Google is Making Broad Advances in AI

Author

Chester Beard
April 22, 2025

News

OpenAI Launches Advanced Reasoning Models and ChatGPT Enhancements

Google AI Previews Cost-Efficient Gemini Model

Microsoft Unveils AI Security Agents to Combat Cyber Threats

Artificial Intelligence Excellence Awards Announce Winners

Alibaba's Qwen2.5 Omni Adds Voice and Video Modes

  • Alibaba has added voice and video modes to its Qwen2.5 Omni, expanding its AI communication tools with new interactive features.

  • Source: Last Week in AI (https://www.lastweekinai.com)

DeepSeek Model Upgrade Aims to Compete with OpenAI

Car Creates Music Based on Landscape with AI

Anthropic Unveils Groundbreaking Interpretability Research

  • Anthropic has introduced groundbreaking research on AI model interpretability, contributing to transparency and trust in AI systems.

  • Source: Last Week in AI (https://www.lastweekinai.com)

New AI Networking Chips for Power Efficiency by Broadcom

AI For Good

Prompt

human centered design with AI

Tools I Use

Cudo Compute is a cloud-based service provider that offers high-performance computing, AI, and deep learning solutions.

Dubsado is great for contract writing and project management.

Folk is the number one AI powered CRM tool.

N8N is the most powerful automation tool

Revolutionizing AI Accessibility

Gemma 3 QAT Models Bring State-of-the-Art Performance to Consumer GPUs

In an exciting development for the AI community, Google has announced the release of Gemma 3 QAT (Quantization-Aware Training) models. These new versions are optimized to dramatically reduce memory requirements while maintaining high quality, making state-of-the-art AI performance accessible on consumer-grade GPUs .

Understanding Quantization-Aware Training

Quantization-Aware Training (QAT) is a technique that incorporates quantization during the training process. Quantization reduces the precision of the numbers used by the model, allowing it to run more efficiently on hardware with limited resources. Traditional quantization can lead to a drop in performance, but QAT helps maintain the model's accuracy by simulating low-precision operations during training .

Gemma 3 QAT models use int4 quantization, which represents each number with only 4 bits. This results in a significant reduction in data size compared to the standard BFloat16 (BF16) precision, which uses 16 bits per number. For example, the Gemma 3 27B model's memory requirements drop from 54 GB to just 14.1 GB when using int4 quantization .

Performance and Accessibility

The Gemma 3 QAT models offer impressive performance while being more accessible than ever before. Here's a breakdown of the memory requirements for each model when using int4 quantization :

  • Gemma 3 27B: 14.1 GB (down from 54 GB with BF16)

  • Gemma 3 12B: 6.6 GB (down from 24 GB with BF16)

  • Gemma 3 4B: 2.6 GB (down from 8 GB with BF16)

  • Gemma 3 1B: 0.5 GB (down from 2 GB with BF16)

These reductions in memory requirements make it possible to run powerful models like Gemma 3 27B on consumer-grade GPUs. For instance, the Gemma 3 27B model can now fit comfortably on a single desktop NVIDIA RTX 3090 (24GB VRAM), while the Gemma 3 12B model can run efficiently on laptop GPUs like the NVIDIA RTX 4060 Laptop GPU (8GB VRAM) .

Seamless Integration with Popular Tools

To make it easy for developers to use these models, Google has partnered with several popular developer tools. Gemma 3 QAT models are available on Hugging Face and Kaggle, and they are supported by the following platforms :

  • Ollama: All Gemma 3 QAT models are natively supported, allowing you to get started quickly with a simple command .

  • LM Studio: This user-friendly interface enables you to easily download and run Gemma 3 QAT models on your desktop .

  • MLX: Optimized for efficient inference on Apple Silicon, MLX allows you to leverage Gemma 3 QAT models on Mac devices .

  • Gemma.cpp: This dedicated C++ implementation enables highly efficient inference directly on the CPU .

  • llama.cpp: This platform offers native support for Gemma 3 QAT models in the GGUF format, making integration into existing workflows seamless .

Community Contributions and Alternatives

In addition to the official QAT models, the vibrant Gemmaverse community offers many alternatives. These community-contributed models often use Post-Training Quantization (PTQ) and provide a wide spectrum of size, speed, and quality trade-offs to fit specific needs. Notable contributors include Bartowski, Unsloth, and GGML, whose models are readily available on Hugging Face .

Get Started Today

To begin exploring the capabilities of Gemma 3 QAT models, you can:

  • Use on your PC with Ollama: Get started quickly with Ollama's native support for Gemma 3 QAT models .

  • Find the Models on Hugging Face & Kaggle: Download the models and explore their capabilities .

  • Run on your phone with Google AI Edge: Experience the power of Gemma 3 QAT models on your mobile device .

The release of Gemma 3 QAT models marks a significant step towards democratizing AI development. By making state-of-the-art AI performance accessible on consumer hardware, Google is empowering developers to leverage cutting-edge capabilities on their own devices. We can't wait to see the innovative applications and solutions that the community will build with these powerful models .

Newsletters I like

Human-Centered AI

In an era where artificial intelligence (AI) is rapidly transforming industries and societies, the concept of human-centered AI (HCAI) has emerged as a critical approach to ensuring that technology serves and empowers people. Unlike traditional AI, which often prioritizes efficiency and automation, human-centered AI places human values, needs, and ethical considerations at the core of its design and development. This newsletter explores the principles, benefits, and real-world applications of human-centered AI, highlighting how it can enhance human capabilities and create more trustworthy and inclusive technologies.

Background on Human-Centered Design in Software

To understand human-centered AI, it's essential to look at its roots in human-centered design, a philosophy that has long been integral to software development. Human-centered design focuses on creating products and services that are usable, accessible, and tailored to the needs and contexts of the people who will use them. This approach involves active user participation throughout the design process, iterative testing, and a deep understanding of user needs and behaviors.

The principles of human-centered design have led to significant improvements in software usability and user satisfaction. By involving users in the development process, designers can identify and address real-world challenges, ensuring that the final product meets the needs of its intended audience. This user-focused approach has been instrumental in creating intuitive and user-friendly software applications that enhance productivity and user experience.

What is Human-Centered AI?

Human-centered AI builds on the principles of human-centered design, applying them to the development of AI systems. At its core, HCAI aims to create AI that enhances human capabilities, supports decision-making, and respects human values and ethical considerations. This approach prioritizes transparency, fairness, and accountability, ensuring that AI systems are understandable and trustworthy to the people who use them.

The primary goals of human-centered AI include:

  1. Augmenting Human Abilities: Rather than replacing human roles, HCAI focuses on augmenting human capabilities. AI systems are designed to assist and support humans, providing them with the tools and information they need to make better decisions and perform tasks more efficiently.

  2. Ensuring Ethical Considerations: HCAI emphasizes the importance of ethical considerations in AI design. This includes transparency, fairness, and accountability, ensuring that AI systems are explainable and respect human rights and privacy.

  3. Involving Users in the Design Process: Human-centered AI involves users throughout the design and development process. This participatory approach ensures that AI solutions are tailored to real-world needs and are accessible and understandable to the people who will use them.

  4. Adapting to Diverse User Needs: HCAI systems are designed to learn from human feedback and adapt to diverse user needs and cultural contexts. This makes them more inclusive and effective in real-world applications.

Key Concepts of Human-Centered AI

Several key concepts underpin the human-centered AI approach:

  1. Human-AI Collaboration: HCAI focuses on creating systems that work collaboratively with humans, augmenting their abilities and supporting their decisions. This means that AI is seen as a tool to assist humans, not replace them. For example, in healthcare, AI might analyze medical data to suggest possible diagnoses, but the final decision remains with the human doctor.

  2. Ethical Considerations: Human-centered AI places a strong emphasis on ethics, ensuring that AI systems are transparent, fair, and accountable. This involves designing systems that are explainable and respect human rights and privacy. Ethical considerations are integrated into the development process to mitigate biases and ensure that the AI's outcomes align with human values.

  3. User Involvement: In HCAI, users are actively involved in the design and development process. This participatory approach ensures that AI solutions are tailored to real-world needs and are accessible and understandable to the people who will use them. Interdisciplinary teams, including psychologists, ethicists, and domain experts, contribute to the design to ensure that the AI is fair, accountable, and aligned with human values.

  4. Adaptability and Context Awareness: Human-centered AI systems are designed to learn from human feedback and adapt to diverse user needs and cultural contexts. This makes them more inclusive and effective in real-world applications. For instance, AI in education can adapt to individual student needs, providing personalized learning experiences.

  5. Transparency and Explainability: HCAI systems are designed to be transparent and explainable, meaning that users can understand how the AI arrives at its decisions. This transparency builds trust and allows users to challenge or correct the AI's outputs if necessary.

  6. Continuous Improvement: Human-centered AI involves a continuous feedback loop where the system learns and improves over time based on human input. This ongoing interaction helps refine the AI's performance and ensures that it remains aligned with human needs and values.

Human-in-the-Loop (HITL)

A crucial aspect of human-centered AI is the concept of Human-in-the-Loop (HITL). HITL involves incorporating human expertise and decision-making into automated processes. Humans provide direct feedback to AI models, especially when the AI's confidence in its predictions is low. This interaction helps improve the accuracy and reliability of AI systems by combining human intelligence with machine automation.

Examples of HITL in action include:

  1. Healthcare: In medical diagnoses, AI systems analyze patient data and suggest possible conditions, but human doctors make the final diagnosis. This collaboration ensures that the AI's recommendations are reviewed and validated by human experts.

  2. Customer Service: AI chatbots handle routine customer queries, but complex or sensitive issues are escalated to human agents. This ensures that customers receive personalized and empathetic support when needed.

  3. Content Moderation: AI systems flag potentially inappropriate content on social media platforms, but human moderators review and make the final decision. This combination of AI and human judgment helps maintain the integrity and safety of online communities.

Benefits of Human-Centered AI

Human-centered AI offers numerous benefits, including:

  1. Improved Accuracy: Human oversight can catch errors and ambiguities that automated systems might miss, leading to higher overall accuracy.

  2. Complexity Handling: Humans can provide insights and solutions for complex tasks that automated systems and processes may struggle with.

  3. Adaptability: Humans can adapt to dynamic situations more effectively than rigid systems and can handle scenarios that weren’t anticipated during system design.

  4. Quality Control: Human reviewers can ensure that outputs meet required standards or compliance requirements.

  5. Efficiency: HCAI automates menial and repetitive tasks, reducing the workload for agents and allowing them to focus on more value-add work.

  6. Customer Satisfaction: In customer service interactions, escalating complex issues or inquiries to human agents drives satisfaction and builds trust.

  7. Risk Mitigation: HCAI models help reduce risk of errors or mitigate potential harm, especially in applications such as autonomous vehicles or healthcare.

Examples of Human-Centered AI in Action

  1. Healthcare: AI assists clinicians by analyzing medical data, but doctors make the final diagnosis and treatment decisions. This ensures that patient care remains personalized and ethical.

  2. Education: Adaptive learning platforms adjust content based on student feedback and performance, with teachers guiding and supplementing the AI’s recommendations. This personalized approach enhances the learning experience for students.

  3. Customer Service: AI chatbots handle routine queries, but complex or sensitive issues are escalated to human agents. This ensures that customers receive personalized and empathetic support when needed.

Conclusion

Human-centered AI represents a significant shift in how we approach the design and development of AI systems. By prioritizing human values, needs, and ethical considerations, HCAI aims to create technologies that enhance human capabilities, support decision-making, and build trust. As AI continues to evolve and integrate into various aspects of our lives, adopting a human-centered approach will be crucial in ensuring that technology serves and empowers people, making AI more trustworthy, inclusive, and beneficial for society.

References

Buzz - Quick Intro

Business Planning Templates.

pensight.com/x/chester-beard/new-meeting/07587cbb-bd44-46bb-925c-8b2e7b9f30a0