THE AIGNOSTIC

Category: Google / Deepmind

  • The Future is Agentic – Deepmind: Project MarinerAgent

    The Future is Agentic – Deepmind: Project MarinerAgent

    On Wednesday, Google introduced its inaugural AI agent designed for web interaction, developed by its DeepMind division and named Project Mariner. This AI, powered by Gemini, operates within the Chrome browser, manipulating the cursor, clicking on elements, and completing forms to navigate and use websites autonomously, much like a human would.

    Fundamentally new UX Paradigm

    The rollout begins with a select group of testers this Wednesday, as Google explores new applications for Gemini, including reading, summarising, and now, actively using websites. An executive at Google has described this development to TechCrunch as indicative of a “fundamentally new UX paradigm shift”, where the interaction with websites transitions from direct user input to managing through an AI intermediary.

    From Clicks to Commands: AI Agents Take Over Your Digital Chores

    AI agents are the current focus in tech because they represent an advanced form of automation, capable of independently performing complex tasks online. This evolution is seen as a significant step beyond traditional AI, promising to change how we interact with digital services, manage our digital lives, and potentially automate many professional tasks. The conversation reflects both excitement about new possibilities and concerns over job displacement and privacy.

  • Google DeepMind’s Recursive Learning Approach and Its Impact

    Google DeepMind’s Recursive Learning Approach and Its Impact

    Google DeepMind’s Socrates Learning

    All 70.000 Project Gutenberg books amount to less than 1 TB (933GB). Imagine the impact of DeepMind’s Recursive Learning approach.

    Google DeepMind’s recursive learning, often referred to as “Socratic Learning,” involves AI systems teaching themselves through iterative processes without human input. This method allows AI to generate its own training data and scenarios, enhancing efficiency and adaptability.

    Not to Create a Better AI, but to Create AI That Can Improve Itself.

    An agent trained within a closed system can master any desired capability, as long as the following three conditions hold: (a) it receives sufficiently informative and aligned feedback, (b) its coverage of experience/data is broad enough, and © it has sufficient capacity and resource. In this position paper, we justify these conditions, and consider what limitations arise from (a) and (b) in closed systems, when assuming that © is not a bottleneck. Considering the special case of agents with matching input and output spaces (namely, language), we argue that such pure recursive self-improvement, dubbed ‘Socratic learning,’ can boost performance vastly beyond what is present in its initial data or knowledge, and is only limited by time, as well as gradual misalignment concerns. Furthermore, we propose a constructive framework to implement it, based on the notion of language games.

    Impact:

    • Autonomy: AI can evolve independently, reducing reliance on human updates for new environments or problems.
    • Data Efficiency: Requires less data for learning, making AI more resourceful.
    • Advancements Towards AGI: Paves the way for Artificial General Intelligence by enabling AI to understand and reason beyond task-specific programming.
    • Ethical and Control Issues: Raises concerns about AI autonomy, necessitating new frameworks for control and ethical considerations.
    • Broad Applications: Potential in fields like personalized education, healthcare, and space exploration, where adaptive learning could lead to innovative solutions.

    Recursive learning introduces complexities regarding control and ethical use of AI, necessitating careful management and oversight.