Understanding Key Terms and Technologies Shaping Business Solutions

AI encompasses a range of technologies, each with its unique capabilities and applications. Understanding the different types of AI technologies is foundational to any business AI strategy.

A Universe of Tools

Artificial intelligence (AI) is thrown around a lot these days. Unfortunately its used far too liberally where marketing just slaps an AI label on and old solution to freshen it up a bit. Perhaps because the definition of AI is so broad. But it's out there, it is real, it's not going away, and when applied properly, AI can transform your business. We just need a little better understanding of things to help separate the wheat from the chaff.

Demystifying the AI Landscape: A Universe of Tools Under One Umbrella

Artificial intelligence is an umbrella encompassing a range of powerful and specific technologies. It's not a single, monolithic technology. It's a vast and ever-evolving universe of tools, each with its own strengths and applications. And Artificial intelligence (AI) is not a one-size-fits-all solution. Understanding the diverse landscape of AI is the cornerstone of crafting a winning AI strategy and crucial for navigating the exciting world of AI. By exploring the different types of AI technologies, you can unlock their potential to streamline processes, enhance decision-making, and achieve meaningful business impact.

Key AI terms:

Artificial intelligence (AI) : AI refers to the field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. It involves the development of algorithms and systems that can acquire knowledge, reason, learn, understand natural language, perceive the environment, and make decisions or take actions based on that understanding.

Machine Learning (ML) : A subset of AI, ML enables computers to learn from data and make predictions or decisions without explicit programming. By analyzing incredibly vast amounts of data, ML models can identify patterns, make predictions, and inform decisions. Machine learning models are crucial for a wide range of applications used today, including:

Facial recognition: Unlocking devices securely through facial recognition software.
Medical imaging analysis: Detecting abnormalities in medical scans to aid diagnoses.
Predictive analytics: Forecasting sales trends, estimating stock prices, or optimizing resource allocation.
Fraud detection: Identifying suspicious patterns in financial transactions to prevent fraud.

There are important considerations when applying ML like the learning process (supervised or unsupervised), data quality and quantity, and bias - ML models can inherit biases from the data they are trained on

Large Language Model (LLM) : A broad category of advanced programming in it's own right, LLMs are a subset and specialized application of Machine Learning (which is of course under the even broader subject of AI) focused on processing and generating human language at a large scale. Unlike traditional ML techniques primarily concerned with data analysis, LLMs excel at the complexities of natural human language processing tasks like understanding and responding to written or spoken language. Deep learning, a powerful technique within ML, plays a significant role in LLM development. Examples of LLM you might use today include:

Chatbots that can provide customer service or answer questions conversationally.
Translation tools that can translate languages accurately and fluently.
Text summarization tools that can condense lengthy documents into concise summaries.

Generative Pre-trained Transformer (GPT) : Many have heard of or even used GPT. Drilling down a little further, GPT refers to a specific type of LLM architecture developed by OpenAI. GPT models are trained on massive amounts of text data, allowing them to generate realistic and creative text formats, like poems, code, scripts, musical pieces, email, letters, etc. While GPT is a powerful and well-known LLM, it's important to remember it's just one example within the broader category of Large Language Models. Examples include: BERT, T5, GPT, and other language models developed by different organizations.

An illustration

Here's an analogy to illustrate the levels, layers, and differences:

AI = music
ML = Rock
LLM = Classic Rock
GPT = Led Zeppelin

This is elementary and could certainly be expanded to include the many of the other subsets of AI.

But wait, there's more

Additionally, there are important distinctions between Artificial General Intelligence (AGI, or general AI) and Artificial Narrow Intelligence (ANI, or narrow AI). AGI, is still a distant dream. While science fiction often portrays AGI as a near-future reality, it's still a highly theoretical concept. Current AI research almost exclusively focuses on ANI, and achieving true AGI remains a significant challenge. The AI in many science fiction movies, refers more to AGI, AI systems with human-like cognitive abilities and the capacity to understand and perform any intellectual task that a human can. In contrast, ANI refers to nearly all of AI systems encountered today. ANI is designed for specific tasks or domains, such as playing chess or diagnosing medical conditions. Today when AI is mentioned, it most often refers to ANI and not AGI. AGI might be consider the "holy grail" or pinnacle of AI advancement - others might say it will be the death and destruction of humanity!

How does Generative AI Work?

Generative AI learns patterns and structures from large datasets during a training phase, typically using deep learning capabilities like neural networks. Once trained, the AI model can generate new content, such as text, images, or music, by sampling from the learned patterns. It can take input data or seed information and use its internal knowledge to produce coherent and contextually relevant output. Feedback from users can be used to improve the model's output quality over time, making it a versatile tool for various creative and practical business applications.

If we were to squeeze Generative AI into the music analogy above we might replace AI = music with Generative AI and bump AI up a notch to "performing arts".

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About Cloud 9

At Cloud 9, we are deeply passionate about the transformative power of emerging technologies like AI (or ANI, wink, wink) and are committed to helping our clients harness its immense potential. We help clients leverage our unparalleled education, resources, and supplier guidance for cutting-edge AI technologies across CX, cybersecurity, cloud, mobility, IoT, and advanced networking. Our engineering and architecture staff are at the forefront of AI-driven business transformation, ready to guide you through identifying and seizing new AI opportunities to meet you most critical business challenges now and as your business evolves.

Decoding AI