Artificial intelligence (AI) has exploded into two major branches over the past few years: generative AI and interactive AI. In this blog, we’ll explain what sets these two types of AI approaches apart and compare their capabilities and use cases.

What is Generative AI?

Generative AI refers to AI systems that can create brand new content, like text, images, audio and video. The most popular examples today include:

- AI image generators like DALL-E 2 and Stable Diffusion, which create original digital artwork based on text prompts.

- AI writing assistants like GPT-3, which can generate essays, stories and even computer code on demand.

- Deepfake algorithms that synthesise fake but photorealistic photos and videos of people.

The key advantage of generative AI is its ability to autonomously create novel, high-quality content at scale. Where human creators are limited by time and manual effort, generative AI can produce infinite amounts of output for minimal cost once the initial model is trained.

Current generative AI Capabilities:

- Text generation – can write blog posts, short stories, poetry, code and more based on prompts

- Image generation – creates photorealistic images from scratch to match text descriptions

- Audio generation – generates speech, music and other audio that matches the style of training data

- Video generation – emerging technology to generate animated videos with imagery and audio

Generative AI does have significant limitations though currently. The most advanced models require massive training datasets and computing power. They can produce nonsensical or biased output if not carefully constrained. Most importantly, current systems lack true reasoning and understanding; they recombine learned patterns rather than creating via conceptual knowledge.

What is Interactive AI?

Interactive AI refers to AI systems designed to intelligently interact with humans in free-flowing conversations. Rather than passively generating content, interactive AI aims for clever dialogue akin to a human assistant or companion.

Examples of interactive AI include:

- Digital assistants like Siri, Alexa and Google Assistant for conversational queries

- Customer service chatbots that offer natural text or voice interactions

- Research prototypes that debate ethics, discuss science or provide tutoring through dialogue

The goal of interactive AI is to hold nuanced, contextual discussions the way humans do. Key abilities include:

- Natural language processing to understand spoken or written exchanges

- Contextual awareness to follow the thread of a conversation

- Ability to ask clarifying questions rather than make assumptions

- Personality and humour that makes conversations more engaging and human

Unlike generative AI, today's interactive AI is narrow – limited to specific domains like scheduling or customer service. But researchers aim to eventually create general artificial intelligence capable of open-ended dialogue on any topic. This requires major breakthroughs in contextual reasoning and common sense.