In 2023, Artificial Intelligence (AI) has become a pivotal force in the stock market, notably with the unexpected emergence of ChatGPT in November 2022. This sparked a rush of AI investing, where investors are backing perceived winners and avoiding lagging tech companies. The AI investing landscape is still evolving, but key themes and players are emerging.

Artificial Intelligence is a broad field of computer science that aims to create machines or systems capable of performing tasks that typically require human intelligence. The goal is to develop machines that can mimic human cognitive functions, such as learning, reasoning, problem-solving, perception, and language understanding. Here’s an overview of key aspects of AI:

  1. Algorithms: Algorithms serve as the building blocks of AI, providing finite sequences of rules and instructions crucial for solving specific problems. Without algorithms, AI would be a far-fetched notion, as they streamline problem-solving sequences and alleviate the computational burden.
  2. Natural Language Processing (NLP): NLP allows machines to interpret, organize, and comprehend human language structurally. Taking a utilitarian approach, NLP analyzes vast amounts of written language, quantifying word relationships. In generative AI applications, NLP crafts coherent text streams, ranging from responses to complete essays.
  3. Large Language Models (LLMs): LLMs are engineered to impart general-purpose language understanding to machines. Trained on massive datasets, they create parameters to categorize the semantic and syntactic nature of words. Advanced LLMs, like ChatGPT, can even fine-tune themselves through prompt engineering.
  4. Artificial Neural Networks (ANNs): Also known as neural nets, ANNs emulate the architecture of human brain neurons. These networks model complex relationships and identify patterns within data using a structure that simulates the human brain’s neuron architecture.
  5. Graphic Processing Units Computing (GPU): GPU Computing, pioneered by Nvidia, accelerates AI applications by leveraging parallel processing. GPU Computing divides tasks across thousands of processors, providing the massive computing power required for tasks like translating words into paths and running convolutional neural nets.
  6. Machine Learning: Fundamental to AI development, machine learning employs algorithms to build models based on training data. These models, created from large bodies of text or images, enable AI systems to make predictions or decisions without explicit programming.
  7. Deep Learning: An advanced form of machine learning, deep learning involves linear-algebra algorithms, massively parallel computation of tensors, and multi-layered structures. This enables applications such as speech recognition to perform more effectively by progressively extracting higher levels of information from raw data.
  8. Inference: In the evolution of AI, inference acts as a form of “machine reasoning.” AI inference involves applying logical rules to the knowledge base, allowing systems to evaluate and analyze new information, leading to informed decision-making.
  9. Word2Vec: Utilizing vectors to represent semantic and syntactic qualities of words, Word2Vec employs mathematical functions like cosine similarity to indicate levels of semantic relationships between words.
  10. Transformers, BERT, and GPT: Transformers, with self-attention mechanisms, are displacing convolutional neural nets (CNNs) in various applications. BERT (Bidirectional Encoder Representations from Transformers) excels in text classification, named entity recognition, and question answering, while GPT (Generative Pre-Trained Transformer) is adept at generating coherent and contextually appropriate text.
  11. Megabyte: In May 2023, Meta Platforms introduced Megabyte, an innovative AI model designed for large-scale creations. This multi-scale decoder architecture claims to enable better performance at reduced costs for training and generation, addressing potential limitations in traditional transformer models.

Understanding AI’s key concepts is crucial. From algorithms streamlining problem-solving to the adaptability of LLMs like ChatGPT, each element contributes to AI’s broad spectrum. ANNs mirror human brain architecture, while GPU Computing, Machine Learning, and Deep Learning enhance AI’s capabilities across diverse applications.

Inference plays a vital role in machine reasoning, guiding AI systems in decision-making. AI models like Word2Vec, Transformers, BERT, and GPT, showcase the depth of AI applications. Megabyte, Meta Platforms’ innovation, addresses limitations and offers enhanced performance at reduced costs.

As AI reshapes the stock market and technology landscape, staying informed and adaptable is key. The integration of AI into investment strategies marks a transformative era, where technology and human advance converge to redefine possibilities in finance.

Frankie Ramos Jr.
Financial Analyst

Disclosures:

This is provided for informational purposes only and should not be interpreted in any way as investment, tax, accounting, legal or regulatory advice. An investor must take into consideration his/her individual circumstances. 

There is no guarantee investment strategies will be successful. Investing involves risks including possible loss of principal. There is always the risk that an investor may lose money. A long-term investment approach cannot guarantee a profit.  All expressions of opinion are subject to change. This article is distributed for educational purposes, and it is not to be construed as an offer, solicitation, recommendation, or endorsement of any particular security, products, or services. Investors should talk to their wealth advisor prior to making any investment decision.

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IBM Research. (2023, October 5). AI Inference Explained. Retrieved from: https://research.ibm.com/blog/AI-inference-explained