AI Alphabet Soup: From Buzzwords to Brilliance!

You've probably seen all sorts of terms?recently, and it can get a bit confusing as to how they all relate to?one another. "Machine learning", "deep learning", and "foundation models." And you've probably seen?other terms like "Generative AI" and "Large Language Models (LLM)." Let's end the?confusion around these “soupy” terms and put them in their place.

There's one thing they all have in common: they are all terms related to the field of artificial intelligence or AI.

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Artificial Intelligence

Artificial Intelligence (AI) is the emulation of human intelligence in machines, enabling them to execute tasks that traditionally demand human cognition. Different manifestations of AI have existed for decades. For instance, you might be familiar with Eliza, a chatbot developed in the mid-1960s, crafted to replicate human-like conversation.

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Machine Learning

Machine Learning (ML) sits within the field of AI.

Now what's machine learning? ML focuses on developing algorithms that allow computers to learn from and?make decisions based on data, rather than being explicitly programmed to perform a specific task. These algorithms use statistical techniques to learn patterns in data and make predictions?or decisions without human intervention.?

But like AI, ML is a very?broad term. It encompasses a range of techniques and approaches from traditional statistical?methods to complex neural networks.

Some of the core categories within ML we can?think of are as follows:

Supervised Learning

Supervised learning is where models are trained on labeled data.

Unsupervised

Unsupervised learning is where the models find patterns in data without predefined labels.

Reinforced Learning

Reinforced learning is where models learn by interacting with the?environment and receiving feedback.

Okay, so where does deep learning come in?

Deep Learning

Deep?learning is a subset of machine learning that focuses on?artificial neural networks with multiple layers,?and we can think of them as looking like this.

These are nodes and all of the connections.? Now, those layers are where we get the deep part?from. While traditional ML techniques might be efficient for linear separations?or simpler patterns, deep learning excels at handling vast amounts of unstructured data?like images or natural language and discovering intricate structures within them.

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Traditional Machine Learning

I’d like to also point out that not all machine learning is deep learning. Traditional machine learning methods?still play a pivotal role in many applications.? Techniques like "linear regression", "decision trees", "support vector machines", or "clustering algorithms."

These are all other types of machine learning, and they've been widely used for a long time. In some?scenarios, deep learning might be overkill?or isn't the most suitable approach.

Okay,?so machine learning, deep learning, what else, oh-yeah, foundation models.?

Foundational Models

Okay, so where?do foundation models fit into this? Well, the term foundation model was popularized in 2021?by researchers at the Stanford Institute and it fits primarily within the realm of deep learning.?

Now, these models are large-scale neural?networks trained on vast amounts of data,?and they serve as a base or a foundation for a multitude of applications.? So instead of training a model from scratch for?each specific task, you can take a Pre-trained?foundation model and fine-tune it for?a particular application, which saves a bunch of time and resources.

Foundation?models have been trained on diverse datasets,?capturing a broad range of knowledge and can be?adapted to tasks ranging from language translation?to content generation to image recognition. So in the grand scheme of things, foundation models sit within the deep learning category?but represent a shift towards more generalized,?adaptable, and scalable AI solutions.

There are some other AI-related?terms that are also worth explaining.

Large Language Models

Language models or LLMs are a specific type of foundation model?that are centered around processing and generating humanlike text. Let's break it down, LLM. The first L that's large, and that refers to the scale of the model.

LLMs possess a vast number?of parameters, often in the billions or even more.? And this enormity is part of what gives LLMs?their nuanced understanding and capability.?? Second, L that language that designed to?understand and interact using human languages, as they are trained on massive data sets.

LLMs can?grasp grammar, context, and even cultural?references. And the last letter and that's for?model at the core that computational models a series of algorithms and parameters working together to process input and produce output.? LLMs can handle a broad spectrum of language?tasks like answering questions, translating or even creative writing.

LLMs are just one example?of foundation models, but there are others. "Vision models" interpret and generate images, there are "Scientific models" used in biology for predicting how proteins behave, etc, and there are "audio models" for generating human-sounding speech.

Finally, one last term that's gaining a lot of traction especially on LinkedIn these days.?

Generative AI

We've all heard?about it. It's Generative AI.? Now this term pertains to models and algorithms?specifically crafted to generate new content.??

Essentially, while foundation models provide?the underlying structure and understanding,?Generative AI is about harnessing that knowledge?to produce something new. It's the “creative expression” that emerges from the vast?knowledge base of these foundation models.?

I hope you enjoyed your bowl of AI alphabet soup.

Reference IBM

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