04 Jun’24

Understanding AI Series: Key Aspects of Artificial Intelligence Every Business Needs to Know

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“… we’re smart enough to invent it and dumb enough to need it. And still so stupid we can’t figure out if we did the right thing.” (Jerry Seinfeld’s comments on AI, Duke Commencement Speech, May 12, 2024)

In this series, we dive into the world of Artificial Intelligence (AI) using various AI tools to provide information on how to view AI as a tool and what to look for when utilizing various AI tools to improve the results. This series is designed to rely on AI tools to provide the roadmap to exploring deeper topics and concerns with the increasing use of AI across a wide range of industries. In an effort to start this series with the basics, this first blog highlights Key terms, Limitations, and things to watch for when using AI tools.

AI is transforming the way businesses operate, offering tools that can enhance productivity, optimize decision-making, and drive innovation. However, understanding the key aspects of AI is essential for leveraging its full potential while being aware of its limitations. In this blog, we’ll explore important AI terms, limitations, and what business users should watch for when integrating AI into their operations. It should be noted that this series will rely on multiple AI tools to develop the content and we will highlight the tools used. In this blog, we conducted the analysis using ChatGPT-4o and Microsoft CoPilot.

Key AI Terms to Know

  1. Artificial Intelligence (AI):

AI refers to the simulation of human intelligence in machines that are programmed to think and act like humans. It encompasses various technologies, such as machine learning, natural language processing, and robotics.

  1. Machine Learning (ML):

A subset of AI, machine learning enables systems to learn from data and improve their performance over time without being explicitly programmed. ML algorithms can analyze large datasets to identify patterns and make predictions or decisions.

  1. Neural Networks:

Neural networks are computational models inspired by the human brain’s structure and function. They consist of interconnected nodes (neurons) organized in layers, widely used in deep learning algorithms to process complex data.

  1. Deep Learning:

A subset of machine learning, deep learning utilizes neural networks with many layers (deep neural networks) to extract high-level features from raw data. It has achieved remarkable success in tasks such as image recognition, natural language processing, and speech recognition.

  1. Natural Language Processing (NLP):

NLP focuses on the interaction between computers and humans through natural language. It enables machines to understand, interpret, and generate human language, facilitating tasks such as language translation, sentiment analysis, and chatbots.

  1. Supervised Learning:

Supervised learning involves training a model on labeled data, meaning the input data is paired with corresponding output labels. The model learns to make predictions or decisions by generalizing from the labeled training examples.

  1. Unsupervised Learning:

In unsupervised learning, the model is trained on unlabeled data, aiming to find hidden patterns or structures in the data without explicit guidance.

  1. Reinforcement Learning:

An agent learns to interact with an environment by taking actions and receiving feedback in the form of rewards or penalties. The goal is to maximize cumulative reward over time by learning optimal strategies through trial and error.

  1. Algorithm Bias:

Systematic errors or prejudices present in AI algorithms can lead to unfair or discriminatory outcomes. Bias can arise from biased training data, flawed algorithm design, or biased decision-making processes.

  1. Ethical AI:

Ethical AI involves the responsible development, deployment, and use of AI technologies in accordance with ethical principles and values. It addresses concerns related to fairness, transparency, accountability, privacy, and societal impact.

  1. Large Language Models (LLMs):

LLMs are advanced NLP models, such as OpenAI’s GPT-3, that are trained on vast amounts of text data. These models can generate human-like text, understand context, and perform a wide range of language-related tasks, from translation to summarization.

  1. Artificial General Intelligence (AGI):

AGI refers to a level of AI where machines possess the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to human intelligence. AGI remains a theoretical concept and has not yet been achieved.


Limitations of AI Tools

While AI offers significant advantages, it is important to recognize its limitations:

  1. Data Dependency:

AI systems rely heavily on data for training. Poor-quality, biased, or insufficient data can lead to inaccurate or biased results.

  1. Complexity and Cost:

Developing and implementing AI solutions can be complex and costly. It requires specialized expertise and substantial computational resources.

  1. Lack of Transparency:

Some AI models, particularly deep learning models, operate as “black boxes,” making it difficult to understand how they arrive at specific decisions.

  1. Ethical Concerns:

AI systems can perpetuate existing biases and inequalities present in training data. Ensuring ethical use and fairness in AI applications is a significant challenge.

  1. Overfitting:

AI models can sometimes learn the training data too well, including noise and outliers, which can lead to poor performance on new, unseen data.


What to Watch For When Relying on AI

  1. Quality of Data:

Ensure that the data used to train AI models is accurate, representative, and free from biases. Regularly update and refine datasets to maintain the model’s relevance and accuracy.

  1. Ethical Considerations:

Implement ethical guidelines and frameworks to govern the use of AI in your business. Regularly audit AI systems to identify and mitigate any biases or unfair practices.

  1. Transparency and Explainability:

Strive for transparency in AI models and decisions. Where possible, use explainable AI techniques to understand how models make decisions and to build trust with stakeholders.

  1. Human Oversight:

AI should complement human decision-making, not replace it. Ensure that there is human oversight to validate and interpret AI-generated insights and decisions.

  1. Regulatory Compliance:

Stay informed about relevant regulations and standards related to AI and data privacy. Ensure that your AI practices comply with legal and ethical requirements.

  1. Continuous Monitoring and Improvement:

AI models should be continuously monitored for performance and accuracy. Regularly update models with new data and refine them to adapt to changing conditions. 

  1. Hallucinations:

Be aware of AI hallucinations, where models generate information that seems plausible but is incorrect or nonsensical. Always verify AI-generated outputs, especially in critical applications, to ensure accuracy and reliability.


Artificial Intelligence holds immense potential for businesses, but understanding its key aspects, limitations, and ethical considerations is crucial. By being informed and vigilant, businesses can effectively integrate AI into their operations, driving innovation and achieving sustainable growth. Embrace AI as a powerful tool, but always keep in mind the responsibility that comes with its use. In future blog posts, we will dive deeper into some of the topics referenced above as well as other topics that present themselves in the quickly evolving AI landscape.

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