Humainity Episode 6
Humainity Episode 6
Using AI for collaboration

In the sixth episode of "Humanity," Richard and I delve into AI's potential for collaboration. The episode begins with reintroducing the concept of "affordances," from Episode 2. "Affordance" is a term coined by the psychologist JJ Gibson, and it refers to the 'act-upon-ability' of objects and surfaces. For instance, a surface at knee height might be perceived as something to sit on, whether it's a chair, rock, or a log. We apply this concept to AI and inquire into the actions and abilities AI might afford, especially in the realm of collaboration.

That idea was elaborated on in the world of design by Don Norman in his book, the Design of Everyday Things. We're using this concept to think about artificial intelligence. What kinds of actions and abilities does AI afford?

Photo of Rajesh and Richard sitting in a well-lit studio, discussing AI collaboration. They are surrounded by screens displaying various AI concepts and diagrams. Richard is pointing to a chart explaining 'affordances'.

The conversation starts with the nuances of face-to-face communication, which remains the dominant form of interaction. Such interactions offer cues from facial gestures, turn-taking, questioning, and answering. The hosts contrast this with AI chatbots like ChatGPT, which lack the embodied features of face-to-face communication. However, as technology advances, there's a growing interest in bridging this gap. AI developers are exploring ways to integrate more human-like elements into chatbots, such as voice intonation and simulated facial expressions. Virtual reality (VR) and augmented reality (AR) platforms are also stepping into the fray, aiming to recreate the depth and richness of in-person conversations. While ChatGPT and its counterparts currently operate primarily through text, the future may see them evolve into more immersive entities, capable of mimicking the subtleties and non-verbal cues that make human communication so intricate and profound.

We get affordances from facial gestures; we get affordances from turn-taking. I stop and you start, we get affordances from, questioning and answering, from back and forth speech.

The discussion moves to the advantages of printed text over face-to-face speech, highlighting the ability to revisit and reference written content. The hosts also touch upon collaborative tools like Google Docs, which allow for both real-time and asynchronous collaboration.

The episode introduces a new platform, Team GPT, which facilitates multiple users interacting with ChatGPT simultaneously. We experiment with it, asking how AI can enhance collaboration. The AI lists several ways, including efficient communication, task management, decision-making, conflict resolution, and more. We further probe one of those responses - efficient communication - with AI suggesting features like automating responses, real-time translation, sentiment analysis, and predictive text. The AI's ability to instantly translate and provide context-aware responses could revolutionize international business meetings and project collaborations. In general, it seems that AI can already conduct itself in the manner of a reasonably competent white-collar worker. Additionally, sentiment analysis can help correct misinterpretations. While humans can misinterpret tone or mood, especially in written communication, AI can objectively analyze and gauge the underlying emotions, potentially preventing misunderstandings.

AI can make collaboration easier in numerous ways (according to AI):

Illustration of a person interacting face-to-face with another individual, juxtaposed with a person chatting with an AI chatbot on a computer. The face-to-face interaction shows the two individuals exchanging facial gestures and cues, while the chatbot interaction displays a series of chat bubbles.

1. Efficient Communication: AI chatbots can automate and simplify communication, allowing team members to focus on more important tasks. Some AI systems can even translate languages in real-time, making it easier for international teams to collaborate.

2. Task Management: AI can automate task assignment based on team member's skills, workload and preferences. It can also monitor project progress and send reminders for deadlines, ensuring tasks get completed on time.

3. Decision Making: AI can analyze large amounts of data and provide insights that help teams make data-driven decisions. This can reduce the amount of time spent on analyzing data manually.

4. Conflict Resolution: AI systems can recognize patterns and predict potential conflicts among team members based on historical data. This can help in proactive conflict resolution and prevention.

5. Training and Development: AI-powered systems can personalize training materials for individual team members, enhancing their skills and performance.

6. Meeting Scheduling: AI can help to schedule meetings by considering everyone's availability. It can also send reminders and set agendas for meetings.

7. Document Collaboration: AI can assist in real-time document collaboration. It can suggest edits, correct grammar, and even generate content.

8. Knowledge Management: AI can help in creating a centralized knowledge base by retrieving, sorting, and categorizing information from various sources. This makes it easy for teams to find and share information.

These are just a few examples of how AI can make collaboration easier. It's important to note that while AI can facilitate and enhance collaboration, it is not a substitute for human interaction or creative thinking.

Vector image of a chatbot interface on a computer screen, with the chatbot taking the role of an 'adversarial partner'. The chatbot highlights flaws in the user's ideas but does so in a constructive manner. The user responds positively, appreciating the chatbot's feedback without feeling offended.

The conversation then veers towards the potential of AI in brainstorming sessions. AI could facilitate idea generation, virtual brainstorming, pattern recognition, and even play the role of a devil's advocate in discussions. The Devil's advocate is particularly interesting because we are so bad at it! We want to fit into a group, not argue against it. In contrast, AI's wont care whether it fits in or not. We contemplate the idea of using AI as an adversarial partner, which could be effective in pointing out flaws without causing offense.

I'm instinctively feeling like having an adversarial partner in the chatbot might be the best possible use of that, because it would be potentially effective in pointing out flaws and I would not be in the least bit offended

While the episode started with the premise of using ChatGPT as a many-to-one collaborative partner, we concluded the episode by reflecting on the potential of using AI as a one-on-one collaborator rather than in group settings.

You know, it's funny because we started the episode thinking about AI as a tool for collaboration, but I'm leaving the episode really thinking more about how could I use a chatbot or, or an image generator as a collaborator. To have an automated collaborator that could play different collaborative roles on command.



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