Embrace the Future with Essential Keywords: Mastering 7 Crucial Concepts in Artificial Intelligence

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• Artificial Intelligence (AI) vs AGI (Artificial General Intelligence)
• AI Hallucination
• Neural Networks
• Machine Learning vs Deep Learning
• Natural Language Processing (NLP)
• Transformer Models
• Large Language Models

AI is advancing at breakneck speed. If you want to keep up with the times, it’s crucial that you know the jargon used by AI companies and marketers. Here are seven AI terms commonly used in everyday discourse to help you become an informed participant.

1 Artificial Intelligence (AI) vs AGI (Artificial General Intelligence)

AI companies often talk about achieving AGI. What is it, and how does it differ from today’s AI ?

The textbook definition of AI is computer systems that can perform tasks requiring human-like intelligence. However, current AI tools don’t fully meet this definition . They only show glimpses of human-like intelligence, focused on specific tasks.

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For example, ChatGPT can write text, code, and generate images using DALL-E. But it can’t create music . Udio can create music but can’t write text. It actually uses GPT-4o for lyrics. True AI should do all these things and more, just like humans can.

This is where AGI comes in. AGI refers to computers that can display human-like intelligence across multiple tasks. Interacting with AGI would be like talking to Commander Datafrom Star Trek.

That said, while we know what AGI should do, we don’t know how it would work . Most AI researchers, including Meta’s AI Chief, think AGI is decades away .

2 AI Hallucination

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ver asked ChatGPT a question and got a perfect-sounding answer—until you realized it was made up? That’s AI hallucination.

AI systems sometimes generate false info with expert-level confidence. They might claim Shakespeare wrote “The Great Gatsby” so convincingly you almost believe it.

These issues were common when ChatGPT first launched with GPT-3.5. Hallucinations have decreased with GPT-4 and GPT-4o, but they still happen. But why?

Well, AI models don’t understand information like we do. They predict words based on learned patterns . This creates grammatically correct, logical sentences. However, they can sometimes end up fabricating data and facts.

Researchers are working to reduce hallucinations, but it’s still a problem. As it stands, the best defense against hallucinations is you. Always do your due diligence when using AI, and double check any facts it generates, especially when it sounds too good, or weird to be true.

3 Neural Networks

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Neural networks are computing systems inspired by the organization of neurons in animal brains—hence the name. The idea is to create computational models that can process information and learn in ways inspired by (not necessarily identical to) biological neural systems.

In the human brain, neurons connect in complex patterns. Their activation influences our memories, thoughts, and actions. In neural networks, artificial neurons (nodes) receive inputs, process them, and send outputs. These nodes connect in intricate patterns. The resulting structure allows the system to perform a wide range of tasks that require human-level intelligence.

Applications of neural networks include image and speech recognition, autonomous vehicles, financial forecasting, and natural language processing, which allows computers to understand and generate human language.

That said, neural networks, especially those designed for complex workloads, require lots of high-quality training data to learn patterns and handle new situations. This can be challenging to obtain .

Also, some neural networks—especially deep learning models (more on that in the next section)—are hard to interpret. Understanding how they reach conclusions can be tricky. This is the black box problem . Researchers are working on more interpretable models and ways to explain AI decisions.

4 Machine Learning vs Deep Learning

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Machine Learning (ML) and Deep Learning (DL) are two of the biggest buzzwords in the tech space. While some people think Machine Learning and Deep Learning are the same, there is a subtle but important difference worth noting.

Machine learning is a broader field in computer science where you feed computers lots of data to find patterns on their own. This contrasts with traditional programming, where developers need to code every possible interaction with the user.

ML programs don’t need this much hand-holding and can generate outputs by referencing patterns in training data—even when you present it with a unique situation not present in the training data. That said, they do need human guidance during training to ensure they’re on the right track. We see some applications of ML systems in spam detection , product recommendations, and weather forecasting.

Now, Deep Learning is a subset of machine learning that uses multi-layered neural networks—hence the name “Deep.” It needs less human input than traditional ML. Human input is mainly involved in the early stages, like designing the base model.

You can use Deep Learning to find patterns in raw data with little human help. It’s great at handling complex data like images or text. It drives advanced computing tasks like image and facial recognition, speech processing, and autonomous vehicles. However, to make this possible, DL models require huge training datasets and lots of computational power.

5 Natural Language Processing (NLP)

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NLP is a field of study in computer science concerned with helping computers understand, interpret, and generate human language. Modern NLP tasks use Deep Learning to achieve impressive results.

Some examples of NLP power tasks include speech recognition, text-to-speech, sentiment analysis, sarcasm detection, text style transfer, and more.

NLP is the backbone of conversational technology like Alexa, Siri, and other AI chatbots. It also helps with translation, content moderation, and auto-generated content. Recent NLP advances have made AI writing more human-like. That’s the reason why ChatGPT sounds so natural.

6 Transformer Models

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Transformer models are a recent and revolutionary tool in the AI space. The idea for transformers was proposed in a 2017 paper titled “Attention Is All You Need “ by researchers at Google Brain.

Transformer models are essentially a neural network architecture used for natural language processing. The basic concept is to first analyze a wide range of input all at once (or in parallel) and then determine which elements are most relevant—a process called “self-attention.”

You can think of it like a super-efficient reader who instantly reads multiple paragraphs and derives meaning from the full context instead of reading each paragraph sequentially, one word at a time.

This self-attention mechanism allows Transformer models to better understand human inputs and, therefore, generate better outputs. It has enabled super-advanced language translation capabilities, text summarization, and, of course, text generation.

Transformer models are the technology behind powerful tools like Google’s BERT (Bidirectional Encoder Representations from Transformers) or ChatGPT (Chat Generative Pre-trained Transformer). However, it’s not just text generation, as these tools are also useful for computer vision and speech recognition.

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7 Large Language Models

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Large Language Models (LLMs) are powerful Transformer models trained on massive text datasets. They have billions or trillions of parameters and primarily focus on language-specific workloads. ChatGPT, or GPT specifically, is an LLM. Claude, Gemini, and Llama are all examples of LLMs.

Thanks to LLMs, we now have advanced level text generation, text summarization, translation , code generation, task understanding & execution, and so much more.

LLMs are trained on diverse internet-scale datasets without specific instructions. They create patterns and connections in data. Sometimes, these patterns can be wrong, which is when humans come in to fine-tune the models for specific tasks and applications.

Currently, one of the main challenges with LLMs is ethical concerns and the potential for misinformation. As the training dataset might contain misinformation and bias, that can pass onto the LLM. This is actually one of the reasons why Google’s Search Generative Experience started to produce misinformation for users .

You’re now armed with knowledge of seven key AI terms. Next time you’re chatting about the latest AI news, you’ll be able to drop these terms like a pro. Just remember, the AI world moves fast, so keep learning and stay curious!