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At Google I/O 2025, one of the most exciting and forward-looking announcements was Project Astra, Google’s latest step toward building a truly intelligent, multimodal AI assistant. Drawing on the power of its Gemini 2.5 Pro model and integrating advancements from Project Astra and Project Astra Live, this is Google’s bold vision for AI that perceives, remembers, and reasons more like a human being. 

In recent years, artificial intelligence (AI) has progressed quite a bit. A large factor in this progression is something known as scaling laws. If you've ever wondered why AI models keep getting bigger and why tech companies are pouring massive resources into training them, scaling laws provide the answer. 

Artificial intelligence (AI) promises transformative potential: smarter automation, better customer experiences, faster insights, and streamlined operations. But with the buzz comes a crucial question for decision-makers: Is AI actually worth the investment? 

Artificial intelligence (AI) is driving transformation across many industries, but many organizations still rely on legacy systems built long before AI became mainstream. These systems, often necessary for the business to function, weren’t designed with modern machine learning models or data pipelines in mind. Yet replacing them from scratch can be costly, disruptive, and time-consuming. 

In the world of machine learning, data is king. But raw, unstructured data is like a library without a catalog, it’s disorganized and difficult to navigate. That’s where data labeling comes in. Data labeling is the process of annotating data with meaningful tags or labels, allowing machine learning models to understand/learn from it. 

Our last blog post covered transfer learning, today we cover a powerful training technique, training techniques is contrastive learning. Unlike traditional learning methods that rely on labeled datasets, contrastive learning trains models by comparing data points to identify meaningful relationships. Like transfer learning, this approach has revolutionized areas like computer vision, natural language processing, and recommendation systems. 

Training large artificial intelligence models can take months; one technique has emerged as a game-changer for developers and data scientists alike: transfer learning. It allows teams to build smarter models faster without starting from scratch. 

Artificial intelligence (AI) has been implemented in a multitude of fields, the government being one of them. From improving public services to enhancing national security, AI is helping governments around the world work smarter, faster, and more efficiently. As public agencies face increasing demands with limited resources, AI offers new ways to automate processes, analyze large datasets, and make better decisions. In this post, we’ll explore 8 practical ways AI is being used in government today. 

Text-to-image models are some of the most fascinating applications of artificial intelligence today. With a simple sentence like “a futuristic city floating above the clouds in watercolor style,” AI can now generate a detailed image that matches that description within seconds. 

There is no doubt artificial intelligence (AI) is reshaping industries and driving innovation, but it also comes with a significant environmental cost. Behind the sleek user interfaces of AI-powered apps and platforms lies an immense infrastructure of servers, cooling systems, and computational resources. As AI models grow larger and more complex, their environmental footprint is becoming a growing concern.