AI 101: The Top Terms Shaping the Future of Technology

Artificial intelligence (AI) is not at all a distant concept anymore; it's here, and it's reshaping our world. As investment in AI continues to grow rapidly, it’s touching every part of our daily lives — from business to personal experiences. But with AI comes a flood of jargon that can be confusing.

We’ll explain some of the major trends we’re seeing first, and then breakdown the building blocks that underpin them. Of course, there’s more to each of these concepts, but these basic explanations will give you a solid starting point.

1. Multimodal AI

   Multimodal AI can understand and process different types of data — like text, images, and sounds — all at once. For instance, in self-driving cars, multimodal AI combines visual data from cameras, spatial information from LiDAR, audio from emergency signals, and text from road signs to navigate safely. This ability to integrate multiple data sources allows AI to make smarter decisions in complex environments.

2. Agentic AI

   Agentic AI refers to systems that act as autonomous agents, capable of making decisions and executing tasks independently. Each agent is designed to focus on specific tasks and collaborate with other agents toward a shared goal. In enterprise settings, multiple AI agents might handle app modernization, manage HR workflows, or perform virtual assistant functions, all working in parallel without needing constant human intervention.

3. Generative AI (GenAI)

   GenAI can create new things — like writing text, producing images, generating voiceovers, or even composing music. It powers tools like ChatGPT, a name that has become ubiquitous in the industry. It also powers our Synechron Nexus and Synechron Nexus Plus Accelerators.

4. Large Language Models (LLMs)

   LLMs are super-smart AI systems that have been trained on huge amounts of text. They help AI understand and generate language, enabling tools like ChatGPT to answer questions, write articles, or hold conversations that sound human. LLMs are central to conversational AI and improving how machines interact with humans in natural, everyday language.

5. Generative Adversarial Networks (GANs)

   GANs are a kind of AI that can create realistic images, videos, or even voices by having two systems "compete" with each other: One generates content, and the other evaluates if it's real. By constantly challenging each other, these systems improve over time, leading to high-quality, lifelike creations. GANs are often used in industries like entertainment and gaming, where generating realistic visuals is crucial.

   Now, let’s break down the basics:

6. Machine Learning (ML)

   Machine learning is a method where computers learn from data to make decisions or predictions without being explicitly told what to do. It's teaching a computer to recognize patterns and improve over time. You’ve seen it in action when applications like Netflix, Amazon, and Spotify recommend what you might like based on your past behavior, or when your phone recognizes your voice commands.

7. Natural Language Processing (NLP)

   NLP is how AI understands and responds to human language, whether written or spoken. It’s the reason virtual assistants like Apple’s Siri or Microsoft’s Cortana can perform. Simply put, it allows machines to "talk" and understand our requests. NLP is key to making human-computer interactions more natural.

8. Hallucination

   In AI, a hallucination refers to instances when the system generates something that’s factually incorrect or even nonsensical. This happens due to various issues, such as problems with the training data or how the system interprets information. For example, when an AI chatbot confidently gives a factually wrong answer, that’s considered a hallucination.

9. Prompt Engineering

   This involves giving precise instructions or questions for AI systems to get the desired output. This process improves the accuracy and quality of responses from AI tools like ChatGPT. As these tools become more widely used, the ability to create effective prompts is becoming an increasingly valuable skill.

10. Democratization of AI

    This means making AI available to everyone, not just big corporations or tech giants. Democratization is about breaking down the most typical barriers of entry, such as cost or complexity, so that small businesses and individuals can use AI to improve their work, solve problems, and innovate.

While these are simplified explanations, the ideas behind them are shaping the future. If you're familiar with these concepts now, you'll be set as AI plays an even bigger role in what we do. Additionally, we've answered some of the most common GenAI questions to ensure you have a clear and practical understanding of the subject.