Understanding What Is AI, Generative AI, And LLM’s
INTRODUCTION
The architecture behind many modern LLMs, like OpenAI’s GPT series, is the Transformer architecture. This type of model has been very successful in various NLP tasks due to its ability to handle long-range dependencies and its scalable nature.
Imagine you’re texting with a friend. You send emojis, abbreviations like “lol” or “brb”, and sometimes even make typos. You understand what your friend says, and they understand you, because you both speak the “language” of texting. But what if you tried texting your computer or your game console in that same way? It wouldn’t understand because machines don’t naturally “get” human language.
NLP, or Natural Language Processing, is like teaching computers and machines how to understand our language—our jokes, our slang, our questions, everything. It’s why tools like Siri or Google Assistant can answer when you ask, “What’s the weather like today?” It’s making computers better at chatting with us!
In the evolving landscape of global business, the adoption and understanding of Artificial Intelligence (AI) is no longer a luxury—it’s a necessity.
As AI technologies continue to weave their way into the very fabric of enterprise operations, there’s an increasing demand for professionals who not only understand AI but can harness its potential for optimizing and innovating workflows.
While AI has been at the forefront of technological discussions, there’s a considerable gap in genuine understanding among the business community. Many professionals recognize the potential of AI but are uncertain about its practical applications in their specific sectors. This has signaled a rising need for informed guidance, especially for businesses aiming to ride the wave of AI-driven transformation.
Our mission at Flomad is to bridge this knowledge gap.
We believe in empowering business professionals with the education and tools they need to successfully integrate AI into their workspaces. From learning to use practical business tools that can increase productivity of a companies employees by 20, 30, 40% or more to automating mundane tasks, extracting insights from vast data sets, or tailoring customer experiences, AI holds the promise to revolutionize the way businesses operate. But without the right knowledge, its potential remains untapped.
At Flomad, we champion a four-pronged approach to AI: research, education, consulting, and collaboration. Our comprehensive suite of services is meticulously designed to cater to professionals eager to harness the transformative power of AI in their businesses. We work with managers, executives, team leads, and other forward-thinking individuals, providing cutting-edge research insights, robust educational content, and tailored consulting solutions. This ensures our partners not only grasp AI’s theoretical concepts but also implement real-world AI strategies that drive tangible results.
In an era where AI stands as a dominant force shaping industries, our goal is to ensure that every business professional, regardless of their technical background, can leverage AI to enhance productivity, foster innovation, and drive impactful results.
The Generative AI Landscape and the Future of Productivity
Flomad educational courses dives into the origins of generative artificial intelligence (AI) and the promising road ahead.
Imagine if, back in the 1960s, computers started understanding our language. Fast forward to the 2010s, and they could turn flat pics into epic 3D visuals. Now? We’ve got next-level AI that can think, learn, and whip up its own creations. But here’s the deal: to truly harness its power, we need to sift through the noise and get the real facts.
We’re standing on the brink of a massive tech revolution, and AI, especially cool ones like ChatGPT, is leading the charge. This isn’t just about gadgets and gizmos – it’s about reshaping our world and transforming our lives.
Artificial Intelligence: A Statistical Insight
In the United States, 63% of job roles are projected to benefit from the integration of AI. This contrasts with a mere 7% that may be potentially replaced by AI technologies. [Source: cnet.com]
A significant 56% of individuals, when presented with the concept of AI-assisted surgery, view it as a substantial medical breakthrough. Another 22% recognize it as a modest advancement, while a small percentage, 5%, do not see its innovative potential. [Source: weforum.org]
Despite prevailing economic uncertainties, 63% of business decision-makers are resolute in their strategy, with plans to either ramp up or sustain their investment in AI technologies. [Source: insiderintelligence.com]
Understanding what is AI, Generative AI, and LLM’s
What is the difference between ai and generative ai?
Imagine you have a big toy box called “AI”. Inside this box, there are lots of different toys that can do various cool tricks. Some toys can play songs, some can solve puzzles, and others can even chat with you!
Now, among these toys, there’s a special group that has magic markers. These toys can draw pictures, write stories, or make up songs all by themselves. They don’t just repeat what they’ve seen before; they create something brand new! We call these special toys with the magic markers “Generative AI”.
So, in simple terms:
AI is like the whole toy box with all the different toys.
Generative AI is like those specific toys that can create new stuff using their magic markers.
Think of AI as a toolbox you’d get for your 18th birthday. Inside this toolbox, there are various tools designed to help with different tasks. Some tools might help you fix things, others can assist with measurements, and some might even help automate tasks.
Now, inside this toolbox, there’s a special set of tools called “3D printers.” Instead of just fixing or measuring, these 3D printers can create entirely new objects based on the designs you feed them or from patterns they’ve learned.
Putting it in context:
AI is the entire toolbox, full of tools designed for various tasks. Generative AI is like the 3D printers in the toolbox, capable of producing brand new items or content. Imagine AI, or Artificial Intelligence, as a smart robot. This robot can do many things that humans do like understanding what you say, helping you find things on the internet, or making choices based on information.
Now, there’s a special kind of smart robot called “Generative AI”. This robot doesn’t just help or answer questions; it can actually create new things, like painting a picture or writing a story all by itself!
Breaking down the jargon:
Purpose:
AI: It’s like a helper robot. It can do many tasks like sorting emails or predicting the weather.
Generative AI: This robot is an artist! It creates new stuff on its own.
Examples:
AI: Think about Siri or Google Assistant that answers questions or recommends songs.
Generative AI: Imagine a robot that can draw a new kind of animal it dreamt up or write a song no one’s ever heard before.
Training (How They Learn):
AI: These robots learn in different ways. Some are taught by showing them examples (like showing photos to recognize cats). Others learn by trial and error.
Generative AI: This robot looks at lots of art, stories, or music and then tries to make something similar but new. It’s like showing a robot lots of paintings and then it paints its own masterpiece.
Outcome (What They Do):
AI: Gives answers, solutions, or suggestions.
Generative AI: Produces something new, like a story, song, or picture.
In short, while all “artist” robots (Generative AI) are smart robots (AI), not all smart robots are artists. Generative AI is special because it can create new things by itself, while AI covers a wide range of tasks.
What is LLM in the context of AI?
Imagine your phone’s predictive text. When you start typing a sentence like “How are you…” your phone might suggest the next word as “doing?” or “feeling?” That’s because it’s trying to guess the next word based on what it has seen before.
When we talk about computers and AI, LLM stands for “Large Language Model”. It’s like a supercharged version of your phone’s predictive text. It’s a tool that has “read” lots of text and can predict or generate the next words in a sequence. So, if you give this tool a beginning like “Once upon a time,” it might come up with a whole story on its own!
Definition:
A Large Language Model (LLM) is a type of generative model that calculates the likelihood of a sequence of words. It’s based on the premise of predicting the next word in a sequence given a history of previous words. These models can generate new text sequences and are commonly used in tasks such as text generation, completion, and more.
Examples:
Text Generation: Given a prompt like “Once upon a time,” an LLM can generate a continuation like “in a land far, far away, there was a kingdom ruled by a just and fair queen.”
Text Completion: For an incomplete sentence like “The Eiffel Tower is located in,” an LLM might suggest the completion: “Paris.”
Machine Translation: Even though sequence-to-sequence models are more common for translation, LLMs can still be used in a way where they evaluate the likelihood of a translation.
Question Answering: Given a passage and a question about the passage, an LLM can generate an answer based on the information present.
The architecture behind many modern LLMs, like OpenAI’s GPT series, is the Transformer architecture. This type of model has been very successful in various NLP tasks due to its ability to handle long-range dependencies and its scalable nature.
Imagine you’re texting with a friend. You send emojis, abbreviations like “lol” or “brb”, and sometimes even make typos. You understand what your friend says, and they understand you, because you both speak the “language” of texting. But what if you tried texting your computer or your game console in that same way? It wouldn’t understand because machines don’t naturally “get” human language.
NLP, or Natural Language Processing, is like teaching computers and machines how to understand our language—our jokes, our slang, our questions, everything. It’s why tools like Siri or Google Assistant can answer when you ask, “What’s the weather like today?” It’s making computers better at chatting with us!