Think of Interactive AI as a human interacting with you. It listens and responds in a human-like language, it can help you perform tasks, answer questions, and hold sustained conversations.
One great advantage is that humans don’t need to conform to a style guide when interacting — language is full of subtleties, but between large language models (LLMs) and Natural Lnaguage Processing (NLP), artificial intelligence (AI) can figure out what you want.
Let’s say you are at the doctor’s surgery, one running at full capacity, and the team asks you to talk to a virtual agent while you wait. You’ll quickly notice the agent is knowledgeable about the human body, and also sympathetic and understanding that you might be stressed.
This is a far cry from the past, where a chatbot might simply try to classify you into “10 common problems that customers face” and put you into a queue for a human agent to speak to.
Over the next few years, we are likely to see Interactive AI incorporated into our daily lives so much more — be it as a personal assistant or out in the world in hospitals, shops, even when booking flights or holidays.
Key Features of Interactive AI
Interactive AI has several key features that set it apart from other forms of AI:
1. Natural Language Understanding
One of the standout features of interactive AI is its ability to understand human language, even with all its complexity. This allows dynamic and context-aware responses, making interactions more natural and engaging.
2. Learning from Human Interactions
Interactive AI leverages Machine Learning (ML) and Deep Learning (DL) techniques to learn and grow from human interactions. It continuously analyzes vast datasets of human communication, identifying patterns, expressions, and tonal nuances, allowing it to adapt and improve its responses over time.
3. Adaptation to Feedback
Just as humans learn and grow from feedback, interactive AI systems are designed to accept and learn from feedback provided by users. This feedback-driven learning approach enables them to improve their future interactions and better cater to individual preferences and needs.
4. Multimodal Interaction
Interactive AI is not limited to a single model — it can talk with us through text, voice, or visuals, and work on different platforms at the same time. If you’ve used Alexa or Siri, you will likely have some experience of this.
5. Sentiment Recognition
Interactive AI can understand and respond to human sentiments and emotions. Using Machine Learning and Deep Learning, it can understand emotions or tones such as annoyance, satisfaction, or curiosity and emphasize with the speaker.
6. Multilingual Capabilities
Language barriers stop being a barrier — with many models trained in more than 50 different languages, allowing large swathes of the world to be involved, all talking to the AI at once.
Challenges of Interactive AI
It’s not all golden, though — with any new tool comes new challenges.
Chief among them is data privacy and security, with assistants handling vast amounts of personal data. If AI has access to medical records, or banking information, or even just personal preferences, it needs to handle that data privately and in respect to the law.
We have enough hacking problems simply interacting with the internet — AI may end up with far more information, and in turn compromise our privacy in so many more alarming ways.
The problem is exasperated by quite how many office workers — a third — are happy to give AI tools like ChatGPT confidential data in daily work life.
As long as concerns surrounding data privacy and security are kept right at the center of the conversation, as well as the level of control we have, it’s going to be an interesting time.
We may leave the bottom thought line to DeepMind cofounder Mustafa Suleyman, who said in a recent interview: “I think that we are obsessed with whether you’re an optimist or whether you’re a pessimist.
“This is a completely biased way of looking at things. I don’t want to be either. I want to coldly stare in the face of the benefits and the threats.
“And from where I stand, we can very clearly see that with every step up in the scale of these large language models, they get more controllable.”