AI Explained: A Video Glossary

Forget crypto and blockchain. ChatGPT is the new tech in town, and you need to know the lingo. Follow along as we boil down all the complex terms around AI, Large Language Models and Machine Learning into bite-sized videos. Soon enough, you'll have a comprehensive understanding of just how this technology is changing the game.

Probabilistic vs. Deterministic Explained

Episode 13 | AI Term: Probabilistic and Deterministic

To truly grasp the complexity of artificial intelligence it’s important to first understand the logic behind how it makes decisions. Some decisions are clear-cut, while others are more complicated with multiple possible outcomes, that’s where probabilistic and deterministic models come into play. Watch the full episode to learn how these decision models work and how they can be used to guide AI to solve problems.

Recent Episodes

Generative AI & LLMs Explained

Episode 01 | AI Term: Generative AI & LLMs

Have you ever wondered how generative AI is transforming app development and content creation? In this episode, we explore the power of large language models and how they're revolutionizing the way we generate new ideas, write stories, and even compose music. With generative AI, creating content has never been easier or more fluent. Tune in to discover how generative AI is changing the game.

Grounding Explained: How to stop AI hallucinations

Episode 02 | AI Term: Grounding

How can we prevent large language models from providing bad information? Well, today we’ll learn about a promising approach called grounding. Grounding has been shown to reduce the likelihood of errors, also known as hallucinations. So, if you want to stop hallucinations, watch to learn how to ground your large language model prompts.

Supervised vs. Unsupervised Learning Explained: What's the difference?

Episode 03 | AI Term: Supervised and Unsupervised Learning

Are you unsure about whether to use supervised or unsupervised learning for your AI project? In this video, you'll learn about the differences between these machine learning techniques and when to use each approach. While they may sound similar, the truth is that they can make a huge difference in how your AI models perform. Choosing the right approach for your AI project can be the key to unlocking its full potential.

Prompt Engineering Explained: Writing effective prompts is crucial.

Episode 04 | AI Term: Prompt Engineering

Are you tired of language models giving you unpredictable and irrelevant outputs? Then it's time to master the art of prompt engineering. We’ll provide you context and direction, so you can ensure that a language model generates the output you want. So why settle for generic responses when you can get precisely what you ask for?

Multimodal Language Models Explained: The next generation of LLMs

Episode 05 | AI Term: Multimodal Language Models

Multimodal language models are revolutionizing the way we interact with computers. This episode explores how these models are opening up a whole new world of possibilities beyond just language. From virtual assistants to automated customer service, these multimodals are set to transform the way we interact with technology, and usher in a new era of human-computer interaction.

Reinforcement Learning Explained: Correcting models with feedback

Episode 06 | AI Term: Reinforcement Learning

Reinforcement learning is transforming the field of AI. But what is it, and how does it work? Well, in this episode learn how this powerful combination of technology and human expertise has enabled ChatGPT to engage in natural and seamless conversations, and what it means for the future of AI.


Speech-to-text and Text-to-speech Explained

Episode 07 | AI Term: Speech-to-text, Text-to-speech

Discover the transformative power of Speech-to-Text and Text-to-Speech technologies, which are revolutionizing the way we communicate with machines. These innovations enable seamless interactions, turning spoken words into written text and vice versa, ultimately enhancing our communication experience with virtual assistants and chatbots.

Annotation Explained

Episode 08 | AI Term: Annotation

Behind every cutting-edge AI lies the crucial process of annotation. Expert annotators provide in-depth information to datasets, essentially creating a detailed guide that helps machine learning algorithms understand, learn, and deliver desired outputs in various scenarios. Watch this episode of AI Explained to learn how important annotation is for everything from ChatGPT to self-driving cars.

Chatbots vs. Conversational AI Explained

Episode 09 | AI Term: Chatbots vs. Conversational AI

Conversational AI is revolutionizing the way we interact with chatbots, allowing for a more natural human-like conversation. In this video we’ll discuss the differences between these two chat experiences and explore how conversational AI enables more efficient and intelligent communication across various applications.

Hallucination Explained

Episode 10 | AI Term: Hallucination

Hallucination sounds like a bad thing but really it’s a crucial aspect of generative AI, enabling imaginative creations. But there’s a trade off, in order to be imaginative you have to distort reality, and unfortunately  this can sometimes lead to incorrect or false information. Watch the full video to discover how engineers are working towards solving this issue and why it's essential to be cautious and not blindly trust AI-generated output.

The Cost of Large Language Models Explained

Episode 11 | AI Term: LLM Costs

Did you know that the cost of creating and maintaining a large language model can rival that of a Boeing 747? While developing such models can be expensive due to their size and complexity, smaller open-source smaller more affordable models also exist. Watch the full video to learn more about the costs of large language models!

Generative AI Explained

Episode 12 | AI Term: Generative AI

Did you know Generative AI can serve as your new creative partner and its core strengths promise to transform the way we interact with technology. Watch the full video to learn more about the process Generative AI follows to rapidly generate human-like content and how it’s changing the way the world works.

Upcoming Episodes

Watch: AI Explained topic, Fine-tuning vs. Instruction-tuning

Fine-tuning vs. Instruction-tuning Explained

Episode 14 | AI Term: Fine-tuning vs. Instruction-tuning

Tuning a machine learning model refers to the technique of tweaking the model in a way to produce a desired outcome. Often referred to as fine-tuning or instruction-tuning, these different techniques can be applied in different ways to produce wildly different outcomes. Watch the full video to learn more about how tuning can be used to optimize AI to perform specific tasks, or even better equip it to adapt to its environment.

Watch: AI Explained topic, Controllability

AI Controllability Explained

Episode 15 | AI Topic: AI Controllability

AI has the potential to transform many aspects of our lives, but it's not always perfect and can sometimes make mistakes with significant consequences. By employing techniques like interpretability, we can better understand and control AI decision-making, ensuring that it remains accurate, safe, and ethical in its applications. Watch the full episode to learn how engineers are deploying these methods get more control over AI

Watch: AI Explained topic, Artificial General Intelligence

Artificial General Intelligence Explained

Episode 16 | AI Topic: Artificial General Intelligence

Artificial General Intelligence (AGI) has the power to unlock new discoveries and tackle some of the world's most daunting challenges. But it’s significantly more complex than narrow AI. Watch the full video to learn how engineers are integrating multiple AI techniques to build these AI systems that continuously improve and can potentially make the world a better place.

Watch: AI Explained topic, Stacking

Stacking Explained

Episode 17 | AI Term: Stacking

Stacking is a technique in AI that combines multiple algorithms to enhance overall performance. By blending the strengths of various AI models, stacking compensates for each model's weaknesses and achieves a more accurate and robust output in diverse applications, such as image recognition and natural language processing. Watch the full video for the full story.

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