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Human AI collaboration and cocreation

5 min read

From: aidotengineer

AI research has undergone significant scaling paradigms in recent years, unlocking new frontiers in product development and fostering new forms of human-AI collaboration [00:41:00]. The progression of AI agents from mere collaborators to co-innovators represents a transformative shift in how humans interact with and leverage artificial intelligence [01:05:00].

Evolution of AI Capabilities

The foundation of modern AI capabilities lies in two primary scaling paradigms [01:28:00]:

  1. Next Token Prediction (Pre-training): This paradigm involves models learning about the world by predicting the next word, string, pixel, or any token in a sequence [01:46:00]. It functions as a “world-building machine,” enabling the model to understand the physics of the world and perform massive multitask learning [01:48:00]. While some tasks like translation are easy, complex tasks such as math, problem-solving, and especially creative writing require significant compute due to the high risk of coherence deterioration [03:11:00]. The era of 2020-2021 saw extensive scaling in pre-training [05:22:00].
  2. Reinforcement Learning (Post-training): This phase, often leveraging Reinforcement Learning from Human Feedback (RLHF) or Reinforcement Learning from AI Feedback (RLAF), refines the model’s usefulness [06:06:00]. Products like GitHub Copilot exemplify this, teaching models to complete function bodies, generate multi-line completions, and predict diffs [06:23:00].
  3. Scaling Reinforcement Learning on Chain of Thought: A more recent paradigm involves scaling RL on Chain of Thought, where models learn to “think” during training and receive good feedback signals in RL [07:05:00]. This allows models to reason through complex problems by creating detailed internal thought processes, enabling them to tackle harder tasks like medical problems or complex codebases [07:45:00].

From Collaborators to Co-innovators

The current stage of AI, particularly models trained with Chain of Thought and real-world tools (browsing, search, computer use) over long horizons, marks the era of “agents” [10:01:00]. The next evolutionary stage envisions AI as “co-innovators” [10:27:00].

Co-innovators

Co-innovators are agents built upon advanced reasoning, tool use, and long context, plus creativity enabled through human-AI collaboration [10:33:00]. This is expected to create new affordances for humans to collaborate better with AI, enabling them to co-create the future [10:52:00].

New Interaction Paradigms and Design Challenges

The increased capabilities of AI agents introduce new interaction paradigms and design challenges [09:02:00]:

  • Streaming Model Thoughts: To avoid long waiting times, one simple approach is to stream the model’s thoughts to the user, providing summaries of its reasoning [09:31:00].
  • Familiar Form Factors for Unfamiliar Capabilities: Presenting powerful new capabilities, like 100K context windows, through familiar interfaces such as file uploads, makes them more accessible [13:39:00].
  • Modular Compositions: Product features should enable modular compositions that can scale with future, higher-capability models [15:21:00].
  • Bridging Real-time and Asynchronous Tasks: A significant challenge is bridging real-time AI interaction with asynchronous task completion (e.g., models researching for hours) [15:42:00]. The key bottleneck is trust, which can be addressed by giving humans new collaborative affordances to verify and edit model outputs, and provide real-time feedback for self-improvement [16:00:00].

Vignettes from Product Development

The Future of Human Interaction with AI

The integration of highly reasoning models allows for a rapid evaluation cycle in product development [11:22:00]. These models can distill knowledge to smaller models, synthetically generate new data for post-training, and create new reinforcement learning environments [11:33:00].

Key areas for the future of AI in improving user experience and integrations include:

  • Creating New Task Classes: Simulating different users or generating synthetic datasets to create new product experiences [11:57:00].
  • Complex RL Environments: Allowing models to use collaborative tools like canvas, search, or browsing within RL environments to learn better collaboration [12:39:00].
  • In-context Learning: Models are extremely good at learning new tools from few-shot examples, accelerating development cycles [13:00:00].
  • Personalized Tutors: Models can adapt to individual learning styles (e.g., visual or auditory) [18:16:00].
  • Generative Entertainment: Enabling non-technical individuals to create games or tools on the fly [18:47:00].
  • Invisible Software Creation: The future suggests an invisible layer of software creation where people can create their own tools directly from mobile devices without coding [21:32:00].
  • AI as Research Partner: Models can assist in research by reproducing papers, leveraging internal knowledge to form new hypotheses, and delegating tasks to AI assistants [20:12:00].
  • Dynamic Interface: The AI interface will be a “blank canvas” that self-morphs based on user intent, becoming an IDE for coders or a writing assistant with tools for brainstorming and visualization for writers [22:42:00].

Ultimately, co-innovation will occur through creative co-direction with highly reasoning agentic systems, leading to the creation of new novels, films, games, science, and knowledge [23:31:00].

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