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🤖 1X launches 'NEO Gamma' humanoid

🤖 1X launches 'NEO Gamma' humanoid

PLUS: Google unveils a multi-agent ‘AI Co-Scientist’

Jaynit Makwana & Dhaval Makwana
February 24, 2025

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Hola, AI fam 🤖

In today’s WakeTheAI edition:

1X launches ‘NEO Gamma‘ humanoid
Google unveils a multi-agent ‘AI Co-Scientist‘
Prompt: Use Eisenhower Matrix To Simplify Task Management
Google has quietly revealed Veo 2’s pricing
Alibaba will invest $52B in AI & Cloud over three years
iOS 18.4 add Priority Notifications, using Apple Intelligence
Pika launches Pikaswaps, letting users replace anything in videos
…and more!

1X
🤖 1X launches ‘NEO Gamma‘ humanoid

Lazy Bits: 1X Technologies introduced NEO Gamma, a next-generation home humanoid robot with improved AI, natural movement, and a safety-first design, bringing humanoid assistants closer to real-world household use.

In-Depth Details:

Home-Friendly Design: NEO Gamma features a minimalist look, soft knit covers for safety, and Emotive Ear Rings for real-time visual feedback.
Lifelike Movement: A whole-body controller trained on human motion data enables NEO to walk, squat, sit, and pick up objects with balance.
Advanced Object Manipulation: A new visual manipulation model allows NEO to grasp and interact with objects even in unfamiliar environments.
Conversational AI: Powered by 1X’s in-house language model, NEO Gamma supports natural conversations, body language interpretation, and real-time responses.
Enhanced Hardware & Audio: NEO is 10x more reliable and 10 dB quieter, with four microphones for accurate voice recognition and a three-speaker system.
Safety-Focused Engineering: Soft covers and Tendon Drive joint actuation improve passive safety, ensuring careful movement around people and objects.

Lazy Conclusion: 1X Technologies is taking a major step toward humanoids as real home assistants, blending AI, robotics, and safety-first engineering. With NEO Gamma, humanoid robots are closer than ever to mainstream adoption, signaling a future where AI-driven assistants become part of daily life.

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GOOGLE
🧑‍🔬 Google unveils a multi-agent ‘AI Co-Scientist’

Lazy Bits: Google introduced AI Co-Scientist, a multi-agent system powered by Gemini 2.0 that generates hypotheses, refines research proposals, and accelerates breakthroughs in biomedicine and other scientific fields.

In-Depth Details:

Multi-Agent AI System: Uses six AI agents to generate, validate, and refine research hypotheses, mirroring the scientific method.
Benchmark Performance: Achieved 80%+ accuracy on expert benchmarks, outperforming both existing AI models and human researchers.
Biomedical Breakthroughs: Identified new drug applications and gene transfer mechanisms in just days during trials at Stanford and Imperial College.
Advanced Reasoning: Analyzes vast scientific literature to uncover novel research insights and bridge interdisciplinary knowledge gaps.
Self-Improvement & Scaling: Uses compute scaling, ranking tournaments, and self-play debates to refine hypotheses and improve research quality over time.

Lazy Conclusion: AI Co-Scientist could revolutionize scientific discovery, outperforming human experts and existing AI models. If successful, it may become an essential AI collaborator in research labs worldwide.

MICROSOFT
🎮 Microsoft introduces ‘Muse‘

Lazy Bits: Microsoft introduced Muse, a generative AI model built on the World and Human Action Model (WHAM) framework. Designed for gameplay ideation, Muse can generate game visuals, controller actions, or both, helping developers create and iterate on new gameplay experiences.

In-Depth Details:

AI-Powered Gameplay Generation: Muse generates realistic gameplay sequences from just 10 frames of real gameplay, predicting how the game will evolve.
Benchmark Performance: Trained on 1 billion images and controller actions, Muse outperforms previous AI models in consistency, diversity, and persistency.
WHAM Demonstrator: Microsoft released the WHAM Demonstrator, a tool for developers to experiment with AI-generated gameplay and iterate on designs.
Advanced Training & Scaling: Muse improved from 128×128 to 300×180 resolution, enhancing physics accuracy, movement fluidity, and environment interactions.

Lazy Conclusion: Muse sets a new standard for AI-driven game development, achieving high benchmarks in consistency, diversity, and realism. If successful, it could transform game design, making AI an essential tool for developers worldwide.

Use Eisenhower Matrix To Simplify Task Management

You can ask ChatGPT to act as your task management expert, helping you implement the Eisenhower Matrix for better decision-making and productivity.

ChatGPT will guide you in categorizing tasks based on urgency and importance, structuring them in a clear table format, and providing strategies for effective time management.

The output will be actionable, easy to implement, and designed to optimize your workflow.

Prompt:

You are an expert in task management and productivity optimization, specializing in the Eisenhower Matrix for effective decision-making. Your task is to develop a structured framework that helps categorize and organize tasks based on urgency and importance to enhance efficiency.

Key Focus Areas:

1. Understanding the Eisenhower Matrix – Explain how the framework helps prioritize tasks effectively.
2. Task Categorization & Prioritization – Guide the user through assigning tasks to the appropriate quadrants.
3. Creating a Structured Task Overview – Present tasks in a clear table format for easy visualization.
4. Optimizing Time Management – Offer strategies for handling each category of tasks efficiently.
5. Boosting Productivity – Provide actionable insights on improving workflow based on the categorized tasks.

Key Information About Me:

Task List: [List tasks that need prioritization]
Deadlines: [Specify due dates for any time-sensitive tasks]
Goals: [Describe both short-term and long-term goals]
Available Time for Tasks: [Define available work hours for task completion]

Output Requirements:

• Present the Eisenhower Matrix in a markdown table format with four columns:
Urgent & Important – Tasks that need immediate attention.
Important but Not Urgent – Tasks that contribute to long-term success but can be scheduled.
Urgent but Not Important – Tasks that should be delegated to free up time.
Neither Urgent nor Important – Tasks that provide little value and should be eliminated or minimized.
• Include a short description of how to manage tasks within each category below the table.
• Use bullet points for additional productivity tips & workflow improvements.
• Ensure recommendations are actionable, structured, and easy to implement.

Result:

Pika launches Pikaswaps, letting users replace anything in videos using uploaded photos or text descriptions for seamless, realistic edits.
Google has quietly revealed Veo 2's pricing of $0.50 per second, totaling $30 per minute or $1,800 per hour.
iOS 18.4 adds Priority Notifications, using Apple Intelligence to highlight important alerts at the top of your Lock Screen for better notification management.
NVIDIA, in partnership with the American Society for Deaf Children, is launching ‘Signs’, an AI platform that teaches ASL with real-time feedback and an expanding dataset.
Alibaba will invest $52B in AI and cloud over three years, surpassing its past decade’s spending and driving a 68% stock surge in 2025.