A.I. in July. I currently use multiple providers at an annual cost of $3,000. I used artificial intelligence to assist in listing and organizing the twenty terms in our lexAIcon.